the relationship between market value and book...
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THE RELATIONSHIP
BETWEEN MARKET VALUE AND BOOK VALUE
FOR FIVE SELECTED JAPANESE FIRMS
Teruyo Omura
MC, the University of Queensland MBA, Kobe University BBA, KwanseiGakuin University
This thesis is submitted to the School of Accountancy in the Faculty of Business at Queensland University of Technology in fulfilment of the degree of Master of Business (Research) in the year 2005.
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Keywords: Error Correction Models, Equilibrium Correction Models, Market to Book Relationship, Time Series, Japanese Firms.
Abstract Studies of the value relevance of accounting number in capital market research are
consistent with the simple view that, in equilibrium, book values are equal to or have
some long-term relationship with market values, and that market returns are related to
book returns.
This dissertation examines the value relevance of annually-reported book values of net
assets, earnings and dividends to the year-end market values of five Japanese firms
between 1950 and 2004 (a period of 54 years). Econometric techniques are used to
develop dynamic models of the relationship between markets, book values and a
number of macro-economic variables. In constructing the models, the focus is to
provide an accurate statistical description of the underlying relationships between
market and book value. It is expected that such research will add to the body of
knowledge on factors that are influential to Japanese stock prices.
The significant findings of the study are as follows: 1) well-specified models of the data
generating process for market value based on the information set used to derive the
models are log-linear in form. Additive, linear models in untransformed variables are
not well-specified and forecast badly out of sample; 2) the book value of net assets has
relevance for market value in the five Japanese firms examined, in the long run.
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TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION ............................................................................................ 1 1.1 PURPOSE OF THE STUDY............................................................................................... 1 1.2 SUMMARY OF PRIOR RESEARCH .................................................................................... 2 1.3 JAPANESE CAPITAL MARKETS AND ACCOUNTING PRACTICES ....................................... 4 1.4 RESEARCH QUESTION................................................................................................... 6 1.5 SIGNIFICANCE OF THE STUDY....................................................................................... 6 1.6 ORGANISATION OF THE THESIS..................................................................................... 7
CHAPTER 2 LITERATURE REVIEW ................................................................................. 9 2.1 INTRODUCTION............................................................................................................. 9 2.2 VALUE RELEVANCE .................................................................................................... 10
2.2.1 Studies using cross-sectional analysis with share price.................................... 10 2.2.2 Studies with share price changes and returns as the dependent variable ...... 12 2.2.3 Combined studies with share price and returns as dependent variables ........ 13 2.2.4 Studies including other fundamental variables................................................. 14
2.3 JAPANESE CAPITAL MARKET RESEARCH ..................................................................... 15 2.3.1 Studies of price level as a dependent variable in the Japanese stock market . 15 2.3.2 Studies of returns as a dependent variable in the Japanese stock market...... 16 2.3.3 Studies of other fundamentals in the Japanese stock market.......................... 17
2.4 STUDIES OF DYNAMIC MODELLING ............................................................................. 19 2.5 SUMMARY ................................................................................................................... 21
CHAPTER 3 THEORETICAL FRAMWORK ..................................................................... 22 3.1 INTRODUCTION........................................................................................................... 22 3.2 ISSUES ARISING FROM CAPITAL MARKET RESEARCH................................................... 23 3.3 INADEQUACY OF THEORY............................................................................................ 23
3.3.1 Inadequacies of method ....................................................................................... 24 3.4 FRAMEWORK FOR THE CURRENT STUDY ..................................................................... 26 3.5 SUMMARY ................................................................................................................... 27
CHAPTER 4 RESEARCH METHOD ................................................................................. 29 4.1 DESCRIPTION OF RESEARCH METHOD RELATIVE TO THE THEORETICAL FRAMEWORK 29 4.2 SAMPLE OF FIRMS....................................................................................................... 33
4.2.1 Selection of firms.................................................................................................. 33 4.3 VARIABLE DEFINITIONS.............................................................................................. 35
4.3.1 Dependent variables ............................................................................................ 35 4.3.2 Independent variables ......................................................................................... 35
4.4 EXPLORATORY DATA ANALYSIS................................................................................... 38 4.5 GENERAL-TO-SPECIFIC APPROACH ............................................................................. 39 4.6 BENCHMARKING AND REPLICATION ........................................................................... 42 4.7 SIGNIFICANT POINTS RELATING TO METHOD.............................................................. 43 4.8 SUMMARY ................................................................................................................... 44
CHAPTER 5 RESULTS....................................................................................................... 45 5.1 INTRODUCTION........................................................................................................... 45 5.2 GENERAL POINTS RELATING TO THE FIVE FIRMS........................................................ 45
5.2.1 Descriptive statistics............................................................................................ 45 5.2.2 Levels of integration of the variables.................................................................. 50 5.2.3 Summary of diagnostic tests ............................................................................... 57
5.3 TOYOTA MOTOR CORPORATION.................................................................................. 60 5.3.1 Historical background.......................................................................................... 60
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5.3.2 Statistical Equilibrium Correction Models......................................................... 66 5.3.3 Development of final model of data generating process for market value ....... 73
5.4 FUJI PHOTO FILM CORPORATION............................................................................... 80 5.4.1 Historical background.......................................................................................... 80 5.4.2 Statistical Equilibrium Correction Models......................................................... 83 5.4.3 Development of final model of data generating process for market value ....... 89
5.5 SONY CORPORATION .................................................................................................. 95 5.5.1 Historical background.......................................................................................... 95 5.5.2 Statistical Equilibrium Correction Models......................................................... 98 5.5.3 Development of final model of data generating process for market value ..... 106
5.6 ITOCHU CORPORATION............................................................................................. 111 5.6.1 Historical background........................................................................................ 111 5.6.2 Statistical Equilibrium Correction Models....................................................... 114 5.6.3 Development of final model of data generating process for market value ..... 120
5.7 SUMITOMO TRUST & BANKING CO. LTD. ................................................................. 125 5.7.2 Statistical Equilibrium Correction Models....................................................... 129 5.7.3 Development of final model of data generating process for market value ..... 136
5.8 SUMMARY ................................................................................................................. 141 CHAPTER 6 DISCUSSION AND CONCLUSIONS ........................................................ 142
6.1 INTRODUCTION......................................................................................................... 142 6.2 ACCOUNTING NUMBERS AS SUFFICIENT STATISTICS FOR MARKET VALUE................ 142 6.3 SUMMARY OF THESIS FINDINGS................................................................................ 153 6.4 LIMITATIONS OF THE STUDY..................................................................................... 155 6.5 FUTURE RESEARCH .................................................................................................. 156
APPENDICES....................................................................................................................... 158 APPENDIX 1: STANDARD PROCEDURE.................................................................................... 158 APPENDIX 2: DEFINITIONS AND SOURCES OF DATA IN INFORMATION SET ............................. 159 APPENDIX 3: FINANCIAL VARIABLES: LEVEL, FIRST DIFFERENCE, LOGGED AND THE FIRST DIFFERENCE LOGGED VARIABLES FOR FOUR FIRMS ............................................................... 160
REFERENCES...................................................................................................................... 168
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INDEX OF FIGURES
Figure 4.1-1: Error Correction Models – Map of testing down strategy for searching for best model................................................................................................... 31
Figure 5-1: Financial data in levels - Toyota Corporation ...................................... 52 Figure 5-2: Macro-economic variables in levels ..................................................... 54 Figure 5-3: Toyota - Comparison of market value.................................................. 63 Figure 5-4: Toyota - Comparison log market value to log variables ...................... 64 Figure 5-5: Toyota Model 1, Model 2, Model 3...................................................... 68 Figure 5-6: Toyota Money Supply Model (Model 7).............................................. 78 Figure 5-7: Toyota Money Supply Model Recursive (Model 7)............................. 79 Figure 5-8 Fuji Photo - Comparison of log market value to log net income and log
book value of net assets.................................................................................... 82 Figure 5-9: Fuji Photo - Comparison of log market value to log variables............. 82 Figure 5-10: Fuji Film Model 1, Model 2 and Model 3 .......................................... 85 Figure 5-11: Fuji Photo Film Simple Book Value Model (Model 10).................... 94 Figure 5-12: Fuji Photo Film Simple Book Value Model Recursive (Model 10)... 94 Figure 5-13: Sony - Comparison of log market value to log variables ................... 97 Figure 5-14: Sony Model 1, Model 2, Model 3..................................................... 100 Figure 5-15: Sony Corporation GDP & CPI Model (Model 6)............................ 110 Figure 5-16: Sony Corporation Real value model recursive (Model 6)................ 110 Figure 5-17: Itochu - Comparison of log market value to log net incomes and book
value of net assets........................................................................................... 113 Figure 5-18: Itochu Corporation - Comparison of log market value to log variables
........................................................................................................................ 113 Figure 5-19: Itochu Model 1, Model 2 and Model 3............................................. 115 Figure 5-20: Itochu Book value, Nikkei index and Exchange rate Model (Model 7)
........................................................................................................................ 124 Figure 5-21: Itochu Book value, Nikkei index and Exchange rate Model (Model 7)
........................................................................................................................ 124 Figure 5-22: Sumitomo Trust & Banking - Comparison of log market value to log
variables.......................................................................................................... 128 Figure 5-23: Sumitomo Model 1, Model 2, Model 3 ............................................ 131 Figure 5-24: Sumitomo Trust & Banking Book Value Model (Model 10)........... 140 Figure 5-25: Sumitomo Trust & Banking Book Value Model Recursive (Model 10)
........................................................................................................................ 141
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INDEX OF TABLES
Table 1.3-1: Summary of TSE Domestic Listing Criteria (First Section)................. 4 Table 4.2-1: Sample firms ....................................................................................... 33 Table 4.5-1: Matrix of basic model construction categories of ECM ..................... 40 Table 4.5-2: Diagnostic tests and RMSE: ............................................................... 41 Table 5.2-1: Descriptive statistics of financial data for five firms (1950-2004) ..... 48 Table 5.2-2: Descriptive statistics for macro-economic variables (1950-2004) ..... 49 Table 5.2-3: Augmented Dickey Fuller (ADF) tests on individual firms ............... 56 Table 5.2-4: Diagnostic Tests for Equilibrium Correction Models for five firms .. 59 Table 5.3-1: Toyota Motor Corporation Statistical Models .................................... 66 Table 5.3-2: Toyota Model 1, Model 2 and Model 3 .............................................. 69 Table 5.3-3: Toyota Model 4 and Model 5.............................................................. 71 Table 5.3-4: Toyota Model 6, Model 7 and Model 8 .............................................. 72 Table 5.3-5: Toyota 10-year forecasting models..................................................... 74 Table 5.3-6: Toyota 10-year forecasting Model 7................................................... 75 Table 5.3-7: Toyota: Model 9 and Model 10 .......................................................... 76 Table 5.3-8: Toyota 10-year forecasting Models 9 and 10 ..................................... 77 Table 5.4-1: Fuji Photo Film: Statistical Models .................................................... 83 Table 5.4-2: Fuji Film Model 1, Model 2 and Model 3 .......................................... 86 Table 5.4-3: Fuji Film Model 4 and Model 5.......................................................... 87 Table 5.4-4: Fuji Film Model 6, Model 7 and Model 8 .......................................... 88 Table 5.4-5: Fuji Film Model 4, Model 5 and Model 7 .......................................... 91 Table 5.4-6: Fuji Film Model 9 and Model 10........................................................ 92 Table 5.4-7: Fuji Film Model 9 and Model 10........................................................ 93 Table 5.5-1: Sony Models ....................................................................................... 98 Table 5.5-2: Sony Model 1, Model 2 and Model 3 ............................................... 103 Table 5.5-3: Sony Model 4 and Model 5............................................................... 104 Table 5.5-4: Sony Model 6, Model 7 and Model 8 ............................................... 105 Table 5.5-5: Sony 10-year forecasting .................................................................. 107 Table 5.5-6: Sony Model 9 and Model 10............................................................. 108 Table 5.5-7: Sony 10-year forecasting .................................................................. 109 Table 5.6-1: Itochu Models .................................................................................. 114 Table 5.6-2: Itochu Model 1, Model 2 and Model 3 ............................................. 117 Table 5.6-3: Itochu Model 4 and Model 5............................................................. 118 Table 5.6-4: Itochu Model 6, Model 7 and Model 8 ............................................. 119 Table 5.6-5: Itochu 10-year forecasting ................................................................ 121 Table 5.6-6: Itochu Model 9 and Model 10........................................................... 122 Table 5.6-7: Itochu 10-year forecasting ................................................................ 123 Table 5.7-1 Rating of Sumitomo Trust & Banking............................................... 127 Table 5.7-2 Sumitomo Models .............................................................................. 130 Table 5.7-3: Sumitomo Model 1, Model 2 and Model 3....................................... 132 Table 5.7-4: Sumitomo Model 4 and Model 5 ...................................................... 134 Table 5.7-5: Sumitomo Model 6, Model 7 and Model 8....................................... 135 Table 5.7-6: Sumitomo 10-year forecasting.......................................................... 137 Table 5.7-7: Sumitomo Model 9 and Model 10 .................................................... 138 Table 5.7-8: Sumitomo 10-year forecasting.......................................................... 139 Table 6.2-1: Book value models for five firms ..................................................... 143
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Table 6.2-2: Best model ........................................................................................ 144 Table 6.2-3: Market to book value ratio over the sample period .......................... 146 Table 6.2-4: Book value models estimated using entire sample ........................... 147 Table 6.2-5: Book value models estimated using entire sample with the index ... 148 Table 6.2-6: Ranking by sufficiency of book value ............................................. 151 Table 6.2-7: Summary of Error Correction Models, RMSE and R2...................... 152
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Statement of Original Authorship
The work contained in this thesis has not been previously submitted for a degree or
diploma at any other higher education institution. To the best of my knowledge and
belief, the thesis contains no material previously published or written by another person
except where due reference is made.
Signed:
Date:
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Acknowledgement I wish to express my sincere gratitude to Professor Roger Willett, my supervisor. Roger
has throughout the writing of this dissertation constantly provided guidance, patience,
enthusiasm and endless support.
I also wish to extend my gratitude to the academic and administration staff at the
Queensland University of Technology, School of Accountancy without whose support
this dissertation would not have been possible.
I would also like to thank to my fellow postgraduate students and friends, Victoria,
Jodie, Zalailah, Eko, Sabri and Rumi, for their considerable support and encouragement,
particularly Steve for his advice and patience.
Finally, I wish to express my appreciation to my husband for his continued support and
encouragement, my sons, my parents and my sister for their understanding, support and
encouragement over the past years, which made the completion of this dissertation
possible.
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CHAPTER 1 INTRODUCTION
1.1 The Purpose of the study
The purpose of this study is to explore the relationship between the market and book
values of five large Japanese firms, using over 50 years of accounting data and other
macro-economic variables. The value relevance literature in the capital market research
(CMR) of accounting numbers is generally consistent with the simple view that, when
in equilibrium, book values are equal to or have some long-term relationship with
market values, and that market returns are related in a systematic fashion to book
returns. However, to date empirical research in this area has thus far given mixed results.
The CMR into the information content of earnings appears to suggest that this has
remained constant over time; while some valuation relevance studies of the association
between stock returns and accounting numbers suggest that the value relevance of
earnings has declined (Lo and Lys, 2000).
Cross-sectional econometric models typically produce weak R2 test statistics and have
yet to describe convincingly any underlying pattern in the relationship between
accounting numbers and share prices in a time series context. This is particularly the
case in the Japanese market. This study uses a dynamic approach to modelling, and
emphasises specification rather than estimation issues (Willett, 2004). Studies on United
States (US) and Indonesian data (Suwardi, 2004) support the usefulness of this approach
in refining our knowledge of the relationship between the market and the book values.
2
The research methods applied in this study develop a similar approach to investigate the
relationship between the market and the book values in Japan.
1.2 Summary of prior research
Valuation theories have been extensively studied by accounting researchers. A number
of recent studies have considered whether the return or price model 1 is the best
mechanism for determining company value. Kothari and Zimmerman (1995) argue that
the price model is less biased, but that the return model has less serious econometric
problems.2 Kothari and Zimmerman suggest that a combined use of both price and
return models may be useful. Collins et al. (1997) investigate the value relevance of
earnings and book value by regressing the price on both variables. They conclude that
the explanatory power of the combination of earnings and book values has increased by
a small degree. The value relevance of earnings to price over time appears to have
decreased while the explanatory power of book value has increased over time.
Using Japanese data from the 1980s, researchers examining the value relevance of
accounting earnings to stock price found that the great volatility experienced in the
Japanese market during that period was unrelated to fundamental variables (French and
Poterba, 1991; Hall et al., 1994; Zielinski and Holloway, 1991). Similarly, Charitou et al.
(2000) provided evidence that Japanese data generate higher earnings response
coefficients than do the US data: this they attributed to a greater conservatism in the
1 Kothari and Zimmerman (1995) define the price model as stock prices regressed on earnings per share. Others like Bartholdy et al. (2003) use models in which price is regressed on book values per share. The return model is defined as stock returns regressed on a scaled earnings variable. 2 Price models are said to have specification problems, referred to as ‘scale effects’, and to lead to more heteroscedasticity and/or misspecification error than return models (White 1980).
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income measurement3 in Japan ( Bae and Kim, 1998; Choi, 1995). They concluded that
further comparative research is desirable to explore the institutional and behavioural
differences between the two countries. Some empirical evidence appears to show the
Japanese reported accounting earnings and management forecasts as having information
content (Sakakibara et al., 1988). Evidence from Japan also suggests that earnings were
associated with security returns, especially during the early 1990s (Hall et al., 1994).
The various economic difficulties faced by Japan are thought to include the need for
reforming policies in the area of political regulations (Cargill et al., 1997), improved
accounting standard setting (Sato, 1999), increased levels of corporate financial
disclosure (Miyajima, 1998), and greater capital market investment (Liaw, 1999). The
CMR suggests that increased market efficiency (with respect to the impounding of
accounting information in share values), fundamental analysis and improvement in the
value relevance of financial reporting may improve capital market investment decisions,
accounting standard settings and corporate financial disclosure decisions (Kothari,
2001).
The research discussed above highlights the significance of conducting fundamental
CMR within a Japanese context. This study examines the value relevance of the
Japanese accounting information by using a systematic approach to dynamic
econometric modelling based upon the time series of individual firms. This feature
distinguishes it from prior accounting research.
3 See subsection 2.4.5 below.
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1.3 Japanese capital markets and accounting practices
Japan has the second largest government-maintained securities market in the world
(IMF, 2001). There are six stock exchanges in Japan, being located in Tokyo, Osaka,
Nagoya, Kyoto, Fukuoka and Sapporo. The Tokyo Stock Exchange Co. Ltd is the oldest
and largest stock exchange in Japan; it was established in 1878 as a profit-making
corporation under the Stock Exchange Law of the same year. In 1948, a Securities and
Exchange Law was enacted with the principal objective of establishing a system of fair
trading in securities and ensuring the protection of the investors. Under this law, the
Tokyo Stock Exchange (TSE) was established in its present form in 1949 with 249
companies being listed at the opening of the TSE. By the end of 2001, 2067 companies
were listed on the TSE. The TSE requires companies seeking listing to undergo a
rigorous examination of their application and to gain the approval of the Ministry of
Finance (MOF). Companies are assigned either to a First or Second Section4 (Liaw,
1999). The First Section requirements are shown in Table 1.3-1.
Table 1.3-1: Summary of TSE Domestic Listing Criteria (First Section) CRITERIA REQUIREMENTS 1. Number of shares listed At least 20,000 shares 2. Market capitalisation (market value) At least 4 billion yen 3. Shareholder’s equity 1 billion yen or greater 4. Net profit before tax Over the past 2 years:
1st year: at least 100 million yen 2nd year: at least 400 million yen
Source: TSE (2002)
4 Generally, newly listed stocks are assigned to the Second Section. At the end of each year, the TSE examines the listed companies to determine whether they meet reassignment criteria (Liaw 1999).
5
Since the "big bang" economic policy took effect in 1996 and with the aim of creating a
free, globalised market5 with a greater transparency, Japan has seen a revolution in its
accounting practices (Carlile and Tilton, 1998; Sato, 1999). The reforms were enhanced
by advancements in information technology, communications and transportation (Liaw,
1999). Trading commissions were 40% higher in Japan than in London prior to the
reforms (Liaw, 1999). The deregulation of the financial system encouraged competition
by, for instance, allowing banks to sell investment trust funds through their branch
networks (Miyajima, 1998). In March 2000, the Japanese Securities and Exchange Law
(JSEL) was amended to require the preparation of consolidated financial statements,
whereas previously, only parent-only statements had been required. It is now mandatory
to include these in the annual report filed with the MOF. As of March 2000, the JSEL
also requires a Statement of Cash Flows to be produced.
The recent adoption of International Accounting Standards (IASs) in Japan is viewed as
a method for dealing with the institutional difficulties of recent years (Tajika 1999). In
2001, the accounting standard setting process underwent a change – from being under
government control – to being administered by a private sector organisation that was
expected to wield less political power (Ali and Hwang, 2000). The Financial
Accounting Standard Foundation, which includes the Accounting Standards Board of
Japan (ASBJ), is the private sector organisation that takes day-to-day responsibility for
the development of accounting standards. The new body was formed to protect Japan’s
position on the International Accounting Standards Board (IASB). Its structure ensures
that Japan's accounting standards are in line with those of other major international
5 See further ‘Comprehensive Reform of the Securities Market’ on June 13, 1997 (Matsuba 2001; Pilat 2002).
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standard-setting bodies (Beamish et al., 2001). It is tacitly assumed in the accounting
literature that countries with private accounting standard setting mechanisms provide
accounting information with a higher value relevance (Ali and Hwang, 2000).
1.4 Research question
The research contained in this thesis is operationalised by the following research
question:
‘What was the nature of the relationship between market values and
reported accounting information for five selected Japanese firms during the
period 1950 to 2004?’
A variety of models have been proposed to characterise this relationship, with either
security returns or earnings as the dependent variables. Like previous CMR studies, this
research adopts an econometric approach, using regression models for the relationship.
However, the emphasis of this study is on dynamic rather than static specification and,
in particular, on autoregressive distributed lag (ADL) models in their re-parameterised
form as ‘equilibrium correction models’ (ECMs). The focus in constructing such
models is on an accurate statistical description of the underlying relationship between
the market and the book values, rather than testing a maintained economic hypothesis.
1.5 Significance of the study
This study focuses on dynamic modelling and uses an econometric approach to the
testing of models – referred to as ‘general-to-specific’ (GETS). This approach places
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less emphasis on prior theoretical formulations of the relationship between market and
book values than is typically the case in CMR. It systematically analyses the dynamic
process underlying the relationship between the market and the book values, as well as
the more commonly investigated, static equilibrium relationships. This is a novel aspect
of the analysis within the accounting literature that is described further in Section 4,
below.
The thesis data is based upon a small sample of five listed Japanese firms collected from
their published financial statements and TSE announcements, for accounting periods
ending in March for the years 1950 to 2004. The five firms selected for investigation are
the Toyota Motor Corporation (Toyota), the Fuji Photo Film Co Ltd. (Fuji Photo Film),
the Sony Corporation (Sony), the Itochu Corporation (Itochu), and the Sumitomo Trust
Banking Co. Ltd. (Sumitomo). The firms were chosen on the basis of the availability of
the data. These firms are the leaders in their respective industries and, compared to
many other firms, supply a more reliable accounting information over a longer
continuous period of time. Through their many subsidiary and associate companies, the
firms have made a major contribution to the economies of Japan and of the world.
1.6 Organisation of the thesis
This thesis is organised as follows: Chapter 2 reviews the prior research, with sections
on previous econometric studies in accounting, finance and economics; and examines
studies that provide a detailed understanding of the Japanese financial markets. Chapter
3 describes the theoretical foundations for the models used in the study and how they
have been adapted from previous research. Chapter 4 examines the research methods
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employed with a detailed discussion of the sample data. Chapter 5 contains the core
results from the econometric analysis, with a focus on answering the first research
question. Chapter 6 discusses the results in the context of the sufficiency of accounting
information for estimating the market value of the five Japanese firms, and concludes
the thesis.
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CHAPTER 2 LITERATURE REVIEW
2.1 Introduction
Ball and Brown (1968) and Beaver (1968) studied the impact on stock returns of the
disclosure of earnings in financial reports on stock returns, which initiated the current
literature referred to as the CMR. During the following three decades, numerous studies
examined the impact of the disclosure of accounting information on share prices and
returns (Bartholdy et al., 2003; Brennan, 1991; Choi, 1995; Collins et al., 1997; Dechow,
1994; Easton, 1999; Fama and French, 1992; Harris et al., 1994; Kothari, 2001; Kothari
and Zimmerman, 1995). Fundamental analysis, a small but growing fraction of the
CMR literature, entails a study of accounting numbers to arrive at the valuation of a
company, the value of which is said to be based on ‘fundamentals’. Fundamental studies
therefore investigate the relationship between accounting numbers and the market value
of shares. Fundamental analysis in the CMR has concentrated on what has recently
become known as ‘value relevance’; that is, comparing the book and the market values,
and assessing the former on how well they correlate with the latter.
This chapter reviews this literature as a background to the theoretical framework for this
thesis (which is covered in Chapter 3).
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2.2 Value relevance
Ohlson (1995) modelled share price as a linear function of the accounting variables
derived from the present value of expected dividends assumption6 (PVED) and a clean
surplus relation assumption7 (CSR). The PVED and the CSR produce the two dynamic
equations that determine the returns in Ohlson’s theory. Most literature pertinent to this
study can be classified as ‘value relevance’ research, which models share price as a
linear function of accounting variables and other fundamental variables. The following
subsection discusses this research and other studies on the Japanese institutional and
financial systems.
2.2.1 Studies using cross-sectional analysis with share price
Until the late 1980s, most CMR focused on the behaviour of abnormal returns, which
emphasised the accounting disclosure issues. Researchers such as Ohlson (1995) and
Penman (1998) intuited the ‘return to fundamentals’ in the late 1980s, which led to the
research interest in value relevance as described above. The development of earnings-
based valuation models reflects an attempt to base company valuation directly on
accounting numbers. Despite the apparent limitations of CMR into abnormal returns
and earnings to inform judgements of market value, recent research has appeared to
support the use of abnormal earnings valuation models (Ohlson, 1995). Ohlson’s (1995)
model develops a residual income notion (RIM) in which firm value is derived as the
sum of the book value of equity and the present value of future abnormal earnings.
Abnormal earnings are defined as observed earnings minus a charge for the cost of
capital. Ou and Penman (1989) tested the hypothesis that current financial statement
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variables can be used to predict future earnings (and hence returns), which display
abnormal returns. They found that a portfolio based upon their predictions of earnings
led to abnormal returns of 8.3% in the first year. These findings suggested that financial
statements capture fundamentals that are not reflected in price, indicating an under-
usage of accounting information.
Collins et al. (1997) used Ohlson’s model to investigate the value relevance of earnings
and book values over a 41-year period. They concluded that the value relevance of
earnings seems to have declined over time, while the combined value relevance of book
value and earnings value had remained constant. The decreasing importance of earnings
appears to have been replaced by an increased value relevance in the book value.
Brennan (1991) argued that market reaction research and valuation studies are not
distinct, insofar as they are concerned with the price effects of the relationship between
new accounting information and the relationship between the level of prices and
accounting variables, respectively. The major difference between the two areas of
research is that market reaction studies generally analyse share price over short-term
intervals while valuation studies are relatively long-term in focus. The long-event-
window methodology has sought to explore the market-to-book relationship by
measuring changes in price and earnings over longer periods (Beaver et al., 1997).
There is concern that rapid price changes mean that the GAAP financial information
possesses less explanatory power in the new ‘information economy’ (Kothari and
Shanken, 2003). Kothari and Shanken found a significant variation in the coefficients of
6 Share price as the present value of expected future dividends discounted at the risk-adjusted expected rate of return. 7 Book value at time (t-1) is calculated from book value of time (t) plus dividends at time (t) minus net income of the current year.
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changing market growth expectations, discount rates and additional variables. They also
provided some indirect evidence of bias, possibly owing to the presence of correlated
omitted variables in value relevance studies.
2.2.2 Studies with share price changes and returns as the dependent variable
Bartov, Radhakrishnan and Krinsky (2001) used a comparative approach to investigate
which independent variable – earnings or cash flows – provided greater information
ability for equity returns within the US, the United Kingdom (UK), Canada, Germany,
and Japan during the period from 1988 to 1996. They concluded that earnings have
greater explanatory power than cash flows for securities returns in the three Anglo-
Saxon countries (the US, UK and Canada). Choi et al. (2002) investigated whether
earnings’ lack of timeliness or noise contributes to the low association between earnings
and returns of knowledge-based and traditional industries during the period from 1980
to 1994. They focused on noise resulting from investor uncertainty about future cash
flows related to intangibles. Choi et al. concluded that timing differences exist between
earnings and stock price changes, which are produced by investor activity, based on an
estimation of firm value derived from expected future benefits. They discussed the
possibility of higher uncertainty regarding future economic benefits leading to greater
information asymmetry between investors and managers and inducing more noise in the
estimated firm value in knowledge-based industries in comparison to the traditional
industries. They found only a weak association with either contemporaneous, past or
future returns.
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2.2.3 Combined studies with share price and returns as dependent variables
Kothari and Zimmerman (1995) considered economic and econometric issues in
investigating whether to use a price model, return model, or a combination of both to
best value shares. They hypothesised that higher accuracy in earnings sensitivity
coefficients can be obtained from levels of prices and earnings rather than first-
difference formulations. They argued that price models lead to economically sensible
earnings response coefficients, while return models suffer from fewer specification
problems. The combined use of both price and return models may thus be useful (Lev
and Ohlson, 1982). From a different perspective, Penman (1998) examined combined
earnings and book value models in equity valuation, arguing that if assets could be
measured at market value, all weight would be placed on book value in regressions,
rather than on earnings. Alternatively, if earnings were sufficient for valuing a firm, a
capitalisation rate to earnings could simply be applied, ignoring book value. However,
Penman’s ideal earnings and book value measures are not typically produced under
generally accepted accounting principles and the two variables cannot simply be added
together, as this would involve double counting.
Shroff (1995) concludes that the earnings of firms with a high price-earnings (P/E) ratio
and a high return on equity (ROE) exhibit greater explanatory power for a firm’s returns
than those with a high P/E and a low ROE. A firm's risk of liquidation was also shown
to affect the value relevance for returns of current earnings. In these circumstances,
therefore, a simple earnings capitalisation model that does not incorporate book value is
likely to be misspecified. Book value could thus be a value relevant factor in its own
right.
14
2.2.4 Studies including other fundamental variables
When modelling the relationship between security prices and accounting earnings,
research tends to use a number of variables. Lev and Thiagarajan (1993) studied the
value relevance of accounting variables, contextual returns-fundamentals analysis, and
the relationship between fundamentals. They included seven fundamental variables in
their models, including percentage changes in loans, net interest revenue, interest
expense, interest revenue, other operation expenses, allowance for loan losses and
number of employees. They constructed an aggregate score of 12 fundamentals for a
sample of firms, in the period between 1974 and 1988. The fundamental scores were
indicative of the expected direction of future earnings changes. Their findings supported
the incremental value relevance of most of the fundamentals studied. When conditioned
on macro-economic variables (e.g. inflation), the returns-fundamentals relation was
considerably stronger. Similarly, Beaver et al. (1997) studied 19 variables to investigate
the price-earnings relationships. They developed a model from the price-earnings
relationship that expresses percentage changes in price as a linear function of the
percentage change in earnings. A second model, comprising a revised version of this
linear regression, was based upon a simultaneous equation approach. The 19 variables
were used as instrumental variables in a first-stage estimation of the endogenous
variables, thereby mitigating bias.
Ali and Hwang (2000) took a comparative approach to exploring the value relevance of
financial accounting data, using US firms as a benchmark. Five country-specific factors
were examined, along with earnings and the book value of equity, relative to their
explanatory power in comparable US firms. The authors concluded that financial data is
15
less value relevant in countries with bank-oriented financial systems. Their finding is
consistent with the theory that banks have direct access to company information in
bank-oriented systems (Mueller et al., 1991; Choi and Mueller, 1992), which leads to a
lower demand for published, value relevant financial reports.
Ali and Hwang’s secondary finding was that there existed low value relevance of
earnings in countries where private sector bodies are not involved in the standard setting
process. They also concluded that lower value relevance exists in Continental-model
countries than in British-American model countries. Furthermore, value relevance
appeared to be lower for those countries in which tax rules significantly influence
financial accounting measurements. These findings are consistent with the belief that
companies in such countries have an incentive to report systematically lower profits to
reduce their taxes, making their accounting information less valid and reliable (Choi and
Mueller, 1992; Joos and Lang, 1994). Finally, Ali and Hwang concluded that the greater
the cost of the external audit, the greater the value relevance of earnings.
2.3 Japanese capital market research
CMR undertaken using Japanese data is reviewed next under similar headings to those
used for general CMR.
2.3.1 Studies of price level as a dependent variable in the Japanese stock market
Ota (2001a) investigated Felthman and Ohlson’s (1995) model of ‘Linear Information
Dynamics’ (LIM) and attempted to adapt the LIM to a Japanese context. As with
Ohlson’s model, LIM attempts to link current information to future abnormal earnings.
16
The market value of equity is calculated as book value plus goodwill, which is greater
on average than book value, as a result of accounting conservatism. The Felthman and
Ohlson (1995) formula contains a variable that denotes information other than abnormal
earnings that has yet to be captured in current financial statements, but which affects
future abnormal earnings (Ota, 2001a; Ota, 2002). This is the theoretical counterpart of
the omitted variables problem in the econometric testing of models. A number of recent
studies have investigated the role of accounting numbers and share valuation in Japan.
However, most such research has focused on the inclusion of other variables or on the
comparison of institutional differences such as parent-earnings with consolidated
earnings. The latter issue is discussed in subsection 2.2.7.
2.3.2 Studies of returns as a dependent variable in the Japanese stock market
Choi and Levich (1991) found little empirical evidence linking fundamental accounting
variables with securities returns in Japan. On the other hand, Alford et al. (1993)
compared the information content of accounting earnings in several countries, using the
US as a benchmark, and found evidence that earnings provide greater information value
for explaining the behaviour of security returns in Japan compared to the US. Similarly,
Chan et al. (1991) found that fundamental variables such as earning yields, book-to-
price ratio and firm size have a relatively positive relationship with excess stock returns
in Japan. They included delisted and listed securities in their sample and examined the
cross-sectional differences in returns on Japanese stocks in the period from 1971 to
1988 based on the four variables: earnings yield, size, book-to-market ratio and cash
flow yield using alternative statistical specifications and various estimation methods. In
contrast to this view, Hall et al. (1994) examined the contemporaneous association
17
between earnings and security returns in Japan and the US over various windows and
found that Japanese investors appeared to make less use of accounting information than
did their US counterparts.
More recently, Herrmann et al. (2001) investigated how the share market adjusts to the
disclosure of consolidated, parent or subsidiary earnings. Their results indicated that the
Japanese stock market adjusts according to parent-only earnings, but appears to
underestimate the significance of subsidiary earnings in current stock prices. Similarly,
in an investigation of the relationship between incremental subsidiary earnings and
future stock returns in Japan, Pope (2001) found that markets seemed to underestimate
the true persistence of subsidiary earnings.
2.3.3 Studies of other fundamentals in the Japanese stock market
In a study conducted over a 15-year period to 1989 (before the Japanese economic
crisis) using monthly data, Choi (1995) examined the relationship between share prices
and factors such as growth rate, price-earnings ratios (P/E), price-to-cash flows ratios
(P/C), and price-to-book value ratios (P/B) in Japan and the US. The study found that
the average P/E was almost 3 times higher, the P/C was about 2 times higher, and the
P/B was almost 3 times higher in Japan compared to the US. The data periods examined
in the study began after the turbulent adjustment period following the institution of a
flexible exchange rate system and the oil price shock of 1973, and ended before the start
of the asset price deflation in 1990. This valuation model identified earnings, exchange
rate changes, growth rates, the relative cost of capital and the dividends payout ratio as
significant independent variables. Evidence of the relaxation of constraints on capital
18
flows is shown through changes in the slope coefficients of exchange rate differences.
This is one of the more notable findings of the study. The results support the notion of
high corporate growth, financed by high retention and low dividends, leading to growth
in firm value in Japan. The relatively high valuation of Japanese stock (Aron, 1989;
French and Poterba, 1991; Hall et al., 1994; Choi, 1995) is shown as a trend 8
characteristic of the so-called ‘bubble economy’. This is likely to have been caused
largely by the institutional peculiarities of the Japanese financial market.
In another comparative study related to Japanese book and market values, Bae and Kim
(1998) investigated the effect of cross-corporate ownership and real estate holdings on
the basis of earnings-to-price ratio (E/P) and book-to-price ratio (B/P) during the 18
year period to 1993. It concluded that high E/P and B/P levels for Japanese stock were
due to market inefficiencies caused by the ‘stock market bubble’. The degree of cross-
corporate holdings (CRH) was calculated as the amount of investment by a firm in
affiliated firms, divided by the book value of the total assets for those firms. The degree
of real estate holding (REH) was calculated as the book value of land and buildings
divided by the book value of the total assets for the firm. It concluded that the
relationship between stock prices and accounting returns was likely to be stronger in
firms with high CRH levels than in firms with low CRH levels. The results from this
study are consistent with the idea that the interests of managers and shareholders in
larger Japanese corporations are more closely aligned with the company in an
environment of low information asymmetry (Hall et al., 1994). Despite this, Bae and
Kim (1998) also found that the ability of earnings and book values to predict future
8 Cargill et al. (1997) describe the movement of the land and stock prices as highly correlated and the long-term trend shows a steady rise from the mid-1970s to the mid-1980s and a sharp acceleration around 1985.
19
stock returns is weak in high-CRH firms. They also concluded that the market takes into
account a firm's real estate holdings when valuing its shares. These results are thought
to be due to the fact that Japanese shareholders hold equity not for the sake of short-
term capital appreciation, but rather to maintain long-term relationships with other
companies (Jacobson and Aker, 1995; Cooke, 1996). This leads Japanese managers to
manage their companies with a long-term perspective (Okumura, 1999). A related study
by Okabe (2002) found that the cross-holding ratio appeared to have declined in recent
years, with accelerated rates of decrease since the mid-1990s.
An example of a specific factor that is peculiar to the analysis of Japanese CMR is the
focus on the impact of real estate investment on financial markets. This echoes the
effects that followed the recent Asian financial markets crises in countries such as
China, Hong Kong and Indonesia (Calmiris and Beim, 2000). As discussed previously,
the Japanese market takes into account a firm's real estate holdings when valuing its
shares (Bae and Kim, 1998).
2.4 Studies of dynamic modelling
Kothari and Shanken (2003) investigated whether the estimated coefficients of equity
on the balance sheet and of income statement variables are influenced by omitted
expected growth or expected return variables. Their findings suggest that using book
value of equity as a deflator in estimating value relevance does not reduce the
relationship between the coefficients on earnings and book values, with growth
expectations and discount rates. Nissim and Penman (2001) also studied time series
regression. They claimed that book value and expected residual earnings are
20
determinants of equity 9 values. They found that changes in nominal interest rates
positively affected successive accounting rates of return and growth in net assets, and
had a negative effect on residual earnings. They stated that equity value would forecast
firm value over many years in the future; however, their conclusions were based on the
assumption that the long run would be similar to the short run.
Bartholdy et al. (2003) developed a theoretical model from the dynamic relationship
between market values and book values, and tested this model directly using a time
series analysis. Their approach was based upon the principle of co-integration using
Hendry’s (1995) ‘general-to-specific’ econometric method and Johansen’s vector error
correction approach. They examined the market to book value of net assets relationship
in the Standard and Poors Industrial 500 index over the period 1963-1998. The variables
of share price and market capitalisation value were used in the dynamic modelling
process to capture the difference between capital provided by shareholders and the book
value of equity. Bartholdy et al. (2003) found a weak linear relationship between book
values and market values in the long run. However, it seemed that the long run was at
least 10 years.
Willett (2003) applied the dynamic modelling approach to the market and book value
relationship in a single firm. Hendry’s (1995) ‘general to specific’ econometric method
was used to demonstrate co-integration between market value and accounting numbers
in the case of a large US company, over the period 1955-2002. Willett (2003) reasoned
that research into a single firm might provide a better understanding of the time series
9 Earnings in excess of required earnings on book values of the investment that involve generating the earnings.
21
behaviour of the relationship between market and book, because the aggregation of
many firms into an index (as in the case of the study by Bartholdy et al.) might obscure
such relationships. Willett (2003) developed a number of equilibrium error collection
models (ECMs) and found evidence of a co-integration relationship between market and
accounting values. However, unlike other models in CMR, the most well-specified
models were multiplicative rather than linearly additive in form. In a later paper using
the same data, Willett (2003) amended his models to conclude that, in the case of the
firm studied, the market seemed to be influenced by income statement rather than
balance sheet figures.
2.5 Summary
Published studies in CMR have focused on archival data of the relationship between
various functions of share prices and accounting information. These studies indicate a
broad diversity in the accounting treatment and methods of investigating the
relationship between share prices and accounting information. The results tend to show
a relationship between market and accounting values; however, other non-firm-specific
variables are also considered to be important in defining the relationship. The
framework and research method for this study have developed out of issues arising from
prior research, and are presented in the following two chapters.
22
CHAPTER 3 THEORETICAL FRAMWORK
3.1 Introduction
This chapter explains how the various issues arising from the literature review impact
upon the research question for this thesis. A number of findings and outstanding issues
from prior research will be used to inform the design of the theoretical framework for
this study. Previous studies in value relevance (as reviewed in Chapter 2) have so far
provided mixed results concerning the nature of the long run relationship between
markets and accounting numbers.
Prior research was categorised under two headings based upon the relationship between
market and book values. One category examined how a change in share price is affected
by changes in firm-specific attributes and other variables (Ball and Brown, 1968;
Landsman and Magliolo, 1988; Ou and Penman, 1989; Brennan, 1991; Kothari and
Zimmerman, 1995; Penman, 1998). The other category examined a possible correlation
between levels of stock prices and levels of firm-specific attributes and other variables
(Bowen, 1981; Mueller et al., 1991; Lev and Thiagarajan, 1993; Beaver et al., 1997; Ali
and Hwang, 2000). The challenge for the research undertaken in this thesis is to clarify,
in the context of Japanese market data, the consistent principles that underlie the
relationship between market and book values, taking into account previous findings
from various CMR studies.
23
3.2 Issues arising from capital market research
Two key and very general issues noted in prior research that require attention in the
design of the current research are: 1) inadequacies of theory; and 2) inadequacies of
method.
3.3 Inadequacy of theory
The work of Ohlson (1995; Ohlson and Zhang, 1998) has become the standard
theoretical benchmark for specifying models that relate a firm’s market value to
accounting numbers. Ohlson’s residual income model (RIM) identifies abnormal
earnings and book value of net assets as the main determinants of firm value, based on
the anticipated generation of value rather than the distribution of value. The RIM relies
on the assumptions noted in the review of the literature. Investors are implicitly
assumed to be risk-neutral, and the discount rate is usually taken as the risk-free rate.
The clean surplus assumption implies that dividends reduce current book value but do
not reduce current earnings, which is consistent with Modigliani and Miller’s (1961)
dividends irrelevancy proposition. Current dividends are assumed to reduce future
expected earnings by the interest the company could have earned on that dividends
(where dividends include all distributions to owner, net of capital contributions). In
addition to the assumptions noted earlier, abnormal earnings (the excess of earnings
over the charge for using capital) are assumed to be governed by a linear, stationary,
autoregressive function, plus a correction factor for information other than accounting
data and dividends.
24
These highly simplistic assumptions are based upon neo-classical economic theory. This
theory focuses on static equilibrium issues and says little about the nature of dis-
equilibrium processes. Ohlson's abnormal return dynamics is an attempt to address this
problem, but is limited by the constraints of the underlying neo-classical economic
assumptions. The CMR described in the previous section is characterised by
coefficients on key variables that often make little sense and are volatile over different
cross-sections. This suggests that existing theory probably does not provide a good basis
for choosing initial models of the relationship between market value and book value.
Consequently, the approach taken here will be to formulate the most general models
possible, using an empirically-driven econometric method to produce more reliable
coefficient estimates. It will then compare the results by way of a benchmark against the
performance of models based on Ohlson's theory.
3.3.1 Inadequacies of method
Inadequacies of method fall under two main headings: 1) failure to replicate prior
findings; and 2) emphasis on cross-sectional regression techniques.
Failure to replicate findings
Generally speaking, models that simply replicate prior research findings on a different
data set are not considered worthy of publication in accounting journals. This fact,
combined with a focus on searching for significant coefficients in regression modelling
and a lack of interest in whether such models are well-specified, has lead to a lack of
knowledge about just how robust or generalisable are the results of previous CMR
studies. The reporting of empirical results from the modelling process is often too
25
imprecise to allow replication to be undertaken with confidence. This issue is addressed
in the current research by selecting studies that are capable of replication, and by using
the thesis data to determine if the model results reflect similar patterns to those
previously reported. The purpose of this exercise is to ascertain if the sample data used
in this thesis research is likely to be robust and generalisable.
Emphasis on cross-sectional methods
CMR takes a predominately cross-sectional approach to testing particular model
specifications suggested by prior theories; usually judging performance of the model by
whether the R2 produced by the model is of an acceptable level. Dynamic model
specification has largely been avoided, presumably because OLS regressions with non-
stationary time series data tend to produce high R2 and significant t statistics, even when
no relationship exists between the dependent variable and independent variables.
Many researchers have tested Ohlson’s (1995) model using cross-sectional techniques.
Those approaches suffer the risk of statistical model misinterpretation from potentially
heterogenous firm level data-generating processes, and disregard potentially important
information about the dynamic properties of the models (Hendry, 1995). Yaekura
(2001) studied recent Japanese accounting and economic changes using Ohlson’s
(1995) model. However, although this study followed Ohlson, the focus of the analysis
was on parent company financial data; it did not address the incorporation of Ohlson’s
information dynamics nor did it use dynamic modelling. Both Qi et al. (2000) and Lo
and Lys (2000) argued that Ohlson’s (1995) model is likely to be misspecified, and that
recent CMR studies that employ a cross-sectional approach in testing that model are
26
likely to result in biased coefficient estimates and inflated R2 values. This thesis research
adopts a general-to-specific approach to dynamic specification that seeks to overcome
these problems.
3.4 Framework for the current study
This thesis focuses on the testing of the annual market to book relationships for TSE-
listed companies over the period 1950-2004, using dynamic modelling. To address the
stated research question it adopts a strategy adapted from that described in Willett
(2004). The strategy in this study consists of two parts. First, models are constructed
using variables chosen on the basis of their suspected explanatory significance from
prior research, and by applying the ‘testing down’ approach (Hendry, 1995) to a search
strategy to find the most parsimonious, well-specified, dynamic model that indicates a
sustainable statistical relationship between market and book values at the firm level.
Second, the resulting models are examined for robustness using the criteria of
forecasting ability in a hold-out sample, in comparison with time series models and two
a priori models (one of which is based upon Ohlson’s theory).
There are three stages in the first part of the framework, which involve the construction
of statistical models for the individual firms. In the first stage, financial data is collected
for each firm. The data is audited with attention given to whether the clean surplus
relation is satisfied. Ohlson’s clean surplus assumption is either explicitly or implicitly
assumed in most CMR, and it is therefore of interest to see if that assumption is
empirically sustainable. The second stage includes an exploratory analysis of the time
series characteristics of the data for the construction of the statistical models. Model
27
construction follows the testing down procedure described in Willett (2003), and is
undertaken using PcGive econometric software. A statistical error correction model
(ECM) for the individual firms results from this process. The mechanics of the GETS
modelling process are described in the next section. In the third stage of the first part of
the framework, the various statistical ECMs resulting from the search strategy are
compared and selected on the basis of their performance in a variety of specification
tests and subject to other econometric considerations. If, as with Willett (2003), the best
models in market and accounting values take a logarithmic form and have a maximum
of one lag in the data, the selected statistical model is used to formulate a simplified
ECM forecasting model of market value, which is based upon the retention of only the
long run component of the ECM.
The second part of the framework uses the best model constructed in the third stage of
part one and assesses its forecasting ability compared to two simple time series models:
one that includes only a constant (in levels), and another that includes a trend. The
autoregressive order of the models is chosen based upon their empirical characteristics.
In addition, the performance of the ECMs is compared to two a priori models: one
founded on Ohlson’s theory (including a calculated abnormal earnings variable), and
the other based upon book value of net assets (which is similar to a market to book ratio
formulation, in the form of the ECM derived later).
3.5 Summary
This chapter provided a discussion of the theoretical framework that underlies the
method used to analyse the relationship between market and book value data in this
28
study. Issues arising from the literature review in Chapter 2 were discussed, including
the dominance of cross-sectional methods in CMR, the need for dynamic modelling,
weaknesses in the nature of the theory underlying CMR, and the importance of
replication. Subsequently, it was explained how the framework addresses these issues;
that is, by taking a clearly defined and replicable approach to constructing statistically
sustainable models that are capable of sensible interpretation.
29
CHAPTER 4 RESEARCH METHOD
4.1 Description of research method relative to the theoretical framework This chapter discusses the methods used to implement the framework for the research
questions discussed in the previous chapter. As explained in Section 3.3, the program of
research embodied in this thesis consists of two stages: 1) gathering and auditing simple
quantitative data on market and book values for specific Japanese firms during the
period 1950-2004; and 2) testing competing models of the relationship between market
and book values, using a systematic, empirically-driven econometric procedure. Details
of the methods for completing these stages are contained in this chapter.
In view of often conflicting empirical results, the difficulties in replicating prior work,
and doubts about the feasibility of conclusively interpreting and testing the theories
upon which prior models are based, this research adopts an empirically-focused GETS
method, supplemented by a systematic search strategy. Although theory is important in
this process, its role is mainly restricted to: 1) determining which variables should be
included in the information set used to construct models; and 2) considering the
interpretation of the final models of the data-generating process for market value (DGP).
One underlying principle of the models is to use variables that are as simple and
publicly verifiable as possible. For this reason, artificial constricts and deflators are
avoided in the initial model formulation. The two dependent variables examined in the
30
models are share price and market value (defined as share price multiplied by the
number of shares outstanding). When price is the dependent variable, the number of
shares is included as a regressor in the models. The purpose of building the two
different classes of model is to check for consistency in results between the two model
forms and to assess that the assumption of the behaviour of share price in the presence
of share splits is empirically justified by the data.
A second class of model results from adjusting the book value of net assets and earnings
to agree with the clean surplus relationship. This will determine if any failure of the
clean surplus assumption alters the results of modelling. A third class of model results
from converting the nominal values of financial variables to their real counterparts, by
deflating the former by the consumer price index (CPI). Finally, a fourth class of model
is based upon taking logs of the variables. The common appearance of heteroscedastic
residuals in CMR regression models suggests this transformation. There are also other
good reasons for making the transformation when economic variables are involved; for
example, the positive values of variables such as share price. The resulting variety of
models and the implied search strategy for well-specified models is outlined in Figure
4.1-1. This search strategy is based upon the work of Willett (2003, 2004).
31
Figure 4.1-1: Error Correction Models – Map of testing down strategy for searching for best model
ECM (PcGive)
Book Value Revised book value
Untransformed Transformed Untransformed Transformed
Share Price
Market value
Share Price
Market value
Share Price
Market value
Share Price
Market value
ECMN1
ECMN1’
ECMN3
ECMN3’
ECM1
ECM1’
ECM3
ECM3’
ECMN2
ECMN2’
ECMN4
ECMN4’
ECM2
ECM2’
ECM4
ECM4’
32
Details of the research design are as follows:
1. Annual accounting and price data for firms listed on the Tokyo Stock Exchange
(TSE) for the period 1950-2004 are used for analysis. Macro-economic variables
identified from the review of prior research are obtained from various sources
(these will be described later).
2. The data is audited to check for internal consistency. The balance sheet identity is
verified, and income and dividends are reconciled to the changes in net asset
(book values) for each year over the sample period. All market data is checked to
confirm the treatment of share splits.
3. Explanatory data analysis is carried out using scatter plots of the variables,
examination of variable correlation matrices, unit root tests, histograms and
autocorrelation functions. This focuses attention on any visual patterns discerned
in the data, and identifies departures from normality and the level of integration
of the modelled variables.
4. ‘Statistical’ models are constructed from the initial information set developed in
steps 1 to 3 by testing down, using PcGive to identify the most parsimonious
statistical models supported by the data.
5. ‘Forecasting’ models are developed from the models resulting from step 4 based
only upon lagged data, to ensure there are no questions about endogeneity and
data snooping in the modelling process.
6. Alternative models based upon ‘pure time series’ and a priori regression analyses
of the long run relationship between market and book are compared to the
33
model’s explanatory power, specification accuracy, parametric stability and
forecasting performance.
4.2 Sample of firms
4.2.1 Selection of firms
The five sample firms were selected from different industries: Toyota falls within the
automobile manufacturing industry; Fuji Photo Film within the chemical industry; Sony
within the electrical industry; Itochu within the retail industry; and Sumitomo within the
banking, insurance and securities industries. The selection was ultimately based on the
availability of a continuous data series over a period of 50-55 years and accessibility;
thus, it is a convenience sample. All of the firms were listed on the TSE (First Section)
from the 1950s. Table 4.2-1 illustrates the firms’ industry codes (which define their
industry), the date of their stock listing on the TSE, as well as balance dates.
Table 4.2-1: Sample firms Company Name Industry Code Stock Listed Date Balance Date
1 Toyota Motor Corporation 7203 May 14 1949 Mar-31 2 Fuji Photo Film 4901 May 14 1949 Mar-31 3 Sony 6758 Dec 01 1958 Mar-31 4 Itochu 8001 July 06 1950 Mar-31 5 Sumitomo Trust Bank 8403 May 14 1949 Mar-31
Industry code: 4000- Chemical, Pharmaceutical and Medical Industries 6000- Electrical Manufacturing 7000- Automobile Manufacturing and others 8000- Wholesales, Retail, Banking, Insurance and Securities Industries
The data includes a range of factors that are considered of interest to a study of the
relationship between book and market values (French and Poterba, 1991; Hall et al.,
1994; Choi, 1995; Kothari and Zimmerman, 1995; Cargill et al., 1997; Bae and Kim,
34
1998; Miyajima, 1998; Ali and Hwang, 2000; Charitou et al., 2000; Kothari, 2001; Ota,
2002; Bartholdy et al., 2003). These factors include the outstanding number of common
stocks, dividends (Choi, 1995; Ohlson, 1995; Ota, 2002; Bartholdy et al., 2003; Willett,
2003; Willett, 2004), accounting earnings (Kothari and Zimmerman, 1995; Ali and
Hwang, 2000; Bartholdy et al., 2003; Willett, 2003; Willett, 2004), book value of equity
(Willett, 2003; Willett, 2004), share price (Choi, 1995; Kothari and Zimmerman, 1995;
Ota, 2002; Bartholdy et al., 2003; Willett, 2003; Willett, 2004) and macro-economic
variables such as CPI, GDP, Money Supply, Official Interest Rate, Foreign Exchange
Rate, Labour Productivity and Nikkei Index10 (Willett, 2003; Willett, 2004). Data for
the period 1949-1999 was collected from the TSE Information Office. Data for the
period 1980-2003 was obtained from DataStream. Data for the period 2000-2004 was
obtained from the individual firms’ web sites. A minimum of 50 consecutive years of
accounting data is available for each firm. Specifically, the sample includes firms that
satisfy the following criteria:
1. Continuously listed in the First Section of the Tokyo Stock Exchange
from the 1950s-2004, and currently included in NIKKEI 22511. (Generally,
newly listed stocks are assigned to the Second Section; at the end of each
year, the TSE examines the listed companies to determine their value12.)
2. Essential financial statement data (consolidated) is available.
3. March is the firm’s financial year end.
10 Calculated continuously since September 7, 1950. 11 The 225 components of the Nikkei Stock average are among the most actively traded issues on the first section of the TSE. 12 See Table 4 for First Section criteria.
35
4.3 Variable definitions
This section provides definitions of the variables used in the econometric models.
4.3.1 Dependent variables
The dependent variables modelled in this study are share prices and market values of
the relevant firms. These are defined as follows:
Share price (PRCCFU): Raw share price quoted at the end of the financial year.
Market value (M): Raw share price at the end of each financial year multiplied by the
number of outstanding shares at the end of each relevant year.
4.3.2 Independent variables
Based upon theoretical and practical considerations, the following variables are
included as independent variables in the models:
Financial variables:
Number of outstanding common shares (CSHOU): Number of outstanding ordinary
shares at the end of the financial year.
Book value of net assets (B): The equity due to common (ordinary or equity)
shareholders per the balance sheet at the end of each fiscal year.
36
Revised book value (REVB): Reconstructed book value is based on Ohlson’s theory of
clean surplus definition; opening book value plus earnings less dividends adjusted for
changes in invested capital equals closing book value.
Earnings (E): Net profits (income) after tax at the end of each financial year, excluding
minority interests but including extraordinary items and prior year adjustments.
Dividends (D): Distributions due to common shareholders as per the balance sheet at the
end of the financial year.
Macro-economic variables:
The relationship between market and book values may be stronger if macro-economic
and other non-firm-specific information is allowed for (Willett 2003). A number of
prior CMR studies have examined the effect of macro-economic variables on equity
values (Bilson, 1999; Nissim and Penman, 2001; Bilson and Brailsford, 2002;
Bartholdy et al., 2003; Nissim and Penman, 2003; Willett, 2003; Suwardi, 2004). The
following macro-economic variables are included in the information set for the initial,
general, unrestricted model, which is formulated as an autoregressive, distributed lag
(ADL) model:
Gross domestic product (G): Nominal Japanese gross domestic product (GDP) at
current prices at the end of the financial year. The GDP data is obtained from the
Statistics Information Site of Economic and Social Research Institute, operated by the
37
Cabinet Office, Government of Japan (Source: www.esri.cao.go.jp) (Cabinet Office,
2004).
Official discount/interest rates (r): The source is the Bank of Japan, taken at the end of
the financial year (Source: www.boj.or.jp) (Bank of Japan, 2004; Cabinet Office, 2004).
Gross official discount (interest) rates (Gr): obtained by IRPRM multiplied by 100.
Foreign exchange index (X): These rates are the yen compared with the US dollar,
expressed as yen per US dollar. The foreign exchange rate was fixed at 360 yen per one
US dollar between 1949 and 1970. This data is obtained from the Bank of Japan
Statistics Information Centre at the end of the financial year. The most current data is
obtained through the Internet web site of the Bank of Japan (Source: www.boj.or.jp).
Consumer Price Index (I): This is the period average of the Japanese consumer price
index, calculated at the end of the financial year. The data was obtained from the
Japanese Statistics Bureau (Source: www.stat.go.jp).
Money Supply (MS): This is defined as the total supply of currency, demand deposits
and quasi money in circulation and outstanding at the end of the relevant period. The
data was collected from three publications by the Bank of Japan: Keizai Tokei Nenpo
(1997), Honpo Keizai Tokei (1965, 1960), and Maiji Iko Honpo Shuyo Keizai Tokei,
(1965). Most of the current data was obtained from the Bank of Japan Information
Centre.
38
Productivity Index (PR): This variable is defined as the Labour Productivity Index of
the manufacturing industry in Japan at the end of the financial year. The data was
obtained from Rodo Seisansei and Bukka Shisu Nenpo, published by the Labour
Productivity Association (1990), with the exception of the indices for the most recent
two years, which were taken from the Labour Productivity Association web site.
4.4 Exploratory data analysis
The previous chapter provided an explanation of the main methodology for the research,
which is dynamic specification modelling. The frequency distributions of individual
variables and their correlations, and the non-stationary characteristics of individual time
series are of importance for this purpose. Descriptive analyses of the time series mean,
standard deviation, skewness and kurtosis are reported. Visual inspection of sequence
plots of the major variables and their autocorrelation functions is undertaken, to
determine whether they give any clues about the likely relationships between the time
series and to identify possible errors in the data set.
Many economic time series data appear to have a unit root (Granger and Newbold,
1974; Engle and Granger, 1987; Gujarati, 1995), which may produce spurious
regression results (Yule, 1926) manifested in a very high R2 (Gujarati, 1995). Therefore,
the following Augmented Dickey-Fuller (ADF) test was used to test variable
stationarity. The ADF(1) tests produces t-values for the coefficient (β-1) in the
following regression:
tttt yyy εγβα +Δ+−+=Δ −− 11)1( (4.1)
39
The coefficient is tested for significance based on the null hypothesis, H0, of non-
stationarity. Rejection of the null hypothesis implies stationarity or that the series is
integrated of order zero, ‘I (0)’. A failure to reject implies that the variable is non-
stationary. The results of the ADF tests are reported in Chapter 5.
4.5 General-to-specific approach
The research method described above endeavours to combine best practice in both
quantitative and qualitative analyses. The PcGive software provides both single
equation and systems of equations approaches to testing for co-integration in time series
data. The modelling approach adopted in this research is restricted to single equations.
The precise details of the testing down approach are taken from the procedure described
in Willett (2003) and are reproduced in Appendix 1.
A basic formula of initial, unrestricted ADL is as follows:
tijt
j
j
i
ij
i
iitit uZyy ++= −
=
=
=
=
=
=− ∑ ∑∑
11
1
2
1
2
1βαα (4.2)
where Yt is either the raw price of a share (P: the trading price of a share at the end of
the financial year) or market value (M: the price of a share multiplied by the number of
outstanding shares), and depends upon the set of regressors Z, including the book value
of accounting variables and a number of macro-economic variables. The set Z includes
not only the current value of the variables but also their past values up to 2 lags. L is a
lag polynomial. In the first stage of the modelling process, the unrestricted ADL model
(4.2) is tested down, eliminating the least significant regressors to estimate the long run
parameters for calculation of the error correction term in the ECM. The ECMs are
40
modelled using first differences of the dependent variable, first differences of
significant regressors and one lag on the ECT, as no lags longer than one period survive
the first part of the testing down process from the original ADL.
ECMs were developed in the four basic forms shown in Table 4.5-1, consistent with the
strategy outlined in Figure 4.1-1. ECM1 and ECM3 contain untransformed and log
transformed variables. ECM 2 and ECM 4 contain explanatory variables including
revised book values instead of book value with macro-economic variables.
Table 4.5-1: Matrix of basic model construction categories of ECM
Dependent variable
Share price
Market value
Regressor Untransformed Transformed Untransformed Transformed
Book value ECM N1 ECM 1 ECM N3 ECM 3
Revised book value
ECM N2 ECM 2 ECM N4 ECM 4
The ECM produced by this process resulted in a formulated model with two parts, as
follows:
1−−=Δ ttt LRDSRDy
i.e.)
( )⎥⎦
⎤⎢⎣
⎡Ζ−−⎥
⎦
⎤⎢⎣
⎡ΔΖ=Δ ∑∑ −−
jjtjt
itit yy 11 βλα (4.3)
41
where Δ represents first differences of the relevant untransformed or transformed
variables. SRD denotes the ‘short run dynamics’ of the model, which are measures of
the contemporaneous change in the relevant variables. LRD represents the ‘long run
dynamics’ or error correction part of the models. The latter shows the imbalance
between the dependent variable and its estimated long run value based upon the
regressors left in the model.
The models were tested using Hendry’s (1995) congruent model criteria (see Appendix
7.1), which is based on specification tests for autoregression, autoregressive conditional
heteroscedasticity, and Ramsey’s Regression Specification Error Test (RESET). Well-
specified models give the promise of more accurate and meaningful estimates of
coefficients, more relevant variables, and a sound functional form for the resulting
regression relationship. A summary of the diagnostic tests and the forecasting criteria
applied to the models is given in Table 4.5-2.
Table 4.5-2: Diagnostic tests and RMSE:
Model specification errors Conditions for testing sequence on the residuals Autocorrelation: AR-1 (AR) Most prevalent form of serial correlation is where the errors in the
current period are positively correlated to those in the previous period. H0: No auto correlation
Autoregressive conditional Heteroscedasticity: (ARCH)
Test for parameter constancy.
Normality test Skewness and excess kurtosis. Heteroscedasticity This test is the constancy of the variance of the model.
If it exists, estimated SE: biased and inconsistent. Ramsey’s Regression Specification Error test (RESET)
This test shows if F value is highly significant, the model is misspecified.
Root Mean Square Error: RMSE
2/1
1
21 )( ⎥⎦
⎤⎢⎣
⎡−= ∑
=
H
tttH fyRMSE
(4.4) H: number of years for prediction; yt the actual values; ft the forecasts obtain from hold-out year.
(Gujarati, 1995; Hendry et al., 2003)
42
4.6 Benchmarking and replication Forecast performance of models in a hold-out sample is recommended for time series
models (Hendry, 1995). To make sense of performance in such tests it is useful to have
comparative benchmarks. The benchmarks adopted in this thesis are the random walk
model with a constant, and the benchmark model with a constant and a trend, following
Willett (2004). The former ‘pure time series’ model is later referred to as the ‘random
walk model’ and the latter as the ‘benchmark ECM’. The reason for the designation
‘benchmark ECM’ is that the trend term can be interpreted as a simple attractor for the
dependent variable in the ECM. The results from the comparisons of the respective
models estimated for each firm’s data will provide an overall impression of model
robustness in describing the behaviour of the dependent variable. The forecasting ability
of the models is compared using the root mean square error measure (RMSE). A four
year hold-out period, ten-year hold-out period and a sequence of ten one-year hold-out
periods were used for calculating the RMSE.
Finally, as noted earlier and again following Willett (2004), for the purposes of further
testing the robustness of the models, the forecasting ability of the best forecasting
models is compared with two a priori models: one based on Ohlson’s theory, with
abnormal earnings and book value of net assets as the regressors, and the other with the
book value of net assets. The Ohlson model in log variables is defined as follows:
ΔMt =k1 - λ { Mt-1
- ( αΑt-1 + βΒt-1) } (4.5) and,
11 −−−= tttt BREA (4.6)
43
where At is Abnormal earnings; Et is Net income; Bt is Book value of net assets and Rt is
the prime interest rate.
The simple book value of net assets regressed on market value model is defined as:
ΔMt =k1 - λ ( Mt-1 - βΒt-1) (4.7)
where Mt is Market value of equity and Bt is Book value of net assets.
4.7 Significant points relating to method
Following are some of the significant and novel features of the method adopted in this
research, in comparison to previous CMR:
1. A dynamic analysis of this type has rarely been employed in CMR; most current
CMR focuses on cross-sectional analyses.
2. Market and book values are kept separate in the econometric models; the market
values are only allowed to appear on the right hand side (RHS) of models in
lagged form, and variables are not allowed to appear on both sides of an equation
to be estimated. Book values are not ‘deflated’ by price and dividends only
appear on one side of an estimated relationship. This restriction to objectively
observable, publicly available variables is novel in CMR. Mixing market and
book values (as is normally done in CMR) has the apparent advantage of
increasing the explanatory power of models, but the disadvantage that it possibly
sets up artificially induced relationships between the data.
44
3. The final models are assessed using a hold-out sample to assess forecasting
performance as the main criteria of model choice. This is occasionally done in the
literature and is always advocated by econometricians but is not often followed in
CMR.
4. The general-to-specific method advocated by Hendry (1995) and implicit in the
software package used for modelling reduces reliance on prior, unreliable
theoretical specification. This approach initially concentrates on the importance
of correct model specification, as opposed to focusing on drawing the best
estimates from possibly incorrectly specified models. The latter ‘specific-to-
general’ approach is typical in CMR. The general-to-specific approach used in
this research has the key advantage of avoiding the serious danger of omitting
significant, relevant, correlated explanatory variables from the tested models.
This reduces the risk of coefficient estimates being biased and inconsistent, only
at the expense of reduced efficiency of the estimates.
4.8 Summary
This chapter described the method used to implement the research framework described
in Chapter 3, outlined the approach to testing the variables used in this study and
defined the methods of model construction. Finally, the chapter described an error
correction modelling approach using the econometric software package that provides an
audit path to the derived models for the purpose of comparability with other studies.
45
CHAPTER 5 RESULTS
5.1 Introduction
This chapter presents the results of the analysis of the relationship between market
values and accounting variables at the individual firm level, using the research methods
described in Chapter 4. The first section of this chapter comprises a general description
of the five firms (Toyota, Fuji Film, Sony, Itochu, and Sumitomo Trust & Banking), and
an exploratory analysis of the macro-economic variables and firm financial data,
including descriptive statistics, unit root tests and diagnostic tests. The following
sections describe a brief historical context and the equilibrium correction model of
market values that results from applying the modelling procedure for each firm in turn.
5.2 General points relating to the five firms
This section describes the general points relating to the five firms, from an exploratory
analysis of the data series used in this study (as described in Chapter 4). Tables of
descriptive statistics for the financial variables for each firm and the macro-economic
variables used in the study are contained in subsection 5.2.1. Subsection 5.2.2 examines
levels of integration of firm-specific financial data and macro-economic variables using
stationarity tests. A summary of the diagnostic tests is given in subsection 5.23.
5.2.1 Descriptive statistics
Table 5.2-1 contains descriptive statistics for the annual data for each of the firms
examined in this study. As stated in Chapter 4, the data covers at least 50 years, within
46
the period 1950-2004. The sample criteria required firms to have a financial year end of
31 March, for their stock to be listed on the TSE in the First Section (as reported in the
Nikkei 225) and for the availability of continuous financial data.
A comparison of the untransformed book value and market value data indicates that
market value in Toyota, Sony and Itochu has a standard deviation that is almost double
that of book value. In the case of Fuji Photo Film, the standard deviation of market
value is approximately 1.5 times that of book value, and for Sumitomo Trust & Banking,
market value is almost three times larger than the standard deviation. Similarly, the
means of book values of net assets in Toyota, Sony and Itochu are about half the means
of their market values. For Fuji Photo Film, the mean of book value of net assets is 64%
of the mean of its market value, and for Sumitomo Trust & Banking, the mean book
value is 40% of its market value. The data series for each variable for all five firms
indicates a continuous upward trend over the 50-year sample period. Toyota displayed
the largest increase among the five firms.
Transforming the data into logs had the effect of subduing the differences noted above.
Market value is naturally positive and taking logs can bring the transformed data closer
to normality (Hendry, 1995, p.19). The log transformation had the expected impact on
the gross differences but appears to have had little impact on normalising the data in this
case, at least in terms of the skewness and kurtosis estimates in Table 5.2-1.
Table 5.2-2 shows equivalent descriptive statistics for the macro-economic variables
used in the study. Similar comments apply to these variables as to the firm-specific
47
variables. The data trends are upward over time, such that the information value of these
statistics is limited. The skewness statistics indicate that all macro-economic variables
exhibit signs of skewness and kurtosis. The testing with levels indicates an abnormal
distribution for GDP and the money supply. The Nikkei and labour productivity series
are skewed to the right; the logged series are skewed left. The kurtosis statistics show
that all macro-economic variables appear to possess a ‘flat’ distribution.
48
Table 5.2-1: Descriptive statistics of financial data for five firms (1950-2004)
First level Mean
Std. Dev. Skewness Kurtosis Logged
Mean
Std. Dev. Skewness Kurtosis
TOYOTA M 4309229 5290038 1.07 -0.07 Log M 13.42 2.85 -0.86 -0.11 N 1970 1540 0.06 -1.85 Log N 6.85 1.70 -1.35 1.02 P 1284 1278 1.16 0.40 Log P 6.55 1.22 -0.23 -1.10 E 194277 234256 2.13 5.80 Log E 11.09 2.01 -1.10 0.84 D 32929 35678 1.00 0.04 Log D 9.31 2.00 -1.02 0.67
B 2143522 2566791 0.91 -0.60 Log B 12.91 2.52 -0.59 -0.81
FUJI FILM M 722604 805939 0.60 -1.30 Log M 11.94 2.37 -0.49 -0.99 N 315176 192650 -0.27 -1.53 Log N 12.27 1.13 -1.43 1.45 P 1515 1519 0.64 -1.17 Log P 6.58 1.36 -0.01 -1.68 E 36372 36036 0.42 -1.36 Log E 9.34 1.96 -0.41 -1.47 D 4111 4125 0.98 -0.46 Log D 7.64 1.36 -0.54 -0.40
B 479316 590298 0.93 -0.67 Log B 11.52 2.25 -0.24 -1.33
SONY M 1375829 1632867 1.53 1.91 Log M 12.87 2.26 -1.12 0.76 N 264513 239407 1.60 2.60 Log N 11.96 1.28 -1.22 1.56 P 3792 3071 1.60 4.57 Log P 7.82 1.08 -0.88 0.09 E 38527 70440 -1.59 10.50 Log E 9.22 3.72 -4.49 25.65 D 8342 7739 0.61 -0.95 Log D 8.18 1.71 -0.96 0.22
B 688465 785070 0.96 -0.42 Log B 12.02 2.35 -0.79 -0.38
ITOCHU M 364214 387191 1.10 0.72 Log M 11.57 2.26 -1.11 0.48 N 758825 555866 0.06 -1.59 Log N 12.85 1.71 -1.63 1.99 P 356 245 1.07 0.97 Log P 5.63 0.73 -0.11 -1.03 E 2045 28043 -1.93 6.19 Log E 5.27 6.99 -1.69 1.26 D 3443 3057 0.77 -0.63 Log D 7.22 2.14 -1.94 3.72
B 163392 185993 0.91 -0.85 Log B 10.88 1.93 -0.74 -0.14
SUMITOMO M 694967 1027898 2.11 4.96 Log M 11.48 2.87 -0.58 -0.85 TRUST & N 688080 541827 0.03 -1.66 Log N 12.67 1.70 -1.11 0.17 BANKING P 639 860 2.43 6.62 Log P 5.73 1.30 0.16 -1.16 E 5463 40459 -1.92 7.05 Log E 6.52 6.05 -2.41 4.89 D 4121 3878 0.54 -1.24 Log D 7.35 1.87 -0.90 -0.26
B 270341 314345 0.84 -1.00 Log B 11.04 2.35 -0.70 -0.58
Note: M: Market value (million Yen); P: share price (Yen); N: number of shares E: Net incomes (million Yen); D: Dividends (Yen); B: Book value of net assets (million Yen)
49
Table 5.2-2: Descriptive statistics for macro-economic variables (1950-2004)
CPI GDP IRPRM MSUPP EXCHRT NIKKIDX PRDINDX LCPI LGDP LIRPRM LMSUPP LEXCHRT LNIKKIDX LPRDINDX
Mean 59.92 231589 4.70 2581106 247 8761 58.55 3.88 11.61 1.16 13.82 5.40 8.36 3.75 Median 69.65 216920 5.50 1868665 239.95 6286 58.54 4.24 12.29 1.70 14.44 5.48 8.63 4.07 Maximum 101 545877 9.00 7010439 360 38916 147 4.62 13.21 2.20 15.76 5.89 10.57 4.99 Minimum 14 5485 0.10 27134 99 166 6 2.66 8.61 -2.30 10.21 4.60 5.11 1.84 Std. Dev. 34.13 198525 2.57 2474047 105 8846 39.22 0.72 1.52 1.21 1.77 0.49 1.41 0.90 Skewness -0.10 0.28 -0.49 0.52 -0.16 1.23 0.36 -0.40 -0.64 -1.77 -0.64 -0.39 -0.47 -0.61 Kurtosis -1.78 -1.56 -0.77 -1.31 -1.71 1.34 -0.91 -1.57 -1.05 2.16 -0.95 -1.57 -0.89 -0.91 Observations 55 55 55 55 55 55 55 55 55 55 55 55 55 55
Note: IRPRM: Interest rate; MSUPP: Money supply; EXCHRT: Foreign exchange rate per US$1; NIKKIDX: Nikkei Index; PRDIDNDX: Labour productivity LCPI: Log CPI; LGDP: Log GDP; LMSUPP: Log Money supply; LEXCHRT: Log Exchange rate; LNIKKIDX: Log Nikkei index; LPRDINDX: Log Productivity index. All macro-economic variables are obtained at the end of financial year
50
5.2.2 Levels of integration of the variables
This sub-section examines the stationarity of the data series, using graphical analysis
and Augmented Dickey-Fuller (ADF) tests. In the graphical analysis, an autocorrelation
function (ACF) was constructed using twenty lags. A graphical analysis of the market
and book value series in levels and first differences is given for Toyota in Figure 5-1,
panels A-D, to illustrate features common to all firms13. Indications of the order of
integration of the macro-economic variables are shown in Figure 5-2, panels A-D. For
co-integration between two or more data series used here, it is strictly necessary that the
series included in the initial ADL are I(1) and that their first differences are I(0). It is
evident from the sequence plots and ACFs for the different firm-specific data series that
this appears to be broadly the case, given the number of observations available. The
pattern is somewhat stronger with the logarithmically transformed data series.
Sequence plots and ACFs for the macro-economic variables are shown in Figure 5.2-2.
First differences and the first difference in logs for interest rate, exchange rate, Nikkei
index and labour productivity indicate stationarity, while the time pattern of these series
in the case of GDP, CPI and money supply suggest these may behave as I(2) variables.
The ADF test results for all variables are shown in Table 5.2-3. These are applied to
levels, first differences in levels, logged levels and first differences of the logged levels.
Here, attention is focused on the logs of the variables, as these are used in all models
reported later. The results show that logged market value (LM) tests marginally
stationary in Toyota, logged earnings (LE) tests marginally stationary in Itochu and
51
more significantly stationary in Sumitomo Trust & Banking. Logged book value tests
stationary in Toyota. Otherwise, the results of the tests support the working assumption
that the accounting variables are non-stationary in levels and stationary in first
differences.
In the case of the macro-economic variables, the consumer price index (LI) and the
logged money supply (LMS) both show deviation from the standard pattern described
above. LI appears to be possibly non-stationary in levels and first differences, again
suggesting a I(2) variable, while LMS also tests marginally significant as non-stationary
in both levels and differences. Consumer price indices commonly test as I(2) (Gujarati,
1995). However, given the weakness of the ADF tests (Davidson and MacKinnon,
1993; Gujarati, 1995), it is not possible to be entirely confident in this assessment.
13 See other firms’ graphical analyses in Appendix 3.
52
Figure 5-1: Financial data in levels - Toyota Corporation
Panel A: Toyota Market value in levels
1950 1960 1970 1980 1990 2000500000
3.5e66.5e69.5e6
1.25e71.55e71.85e7 Toyota Market value
0 5 10 15 20
0
1Toyota ACF-Market value
1950 1960 1970 1980 1990 2000
10
15Toyota Logged market value
0 5 10 15 20
0
1Toyota ACF-Logged market value
1950 1960 1970 1980 1990 2000
-5e6
0
5e6Toyota First difference of market value
0 5 10 15 20
0
1Toyota ACF-First difference of market value
1950 1960 1970 1980 1990 2000
0
1
2Toyota First difference of logged market value
0 5 10 15 20
0
1Toyota ACF-First difference of logged market value
Panel B: Toyota Book value of net assets in levels
1950 1960 1970 1980 1990 2000500000
2.5e6
4.5e6
6.5e6
8.5e6 Toyota-Book value of net assets
0 5 10 15 20
0
1Toyota ACF-Book value of net assets
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5
15.0 Toyota-log book value of net assets
0 5 10 15 20
0
1Toyota ACF-log book value of net assets
1950 1960 1970 1980 1990 2000
0
250000
500000
750000Toyota difference of book value of net assets
0 5 10 15 20
0
1Toyota ACF-Difference of book value of net assets
1950 1960 1970 1980 1990 2000
0.00
0.25
0.50 Toyota difference of log book value of net assets
0 5 10 15 20
0
1Toyota ACF-Difference of logged book value of net assets
53
Panel C: Toyota Dividends in levels
1950 1960 1970 1980 1990 2000
50000
100000
150000Toyota Dividends
0 5 10 15 20
0
1Toyota ACF-Dividends
1950 1960 1970 1980 1990 2000
5.0
7.5
10.0
12.5Toyota-Log dividends
0 5 10 15 20
0
1Toyota ACF-Log dividends
1950 1960 1970 1980 1990 2000
0
20000
40000Toyota-Difference of dividends
0 5 10 15 20
0
1Toyota ACF-Difference of dividends
1950 1960 1970 1980 1990 2000
0
1
2Toyota Difference of log dividends
0 5 10 15 20
0
1Toyota ACF-Difference of logged dividends
Panel D: Toyota Net incomes in levels
1950 1960 1970 1980 1990 2000
500000
1e6Toyota Net income
0 5 10 15 20
0.5
1.0Toyota ACF-Net income
1950 1960 1970 1980 1990 2000
5.0
7.5
10.0
12.5
15.0Toyota Log net income
0 5 10 15 20
0
1Toyota ACF-Log net income
1950 1960 1970 1980 1990 2000
0
200000
400000Toyota Difference of net income
0 5 10 15 20
0
1Toyota ACF-Difference of net income
1950 1960 1970 1980 1990 2000
0
1
2 Toyota Difference of log net income
0 5 10 15 20
0
1Toyota ACF-Difference of log net income
54
Figure 5-2: Macro-economic variables in levels
Panel A: Raw Level
1960 1980 2000
25
50
75
100 CPI
0 10 20
0
1ACF for CPI
1960 1980 2000
2.5
5.0
7.5
10.0Interest rate
0 10 20
0
1ACF for Interest rate
1960 1980 2000
100
200
300
400Exchange rate
0 10 20
0
1ACF for exchange rate
1960 1980 2000
250000
500000GDP
0 10 20
0
1ACF for GDP
1960 1980 2000
2.5e6
5e6
7.5e6Money supply
0 10 20
0
1ACF for money supply
1960 1980 2000
50
100
150Productivity index
0 10 20
0
1ACF for productivity index
1960 1980 2000
10000
20000
30000
40000Nikkei index
0 10 20
0
1ACF for Nikkei index
Panel B: Macro-economic variables in the first difference level
1960 1980 2000
0
5
10Difference of CPI
0 10 20
0
1ACF-Difference of CPI
1960 1980 2000
-2.5
0.0
2.5
5.0 Difference of interest rate
0 10 20
0
1ACF-Difference of interest rate
1960 1980 2000
-50
0
50Difference of exchange rate
0 10 20
0
1ACF-Difference of exchange rate
1960 1980 2000
0
20000
40000 First difference of GDP
0 10 20
0
1ACF-Difference of GDP
1960 1980 2000
0
200000
400000
600000Difference of money supply
0 10 20
0
1ACF-Difference of money supply
1960 1980 2000
-10
0
10
20First difference of productivity
0 10 20
0
1ACF-Difference of productivity
1960 1980 2000
-10000
0
10000Difference of Nikkei index
0 10 20
0
1ACF-Difference of Nikkei index
55
Panel C: Macro-economic variables in the log level
1960 1980 2000
3
4
5Log CPI
0 10 20
0
1ACF-Log CPI
1960 1980 2000
-2
0
2Log interest rates
0 10 20
0
1ACF-Log interest rates
1960 1980 2000
5.0
5.5
6.0Log exchange rate
0 10 20
0
1ACF-log exchange rate
1960 1980 2000
10
12
14Log GDP
0 10 20
0
1ACF for log GDP
1960 1980 2000
12.5
15.0Log money supply
0 10 20
0
1ACF for log money supply
1960 1980 2000
2
3
4
5Log productivity index
0 10 20
0
1ACF for log productivity index
1960 1980 20005 07 510 012 5
0 10 20
0
1ACF for log nikkei index
1960 1980 2000
8
10Log Nikkei indx
Panel D: Macro-economic variables in the first difference of log level
1960 1980 2000
0
5
10 First difference of CPI
0 10 20
0
1 ACF-First difference of CPI
1960 1980 2000-2.50.02.55.0 First difference of interest rate
0 10 20
0
1 ACF-First difference of interest rate
1960 1980 2000
-50
0
50 First difference of exchange rate
0 10 20
0
1 ACF-First difference of exchange rate
1960 1980 2000
02000040000 First difference of GDP
0 10 20
0
1 ACF-First difference of GDP
1960 1980 20000
200000400000600000 First difference of money supply
0 10 20
0
1 ACF-First difference of money supply
1960 1980 2000-10
01020 First difference of productivity
0 10 20
0
1 ACF-First difference of productivity
1960 1980 2000
-10000
0
10000 First difference of Nikkei index
0 10 20
0
1 ACF-First difference of Nikkei index
56
Table 5.2-3: Augmented Dickey Fuller (ADF) tests on individual firms Financial TOYOTA FUJI FILM SONY ITOCHU
SUMITOMO TRUST BANK
variables
t value for ADF Stationary?
T value for ADF Stationary?
t value for ADF Stationary? t value for ADF Stationary? t value for ADF Stationary?
M -2.46 -0.45 -2.03 -2.16 -2.03 ΔM -3.02 ** -6.49 ** -7.76 ** -4.09 ** -2.80 LM -3.72 * -1.49 -1.53 -1.97 -0.87 ΔLM -5.96 ** -6.04 ** -5.80 ** -4.63 ** -5.96 ** E 0.51 0.43 -1.55 -3.51 -7.23 ** ΔE -3.51 * -3.85 ** -3.64 * -6.37 ** -5.02 ** LE -2.39 -1.62 -1.87 -3.78 * -4.71 ** ΔLE -5.35 ** -5.35 ** -4.91 ** -6.40 ** -7.48 ** D 0.66 -0.31 -0.07 -4.08 * -2.23 ΔD -3.20 -9.32 ** -8.09 ** -6.31 ** -2.07 LD -2.37 -1.08 -1.38 -1.98 -1.55 ΔLD -4.85 ** -8.64 ** -8.01 ** -5.86 ** -6.70 ** B -0.02 5.98 1.53 -1.72 -1.44 ΔB -1.46 -1.69 -4.25 ** -2.81 -2.64 LB -3.66 ** -1.64 -1.79 -1.25 -1.32 ΔLB -5.35 ** -5.44 ** -6.50 ** -3.80 * -5.14 **
ADF tests: macro-economic variables (MEC)
MEC t value for ADF Stationary? MEC
t value for ADF Stationary? MEC t value for ADF
Stationary?
I -1.37 MS -2.40 NIK 1.65 ΔI -1.83 ΔMS -2.32 ΔNIK -1.54 * LI -0.45 LMS -3.45 * LNIK -0.53 ΔLI -2.08 ΔLMS -3.72 * ΔLNIK -5.98 ** G -2.35 X -2.47 Note: ΔG -2.85 ΔX -4.88 ** M: Market Value I: Consumer Price Index LG 0.01 LX -2.42 E: Net Income G: Gross Domestic Products
ΔLG -3.73 * ΔLX -5.20 ** D: Dividends r: Official Interest rate
R -3.24 PR -3.75 * B: Book value of net assets MS: Money Supply Δr -5.38 ** ΔPR -6.02 ** Δ: First difference X: Foreign Exchange rate Lr -0.07 LPR -1.47 L: Logged on variables PR: Labour Productivity Index
ΔLr -6.38 ** ΔPR -7.84 ** ΔL: First difference of logged variables NIK: Nikkei Index
** 1%;* 5% level of significance to reject the null hypothesis: H (0): Non-stationary
57
5.2.3 Summary of diagnostic tests
An unrestricted autoregressive distributed lag (ADL) model was created and tested
down. All ECM models derived are described in Figure 4.1-1 and follow the standard
procedure (described in Appendix 7.1). The variables were operationalised with two
lags. However, models with two lagged variables produced statistically insignificant
results compared to those containing one lag. The results of ECM model diagnostic tests
are shown in Table 5.2-4. ECMs N1, N1’, N2, N2’, N3, N3’, N4 and N4’ were
formulated with untransformed dependent and independent variables for all five firms.
ECMs 1, 1’, 2, 2’, 3, 3’, 4 and 4’ were formulated with transformed dependent and
independent variables. ECMs 1 and 2 were regressed on share price and ECMs 3 and 4
were regressed on market value (see Table 4.5-1). ECMs 1 and 3 contained book value
of net assets and dividends as dependent variables, whereas ECMs 2 and 4 instead
contained revised book value as a dependent variable. As discussed earlier, revised
book value is calculated by cumulating book value and cumulating dividends, based on
Ohlson’s (1995) theory of the clean surplus concept.
The models were considered for potential misspecification, including functional form,
by tests for heteroscedasticity, autocorrelation and the RESET 14 test. Furthermore,
selection of the models involved the measurement of their fit and forecasting
performance based upon the R2 statistics and root mean squared error (RMSE)
respectively. Diagnostic tests for the models were described in Chapter 4. The results of
these diagnostic tests are shown in Table 5.2-4. One asterisk indicates a 5% level of
significance, reflecting violation of an assumption of the model, while two asterisks
58
indicate a 1% level of significance. The results indicate that ECM15 models with an
additive form (ECMs N1, N2, N3 and N4) and with untransformed variables violate the
most tests for all the five firms. The ECM models with multiplicative form (ECMs 1, 2,
3 and 4) and with transformed variables are less misspecified.
ECMs 1 and 2 (the share price models) also appear to suffer from a number of
misspecification problems. Generally, therefore, models with dependent and
independent variables in raw, untransformed form and models with transformed
variables regressed on share price did not result in congruent models. ECMs 2 and 4,
(the revised book value models) additionally suffered from either significant departures
from normality, misspecification of functional form, or poor forecasting performance.
Therefore, ECM 3 remained as the only congruent model type. ECM 3 is of
multiplicative form in the untransformed variables and the dependent variable is
interpreted as the proportional growth rate of market value. In the following sections,
ECM 3 is referred to as ‘statistical Model 1’. Those models referred to as ‘Model 2’
exclude possible non-stationary variables, while ‘Model 3’ contains financial variables
deflated by the CPI.
14 ‘Ramsey’s Regression Specification Error test’: see Table 4.5.2. 15 ECM models are shown in Figure 4.1.1. ECM Ns models produced with untransformed variables and ECMs containing transformed variables. ECM models with asterisk were produced without macro-economic variables and ECMs without asterisk contain macro-economic variables.
59
Table 5.2-4: Diagnostic Tests for Equilibrium Correction Models for five firms
AR ARCH Normal Hete RESET AR ARCH Normal Hete RESET AR ARCH Normal Hete RESET AR ARCH Normal Hete RESET AR ARCH Normal Hete RESETECM N1' * ** ** ** ** * ** * * * * * * ** **ECM N1 * ** ** ** ** ** * * ** ** * ** *ECM N2' * ** ** ** * ** ** * * * ** ** * * * ** * ** * ** *ECM N2 * ** ** ** * * ** * * * * * * * * * * *ECM N3' * ** ** * ** * * * * * * * * * * * * ** * *ECM N3 ** * ** * * * ** * * ** * ** *ECM N4' ** * ** * * * ** * * * ** * * * *ECM N4 ** * ** * * * ** * * * * * * *ECM1' * * ECM1ECM2' * ** ** * *ECM2 *ECM3' ECM3ECM4' * * *ECM4NOTES: 1.ECM N1', ECM N2', ECM N3' and ECM N4' models are used untransformed dependent and independent variables and
macro-economic variables of GDP, interest rate, CPI, foreign exchange rate, money supply and productivity index.2.ECM N1, ECM N2, ECM N3 and ECM N4 models are used untransformed variables and macro-economic variables of GDP, interest rate and CPI.3.ECM 1', ECM2', ECM3', and ECM 4' models are used transformed variables and macro-economic variables of GDP, interest rate, CPI, foreign exchange rate, money supply and productivity index. 4.ECM1, ECM2, ECM3 and ECM4 models are used transformed variables and macro-economic variables of GDP, interest rate and CPI.5. Models Results of significance in AR, ARCH, Normality, hetero test, RESET tes (* indicates 5% and ** 1% level of significance.)
SUMITOMO TRUST BANKTOYOTA FUJIFILM SONY ITOCHU
60
5.3 Toyota Motor Corporation
5.3.1 Historical background
This subsection outlines the history of Toyota Motor Corporation (Toyota) during the
period 1950-2004. The Japanese economy has grown rapidly in the 20th century (Okabe,
2002; Morck and Nakamura, 2004), and companies such as Toyota have contributed to
this economic growth. Toyota currently operates primarily as an automotive company
but has other interests in industries such as housing, information and communications,
finance, intelligent transit systems, marine business, biotechnology and forestry.
Toyota was originally founded from the automobile section of Toyoda Automatic Loom
Co. in 1933. At the time, operations were focused on the production of military trucks.
The founding of Toyota coincided with the Japanese Government’s provision of special
subsidies for the production of vehicles for military use in 1932, as well as subsidies for
shipbuilding and the provision of tax credits and protection of the oil refining industry
in the following year (Flath 2000). On 28 August 1937, Toyota was established as an
independent company. Since this time, it has continuously expanded its operations. On
14 May 1949, Toyota listed on the First Section16 of the Tokyo, Osaka and Nagoya
Stock Exchanges. Its 4,020,000 ordinary shares were then priced at a face value of 50
yen.
Labour disputes occurred in 1949 as Toyota introduced a system that focused largely on
increasing labour productivity (Okazaki 1998). These disputes were settled with the
introduction of a wage and productivity incentive system, which contributed to an
16 See Chapter 1.3, Table 1.3.1.
61
increase in worker loyalty and strengthened Toyota’s management system (Okazaki
1998). Toyota then altered its financial position from a reliance on external debt to
retained profits (Monden 1993). Toyota reported a net loss in the 1950 interim report.
The annual report for the 1950 financial year disclosed a 112 million yen net profit
(A$14 million). This interim result was the only loss reported for any period in the 54
years from 1950 to 2004. The announcement of the profit was received favourably and
reflected in a rise in the share price to 52 yen in September 1951.
Toyota’s retained earnings expanded between 1967 and 198517. Between 1988 and 1994,
retained earnings declined slightly and from 1999 to 2004, the average increase in
reported retained profits was 9%. It is usually asserted that Japanese creditors and
investors work together closely to share information about company operations and that
Japanese investors are concerned less with higher debt ratios (total liabilities divided by
total assets) than is typical of investors in Western countries. However, by Japanese
standards, there was relatively little debt in Toyota’s financing structure during the
sample period. From 1951 to 1979, the reported average debt ratio was 49%. Since 1980,
reported debt ratio has been 16% on average, with a minimum of 0.1% in 1986 and a
maximum of 35% in 2003.
The company actively issued shares until 1998. The average annual number of shares
issued between financial years 1952-1977 was approximately 71 million. From 1978 to
1981, the company issued 196 million, 209 million, 336 million and 236 million shares
in each year, respectively. From 1982 to 1998, the issue of stock slowed; the annual
17 The average increase of retained profit between 1967 and 1985 was 2920.25 million yen (110%; between 1966 and 1967 it was 14% on average).
62
average for the period was 14 million shares. In 1999, Toyota began a share repurchase
scheme: 43 million shares were repurchased in 1999, 11 million in 2000, 64 million in
2001, 77 million in 2002, 154 million in 2003 and 64 million in 2004.
In 1970, Toyota reported a 6% reduction in net income, from 81,703 million yen in
1969 (approximately A$1,021 million) to 76,373 million yen (approximately A$955
million). During the same period, Toyota’s share price increased by approximately 30%
to 373 yen between 1969 and 1971, and continued to rise in the following year (by 65%
to 650 yen). A decrease in net income and a 43% drop in the share price in 1974 could
have been associated with the oil crisis in the aftermath of the Yom Kippur war. After
this, however, net income and share price both showed steady continuous growth until
1989. The sharp increases in price were more marked than those of income and were
likely associated with the ‘bubble economy’ phenomena. The 1980 share price doubled
by the end of 1981. In contrast, there was a steady (10-20%) increase in net income
during the 1980s.
Sequence plots of the log of market value compared to the log of book value and net
income are shown in Figure 5-3, panels A and B. The log of market value and the log of
book value of net assets were moving in similar patterns and some of the points were
overlapping, indicating a relationship between the two series (panel A), while the log of
net income and the log of market value appeared to move toward each other (panel B).
In Figure 5-4, the relation of other log variables plotted with the log of market value
were displayed. The log of market value and the log of money supply show similar
movement during the sample period, possibly indicating a relationship.
63
Figure 5-3: Toyota - Comparison of market value
Panel A: Comparison of log market value to log net income
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
6
8
10
12
14
16 TMC Logged market value TMC Logged net income
Panel B: Comparison of log market value to log book value of net assets
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
8
10
12
14
16Logged market value Logged book value of net assets
64
Figure 5-4: Toyota - Comparison log market value to log variables
1950 1960 1970 1980 1990 2000
5
10
15 Log market value Log dividends
1950 1960 1970 1980 1990 2000
10
15Log M Log money supply
1950 1960 1970 1980 1990 2000
5
10
15 Log market value Log CPI
1950 1960 1970 1980 1990 2000
0
10
20Log market value Log interest rates
1950 1960 1970 1980 1990 2000
5
10
15 Log market value Log exchange rate
1950 1960 1970 1980 1990 2000
10
15Log market value Log GDP
1950 1960 1970 1980 1990 2000
5
10
15 Log market value Log productivity index
1960 1970 1980 1990 2000
10
15Log market value Log nikkei index
There was a drop in net income, share price and retained profits in 1989, corresponding
to the introduction of the Super 301 clause of the Omnibus Trade and Competitiveness
Act 1988 by the US. This Act changed US policies to the framing of trade by requiring
the identification of countries with a ‘consistent pattern of trade barriers and market
distorting practices’. The subsequent effect has been treatment of Japanese exports to
the US (Nakamura 2001). The Act came into force in 1993, imposing tariffs of 100% on
13 Japanese-made luxury car models. These arrangements forced Japanese automobile
companies to purchase US-made car parts. Japanese automobile manufacturers
responded by building factories in the US to produce their own car parts. A drop in net
income of nearly 40% followed in 1994. Between 1994 and 1995, the market value of
Toyota fell 60% against the backdrop of Japan’s stagnant economy. Toyota nevertheless
maintained stable dividend payments during the 1990s and its share price rose by 63%
in 1997. Since 1997, the share price has continued to rise.
65
Today, Toyota has factories all over the world including manufacturing and assembly
plants. In keeping with the business philosophy of many Japanese firms, one of
Toyota’s avowed corporate policies is the continuous innovative improvement in
technology, combined with cost-cutting strategies to keep the most recent technological
advances within reach of local consumers’ purchasing ability (Liker, 2004). In the year
to 30 June 2004, Toyota reported that it produced roughly 6.71 million vehicles
worldwide, earned 17.29 trillion yen (A$0.22 trillion) in revenue (an 11.6% increase
from 2003) and paid 151.2 billion yen (A$1.89 billion) in total dividends from 554
consolidated subsidiaries with 228 affiliates.
In the next subsection, the basic equilibrium error correction models of type ECM3 (as
described in the previous section) are constructed for Toyota. As can be seen from the
following modelling process, many factors could explain the movements in Toyota’s
market value over time, including firm-specific accounting numbers and changes in
non-firm-specific macro-economic indicators. This is true, naturally, of all the other
firms examined in this research. The modelling process therefore endeavours to
ascertain which (if any) of the possible explanatory variables in the information set
described in Chapter 4 is most useful in generating a robust model of the behaviour of
market value over the sampled time period.
The purpose of the implementation of the general-to-specific method of modelling
reported in the next subsection is to formulate a congruent statistical model from the
information set. The forecasting ability of these statistical models is reported in the
context of the four-year hold-out sample periods and compared with the forecasting
66
performance of pure time series models. The statistical model is subsequently simplified
in Section 5.3.3 to eliminate concerns about endogeneity. The most promising resultant
model is selected for comparison against the performance in different hold-out sample
periods of the two most commonly held a priori models - one based upon Ohlson’s
(1995) theory, the other on the book-to-market ratio.
5.3.2 Statistical Equilibrium Correction Models
Statistical ECMs are constructed in the first instance using the standard testing down
procedure (described in Appendix 7.1) and tested in a four-year hold-out period from
2001–2004. Application of the testing down procedure to an initial ADL formulated on
Toyota data, with two lags on each logged variable and with market value as the
dependent variable, produced the three statistical models (see Table 5.3-1).
Table 5.3-1: Toyota Motor Corporation Statistical Models
Model 1
(Mt /Mt-1) =
1
(Dt /Dt-1)0.85
(MSt /MSt-1)1.8
{139.8(Dt-1)0.66(MSt-1)0.89 / Mt-1 }0.59
(SE) (0.104) (0.054) (0.783) (0.094) R2=0.53
Model 2
(Mt /Mt-1) =
1.34
(MSt /MSt-1)1.04
{139.8(MSt-1)1.37 / Mt-1 } 0.42
(SE) (0.071) (0.331) (0.134) R2=0.37
67
Model 3
(Mt /Mt-1) =
1
(Dt /Dt-1)0.96
(MSt /MSt-1)2.25
{116. MSt-1)0.75Mt-1)(Dt-1)0.64 } 0.61
(SE) (0.104) (0.053) (0.783) (0.089) R2=0.55
The first model (Model 1) is the result of applying this standard testing down procedure
to the initial ADL formulated on logs of all the observed, nominal data in the Toyota
information set (excluding any ‘clean surplus’ adjustments to book value). Model 2 is
constructed by following the standard procedures after excluding any I(2) variables, and
Model 3 is based on financial variables deflated by the CPI. A comparison of the
sequence of actual and fitted values in the estimated and forecast periods for each of
these models is shown in Figure 5-5. The specifications of the three models, their mean
errors, the RMSEs for each year of the four-year hold-out period and their R2 values are
shown in Table 5.3-2. The RMSE is computed for each hold-out period using the
formula described in Section 4.1.
68
Figure 5-5: Toyota Model 1, Model 2, Model 3
Model 1
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50Raw return (gross) Fitted
Model 2
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50Raw return (gross) Fitted
Model 3
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 20050.50
0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50Raw return (gross) Fitted
69
Table 5.3-2: Toyota Model 1, Model 2 and Model 3
One year ahead of forecast performance of three statistical equilibrium correction models – Models 1, 2 and 3 – in the four-year hold-out period (2001-2004)
Models 1 2 3 Forecast errors 2001 -0.10 -0.14 -0.08 2002 0.04 0.12 0.06 2003 -0.57 -0.48 -0.55 2004 -0.17 0.16 -0.17 Mean -0.20 -0.08 -0.19 RMSE 0.30 0.27 0.29 R2 0.53 0.37 0.55
Toyota Model specifications:
1 Mt/Mt-1 = k1(Dt/D t-1)a(MSt/MSt-1)b( k2 D α t-1 MS8
t-1 / Mt-1 }λ
k1=1; k2=139; a=0.85; b=1.8; α=0.64; β =0.89; λ =0.59; k3= 0.06
2 (Mt/Mt-1) =k1 (MSt/MSt-1)a( k2 MS β t-1 / Mt-1}λ
k1=1.34; k2=139.8; a=1.04; α =1.37; λ =0.42
3 (Mt/Mt-1) =k1 (Dt/ Dt-1)a (MSt/MSt-1)b( k2 MS α t-1 / Mt-1 Dβ
t-1) λ
k1= 1; k2=116; a=0.96; b=2.25; α =0.75; β =0.64;λ =0.61
Mt : Market value; Dt: Dividends; MSt: Money Supply The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT:
1 ECT tests stationary 2 ECT tests stationary 3 ECT tests stationary
Notes: The above models result from applying the testing procedure described in Table 1. Model 1 uses the entire information set as the starting point for the reduction sequence. Model 2 results from dropping series in dividends, GDP and the CPI, all of which are indicated as possibly being I(2) by Augmented Dickey Fuller (ADF) tests. Model 3 is based on ‘real’ data, where nominal financial data is divided by the CPI and multiplied by 100.
70
In all three models, money supply appears to be the most important, explanatory
independent variable for market value. The RMSEs of the different models are similar,
lying between 27% and 37%, with Model 2 showing the lowest RMSE and Model 1
(marginally) the highest. Model 2 has the lowest R2, representing its (possibly
misleading) ability to track the actual time series in the estimation period. The R2 of
Models 1 and 3 are higher, at 39% and 55% respectively.
For benchmarking purposes, a simple random walk model with a constant (Model 4)
and a constant and trend (Model 5) were constructed18. These results are reported in
Table 5.3-3. The RMSE of both Models 4 and 5 is 47%. The RMSEs of Models 1-3 are
lower than those of Models 4 and 5.
The error correction term (ECT) in the statistical ECMs appears of more significance
than the contemporaneous first differenced variables or ‘short run terms’ (SRTs). In
addition, the variability of the latter leads to a decrease in the forecast performance of
the statistical models relative to the benchmark models, suggesting potential elimination
of these from the models. Models 6, 7 and 8 are therefore constructed by excluding the
SRTs from Models 1, 2 and 3 respectively (see Chapter 4.5). The forecasting results
from Models 6, 7 and 8 are shown in Table 5.3-4. The RMSE of Model 8 improves in
comparison to Model 3 (dropping from 55% to 20%). In contrast, the RMSE of both
Models 6 and 7 worsen when compared to their statistical counterparts (Models 1 and 2,
respectively).
18 These time series models were suggested by examination of the data.
71
Table 5.3-3: Toyota Model 4 and Model 5 One year ahead forecast performance of random walk model and benchmark equilibrium
correction model in the four-year hold-out period (2001-2004)
Models Random walk model Benchmark ECM 4 5 Forecast errors (%) 2001 -0.42 -0.39 2002 -0.07 -0.11 2003 -0.73 -0.76 2004 0.12 -0.03 Mean -0.27 -0.32 RMSE 0.43 0.43 R2 0.00 0.28
Model specifications:
4 (Mt/Mt-1) = k k = 1.24
5 (Mt/Mt-1) =k1{exp(k2+.at) /Mt-1 } λ
k1=1; k2=51200.3; a=0.14;λ =0.17
Mt : Market value of equity. Behaviour of ECT:
4 Not applicable 5 Graphical analysis and some ADF tests indicate non-stationary behaviour
The results suggest that the most appropriate model of market value for Toyota is
Model 8; that is, one including the explanatory variables of money supply and dividends.
Model 8 contains the smallest forecasting errors in the four-year hold-out period
between 2001 and 2004. However, other hold-out periods may give different results.
These are therefore reported next for: a) the ten-year period from 1995-2004 (based on
estimates computable in 1994); and b) the more-than-ten-year period from 1995-2004
(based on estimates computable at the end of each preceding year). In addition, for the
72
purposes of further testing the robustness of the models, their forecasting ability is
compared to the two a priori models discussed earlier.
Table 5.3-4: Toyota Model 6, Model 7 and Model 8
One year ahead forecast performance of three statistical equilibrium correction models after elimination of short run variables – Models 6, 7 and 8 – in the four-year hold-out
period (2001-2004)
Models
Dividend Money supply model
Money supply model
Real value model
6 7 8 Derived from Model 1 2 3 Forecast errors (%) 2001 -0.37 -0.21 0.26 2002 -0.13 0.03 0.15 2003 -0.76 -0.58 -0.25 2004 -0.18 0.05 0.01 Mean -0.36 -0.18 0.05 RMSE 0.44 0.31 0.20 R2 0.17 0.20 0.14 Model specifications:
6 (Mt/Mt-1)= k1( k2 (Dαt-1ΜS β
t-1 /Mt-1)λ
k1=1.34;k2=139;α=0.64;β=0.89; λ =0.59
7 (Mt/Mt-1)= k1 (k2 MS αt-1 /Mt-1)λ
k1=1.12; k2=139; α=1.37; λ =0.42
8 (Mt/Mt-1)= k1 ( k2ΜSαt-1/ Mt-1D β
t-1)λ k1= 1.33; k2=2.25; α=0.75; β=0.64; λ =0.61
Mt : Market value; Dt: Dividends; MSt: Money Supply The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT:
6 As for Model 1 – see Table 5.3-2 7 As for Model 2 – see Table 5.3-2 8 As for Model 3 – see Table 5.3-2
73
5.3.3 Development of final model of data generating process for market value Model 8, the best performing model on the RMSE criteria in the four-year hold-out
period, is compared to Models 4 and 5 by two further forecast tests, both conducted
over the ten-year period from 1995-2004. One set of forecasts (F1) is for the whole ten-
year period, based on estimates of the ECT computable in 1994, while the other set (F2)
is for one year ahead, based on estimates computable in the year preceding the
forecast19. A comparison of these results is shown in Table 5.3-5. The RMSEs of the
random walk model and ‘benchmark ECM’ (i.e. in this case, the random walk with drift
model) are both 31%, and therefore well above that of Model 8 (23%) in the case of the
F1 forecasts. However, the RMSE of Model 8 is now worse than that of the four-year
hold-out period. Furthermore, in the case of the F2 forecasts, the RMSE for the random
walk and benchmark models are 31% and 28% respectively, while the RMSE for Model
8 increased to 32%.
In view of the poor performance of Model 8 in the F2 forecasts, Model 7 (the next best
model on the basis of previous forecast performance) is tested for its F1 and F2 forecast
performance. The results are shown in Table 5.3-6. The RMSE of this model is 22%,
down by 5% on its value in the four-year hold-out period, and noticeably better than
either the random walk or benchmark ECM models. As the apparent ‘best’ performer of
the final models derived by the empirical testing down procedure, this model is
therefore tested against the Ohlson and book value models.
19 These denotations will be used throughout the remainder of the thesis in reference to the relevant forecast periods.
74
Table 5.3-5: Toyota 10-year forecasting models One year ahead forecast performance of random walk, benchmark and real value equilibrium correction models in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of random walk, bench mark and real value correction models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 4: Random walk model Model 4: Random walk model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.22 0.49 0.27 1995 0.22 0.49 0.27 1996 0.22 0.22 0.00 1996 0.22 0.22 0.00 1997 0.22 0.06 -0.16 1997 0.22 0.06 -0.16 1998 0.22 0.05 -0.17 1998 0.22 0.05 -0.17 1999 0.22 0.23 0.01 1999 0.22 0.23 0.01 2000 0.22 -0.11 -0.33 2000 0.22 -0.11 -0.33 2001 0.22 -0.21 -0.43 2001 0.21 -0.20 -0.41 2002 0.22 0.14 -0.08 2002 0.20 0.14 -0.06 2003 0.22 -0.51 -0.73 2003 0.20 -0.51 -0.71 2004 0.22 0.33 0.11 2004 0.19 0.33 0.14
Mean error -0.15 RMSE 0.31 Mean error -0.14 RMSE 0.31 Model 5: Benchmark ECM Model 5: Benchmark ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.22 0.49 0.27 1995 0.22 0.49 0.27 1996 0.16 0.22 0.06 1996 0.20 0.22 0.02 1997 0.14 0.06 -0.08 1997 0.19 0.06 -0.12 1998 0.16 0.05 -0.10 1998 0.19 0.05 -0.13 1999 0.17 0.23 0.06 1999 0.18 0.23 0.04 2000 0.16 -0.11 -0.27 2000 0.17 -0.11 -0.28 2001 0.20 -0.21 -0.41 2001 0.18 -0.21 -0.39 2002 0.27 0.14 -0.13 2002 0.19 0.14 -0.05 2003 0.27 -0.51 -0.78 2003 0.18 -0.51 -0.69 2004 0.39 0.33 -0.05 2004 0.17 0.33 0.16
Mean error -0.14 RMSE 0.31 Mean error -0.12 RMSE 0.28 Model 8: Real value model Model 8: Real value model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.30 0.49 0.20 1995 -0.08 0.49 0.57 1996 0.12 0.22 0.10 1996 0.41 0.22 -0.19 1997 0.06 0.04 -0.02 1997 -0.32 0.04 0.36 1998 0.05 0.05 0.00 1998 0.08 0.05 -0.02 1999 0.05 0.23 0.18 1999 -0.05 0.23 0.29 2000 -0.01 -0.10 -0.09 2000 -0.15 -0.10 0.05 2001 0.05 -0.20 -0.24 2001 -0.02 -0.20 -0.18 2002 0.15 0.15 0.00 2002 0.16 0.15 -0.01 2003 0.11 -0.51 -0.62 2003 0.10 -0.51 -0.61 2004 0.33 0.33 0.01 2004 0.43 0.33 -0.10
Mean error -0.05 RMSE 0.23 Mean error 0.02 RMSE 0.32
75
Table 5.3-6: Toyota 10-year forecasting Model 7 One year ahead forecast performance of a money supply model denoted in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of a money supply model in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 7: Money supply ECM Model 7: Money supply ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.28 0.49 0.21 1995 0.28 0.49 0.211996 0.09 0.22 0.13 1996 0.10 0.22 0.121997 0.02 0.06 0.05 1997 0.02 0.06 0.041998 0.01 0.05 0.04 1998 0.02 0.05 0.031999 0.01 0.22 0.22 1999 0.02 0.22 0.202000 -0.07 -0.11 -0.04 2000 -0.05 -0.11 -0.062001 -0.01 -0.21 -0.20 2001 0.01 -0.20 -0.212002 0.10 0.14 0.04 2002 0.10 0.14 0.042003 0.05 -0.51 -0.56 2003 0.05 -0.51 -0.562004 0.27 0.33 0.06 2004 0.27 0.33 0.06
Mean error -0.01 RMSE 0.22 Mean error -0.01 RMSE 0.22 The Ohlson model is defined as a regression of market value on abnormal earnings and
the book value of the net assets and labelled ‘Model 9’. The simple model of the book
value of net assets regressed on market value is labelled ‘Model 10’. The specification
results of Models 9 and 10 are reported in Table 5.3-7.
The RMSEs of Models 9 and 10 are 47% and 36% respectively, so that Model 7 is
lower in its forecasting error based upon the four-year hold-out period. A comparison of
the F1 and F2 forecasts for Models 7, 9 and 10 are reported in Table 5.3-8. The RMSEs
of Models 9 and 10, based on the F1 and F2 forecasts, lie between 28% and 31%, while
the RMSE of Model 7 is lower in both cases at 22%. Consequently, Model 7, which has
an R2 of 34% in the 10-year period on which the F1 forecasts are based, is chosen by the
procedure adopted here as the best description of the data generating process for the
market value of Toyota, given the initial information set used for modelling. Model 7
76
contains money supply as the only significant regressor, other than the lagged
dependent variable.
Table 5.3-7: Toyota: Model 9 and Model 10
One year ahead forecast performance of Ohlson-type and simple book value of net assets
equilibrium correction models in the four-year hold-out period (2001–2004)
Models Ohlson Book value 9 10 Forecast errors (%) 2001 -0.34 -0.28 2002 -0.17 -0.06 2003 -0.81 -0.67 2004 -0.31 -0.05 Mean error -0.41 -0.26 RMSE 0.47 0.36 R2 0.28 0.32 Notes:
9 (Mt /Mt-1) =k1{(Ααt-1) ( Β β
t-1) /Mt-1 } λ k1=0.78; α=0.29; β=0.87; λ =0.54
10 (Mt /Mt-1) =k1 {(Βαt-1) /Mt-1
} λ k1=1.33; α=1.03; λ =0.43
Mt : Market value of equity; At: Abnormal earnings; Bt : Book value of net assets
Behaviour of ECT
9 ECT tests mostly non-stationary. At is only significant in the ECT at the 10% level.
10 ECT tests non-stationary at all lags
77
Table 5.3-8: Toyota 10-year forecasting Models 9 and 10 One year ahead forecast performance of Ohlson model and book value of net assets model in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of Ohlson model and book value of net assets model in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 9: Ohlson model Model 9: Ohlson model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.15 0.49 0.34 1995 0.30 0.49 0.19 1996 0.00 0.22 0.22 1996 0.13 0.22 0.09 1997 0.07 0.06 -0.01 1997 0.07 0.06 -0.01 1998 0.14 0.05 -0.09 1998 0.09 0.05 -0.03 1999 0.17 0.23 0.05 1999 0.09 0.22 0.13 2000 0.01 -0.11 -0.12 2000 0.01 -0.11 -0.12 2001 0.13 -0.20 -0.33 2001 0.08 -0.20 -0.28 2002 0.31 0.14 -0.17 2002 0.19 0.14 -0.04 2003 0.29 -0.51 -0.80 2003 0.14 -0.51 -0.65 2004 0.65 0.33 -0.32 2004 0.33 0.33 0.01
Mean error -0.12 RMSE 0.33 Mean error -0.07 RMSE 0.24 Model 10: Book value of net assets ECM Model 10: Book value of net assets ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.33 0.49 0.16 1995 0.33 0.49 0.16 1996 0.14 0.22 0.08 1996 0.14 0.22 0.08 1997 0.07 0.06 -0.01 1997 0.07 0.06 -0.01 1998 0.08 0.05 -0.03 1998 0.09 0.05 -0.04 1999 0.09 0.23 0.14 1999 0.09 0.22 0.13 2000 0.00 -0.11 -0.11 2000 0.00 -0.11 -0.11 2001 0.07 -0.21 -0.28 2001 0.08 -0.20 -0.28 2002 0.19 0.14 -0.05 2002 0.19 0.14 -0.05 2003 0.15 0.51 -0.66 2003 0.14 -0.51 -0.65 2004 0.37 0.33 -0.04 2004 0.36 0.33 -0.03
Mean error -0.08 RMSE 0.24 Mean error -0.08 RMSE 0.24
The sequence plots of the fitted and actual values of the proportionate changes in market
value over the entire sample period and the 10-year forecast period are displayed in the top
left hand panel of Figure 5-6. Generally speaking, the model (based on data of the preceding
year) tracks the actual series quite well. This tracking of the actual series continues in the
hold-out period, as indicated in Figure 5-6 (second panel graph, second column). The
forecast errors are comparable with the residuals in the estimation period, and the residuals
78
show a reasonably ‘normal’ shape for the size of the sample. In the bottom right-hand panel
of Figure 5-6, the residuals appear similar in pattern to a white noise process. Finally, from
the recursive graphics shown in Figure 5-7, it appears that the coefficients on the ECT are
quite stable over time, increasing in significance and explanatory power as the time series
develops.
Figure 5-6: Toyota Money Supply Model (Model 7)
1960 1970 1980 1990 2000
-0.5
0.0
0.5
1.0
1.5Raw return Fitted
1995 2000 2005
-0.5
0.0
0.5
1.01-step Forecasts Raw return
-3 -2 -1 0 1 2 3 4
0.2
0.4
DensityResiduals N(0,1)
0 5 10 15 20
-0.5
0.0
0.5
1.0ACF-residuals
This is a case where accounting-specific data appears not to provide the best model of the
DGP for market value. Money supply apparently contains as much information for the
determination of market value of Toyota as does its reported accounting numbers. However,
the final performance of the book value model is almost as good as Model 7, despite the fact
that it did not emerge as a contender for the best model by the testing down procedure. The
RMSE of the book value model in the F2 forecast scenario - arguably the acid test of the
models - is 24%. This is only 2% above that of Model 7 and substantially better than the
random walk and benchmark models. Similarly, the R2 of the book value model is only 2%
79
worse than that of Model 7 for the 1950-1994 estimation periods. This matter is discussed
further in the next chapter, wherein the modelling results for the five firms are considered
together.
Figure 5-7: Toyota Money Supply Model Recursive (Model 7)
Note: ECT-M7: Error Correction Model 7 (page 72)
1960 1970 1980 1990
-0.25 0.0
0.2
0.5
0.7Constant × +/-2SE
1960 1970 1980 1990
-0.75
-0.50
-0.25
0.0ECT-M7_1 × +/-2SE
1960 1970 1980 1990
1.0
1.5
2.0
2.5t: Constant
1960 1970 1980 1990
-4
-3
-2t: ECT-M7_1
1960 1970 1980 1990
2
3
4Residual sum of squares
1960 1970 1980 1990
-1
0
1 Res1Step
80
5.4 Fuji Photo Film Corporation
As with the other firms analysed in this chapter, the basic procedure for developing the
data generating process (DGP) for market value is similar to that described for Toyota
in Section 5.3. Consequently, the discussion of the development of the best model for
each firm is more abbreviated than in the case of Toyota.
5.4.1 Historical background
Fuji Photo Film Corporation (Fuji Film) was founded in 1934 with a capital of 3 million
yen (A$37,500), as the ‘spin-off’ of the film manufacturing division of Dainippon
Celluloid Company (DCC). In the 1930s, DCC was the largest manufacturer of
celluloid in the world. In 1933, DCC built its first film manufacturing factory at the foot
of Mt. Fuji, where the abundant natural spring water and clean air were important
aspects of the film manufacturing industry.
In the 1930s, Fuji Film was reliant on public investment from a Japanese government
keen to build a domestic capability in film making. Fuji Film subsequently produced
photographic film and other photographic sensitive material, chemical products,
precision optic materials and equipment such as optical glass and photo equipment.
Today, these remain among Fuji Film’s main products. The company is now widely
recognised as a leader in the fields of photo materials, equipment and services (Fuji
Photo Film, 2004). According to its 2004 annual report, Fuji Film had 73,164
employees at 178 consolidated subsidiaries across more than 20 countries.
81
Fuji Film stock was first listed on the TSE on the 14th of May, 1949 with a capital of
120 million yen (A$1,500,000). From 1949 to 1970, foreign exchange rates were fixed
at 360 yen to US$120. Under threat of a US levy of 10% on imports of textiles, the
Japanese authorities raised exchange rates by 16.88% to 308 yen (A$3.85) in October
1970. In August 1971, US dollar convertibility into gold was terminated. In January
1973, the Bank of Japan officially announced the end of the fixed foreign exchange rate
system. The Fuji Film share price dropped sharply in 1950 and again in 1970, as well as
at the time of the first oil crisis during the early 1970s. In the 1980s, Fuji Film’s share
price increased, along with most Japanese shares. Fuji Film avoided the general share
price declines of 1990 but not those which occurred in 1997 that were generally
associated with the introduction of a goods and services tax (GST). Fuji Film’s reported
earnings doubled between 2003 and 2004 to a reported 33.738 billion yen (A$421.75
million).
Sequence plots of the log of market value compared to the log of net income and book
value are shown in Figure 5-8. Although the log of market value and net income
showed similar movement over the sample period, the variables did not cross at any
point. On the other hand, the log of book value of net assets and the log of market value
were integrated at various points, indicating a possible relationship between the two
variables. The comparison of the log of market value to the log of other variables used
in this research was plotted in Figure 5-9. The log of market value and the log of GDP
moved closely together, suggesting the possibility of a relationship.
20 The foreign exchange rate emerges as a significant regressor in this firm’s market value models; hence these details are provided to aid interpretation of the models.
82
Figure 5-8 Fuji Photo - Comparison of log market value to log net income and log book value of net assets
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
7.5
10.0
12.5
15.0Log market value Log net incomes
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
7.5
10.0
12.5
15.0Log market value Log book value
Figure 5-9: Fuji Photo - Comparison of log market value to log variables
1950 1960 1970 1980 1990 2000
5
10
15FPF Log market value Log dividends
1950 1960 1970 1980 1990 2000
5
10
15FPF Log market value Log CPI
1950 1960 1970 1980 1990 2000
0
5
10
15FPF Log market value Log interest rate
1950 1960 1970 1980 1990 2000
5
10
15FPF Log market value Log exchange rate
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5
15.0FPF Log market value Log GDP
1950 1960 1970 1980 1990 2000
10
15FPF Log market value Log Money Supply
1950 1960 1970 1980 1990 2000
5
10
15FPF Log market value Log Productivity index
1960 1970 1980 1990 2000
10
15FPF Log market value Log Nikkei index
83
5.4.2 Statistical Equilibrium Correction Models
As for Toyota, the same testing down procedure was applied to the initial ADL
formulated with Fuji Film data. Two lags on each variable resulted in the three
statistical models reported in Table 5.4-1.
Table 5.4-1: Fuji Photo Film: Statistical Models Model 1
(Mt /Mt-1) =
0.09
(Et /Et-1)0.82
(Bt /Bt-1)1.03
(It /It-1)1.47
(63Et-1
0.84 Bt-10.94 Xt-1
0.66 /Mt-1 It-1
1.58}0.88
(SE) (0.042) (0.133) (0.202) (0.641) (0.132) R2=0.73
Model 2
(Mt /Mt-1) =
1
(Bt /Bt-1)0.02
( 792 Bt-1
1.21 Xt-10.64/Mt-1 ) 0.58
(SE) (0.044) (0.227) (0.121) R2=0.60
Model 3
(Mt /Mt-1) =
1
(Et /Et-1)0.64
(Bt /Bt-1)1.10
(1.03E t-10.65Dt-1
0.39 Bt-10.38 / Mt-1
) λ
(SE) (0.038) (0.144) (0.222) (0.114) R2=0.55
As with Toyota, Model 1 is the result of testing down using nominal observed values of
the data in the initial information set, Model 2 is constructed after excluding any
possibly I(2) variables, and Model 3 uses financial variables deflated by the CPI. The
sequence plots of actual and fitted values in the estimated and forecast periods for each
of the three models are presented in Figure 5-10. The specification statistics for the
84
three models, their mean errors, the RMSEs for each year of the four-year hold-out
period and their R2 statistics are shown in Table 5.4-2. The RMSEs for the models are
similar - between 26% and 34% - with Model 2 showing the lowest RMSE and Model 1
the highest RMSE over the hold-out period from 2001 to 2004. Model 3 had the lowest
R2 at 55% and Model 1 the highest at 73%.
85
Figure 5-10: Fuji Film Model 1, Model 2 and Model 3
Model 1
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.25
0.00
0.25
0.50
0.75
1.00
1.25Raw return (gross) Fitted
Model 2
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.25
0.00
0.25
0.50
0.75
1.00
1.25Raw retrun (gross) Fitted
Model 3
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.25
0.00
0.25
0.50
0.75
1.00
1.25 Raw return gross Fitted
86
Table 5.4-2: Fuji Film Model 1, Model 2 and Model 3
One year ahead of forecast performance of three statistical equilibrium correction models
– Models 1, 2 and 3 – in the four-year hold-out period (2001-2004)
Models 1 2 3
Forecast errors 2001 -0.42 -0.16 -0.34 2002 -0.40 -0.36 -0.20 2003 0.00 -0.27 -0.07 2004 -0.37 -0.22 -0.36 Mean -0.30 -0.25 -0.20 RMSE 0.34 0.26 0.27 R2 0.73 0.60 0.55
Model Specifications:
1 (Mt /Mt-1)= k1(Et/ Et-1)a(Bt /Bt-1)b( It-1/ It)c( k2 Eα t-1 Β β
t-1 X γ t-1 /Mt-1 I ε
t-1) λ
k1= 0.99; a=0.82; b=1.03; c=1.47; k2=62.9 ;α=0.84;β=0.94; γ=0.66; ε=1.58;λ =0.88
2
(Mt /Mt-1) =k1 (Bt /Bt-1) a ( k2 Bαt-1 Xβ
t-1 /Mt-1) λ
k1= 1 ; k2=792.37; a=1.039;α=1.21 ;β=0.84;λ =0.58
3
(Mt /Mt-1)= k1 (Et /E-1)a (Bt/Bt-1)b ( k2 Eα t-1Dβ t-1 Β γ
t-1 / Mt-1 ) λ
k1= 1; a=0.64;b=1.10; k2= 1.03; α=0.65; β=0.39; γ=0.38; λ =0.60
Mt : Market value; Dt: Dividends; Et: Reported earnings Bt : Book value of equity; It : CPI: Xt-1 : Exchange rates; The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT:
1 ECT tests stationary 2 ECT tests stationary 3 ECT tests stationary
Notes: The above models result from applying the testing procedure described in Table 1. Model 1 uses the entire information set as the starting point for the reduction sequence. Model 2 results from dropping series in dividends, GDP and the CPI, all of which are indicated as possibly being I(2) by Augmented Dickey Fuller (ADF) tests. Model 3 is based on ‘real’ data, where nominal financial data is divided by the CPI and multiplied by 100.
87
Table 5.4-3: Fuji Film Model 4 and Model 5 One year ahead forecast performance of random walk model and benchmark equilibrium
correction model in the four-year hold-out period (2001-2004)
Models Random walk model Benchmark ECM 4 5 Forecast errors (%) 2001 -0.17 -0.26 2002 -0.35 -0.47 2003 -0.26 -0.45 2004 -0.11 -0.35 Mean -0.22 -0.38 RMSE 0.24 0.39 R2 0.00 0.24
Model specifications:
4 (Mt /Mt-1) = k k = 1.18
5 (Mt /Mt-1) =k1{exp(k2+.at) /Mt-1 } λ
k1=1; k2=8472.9;
a = 0.14; λ = 0.25 Mt: Market value of equity. Behaviour of ECT:
4 Not applicable 5 Graphical analysis and some ADF tests indicate non-stationary behaviour
These models resulted in high R2 values and relatively high RMSEs compared to
Toyota. Although Model 1 had the highest RMSE of the three models, the sequence
plot of the fitted values from this model appears to incorporate information that is lost in
Models 2 and 3.
Table 5.4-3 shows the comparable statistics for the pure time series models. In this case,
the random walk model (Model 4), not the benchmark ECM (Model 5), outperforms the
88
three statistical ECMs in terms of forecasting performance over the four-year hold-out
period. The random walk model, of course, has no explanatory power (i.e. R2 = 0).
Table 5.4-4: Fuji Film Model 6, Model 7 and Model 8
One year ahead forecast performance of three statistical equilibrium correction models after elimination of short run variables – Models 6, 7 and 8 – in the four-year hold-out
period (2001-2004)
Models
Earnings, Book value & Exchange
rate model
Book value and Exchange rate
ECM Real value
ECM 6 7 8 Derived from Model 1 2 3 Forecast errors (%) 2001 -0.23 -0.23 -0.21 2002 -0.77 -0.56 -0.55 2003 -0.57 -0.54 -0.45 2004 -0.09 -0.37 -0.15 Mean -0.42 -0.43 -0.34 RMSE 0.49 0.45 0.37 R2 0.23 0.28 0.25
Model specifications:
6 (Mt /Mt-1 )= k1 ( k2 Eα t-1 Β βt-1 X γ
t-1 / Mt-1 I ε t-1) λ
k1= 1.20 ; k2=62.9 ;α=0.84;β=0.94; γ=0.66;ε=1.58;λ =0.88
7
(Mt /Mt-1) =k1 ( k2 Bαt-1 Xβ
t-1 /Mt-1) λ k1= 1.23; k2 =792.37;α=1.21 β=0.84;λ =0.64
8 (Mt /Mt-1)= k1 ( k2 Eα t-1Dβ t-1 Βt
γ -1 / Mt-1 ) λ
k1= 1.19; k2=1.03; α=0.65; β=0.39; γ=0.38; λ =0.35
Mt : Market value; Dt: Dividends; Et: Reported earnings Bt : Book value of equity; It : CPI: Xt-1 : Exchange rates The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT:
6 As for Model 1 – see Table 5.4-2 7 As for Model 2 – see Table 5.4-2 8 As for Model 3 – see Table 5.4-2
89
Models 6, 7 and 8 are produced by dropping the SRTs from Models 1, 2 and 3, as
shown in Table 5.4-4. Again in this case, unlike Toyota, the RMSEs for the three
simplified forecasting models are worse than their statistical counterparts, suggesting
that in the statistical counterpart models for Fuji Film, the short-term dynamics have
greater significance than the ECT. These three models are carried forward with the
random walk and benchmark ECM for assessment over the same F1 and F2 forecast
scenarios, as was done for Toyota.
5.4.3 Development of final model of data generating process for market value
The results of testing Models 4, 5 and 7 in the F1 and F2 forecasting scenarios are
shown in Table 5.4-5. Models 6 and 8 are similar in their RMSEs: 37% and 33%
respectively for F1, and 27% and 23% respectively for F2. Again, the random walk
model appears to significantly outperform Model 7 in forecast performance despite its
lack of explanatory power. This suggests there is no real value relevance in the book
value element in the ECM in Models 2 and 7. However, when Model 7 is compared to
the same a priori models as for Toyota, this appears not to be the case. The comparable
RMSEs for the Ohlson and book value ECMs in the four-year period (2001-2004) and
the F1 and F2 forecast scenarios produces the results exhibited in Table 5.4-6 and Table
5.4-7. The initial four-year hold-out period gives quite high RMSEs for both a priori
models, and similar R2 values to those obtained for Models 5 – 7. However, under F1
and F2 conditions, both models perform better than the random walk, with the book
value ECM producing an RMSE of 15% in the ‘acid test’ F2 forecast.
90
Given that the book value ECM has explanatory power of 20% in the original
estimation period covering 1950 – 2000 (compared to 28% for Model 7) and that it
forecasts relatively well, it appears that book value is value relevant for Fuji Film. The
significance of this result points to a failure of the testing down procedure to uncover
this simple model, and to the importance of comparing the best model from the
empirical testing down procedure to maintained theories and a priori hypothesised
models.
91
Table 5.4-5: Fuji Film Model 4, Model 5 and Model 7
One year ahead forecast performance of a random walk model, benchmark and earnings capitalisation equilibrium correction models in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of a random walk model and benchmark and productivity equilibrium correction models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 4: Random walk model Model 4: Random walk model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.18 0.09 -0.09 1995 0.18 0.09 -0.09 1996 0.18 0.26 0.08 1996 0.18 0.26 0.08 1997 0.18 0.29 0.11 1997 0.18 0.29 0.11 1998 0.18 -0.02 -0.2 1998 0.18 -0.02 -0.2 1999 0.18 -0.24 -0.42 1999 0.18 -0.24 -0.42 2000 0.18 0.19 0.01 2000 0.17 0.19 0.02 2001 0.18 0 -0.18 2001 0.17 0 -0.17 2002 0.18 -0.18 -0.36 2002 0.17 -0.18 -0.34 2003 0.18 -0.09 -0.27 2003 0.16 -0.09 -0.25 2004 0.18 0.06 -0.12 2004 0.15 0.06 -0.09
Mean error -0.14 RMSE 0.22 Mean error -0.14 RMSE 0.21 Model 5: Benchmark ECM Model 5: Benchmark ECM
Horizon Forecast Actual Error Horizon Forecast Actual Error 1995 0.22 0.09 -0.13 1995 0.43 0.09 -0.34 1996 0.23 0.25 0.02 1996 0.37 0.26 -0.11 1997 0.2 0.28 0.08 1997 0.3 0.29 -0.02 1998 0.17 -0.02 -0.19 1998 0.25 -0.02 -0.27 1999 0.2 -0.24 -0.44 1999 0.26 -0.24 -0.5 2000 0.29 0.19 -0.1 2000 0.28 0.18 -0.1 2001 0.28 0 -0.28 2001 0.25 0 -0.25 2002 0.31 -0.18 -0.49 2002 0.24 0.18 -0.42 2003 0.38 -0.08 -0.46 2003 0.23 -0.09 -0.32 2004 0.43 0.06 -0.37 2004 0.21 0.06 -0.14
Mean error -0.24 RMSE 0.31 Mean error 0.21 RMSE 0.39 Model 7: Book value and Exchange rate ECM Model 7: Book value and Exchange rate ECM
Horizon Forecast Actual Error Horizon Forecast Actual Error 1995 0.34 0.09 -0.25 1995 0.43 0.09 -0.25 1996 0.31 0.25 -0.06 1996 0.3 0.25 -0.05 1997 0.26 0.28 0.02 1997 0.25 0.28 0.03 1998 0.2 -0.02 -0.22 1998 0.19 -0.02 0.21 1999 0.19 -0.24 -0.43 1999 0.18 -0.24 -0.42 2000 0.28 -0.19 -0.09 2000 0.26 0.19 -0.07 2001 0.26 0 -0.26 2001 0.23 0 -0.23 2002 0.41 -0.18 -0.59 2002 0.37 -0.18 -0.55 2003 0.49 -0.09 -0.58 2003 0.41 -0.09 -0.5 2004 0.46 0.06 -0.4 2004 0.36 0.06 -0.3
Mean error -0.29 RMSE 0.35 Mean error -0.21 RMSE 0.31
92
Table 5.4-6: Fuji Film Model 9 and Model 10
One year ahead forecast performance of Ohlson-type and simple book value of net assets
equilibrium correction models in the four-year hold-out period (2001–2004)
Models Ohlson Book value 9 10 Forecast errors (%) 2001 -0.26 -0.25 2002 -0.53 -0.46 2003 -0.50 -0.46 2004 -0.30 -0.34 Mean error -0.40 -0.37 RMSE 0.41 0.38 R2 0.28 0.20 Notes:
9 (Mt /Mt-1) =k1{(Ααt-1) ( Β β
t-1) /Mt-1 } λ
k1=1.80; α=0.32; β=0.73; λ =0.56
10 (Mt /Mt-1) =k1 {(Βαt-1) /Mt-1
} λ k1=1.28; α=1.02; λ =0.40
Mt : Market value of equity; At: Abnormal earnings; Bt : Book value of net assets
Behaviour of ECT
9 ECT tests mostly non-stationary. At is only significant in the ECT at the 10% level.
10 ECT tests non-stationary at all lags
93
Table 5.4-7: Fuji Film Model 9 and Model 10 One year ahead forecast performance of Ohlson model and simple book value of net assets model in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of Ohlson model and simple book value of net assets model in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 9: Ohlson ECM Model 9: Ohlson ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.12 0.09 -0.03 1995 0.28 0.09 -0.2 1996 0.15 0.26 0.11 1996 -0.01 0.26 0.27 1997 0.16 0.29 0.13 1997 0.13 0.29 0.16 1998 0.15 -0.02 -0.17 1998 0.07 -0.02 -0.09 1999 0.12 -0.24 0.36 1999 0.10 -0.24 -0.35 2000 0.14 0.19 0.05 2000 0.17 0.19 0.02 2001 0.12 0.00 -0.12 2001 0.11 0.00 -0.11 2002 0.1 -0.18 -0.28 2002 0.18 -0.18 -0.36 2003 0.11 -0.09 -0.2 2003 0.19 -0.09 -0.28 2004 0.09 0.06 -0.03 2004 0.13 0.06 -0.07
Mean error -0.09 RMSE 0.18 Mean error -0.1 RMSE 0.22 Model 10: Book value ECM Model 10: Book value ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.17 0.09 -0.08 1995 0.17 0.09 -0.08 1996 0.15 0.26 0.11 1996 0.12 0.26 0.13 1997 0.08 0.29 0.2 1997 0.06 0.29 0.22 1998 0.01 -0.02 -0.03 1998 0.00 -0.02 -0.03 1999 0.04 -0.24 -0.28 1999 0.03 -0.24 -0.27 2000 0.13 0.19 0.06 2000 0.08 0.19 0.11 2001 0.08 0.00 -0.08 2001 0.04 0.00 -0.04 2002 0.10 -0.18 -0.28 2002 0.05 -0.18 0.23 2003 0.18 -0.09 -0.27 2003 0.09 -0.09 -0.17 2004 0.21 0.06 -0.14 2004 0.09 0.06 -0.02
Mean error -0.08 RMSE 0.17 Mean error 0.09 RMSE 0.15
The performance graphics for Model 10 are shown in Figure 5-11. The results are
visually parallel to those of Toyota, as presented in the previous section. The forecasts
seem to track the movement of the actual data and the ACF gives the appearance of
white noise, though perhaps with a hint of autocorrelation. However, the residuals show
a greater departure from normality than in the case of Toyota and the scaled forecast
errors are large relative to the residuals in the estimation period. Nevertheless, the
94
recursive graphics in Figure 5-12 show stability in the behaviour and growing
significance of the error correction coefficient over time.
Figure 5-11: Fuji Photo Film Simple Book Value Model (Model 10)
Figure 5-12: Fuji Photo Film Simple Book Value Model Recursive (Model 10)
1970 1980 1990
0.0
0.2
0.4Constant × +/-2SE
1970 1980 1990
-1.5
-1.0
-0.5
0.0ECT-FM10_1 × +/-2SE
1970 1980 1990
2
4
6t: Constant
1970 1980 1990
-6
-5
-4
-3t: ECT-FM10_1
1970 1980 1990
1
2
3
4Residual sum of squares
1970 1980 1990
-0.5
0.0
0.5Residuals 1Step
Note: ECT-FM: Error Correction Fuji Film Model 10 (page 92)
1950 1960 1970 1980 1990 2000
0.0
0.5
1.0 Raw return Fitted
1995 2000 2005
0.0
0.5
1.0 1-step Forecasts Raw return (gross in logs)
-3 -2 -1 0 1 2 3
0.2
0.4
Density Residuals from model N(0,1)
0 5 10 15 20
-0.5
0.0
0.5
1.0ACF-residuals
95
5.5 Sony Corporation
5.5.1 Historical background
Sony Corporation was established in Tokyo in 1946 for the purpose of researching and
manufacturing telecommunications and measuring equipment, with a start-up capital of
190,000 yen (A$2,375). Its first major invention was a magnetic tape recorder in 1950.
In 1955, it launched a transistor radio product and listed on the TSE at 136 yen per share
(A$1.70) with 4 million shares. Unlike a typical Japanese company, Sony has enlarged
its operations and financed research through issuing stock as opposed to traditional
financial means (Miwa and Ramseyer, 2000; Morck and Nakamura, 2004).
Sony focuses its business operations on the manufacture of electronic and electrical
machines and equipment, medical instruments, optical instruments and metal, chemical
and ceramic industrial products It also designs, produces and sells audiovisual software
and computer software programs (Sony Corporation, 2004). Since its inception, Sony
has reported positive profits in all years except 1995. Despite an increase of 6.7% in net
sales between 1994 and 1995, Sony disclosed a negative profit of 2,930 million yen due
to the amortisation of goodwill in its movie division. Nevertheless, dividends have been
declared every year since operations began in 1946. In the last five years (2000-2004)
the cost of research and development was reported to be an average of 5.6% of net
sales.
The Sony share price dropped in 1964, which was possibly due to a detrimental change
in the Japanese government’s export policy (Malcolm, 2001). This change had a broad
impact over large Japanese firms, leading to huge losses to the then market leader,
96
Yamaichi Securities (Branstetter, 2003). The Bank of Japan came to the rescue, buying
stocks with public funds generated in the preceding year, and effecting a recovery in
share price (Branstetter, 2003). GDP growth at the time was 16%, close to the
maximum of 18% per annum during the total 50-year period (1950-2004). However, the
first oil crisis was associated with a drop in the Sony share price (and most other
Japanese stocks). In 1982, the opening of Tokyo Gold exchange and the effect of the
‘bubble economy’ may have increased Sony’s share price (Chan-Lau, 2002). From 1989
to 1991, a small fall in share price ensued, probably due to the end of the bubble
economy and a drop in land prices at that time (Bae and Kim, 1998; Stern et al., 2003).
The introduction of the GST in 1997 also possibly contributed to the fall in share prices
in that year. In 2000, a sharp drop in the Sony share price was attributed to the
announcement by the Bank of Japan of the 0% official interest rate (Shinotsuka, 2000;
Stern et al., 2003). In 2004, Sony’s book value increased by 4%, while its reported
earnings decreased by 23% compared to 2003. However, the annual report of that year
promised increased returns in the following year.
Sequence plots of the Sony market value to net income, book value and other variables
in the initial information set (in logs) are shown in Figure 5-13. The log of market value
appears to track alongside the log of book value quite closely, while net income shows
huge gaps in 1995. The loss suffered in 1995 might upset the estimation of any
relationship between the log of market value and the log of net income. The trend in the
log of market value is also reflected in the trends of some of the macro-economic
variables, especially GDP, money supply and CPI. The function of the testing down
procedure reported in the next subsection attempts to identify the non-spurious and most
97
useful of these possible drivers of the trend in market value, assuming at least one is
relevant.
Figure 5-13: Sony - Comparison of log market value to log variables
Panel A: Log Market value to Log Net income and Log Book value
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-10
0
10
SNY Log Market value Log Net incomes
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
7.5
10.0
12.5
15.0 SNY Log Market value Log Book value
Panel B: Log Market value to other variables;
1960 1970 1980 1990 2000
5
10
15 Log Market value Log dividends
1960 1970 1980 1990 2000
5
10
15 Log Market value Log CPI
1960 1970 1980 1990 200005
1015 Log Market value Log Interest rates
1960 1970 1980 1990 2000
5
10
15 Log Market value Log Exchange rate
1960 1970 1980 1990 2000
10
15Log Market value Log GDP
1960 1970 1980 1990 2000
10
15Log Market value Log Money supply
1960 1970 1980 1990 2000
51015 Log Market value Log Productivity index
1960 1970 1980 1990 2000
10
15Log Market value Log Nikkei index
98
5.5.2 Statistical Equilibrium Correction Models
Application of the testing down procedure to the initial ADL formulated on the Sony
data, again with two lags on each variable, resulted in the three statistical models shown
in Table 5.5-1.
Table 5.5-1: Sony Models Model 1
(Mt /Mt-1) =
1.00
(Gt /Gt-1)3.47
(It-1 /It
)4.12
(900765Gt-13.40/Mt-1 It-1 3.48
)0.51 (SE) (0.101) (1.17) (1.72) (0.125) R2=0.48
Model 2
(Mt /Mt-1) =
1.00
(PRt /PRt-1)0.92
( 29.08PRt-1
2.49 /Mt-1)0.46
(SE) (0.076) (0.468) (0.0863) R2=0.38
Model 3
(Mt /Mt-1 )=
1.09
(It-1 /It
)4.12
(Gt /G-1)3.47
(900765 Gt-13.40/Mt-1
I t-14.48 )0.51
(SE) (0.101) (1.171) (1.723) (0.125) R2=0.48
As before, Model 1 is obtained by testing down from logs of nominal values of all the
Sony variables in the initial information set; Model 2 excludes any suspected I(2)
variables, and Model 3 deflates the financial variables by the CPI. Sequence plots of
fitted against actual values for the estimation period and the four-year hold-out period
for these three models are shown in Figure 5-13. The specification of the three models,
their mean errors, the RMSEs for the hold–out period and their R2 values are shown in
99
Table 5.5-2. In all three models, the key independent variables retained as regressors for
market value were the macro-economic variables, GDP, CPI, and the Productivity Index.
The RMSEs of all these models are between 22% and 32%; where Model 1 has the
lowest value and Model 3 the highest. Models 1 and 3 each have an R2 of 48%,
indicating a similar ability to track the actual time series in the estimated period. The R2
of Model 2 is lower at 38%.
100
Figure 5-14: Sony Model 1, Model 2, Model 3
Model 1
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.5
0.0
0.5
1.0
1.5Raw gross Fitted
Model 2
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.5
0.0
0.5
1.0
1.5Raw retrun (gross) Fitted
Model 3
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-1.0
-0.5
0.0
0.5
1.0
1.5Raw retrun (gross) Fitted
101
As in the case of the other companies, Model 4 assumes that the log of market value is a
simple random walk and is constructed simply by estimating a constant. These results
are contained in Table 5.5-3. Model 5 assumes a drift term in the random walk, and also
incorporates a trend. The RMSEs of Models 4 and 5 are similar; 34% and 32%
respectively. The RMSEs of Models 1 and 2 are therefore lower in the four-year hold-
out period than those of Models 4 and 5, while Model 3 equals that of the benchmark
ECM.
In the six abovementioned models, the forecast errors for each four-year hold-out period
exhibit similar patterns in sign, i.e. negative in 2001, 2003 and 2004 (except for Model
2, which is positive in 2001, and Model 3, which is positive in 2004). Visually, the
forecasts in Model 1 are perhaps slightly more convincing than those of Model 2.
Those of Model 3, while not strictly comparable with Models 1 and 2, also seem to have
a smoothing effect on the actual data series.
As with Toyota, but unlike Fuji Film, the ECTs in these models have greater statistical
significance than the short run contemporaneous variables. Models 6, 7 and 8 are
constructed by eliminating the SRTs from Models 1, 2 and 3 respectively. The results of
Models 6, 7 and 8 are presented in Table 5.5-4. The RMSEs in Model 7 and 8 improve
compared to their statistical counterparts (Models 2 and 3); dropping by 4% in each
case to 25% and 28 % respectively. The RMSE of Model 6 worsens by 2% to 24%.
At this point, the results suggest that the most appropriate model of the DGP for the
market value of Sony is Model 1; the model that includes the explanatory variables of
102
GDP and CPI. Given that the coefficients on these variables are close and opposite in
sign, the ratio of these two variables could be interpreted as ‘real GDP’.
103
Table 5.5-2: Sony Model 1, Model 2 and Model 3
One year ahead of forecast performance of three statistical equilibrium correction models – Models 1, 2 and 3 – in the four-year hold-out period (2001-2004)
Models 1 2 3 Forecast errors 2001 -0.07 0.02 -0.12 2002 0.29 0.31 0.37 2003 -0.28 -0.43 -0.20 2004 -0.14 -0.22 0.47 Mean -0.05 -0.08 0.13 RMSE 0.22 0.29 0.32 R2 0.48 0.38 0.48
Model specifications:
1
(Mt /Mt-1)= k1 (Gt/Gt-1)a(It-1/It )b { k2 (G α
t-1) / Mt-1 (I β t-1) }λ
k1 = 1; k2=900765;a= 3.47 ; b- 4.12; α = 3.40; β = 3.48 ; λ = 0.51
2
(Mt /Mt-1) =k1 ( PRt /PRt-1) a {k2 (PR α t-1) / Mt-1} λ
k1= 1 ; k2=29.08 ; a=0.92; α=2.49; λ =0.46
3
(Mt /Mt-1) = k1 (Gt /Gt-1)a(It-1/It )b { k2 (G α
t-1) / (Mt-1 ) (I β t-1) } λ
k1= 1; k2 =900765;a=3.47 ;b=4.12; α=3.40; β=4.48; λ =0.51
Mt : Market value; It : CPI: PRt :Productivity index; Gt: GDP The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT: 1 ECT tests stationary 2 ECT tests stationary 3 ECT tests stationary
Notes: The above models result from applying the testing procedure described in Table 1. Model 1 uses the entire information set as the starting point for the reduction sequence. Model 2 results from dropping series in dividends, GDP and the CPI, all of which are indicated as possibly being I(2) by Augmented Dickey Fuller (ADF) tests. Model 3 is based on ‘real’ data, where nominal financial data is divided by the CPI and multiplied by 100.
104
Table 5.5-3: Sony Model 4 and Model 5
One year ahead forecast performance of random walk model and benchmark equilibrium correction model in the four-year hold-out period (2001-2004)
Models Random walk
model Benchmark ECM 4 5 Forecast errors (%) 2001 -0.15 -0.13 2002 0.11 0.11 2003 -0.62 -0.56 2004 -0.17 -0.25 Mean -0.21 -0.21 RMSE 0.34 0.32 R2 0.00 0.34
Model specifications:
4 (Mt /Mt-1) =k k = 1.23
5 (Mt /Mt-1) =k1{exp(k2+.at) /Mt-1 }
k1=1.03;k2=43634.4; a =0.12 λ =0.27
Mt: Market value of equity. Behaviour of ECT:
4 Not applicable 5 Graphical analysis and some ADF tests indicate non-stationary behaviour
105
Table 5.5-4: Sony Model 6, Model 7 and Model 8
One year ahead forecast performance of three statistical equilibrium correction models after elimination of short run variables – Models 6, 7 and 8 – in the four-year hold-out
period (2001-2004)
Models GDP &
CPI model Productivity
ECM Real value
ECM 6 7 S8 Derived from Model 1 2 3 Forecast errors (%) 2001 0.16 -0.06 0.27 2002 0.26 0.25 0.46 2003 -0.32 -0.37 -0.16 2004 -0.15 -0.21 0.10 Mean 0.01 -0.09 0.17 RMSE 0.24 0.25 0.28 R2 0.35 0.32 0.23
Model specifications:
6 (Mt /Mt-1) =k1 { k2 (G α
t-1) / Mt-1 (I β t-1) }λ
k1 = 1; k2=900675;α = 3.40; β = 3.48 ; λ = 0.61
7 (Mt /Mt-1) =k1 {k2 (PR α
t-1) / Mt-1} λ k1= 1.07 ; k2=29.08; a =2.49; λ =0.38
8 (Mt /Mt-1) == k1 { k2 (G α
t-1) / (Mt-1 ) (I β t-1) } λ
k1=1.09; k2=900675,α = 3.40; β = 4.48; λ =0.63,
Mt : Market value; It : CPI: PRt: Productivity index; Gt: GDP The prefix R denotes the variable is real, i.e. has been deflated by the CPI
Behaviour of ECT:
6 As for Model 1 – see Table 5.5-2 7 As for Model 2 – see Table 5.5-2 8 As for Model 3 – see Table 5.5-2
106
5.5.3 Development of final model of data generating process for market value
In this subsection, Model 6 is compared to Models 4 and 5 in the two 10-year
forecasting scenarios F1 and F2, as per the method used for Toyota and Fuji Film.
Comparisons of these results are shown in Table 5.5-5. The RMSEs of Models 4 and 5
in the F1 ten-year hold-out period worsen by 9% (to 43%) and by 6% (to 38%)
respectively, while that of Model 6 improves by 2% (to 22%). The R2 of Model 5
worsens to 38%, while Model 6 improves to 50%. The RMSE performance of Model 1
relative to the time series models is not so clearly different in the F2 scenario but is still
superior (33% compared to 38% obtained for the benchmark ECM and 42% for the
random walk).
Results from the Ohlson-type model (Model 9) and book value model (Model 10) are
contrasted in Table 5.5-6. The RMSEs of Models 9 and 10 in the four-year hold-out
period are 23% and 24% respectively. The RMSE of Model 6 is lower than that of the
book value model (Model 10) but higher than that of the Ohlson model (Model 9). The
R2 values of Models 9 and 10 are both 22% in the four-year hold-out period, i.e. lower
than for Model 6. The performance of Models 9 and 10 in the F1 and F2 forecasting
scenarios are reported in Table 5.5-7. Under F1, the RMSEs of Models 9 and 10 worsen
to 36% and 34% respectively, while their R2 values improve to 29% and 24%
respectively. Under F2, the RMSEs of Models 9 and 10 worsen to 39% and 37%
respectively.
107
Table 5.5-5: Sony 10-year forecasting
One year ahead forecast performance of a random walk model, benchmark and earnings capitalisation equilibrium correction models in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of a random walk model and bench mark and Productivity equilibrium correction models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 4: Random walk model Model 4: Random walk model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.22 -0.25 -0.47 1995 0.22 -0.25 -0.47 1996 0.22 0.39 0.17 1996 0.21 0.40 0.18 1997 0.22 0.40 0.18 1997 0.22 0.41 0.19 1998 0.22 -0.24 -0.46 1998 0.22 -0.24 -0.46 1999 0.22 0.80 0.57 1999 0.21 0.80 0.58 2000 0.22 -0.50 -0.72 2000 0.22 -0.50 -0.72 2001 0.22 0.05 -0.17 2001 0.20 0.05 -0.15 2002 0.22 0.32 0.10 2002 0.20 0.32 0.12 2003 0.22 -0.41 -0.63 2003 0.21 -0.41 -0.62 2004 0.22 0.04 -0.18 2004 0.19 0.04 -0.15
Mean error -0.16 RMSE 0.43 Mean error -0.15 RMSE 0.42 Model 5: Benchmark ECM Model 5: Benchmark ECM
Horizon Forecast Actual Error Horizon Forecast Actual Error 1995 0.18 -0.25 -0.43 1995 0.18 -0.25 -0.43 1996 0.28 0.40 0.11 1996 0.21 0.40 0.18 1997 0.20 0.41 0.20 1997 0.16 0.41 0.25 1998 0.12 -0.24 -0.37 1998 0.11 -0.24 -0.35 1999 0.23 0.80 0.57 1999 0.16 0.80 0.63 2000 0.04 -0.50 -0.54 2000 0.04 -0.50 -0.54 2001 0.21 0.05 -0.16 2001 0.16 0.05 -0.11 2002 0.23 0.32 0.09 2002 0.17 0.32 0.15 2003 0.18 -0.41 -0.59 2003 0.12 -0.41 -0.54 2004 0.33 0.04 -0.28 2004 0.21 0.04 -0.17
Mean error -0.14 RMSE 0.38 Mean error -0.09 RMSE 0.38 Model 6: GDP & CPI ECM Model 6: GDP & CPI ECM
Horizon Forecast Actual Error Horizon Forecast Actual Error 1995 0.24 -0.25 -0.15 1995 0.29 -0.25 -0.54 1996 0.26 0.40 -0.01 1996 0.43 0.40 -0.04 1997 0.20 0.38 0.09 1997 0.28 0.38 0.12 1998 0.09 -0.24 -0.12 1998 0.03 -0.24 -0.27 1999 0.07 0.80 -0.31 1999 0.11 0.80 0.69 2000 0.20 -0.49 -0.01 2000 -0.41 -0.49 -0.09 2001 0.20 0.06 -0.20 2001 -0.11 0.06 0.16 2002 0.19 0.33 -0.36 2002 0.05 0.33 0.27 2003 0.21 -0.41 -0.30 2003 -0.08 -0.41 -0.33 2004 0.31 0.04 -0.25 2004 0.19 0.04 -0.15
Mean error -0.16 RMSE 0.22 Mean error -0.02 RMSE 0.33
108
Table 5.5-6: Sony Model 9 and Model 10
One year ahead forecast performance of Ohlson-type and simple book value of net assets equilibrium correction models in the four-year hold-out period (2001 - 2004)
Models Ohlson Book value 9 10 Forecast errors (%) 2001 -0.05 -0.06 2002 0.21 0.21 2003 -0.40 -0.41 2004 -0.10 0.10 Mean error -0.09 -0.09 RMSE 0.23 0.24 R2 0.22 0.22 Notes:
9 (Mt /Mt-1) =k1{(Ααt-1) ( Β β
t-1) /Mt-1 } λ k1=3.92; α=0.003; β=0.83; λ =0.40
10 (Mt /Mt-1) =k1 {(Βαt-1) /Mt-1
} λ k1=3.91; α=0.83; λ =0.40
Mt:Market value of equity; At: Abnormal earnings; Bt: Book value of net assets Behaviour of ECT
9 ECT tests mostly non-stationary. At is only significant in the ECT at the 10% level.
10 ECT tests non-stationary at all lags
Figure 5-15 and Figure 5-16 show the performance graphics for Model 6. These show
reasonably good forecast performance in the period between 2001-2004, forecast errors
that compare well to the size of residuals in the estimation period, a reasonably normal
look to the distribution of the residuals, and a white noise pattern to the time series of the
residuals. The recursive graphics confirm apparent stability and increasing significance in
the ECT coefficient over the sample period. The results therefore suggest that the most
appropriate model of the DGP for log of market value for Sony, based on the initial
information set, is Model 6; that is, a model that contains no accounting numbers.
109
Table 5.5-7: Sony 10-year forecasting One year ahead forecast performance of Ohlson model and book value of net assets models in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of Ohlson model and book value of net assets models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 9: Ohlson model Model 9: Ohlson model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.31 -0.25 -0.56 1995 0.20 -0.25 -0.45 1996 0.35 0.40 0.04 1996 0.13 0.40 0.26 1997 0.03 0.41 0.37 1997 -0.13 0.41 0.54 1998 -0.06 -0.24 -0.18 1998 -0.20 -0.24 -0.04 1999 0.08 0.80 0.71 1999 -0.04 0.80 0.84 2000 -0.20 -0.50 -0.30 2000 -0.32 -0.50 -0.18 2001 0.04 0.05 0.01 2001 -0.04 0.05 0.09 2002 0.06 0.32 0.26 2002 -0.03 0.32 0.35 2003 -0.05 -0.41 -0.36 2003 -0.15 -0.41 -0.26 2004 0.06 0.04 -0.02 2004 0.01 0.04 0.03
Mean error -0.0025 RMSE 0.36 Mean error 0.12 RMSE 0.39 Model 10: Book value ECM Model 10: Book value ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.14 -0.25 -0.39 1995 -0.01 -0.25 -0.24 1996 0.15 0.40 0.24 1996 0.01 0.40 0.39 1997 0.05 0.41 0.36 1997 -0.10 0.41 0.51 1998 -0.04 -0.24 -0.20 1998 -0.18 -0.24 -0.06 1999 0.12 0.80 0.68 1999 -0.01 0.80 0.80 2000 -0.18 -0.50 -0.32 2000 -0.32 -0.50 -0.18 2001 0.06 0.05 -0.01 2001 -0.04 0.05 0.09 2002 0.06 0.32 0.26 2002 -0.04 0.32 0.36 2003 -0.06 -0.41 -0.35 2003 -0.16 -0.41 -0.26 2004 0.09 0.04 -0.05 2004 0.01 0.04 0.03
Mean error 0.02 RMSE 0.34 Mean error 0.14 RMSE 0.37
110
Figure 5-15: Sony Corporation GDP & CPI Model (Model 6)
1960 1970 1980 1990 2000
0
1
Raw return Fitted
1995 2000 2005
-1.0
-0.5
0.0
0.5
1.0
1.51-step Forecasts Raw return
-3 -2 -1 0 1 2 3 4
0.2
0.4
DensityResiduals N(0,1)
0 5 10 15 20
-0.5
0.0
0.5
1.0ACF-residuals
Figure 5-16: Sony Corporation Real value model recursive (Model 6)
1970 1975 1980 1985 1990 1995
0.25
0.50
0.75
1.00 Constant × +/-2SE
1970 1975 1980 1985 1990 1995
-0.75
-0.50
-0.25
0.00 ECT-SNYGDPonly_1 × +/-2SE
1970 1975 1980 1985 1990 1995
4.00
4.25
4.50t: Constant
1970 1975 1980 1985 1990 1995
-3.0
-2.5
-2.0t: ECT-SNYGDPonly_1
1970 1975 1980 1985 1990 19952
4
6Residual sum of squares
1970 1975 1980 1985 1990 1995
-1
0
1 Residual 1Step forecast
Note: ECT-SNYGDP: Error Correction Sony GDP Model (page 105)
111
5.6 Itochu Corporation
5.6.1 Historical background
Itochu Corporation (Itochu) was founded by Ito Chubei in 1858 as a deliverer of linens
for trade. Itochu was transformed into a public company with a capital of 1 million yen
(A $12,500) in 1918. In 1948, when Itochu’s capital was 400 million yen (A$5 million),
the conglomerate break-up law forced it to separate its manufacturing interests from its
export and trading interests. Itochu is a global trading company operating in over 80
countries, with 18 offices in Japan, 135 offices overseas and 173 subsidiaries around the
world. Itochu’s operations cover a range of industries, including 31 subsidiaries in
textiles, 14 in machinery, 27 in aerospace, electronics and multimedia, 11 in energy,
metals and minerals, 27 in chemicals, forest products and general merchandise, 20 in
food, and 43 in finance, realty, insurance and logistical services.
Itochu’s stock was listed on the Tokyo, Osaka and Nagoya stock exchanges in 1949 as a
re-established trading company, with a capital of 150 million yen (A$1.875 million), a
share price of 50 yen per share and 3 million outstanding shares. Currently, Itochu has
a capital of 202.241 billion yen (A$2.528 billion). In 1954, Itochu’s share price fell,
along with other Japanese stocks. In 1956, the share price rose sharply, possibly related
to Japan joining the United Nations, and then rose again in 1964; the year of the
Japanese Olympics and of the entry of Japan into the OECD (Malcolm, 2001). In 1971,
the floating of the exchange rate may also have given an upward impetus to Itochu’s
stock price, while the oil crisis in 1975, the end of the bubble economy in 1989 – 1990,
and the introduction of GST in 1997 probably served to depress Itochu’s share price, in
112
the same way as it did for most other quoted Japanese stocks. Itochu reported negative
earnings (down by 12%) and a book value down by 0.7% in its 2004 annual report.
However, its share price rose by 66% between 2003 and 2004, and the CEO expressed
confidence in the company’s performance for the following year.
The sequence plots in Figure 5-17 of the variables in the initial information set indicate
that some variables may have a significant long run relationship with market value, the
book value of net assets and GDP, and perhaps the money supply. On the other hand, net
income does not present an obvious visual relationship with market value.
113
Figure 5-17: Itochu - Comparison of log market value to log net incomes and book value of net assets
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
7.5
10.0
12.5
Log Market value Log Net income
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
7.5
10.0
12.5
Log Market value Log Book value
Figure 5-18: Itochu Corporation - Comparison of log market value to log variables
1950 1960 1970 1980 1990 2000
5
10
15Log Market value Log Dividends
1950 1960 1970 1980 1990 2000
5
10
15Log Market value Log CPI
1950 1960 1970 1980 1990 200005
1015
Log Market value Log Interest rates
1950 1960 1970 1980 1990 2000
5
10
15Log Market value Log Eexchange rate
1950 1960 1970 1980 1990 2000
7.510.012.515.0
Log Market value Log GDP
1950 1960 1970 1980 1990 2000
10
15Log Market value Log Money supply
1950 1960 1970 1980 1990 2000
5
10
15Log Market value Log Productivity index
1950 1960 1970 1980 1990 2000
7.510.012.515.0
Log Market value Log Nikkei index
114
5.6.2 Statistical Equilibrium Correction Models
Application of the testing down procedure to the initial ADL formulated with two lags
on each variable for Itochu data resulted in the three statistical models shown in Table
5.6-1. The first model (Model 1) is, as with the other companies, formulated using the
logged nominal values of variables in the information set; Model 2 excludes possible
I(2) variables; and Model 3 deflates the financial variables by the CPI. Comparison of
sequence of actual and fitted values in the estimated and forecast periods for each of
those models is shown in Figure 5-19. Models 1 and 3 behave quite erratically in the
forecast period whereas Model 2 at least appears to track changes in direction of the
actual series.
Table 5.6-1: Itochu Models
Model 1
(Mt /Mt-1) =
1.00
(MSt /MSt-1)3.38
(Et /Et-1)0.17
(36739E t-10.62MSt-1
4.99 / Mt-1It-1
1.58) 0.63 (SE) (0.099) (0.783) (0.0544) (0.0939) R2=0.55
Model 2
(Mt /Mt-1) =
1.02
(Bt /Bt-1) 1.05
(197382Bt-1
0.66Xt-11.46Vt-1/ Mt-1)0.57
(SE) (0.0702) (0.327) (0.126) R2=0.38
Model 3
(Mt /Mt-1) =
1.00
(Et /E-1)0.17
(MSt /MSt-1)3.58
(29377Et-1
0.63 Gt-1 5.11MSt-15.43/ Mt-1 )0.63
(SE) (0.098) (0.053) (0.783) (0.0895) R2=0.56
115
Figure 5-19: Itochu Model 1, Model 2 and Model 3
Itochu Corporation: Comparison of actual and fitted series for raw return (gross) for
three statistical equilibrium correction models (Itochu Model 1, 2 and 3)
Model 1
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25Raw retrurn (gross) Fitted
Model 2
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25Raw retrun Fitted
Model 3
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25Raw retrun Fitted
116
The specification of the three models, their mean errors, the RMSEs for each year of the
four-year hold-out period and their R2 values are shown in Table 5.6-2. The competitor
explanatory variables in these models seem, on the one hand, to resolve into earnings,
money supply and the CPI (Models 1 and 3), and on the other, into the book value of
net assets, the exchange rate and the Nikkei market index (Model 2). The RMSEs of
Models 1 and 3 are similar (60% and 61% respectively), while that of Model 2 is much
smaller (27%) for the four-year hold-out period. The R2 of Model 2 is the lowest at 38%,
compared with 55% and 56% for Models 1 and 3, respectively.
The comparable results for the times series, simple random walk model with a constant
(Model 4) and constant and trend (Model 5) are reported in Table 5.6-3. The RMSEs of
Models 4 and 5 are 33% and 36% respectively. The RMSEs of Models 1 and 3 are thus
higher than benchmark models, while that of Model 2 is lower. The pattern of signs in
each year’s forecast errors is similar: negative in 2001, 2002 and 2003 and positive in
2004.
When Models 1, 2 and 3 are re-constructed by eliminating the SRTs, the results
(reported as Models 6, 7 and 8 respectively) are as shown in Table 5.6-4. The RMSE of
Model 7 is again lowest (24%, improved from Model 2), while those of Model 6 and
Model 8 worsen to 80% and 79% respectively; much higher than the benchmark models
in RMSE. The R2 of Model 7 falls to 25% and Models 6 and 8 to 26%. Thus, the most
appropriate model of the DGP for market value for Itochu appears to be Model 7; that is,
with the book value of net assets, the Nikkei index and the exchange rate as explanatory
variables.
117
Table 5.6-2: Itochu Model 1, Model 2 and Model 3 One year ahead of forecast performance of three statistical equilibrium correction models
– Models 1, 2 and 3 – in the four-year hold-out period (2001-2004)
Models 1 2 3 Forecast errors 2001 -0.43 -0.10 -0.43 2002 -0.82 -0.04 -0.81 2003 -0.21 -0.18 -0.20 2004 0.73 0.50 0.76 Mean -0.18 0.04 -0.17 RMSE 0.60 0.27 0.61 R2 0.55 0.38 0.56
Model specification
1 (Mt /Mt-1 )= k1 (Et /Et-1)a(MSt /MSt-1)b (k2 E α t-1ΜSβ
t-1 / Mt-1 I γ t-1) λ
k1= 1; k2=367397; a= 0.17; b=3.38;α=0.62;β=4.99; γ=1.58;λ =0.63
2 (Mt /Mt-1 )=k1 (Bt /Bt-1) a ( k2 Bα t-1 Xβ t-1 V γ
t-1 / Mt-1) λ k1= 1.02; k2= 197382.75; a=1.05;α=0.66;β=1.46; γ=1;λ =0.57
3 (Mt /Mt-1)= k1(Et /E-1)a(MSt /MSt-1)b (k2 Eα t-1 G β
t-1MS γt-1 / Mt-1
) λ k1= 1; k2=29377;a=0.17;b=3.58;α=0.63; β=5.11; γ=5.43; λ =0.63
Mt: Market value; Dt: Dividends; MSt: Money Supply; Et: Reported earnings Bt: Book value of equity; It: CPI: Xt-1 : Exchange rates; Vt: Nikkei Index; Gt: GDP The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT:
1 ECT tests stationary 2 ECT tests stationary 3 ECT tests stationary
Notes: The above models result from applying the testing procedure described in Table 1. Model 1 uses the entire information set as the starting point for the reduction sequence. Model 2 results from dropping series in dividends, GDP and the CPI, all of which are indicated as possibly being I(2) by Augmented Dickey Fuller (ADF) tests. Model 3 is based on ‘real’ data, where nominal financial data is divided by the CPI and multiplied by 100.
118
Table 5.6-3: Itochu Model 4 and Model 5
One year ahead forecast performance of random walk model and benchmark equilibrium
correction model in the four-year hold-out period (2001-2004)
Models Random walk model
Benchmark ECM
4 5 Forecast errors (%) 2001 -0.31 -0.37 2002 -0.14 -0.24 2003 -0.40 -0.52 2004 0.40 0.23 Mean -0.11 -0.22 RMSE 0.33 0.36 R2 0.00 0.14
Model specifications:
4 (Mt /Mt-1 )= k k = 1.12
5 (Mt /Mt-1 )= k1{exp(k2+.at) /Mt-1 } λ
k1=1; k2=24921.8; a=0.09;λ =0.14
Mt: Market value of equity. Behaviour of ECT:
4 Not applicable 5 Graphical analysis and some ADF tests indicate non-stationary
behaviour
119
Table 5.6-4: Itochu Model 6, Model 7 and Model 8
One year ahead forecast performance of three statistical equilibrium correction models after elimination of short run variables – Models 6, 7 and 8 – in the four-year hold-out
period (2001-2004)
Models Statistical
model Book, Nikkei and Exchange ECM
Real value ECM
6 7 8 Derived from Model 1 2 3 Forecast errors (%) 2001 -0.24 -0.19 -0.23 2002 -0.84 -0.11 -0.81 2003 -1.05 -0.23 -1.03 2004 0.83 0.35 0.84 Mean -0.32 -0.05 -0.31 RMSE 0.80 0.24 0.79 R2 0.26 0.25 0.26
Model specifications:
6
(Mt /Mt-1 )= k1 (k2 E α t-1ΜSβ
t-1 / Mt-1 I γ t-1) λ
k1= 1.35; k2=367397; α=0.62; β=4.99; γ=4.43; λ =0.63
7 (Mt /Mt-1 )= k1 ( k2 Bα t-1 Xβ t-1 V γ t-1 / Mt-1) λ
k1= 1.18 ; k2= 197382.75; α=0.66 ;β=1.46; γ=1; λ =0.54
8 (Mt /Mt-1 )= k1 (k2 Eα
t-1 G β t-1MS γ
t-1 / Mt-1 ) λ
k1= 1.35; k2=29377;α=0.63; β=5.11; γ=5.43; λ =0.40
Mt: Market value; Dt: Dividends; MSt: Money Supply; Et: Reported earnings Bt : Book value of equity; It : CPI: Xt-1 : Exchange rates; Vt: Nikkei index; Gt: GDP The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT:
6 As for Model 1 – see Table 5.6-2 7 As for Model 2 – see Table 5.6-2 8 As for Model 3 – see Table 5.6-2
120
5.6.3 Development of final model of data generating process for market value
When Model 7 is compared to Models 4 and 5 under the F1 and F2 forecasting
scenarios, the results are as shown in Table 5.6-5. The F1 RMSE of Model 7 is now
34%, while the respective values for the RMSEs of Models 4 and 5 are 43% and 38%.
Additionally, the R2 of Model 5 falls to 13%. In the case of F2, the RMSEs of Models 7,
4 and 5 are 36%, 42% and 38% respectively.
Results from the a priori Ohlson and book value models based on the RMSE criteria
using the four-year hold-out sample period from 2001-2004 are shown in Table 5.6-6.
The RMSEs of Models 9 and 10 are 36% and 34% respectively, while the R2 values are
low, at only 9% and 8% respectively. When these models are assessed under the F1 and
F2 scenarios, the results shown in Table 5.6-7 are obtained. The RMSEs of both models
are close to 50%, far higher than for Model 7.
121
Table 5.6-5: Itochu 10-year forecasting
One year ahead forecast performance of a random walk model, benchmark and earnings capitalisation equilibrium correction models in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of a random walk model and bench mark and Productivity equilibrium correction models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 4: Random walk model Model 4: Random walk model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.22 -0.25 -0.47 1995 0.22 -0.25 -0.47 1996 0.22 0.39 0.17 1996 0.21 0.40 0.18 1997 0.22 0.40 0.18 1997 0.22 0.41 0.19 1998 0.22 -0.24 -0.46 1998 0.22 -0.24 -0.46 1999 0.22 0.80 0.57 1999 0.21 0.80 0.58 2000 0.22 -0.50 -0.72 2000 0.22 -0.50 -0.72 2001 0.22 0.05 -0.17 2001 0.20 0.05 -0.15 2002 0.22 0.32 0.10 2002 0.20 0.32 0.12 2003 0.22 -0.41 -0.63 2003 0.21 -0.41 -0.62 2004 0.22 0.04 -0.18 2004 0.19 0.04 -0.15
Mean error -0.16 RMSE 0.43 Mean error -0.15 RMSE 0.42 Model 5: Benchmark ECM Model 5: Benchmark ECM
Horizon Forecast Actual Error Horizon Forecast Actual Error 1995 0.18 -0.25 -0.43 1995 0.18 -0.25 -0.43 1996 0.28 0.40 0.11 1996 0.21 0.40 0.18 1997 0.20 0.41 0.20 1997 0.16 0.41 0.25 1998 0.12 -0.24 -0.37 1998 0.11 -0.24 -0.35 1999 0.23 0.80 0.57 1999 0.16 0.80 0.63 2000 0.04 -0.50 -0.54 2000 0.04 -0.50 -0.54 2001 0.21 0.05 -0.16 2001 0.16 0.05 -0.11 2002 0.23 0.32 0.09 2002 0.17 0.32 0.15 2003 0.18 -0.41 -0.59 2003 0.12 -0.41 -0.54 2004 0.33 0.04 -0.28 2004 0.21 0.04 -0.17
Mean error -0.14 RMSE 0.38 Mean error -0.09 RMSE 0.38 Model 7: Book value, Nikkei index and
Exchange rate ECM Model 7: Book value, Nikkei index and
Exchange rate ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.06 -0.25 -0.31 1995 -0.07 -0.25 -0.32 1996 0.21 0.40 0.18 1996 0.18 0.40 0.21 1997 0.12 0.41 0.29 1997 0.08 0.41 0.32 1998 0.001 -0.24 -0.24 1998 -0.07 -0.24 -0.17 1999 0.07 0.80 0.72 1999 0.05 0.80 0.74 2000 -0.17 -0.50 -0.33 2000 -0.14 -0.50 -0.36 2001 0.08 0.05 -0.03 2001 0.03 0.05 0.02 2002 0.04 0.32 0.28 2002 0.02 0.32 0.30 2003 -0.07 -0.41 -0.34 2003 0.04 -0.41 -0.45 2004 0.20 0.04 -0.16 2004 0.25 0.04 -0.21
Mean error 0.006 RMSE 0.34 Mean error 0.08 RMSE 0.36
122
Table 5.6-6: Itochu Model 9 and Model 10 One year ahead forecast performance of Ohlson-type and simple book value of net assets
equilibrium correction models in the four-year hold-out period (2001 – 2004)
Models Ohlson Book value 9 10 Forecast errors (%) 2001 -0.37 -0.31 2002 -0.26 -0.21 2003 -0.55 -0.52 2004 0.15 0.21 Mean error -0.26 -0.21 RMSE 0.36 0.34 R2 0.07 0.07
9 (Mt /Mt-1 )= k1{(Ααt-1) (Β β
t-1) /Mt-1 } λ k1=1.80; α=0.32; β=0.73; λ =0.56
10 (Mt /Mt-1 )= k1 {(Βαt-1) /Mt-1
} λ k1=1.23; α=1.04; λ =0.21 Mt: Market value of equity; At: Abnormal earnings; Bt: Book value of net assets Behaviour of ECT
9 ECT tests mostly non-stationary. At is only significant in the ECT at the 10% level.
10 ECT tests non-stationary at all lags
123
Table 5.6-7: Itochu 10-year forecasting
One year ahead forecast performance of a random walk model, benchmark and earnings capitalisation equilibrium correction models in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of a random walk model and bench mark and productivity equilibrium correction models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 9: Ohlson ECM with constant Model 9: Ohlson ECM with constant Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.22 -0.27 -0.49 1995 0.22 -0.27 -0.491996 0.06 0.35 0.29 1996 0.05 0.35 0.31997 0.13 -0.21 -0.34 1997 0.13 -0.21 -0.341998 0.23 -0.64 -0.87 1998 0.22 -0.64 -0.861999 0.42 -0.26 -0.68 1999 0.36 -0.26 -0.632000 0.39 0.8 0.4 2000 0.29 0.8 0.512001 0.2 -0.21 -0.41 2001 0.17 -0.21 -0.372002 0.27 -0.03 -0.3 2002 0.22 -0.03 -0.252003 0.3 -0.3 -0.6 2003 0.24 -0.3 -0.532004 0.44 0.51 0.07 2004 0.32 0.51 0.19
Mean error -0.29 RMSE 0.5 Mean error -0.25 RMSE 0.49Model 10: Book value of net assets ECM Model 10: Book value of net assets ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.2 -0.27 -0.47 1995 0.2 -0.27 -0.471996 0.25 0.35 0.1 1996 0.24 0.34 0.11997 0.18 -0.21 -0.39 1997 0.17 -0.21 -0.381998 0.24 -0.64 -0.88 1998 0.23 -0.64 -0.871999 0.34 -0.26 -0.6 1999 0.29 -0.26 -0.552000 0.34 0.8 0.46 2000 0.26 0.8 0.542001 0.13 -0.21 -0.34 2001 0.1 -0.21 -0.312002 0.21 -0.03 -0.24 2002 0.17 -0.03 -0.22003 0.27 -0.3 -0.57 2003 0.22 -0.3 -0.522004 0.36 0.51 0.15 2004 0.28 0.51 0.23
Mean error -0.28 RMSE 0.48 Mean error -0.24 RMSE 0.47
The results show Model 7 as the most appropriate DGP for market value model of
Itochu based upon the information set; i.e. an ECT that is based upon book value of net
assets, the exchange rate and the market index. Figures 5.6-3 and 5.6-4 show the
performance graphics. The one step forecasts in the four-year hold-out period track the
pattern of the actual data quite well, the residuals appear visually normal and similar in
scale to the forecast errors. However, there is some indication of autocorrelation in the
124
ACF of the residuals. As with the best models for the other firms, the coefficient
estimates show stability and increasing significance over time.
Figure 5-20: Itochu Book value, Nikkei index and Exchange rate Model (Model 7)
1960 1970 1980 1990 2000
-0.5
0.0
0.5
1.0
1.5Raw return Fitted
1995 2000 2005
-0.5
0.0
0.5
1.01-step Forecasts Raw return
-3 -2 -1 0 1 2 3 4
0.2
0.4
DensityResiduals N(0,1)
0 5 10 15 20
-0.5
0.0
0.5
1.0ACF-residuals
Figure 5-21: Itochu Book value, Nikkei index and Exchange rate Model (Model 7)
Note: ECT-I7; Error Correction Itochu Model 7 (page 119)
1960 1970 1980 1990
-0.25
0.00
0.25
0.50
0.75Constant × +/-2SE
1960 1970 1980 1990
-0.75
-0.50
-0.25
0.00 ECT-I7_1 × +/-2SE
1960 1970 1980 1990
1.0
1.5
2.0
2.5t: Constant
1960 1970 1980 1990
-4
-3
-2 t: ECT-I7_1
1960 1970 1980 1990
2
3
4 Residual sum of squares
1960 1970 1980 1990
-1
0
1 Res1Step
125
5.7 Sumitomo Trust & Banking Co Ltd.
5.7.1.1 Historical background
The Sumitomo Trust & Banking Co Ltd (Sumitomo) was originally formed as one
member of the Sumitomo Zaibatsu conglomerate group of companies. The Sumitomo
Corporation was originally established in the 17th century, as the biggest copper refining
company in Japan. It also exported thread, textiles, sugar and medicines. In the late 19th
century, the Sumitomo Bank was established to address the need for a main bank for the
whole house of Sumitomo. The Sumitomo Bank was established in 1925 as a specialist
in trusts and collateralised corporate bond business for the house of Sumitomo, with a
capital of 20 million yen (A$2.5 million). After World War II, the Occupation
Administration ordered the break-up of four big Zaibatsu groups including Sumitomo
(the others being Mitsui, Mitsubishi and Yasuda). In 1948, the company was obliged to
change its name to the Fuji Trust & Banking Co. by the Occupation Administration. In
1949 it was listed on the TSE. The name of the company was changed back to
Sumitomo Trust & Banking in 1952 along with development of currency exchange
operations and custody services for securities and investment trust business.
As at March 31, 2004, Sumitomo had 18 consolidated subsidiaries, five associates, a
head office, 50 branches and 15 satellite offices in Japan. It also has 40 trust agencies,
three overseas branches and five overseas representative offices. The company is
engaged in trust banking operations and financing services. Its principle activities
include, both domestically and internationally, 24 categories of business activities, such
as asset management, securitisation, foreign exchange, custodial services, real estate
126
management and, above all, related business and consultancies. Its bonds are rated A- in
the S&P rating and A2 in Moody’s rating (Sumitomo Trust & Banking, 2004).
The Sumitomo suffered losses in five financial years during the sample period between
1950 and 2004 - all were due to the write-off of non-performing loans resulting from
either general deflationary conditions or large-scale bankruptcies in the construction and
retail industries (or both). Losses were recorded in 1996 (159.1 billion yen - A$1988.5
million); 1998 (50.1 billion yen - A$626.8 million); 1999 (136.7 billion yen - A$1709.3
million); 2002 (42.4 billion yen - A$531 million); and 2003 (72.9 billion yen - A$912
million). In 2004, Sumitomo finally reported a net profit of 79.6 billion yen (A$995.3
million). The proportion of foreign investors among Sumitomo’s total shareholders
increased sharply in 2003 and 2004, to approximately 50% and 43%, respectively. The
proportion of financial institution shareholders decreased approximately 10% in each
year from 2002 to 2004 (Imai, 2002; Osaki, 2005).
The share price of Sumitomo was stable between 1950 and 1983. From 1984 to 1987,
the price started to move upward. At the same time, Sumitomo increased the number of
shares by 25% (from 911 billion shares in 1983 to 1140 billion shares in 1984). From
1983 to 1986, its price increased approximately 80% each year, and in 1987, it
increased by 1.25 times more than 1986, presumably due to the effects of the bubble
economy. After an upward movement for five years, Sumitomo’s price suffered a
significant drop between 1988 and 1990. The share price increased by 57% in 1994 but
dropped in the following year and fell consistently downward until 2004. This has been
attributed to the sluggish Japanese economy and a lack of success in redirecting the
127
flow of money to benefit the company (Osaki, 2005). The share price of Sumitomo fell
sharply along with the rest of the Japanese stock market in March 2003. That particular
decline was attributed to the large banking groups and major banks reporting huge
losses from writing-off non-performing loans (Kashyap and Hoshi, 2004) and the
impact of this decline on the value of their stock portfolios. Sumitomo has now disposed
of its non-performing loans (see the Director’s report of 2004), and currently enjoys
relatively favourable ratings by S&P and Moody’s (see Table 5.7-1).
Table 5.7-1 Rating of Sumitomo Trust & Banking
Ratings of the Sumitomo Trust & Banking Co. Ltd. (Latest Update: June 13, 2005)
S & P Moody's FITCH JCR R&I
L/T Bonds -
L/T Deposits A
A2 A- A+ A
S/T A-1 P-1 F1 - a-1
Outlook Stable Stable Positive - Stable
Financial/Individual - D C - -
Source: (Sumitomo Trust & Banking, 2004)
128
Figure 5-22: Sumitomo Trust & Banking - Comparison of log market value to log variables
Panel A: Comparison of log market value to log net income and book value
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
5.0
7.5
10.0
12.5
15.0 Log Market value Log Net incomes
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
5.0
7.5
10.0
12.5
15.0 Log Market value Log Book value
Panel B: Comparison of log market value to log variables
1950 1960 1970 1980 1990 2000
5
10
15 Log Market value Log Dividends
1950 1960 1970 1980 1990 2000
5
10
15 Log Market value Log CPI
1950 1960 1970 1980 1990 200005
1015 Log Market value Log Iinterest rates
1950 1960 1970 1980 1990 2000
5
10
15 Log Market value Logged exchange rate
1950 1960 1970 1980 1990 2000
5
10
15 Log Market value Log GDP
1950 1960 1970 1980 1990 2000
5
10
15 Log Market value Log Money supply
1950 1960 1970 1980 1990 2000
51015 Log Market value Log Productivity index
1950 1960 1970 1980 1990 2000
5
10
15 Log Market value Log Nikkei index
129
5.7.2 Statistical Equilibrium Correction Models
Application of the testing down procedure to the initial ADL formulated with two lags
on each variable for Sumitomo data resulted in the three statistical models shown in
Table 5.7-2. In the following subsection, the results of these models are reported with
further testing results.
The sequence plots for Models 1 (using all the nominal variables in the initial ADL), 2
(excluding I(2) variables) and 3 (using deflated data) are shown in Figure 5.23. The
specification of the three models, their mean errors, the RMSEs for each year for the
four-year hold-out period and their R2 values are shown in Table 5.7-3.
130
Table 5.7-2 Sumitomo Models
Model 1
(Mt /Mt-1) =
1.00
(Bt-1/Bt)0.48
(Gt /Gt-1)3.03
(Vt /Vt-1)0.42
(Gt-1
1.93Vt-11.83/6.16E+5Bt-1
1.18 Mt-1 }0.41
(SE) (0.071) (0.169) (0.684) (0.173) (0.072) R2=0.55
Model 2
(Mt /Mt-1)=
1.00
(Bt-1/Bt)0.46
(Et-1 /Et)0.64
(PRt /PRt-1)0.93
(Vt /Vt-1)0.55
(9.67E+12PRt-1
2.80Vt-12.52/Et-1
3.26Bt-11.34Mt-1 ) 0.41
(SE) (0.063) (0.271) (0.268) (0.313) (0.184) (0.063) R2=0.57
Model 3
(Mt /Mt-1) =
1.00
(Gt /G-1)2.19
(Bt-1/Bt
)0.59
(Vt/Vt-1)0.44
(Gt-1
4.13V t-1 2.15/9.32E+8Mt-1Bt-1
1.84)0.39
(SE) (0.064) (0.738) (0.169) (0.171) (0.058) R2=0.59
131
Figure 5-23: Sumitomo Model 1, Model 2, Model 3 Sumitomo Trust & Banking: Comparison of actual and fitted series for raw returns (gross) for three statistical equilibrium correction models (Models 1, 2 & 3)
Model 1
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00Raw return Fitted
Model 2
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50Raw return (gross) Fitted
Model 3
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005-0.50
-0.25
0.00
0.25
0.50
0.75
1.00Raw return Fitted
132
Table 5.7-3: Sumitomo Model 1, Model 2 and Model 3
One year ahead forecast performance of three statistical equilibrium correction models – Models 1, 2 and 3 – in the four-year hold-out period (2001-2004)
Models 1 2 3 Forecast errors 2001 -0.06 -0.04 0.03 2002 -0.07 -0.08 -0.11 2003 -0.31 -0.38 -0.37 2004 1.05 -1.73 0.96 Mean 0.15 -0.56 0.13 RMSE 0.55 0.88 0.52 R2 0.55 0.57 0.59 Model specifications:
1 (Mt /Mt-1)= k1{Bt-1/Bt)a(Gt /Gt-1)b(Vt /Vt-1)c{
G αt-1Vβ
t-1 / k2 B γ
t-1Mt-1 } λ
k1 = 1; k2=6.16E+5;a= 0..48 ; b=3.03; c= 0.42;α = 1.18; β = 1.93;γ=1.83; λ = 0.41;k2=616245
2 (Mt /Mt-1) =k1 (Bt-1/Bt)a(Et-1/Et)b(PRt /PRt-1)c (Vt /Vt-1)d (k2
PR α t-1 V β
t-1 / E γ t-1 B ε
t-1 Mt-1 )λ
k1 = 1; k2=9.67E+12;a= 0.46 ; b=0.64; c= 0.93;d= 0.55; α = 2.80; β = 2.52 ;γ=3.26; ε=1.34;λ = 0.41
3 (Mt /Mt-1)= k1(Gt /G-1(Vt /Vt-1) b (Bt-1/Bt
)c ( k2G αt-1V βt-1Mt-1
B γ t-1) λ k1= 1; a=2.19 ;b=0.44;c=0.59; k2= 9.327E+8; α=4.13; β=2.15; γ=1.84;λ =0.39
Mt : Market value; Et: Reported earnings; Bt: Book value of net assets; PR t-1: Productivity index; Vt: Nikkei Index ; Gt: GDP The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT:
1 ECT tests stationary 2 ECT tests stationary 3 ECT tests stationary
Notes: The above models result from applying the testing procedure described in Table 1. Model 1 uses the entire information set as the starting point for the reduction sequence. Model 2 results from dropping series in dividends, GDP and the CPI, all of which are indicated as possibly being I(2) by Augmented Dickey Fuller (ADF) tests. Model 3 is based on ‘real’ data, where nominal financial data is divided by the CPI and multiplied by 100.
133
While the R2 values for these models are quite high (greater than 55%), the forecasts for
the models (with the exception of the 2004 forecast in Model 2) are very damped
versions of the actual data series and have relatively high RMSEs (over 52% and as
high as 88%). Furthermore, the interpretation of the ECT is contrary to what would
normally be expected, since book and market both have the same sign in all three
models.
The performance of the comparative time series models are shown in Table 5.7-4. The
RMSEs of Models 4 and 5 are similar to those obtained in Model 2 (88% and 84%
respectively). Constructing Models 6, 7 and 8 as in previous sections, gives the results
reported in Table 5.7-5. The RMSEs of Models 6, 7 and 8 improve slightly from
Models 1, 2 and 3, dropping to 42%, 43% and 39% respectively. The results suggest
that the most appropriate model of the DGP for market value for Sumitomo appears to
be Model 8; i.e. a model with book value of net assets, earnings, and the productivity
index.
134
Table 5.7-4: Sumitomo Model 4 and Model 5
One year ahead forecast performance of random walk model and benchmark equilibrium correction model in the four-year hold-out period (2001-2004)
Models Random walk model Benchmark ECM
4 5 Forecast errors (%) 2001 -0.28 -0.22 2002 -0.49 -0.45 2003 1.13 1.14 2004 -1.24 -1.12 Mean -0.22 -0.16 RMSE 0.88 0.84 R2 0.00 0.16
Model specifications:
4 (Mt /Mt-1)= k k = 1.20
5 (Mt /Mt-1)= k1{exp(k2+.at) Mt-1 } λ
k1=1; k2=77870; a=0.08; λ =0.086
Mt: Market value of equity. Behaviour of ECT:
4 Not applicable 5 Graphical analysis and some ADF tests indicate non-stationary
behaviour
135
Table 5.7-5: Sumitomo Model 6, Model 7 and Model 8
One year ahead forecast performance of three statistical equilibrium correction models after elimination of short run variables – Models 6, 7 and 8 – in the four-year hold-out
period (2001-2004)
Models
Book value, GDP, Nikkei index model Statistic ECM
Real value ECM
6 7 8 Derived from Model 1 2 3 Forecast errors (%) 2001 -0.19 -0.28 -0.01 2002 -0.30 -0.22 -0.16 2003 -0.54 -0.41 -0.44 2004 0.53 0.68 0.64 Mean -0.12 -0.06 0.01 RMSE 0.42 0.43 0.39 R2 0.26 0.31 0.31
Model specifications:
6
(Mt /Mt-1)= k1 {
G αt-1Vβ
t-1 / k2 B γ
t-1Mt-1 } λ k1 = 1; k2=616245;α = 1.18; β = 1.93; γ=1.83; λ = 0.35
7
(Mt /Mt-1)= k1 (k2
PR α t-1 V β
t-1 / E γ t-1 B ε
t-1 Mt-1 )λ k1 = 1.07; k2=9.67E+13;α = 0.07; β = 3.05;γ=1.76;ε=1.10; λ = 0.31
8
(Mt /Mt-1)= k1 ( k2G α
t-1V βt-1Mt-1 B γ t-1) λ
k1= 1.08; k2= 9.32E+8; α=4.13; β=2.15; γ=1.84;λ =0.33
Mt : Market value; Dt: Dividends; MSt: Money Supply; Et: Reported earnings Bt: Book value of equity; It : CPI: Xt-1 : Exchange rates; Vt: Nikkei index; Gt: GDP The prefix R denotes the variable is real, i.e. has been deflated by the CPI.
Behaviour of ECT: 6 As for Model 1 – see Table 5.7-3 7 As for Model 2 – see Table 5.7-3 8 As for Model 3 – see Table 5.7-3
136
5.7.3 Development of final model of data generating process for market value Comparisons of the ‘best’ statistical model with the time series models in the
forecasting scenarios F1 and F2 are shown in Table 5.7-6. The RMSE of Model 8 is
higher than the time series models under both scenarios and also performs badly in
comparison to the a priori Ohlson and book value models. Although both of the latter
fare poorly in the four-year hold-out sample period (see Table 5.6-7), they marginally
outperform the time series models over the longer 10-year period (see Table 5.7-7).
The conclusion therefore, especially in view of the counter-intuitive composition of the
statistical ECTs in this case, is that the a priori book value model is probably the best
ECT in this case, given that it outperforms the time series model in its RMSE (and
naturally in R2). The Ohlson model performs at the same level of effectiveness, but this
is probably due to the inclusion of the book value term, since the earnings appears
prima facie unlikely in this case to have explanatory power for market value (see Figure
5.7-1). Also, there is no evidence in any of the results that the other variable in the
Ohlson model, interest rate, has value relevance in the long run. Sumitomo is therefore
the second of the five firms examined for which a simple a priori ECT based upon book
value outperforms the model derived by the testing down procedure.
137
Table 5.7-6: Sumitomo 10-year forecasting One year ahead forecast performance of a random walk model, benchmark and earnings capitalisation equilibrium correction models in the 10-year period from 1995 to 2004, based on estimates computable in 1994
One year ahead forecast performance of a random walk model and bench mark and productivity equilibrium correction models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year
Model 4: Random walk model Model 4: Random walk model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.22 -0.27 -0.49 1995 0.22 -0.27 -0.49 1996 0.22 0.23 0.01 1996 0.21 0.23 0.02 1997 0.22 -0.40 -0.62 1997 0.21 0.40 -0.61 1998 0.22 -0.14 -0.36 1998 0.20 -0.14 -0.34 1999 0.22 -0.55 -0.78 1999 0.19 -0.55 -0.74 2000 0.22 0.62 0.40 2000 0.18 0.62 0.44 2001 0.22 -0.10 -0.32 2001 0.18 -0.10 -0.28 2002 0.22 -0.31 -0.53 2002 0.18 -0.31 -0.49 2003 0.22 1.31 1.09 2003 0.17 1.31 1.14 2004 0.22 -1.05 -1.28 2004 0.19 -1.05 -1.24
Mean error -0.29 RMSE 0.69 Mean error -0.26 RMSE 0.68 Model 5: Benchmark ECM Model 5: Benchmark ECM
Horizon Forecast Actual Error Horizon Forecast Actual Error 1995 0.30 -0.27 -0.57 1995 0.29 -0.27 -0.56 1996 0.39 0.23 -0.16 1996 0.27 0.23 -0.04 1997 0.38 -0.40 -0.78 1997 0.24 0.40 -0.64 1998 0.50 -0.14 -0.64 1998 0.19 -0.14 -0.33 1999 0.56 -0.55 -1.12 1999 0.14 -0.55 -0.69 2000 0.71 0.62 -0.09 2000 0.02 0.62 0.61 2001 0.62 -0.10 -0.72 2001 0.12 -0.10 -0.22 2002 0.68 -0.31 -0.99 2002 0.10 -0.31 -0.40 2003 0.78 1.31 0.54 2003 0.05 1.31 1.26 2004 0.55 -1.05 -1.60 2004 0.13 -1.05 -1.18
Mean error -0.61 RMSE 0.83 Mean error -0.219 RMSE 0.67 Model 8: Real value ECM Model 8: Real value model Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.56 -0.27 -0.83 1995 0.62 -0.27 -0.89 1996 -0.61 0.23 0.84 1996 -0.61 0.23 0.84 1997 0.64 -0.42 -1.06 1997 0.03 -0.42 -0.45 1998 -0.48 -0.14 0.34 1998 0.63 -0.14 -0.77 1999 0.26 -0.55 -0.81 1999 0.26 -0.55 -0.81 2000 -0.59 0.63 1.22 2000 -0.23 0.63 0.86 2001 0.82 -0.09 -0.91 2001 -0.78 -0.09 0.69 2002 -0.64 -0.3 0.34 2002 -0.52 -0.3 0.22 2003 0.09 1.32 1.23 2003 0.98 1.32 0.34 2004 -0.56 -1.05 -0.49 2004 -0.56 -1.05 -0.49
Mean error -0.013 RMSE 0.86 Mean error -0.046 RMSE 0.68
138
Table 5.7-7: Sumitomo Model 9 and Model 10 One year ahead forecast performance of Ohlson-type and simple book value of net assets
equilibrium correction models in the four-year hold-out period (2001 – 2004)
Models Ohlson Book value 9 10
Forecast errors (%) 2001 -0.25 -0.12 2002 -0.47 -0.33 2003 1.38 1.28 2004 -0.93 -1.09 Mean error -0.07 -0.06 RMSE 0.87 0.86 R2 0.13 0.13
Notes: 9
(Mt /Mt-1) =k1{(Ααt-1) ( Β β
t-1) /Mt-1 } λ
k1=1.02; k2=2.04E+7; α=0.30; β=0.01; λ =0.03
10
(Mt /Mt-1) =k1 {(Βαt-1) /Mt-1
} λ
k1=2.37; α=9.77; λ =0.01
Mt : Market value of equity; At: Abnormal earnings; Bt: Book value of net assets
Behaviour of ECT
9
ECT tests mostly non-stationary. At is only significant in the ECT at the 10% level.
10 ECT tests non-stationary at all lags
139
Table 5.7-8: Sumitomo 10-year forecasting
One year ahead forecast performance of a random walk model, benchmark and earnings capitalisation equilibrium correction models in the 10-year period from 1995 to 2004, based on estimates computable in 1994.
One year ahead forecast performance of a random walk model and bench mark and productivity equilibrium correction models in the 10-year period from 1954 to 2004, based on estimates computable at the end of each preceding year.
Model 9: Ohlson-type ECM with constant Model 9: Ohlson-type ECM with constant Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 -0.01 -0.27 -0.25 1995 -0.02 -0.27 -0.25 1996 -0.03 0.23 0.26 1996 -0.03 0.23 0.26 1997 -0.04 -0.40 -0.36 1997 -0.04 -0.40 -0.36 1998 0.09 -0.14 -0.23 1998 0.09 -0.14 -0.23 1999 -0.02 -0.55 -0.53 1999 -0.03 -0.55 -0.53 2000 -0.03 0.62 0.65 2000 -0.03 0.62 0.65 2001 0.11 -0.10 -0.21 2001 0.11 -0.10 -0.21 2002 0.11 -0.31 -0.42 2002 0.11 -0.31 -0.42 2003 -0.02 1.31 1.33 2003 -0.02 1.31 1.33 2004 -0.03 -1.05 -1.02 2004 -0.03 1.05 -1.02
Mean error -0.13 RMSE 0.64 Mean error -0.08 RMSE 0.64 Model 10: Book value of net assets ECM Model 10: Book value of net assets ECM Horizon Forecast Actual Error Horizon Forecast Actual Error
1995 0.05 -0.27 -0.32 1995 0.05 -0.27 -0.32 1996 0.05 0.23 0.17 1996 0.02 0.23 0.20 1997 0.06 -0.40 -0.46 1997 0.06 -0.40 -0.46 1998 0.06 -0.14 -0.20 1998 0.03 -0.14 -0.17 1999 0.07 -0.55 -0.62 1999 0.02 -0.55 -0.57 2000 0.07 0.62 0.56 2000 -0.03 0.62 0.65 2001 0.07 -0.10 -0.17 2001 0.03 -0.10 -0.12 2002 0.06 -0.31 -0.37 2002 0.02 -0.31 -0.32 2003 0.07 1.31 1.24 2003 0.01 1.31 1.30 2004 0.07 1.05 -1.12 2004 0.02 1.05 -1.07
Mean error -0.13 RMSE 0.64 Mean error 0.41 RMSE 0.64
140
Thus, Model 10 appears to be the most appropriate DGP for market value model in
Sumitomo’s case. Figure 5-24 displays the performance graphics. The estimated
forecast seems to track the movement of the actual data and the ACF appears to be
white noise. However, the RMSE is larger than the best performance models of Toyota
and the 1 step forecast line is flat. Similar to the results obtained for Fuji Film, the ACF
gives an indication of possible autocorrelation. However, the residuals show a larger
departure from normality than in the case of Itochu and the scaled forecast errors are
larger in most of the Sumitomo models than in the models of the four other firms. The
recursive graphics show coefficient estimates with relatively stable trends but close to
zero.
Figure 5-24: Sumitomo Trust & Banking Book Value Model (Model 10)
1950 1960 1970 1980 1990 2000
-1.0
-0.5
0.0
0.5
1.0
1.5Raw return Fitted
1995 2000 2005
-1.0
-0.5
0.0
0.5
1.0
1.51-step Forecasts Raw return
-3 -2 -1 0 1 2 3
0.1
0.2
0.3
0.4
0.5
DensityResiduals N(0,1)
0 5 10 15 20
-0.5
0.0
0.5
1.0ACF-residuals
141
Figure 5-25: Sumitomo Trust & Banking Book Value Model Recursive (Model 10)
1960 1970 1980 1990
-5.0
-2.5
0.0
2.5Constant × +/-2SE
1960 1970 1980 1990
0.0
0.1
0.2
0.3ECT-STBM10-10June_1 × +/-2SE
1960 1970 1980 1990
0
1t: Constant
1960 1970 1980 1990
1
2t: ECT-STBM10-10June_1
1960 1970 1980 1990
2
4
Residual ssum of squares
1960 1970 1980 1990
0
1Residual 1 step forecast
Note: ECT-STBM10; Error Correction Sumitomo Trust Bank Model 10 (page 138)
5.8 Summary
This chapter has reported the results of modelling each firm’s data set. The construction
of models was based on a standard procedure described by Willett (2004), using each
firm’s financial data and macro-economic variables over the 54 year period from 1950
to 2004. The best performance model for each firm is largely different in terms of the
included variables but share similarities in functional form. The results also indicate
some degree of relationship of market value to book value in four of the five firms.
In the final chapter, these results are further explored and interpreted in the context of
the question of the ‘sufficiency’ of accounting information for estimating the market
value of the five firms.
142
CHAPTER 6 DISCUSSION AND CONCLUSIONS
6.1 Introduction
This chapter interprets the results reported in the previous chapter by discussing the
significance of accounting numbers in the models, relative to other data. To what extent
is accounting information ‘sufficient’ to explain market value, without additional
information from other sources (e.g. macro-economic sources)? This issue raises
questions about the ‘value relevance’ of accounting information and has implications
for the disclosure quality of the financial statements of the firms covered by the study.
The discussion of sufficiency in Section 6.2 concludes the development of the thesis.
Section 6.3 summarises the content of the thesis and Section 6.4 discusses the
limitations of the research. The final section, Section 6.5, outlines a number of possible
future research directions.
6.2 Accounting numbers as sufficient statistics for market value
One theme in CMR is concerned with whether accounting numbers are, in some sense,
sufficient statistics for the valuation of firm market value. Much classical theory in
economics and finance assumes as much (see, for example ( Gordon and Khumawala,
1999; Modigliani and Miller, 1961). Ohlson’s (1995) theory has important elements that
follow in this tradition and currently influences the specification of regression models in
CMR. In the context of this research, the idea that book value may be sufficient for
market is interpreted analogously but non-rigorously to the statistical principle of
143
sufficiency; that is, as being that ‘inference about market value based upon book value
B depends only on B’, so that other information such as macro-economic data is
irrelevant for market value.
The models of market value reported in the previous chapter included variables other
than accounting variables, and sometimes did not include accounting variables at all
(Table 6.2-7). However, it was noted that models of market value based on book value
often appeared to come a close runner-up for the title of ‘best model’ in those cases
where a simple book value model was not, in fact ‘best’. Table 6.2-1 summarises the
coefficient estimates and other basic statistical information about the book value models
for each of the five firms studied. Table 6.2-2 reproduces the final models from Chapter
4, including the comparative RMSE and R2 data for the time series models.
Table 6.2-1: Book value models for five firms
K1 K2 β1 λ R2 F1 F2 RMSE RMSE Toyota 1.07 ** 2.25 1.00 *** -0.42 *** 32% 0.24 0.24 Fuji Film 1.00 1.02 1.04 *** -0.34 * 18% 0.17 0.15 Sony 1.16 * 35.23 ** 0.78 *** -0.38 ** 24% 0.34 0.37 Itochu 1.16 *** 1.57 1.04 *** -0.23 *** 6% 0.48 0.47 Sumitomo 1.04 *** 1.94E+52 9.77 -0.006 *** 9% 0.64 0.64 Book Value model :Mt/Mt-1 = k1{k2
( Mt-1 /(Bt-1)β} –λ
F1: The whole ten-year period, based on estimates of the ECT computable in 1994
F2: For one year ahead, based on estimates computable in the year preceding the forecast
144
Table 6.2-2: Best model
K1 K2 variable β1 variable β2 variable β3 λ R2 RMSE
Toyota 1.11 * 189.99 ** Money supply 1.38 *** -0.42 *** 34% 22%
Fuji Film 1.02 Book value 1.04 -0.34 18% 17%
Sony 1.43 *** 630772 *** GDP 3.05 *** CPI 2.63 * -0.52 *** 27% 22%
Itochu 1.02 *** 197383 *** Book value 0.66 ** Exchange
rate 1.46 *** Nikkei index 1 *** -0.51 *** 23% 37%
Sumitomo 1.04 *** 1.94E+52 Book value 9.77 -0.006 * 9% 64%
*** 1% level of significance; ** 5% level of significance; * 10% level of significance
145
A possible interpretation of the coefficient λ in the book value models is that they
measure the strength of the reversion of market value to a long term relationship with
book value. The signs and magnitudes of the coefficients appear to support such an
interpretation, since they lie between 0 and -1 - as they must do if they are to reflect a
dampening, equilibrium seeking effect on the behaviour of market value over time.
However, it is necessary for statistical confidence to factor in consideration of the
explanatory power of the models and a sensible interpretation of the remaining
coefficients in the models. For example, the λ coefficient in a model with a low
adjusted R2 must have less credibility than one produced from a model with a high R2.
Also, a simple book value error correction interpretation implies that k1 and β should be
close to unity and that k2 should reflect a simple, long term relationship between market
and book values.
Of the five companies, three of the book value models have some credibility as possible
estimates of an error correction adjustment of market to book value over a period of
approximately one year. Toyota, Fuji Film and Itochu have estimated long run
coefficients that are easily interpretable, in that the overall multiplier (k1) and the index
on Bt-1 (β) are close to unity (and not significantly different at the 5% level), while the
multiplier on book value (k2) is of the same order as the relative size of the average
market to book ratio over the entire sample period (see Table 6.2-3). The latter
coefficient could, for instance, be interpreted as a premium attaching to the share
market’s assessment of the fair value of the assets of the firm, which depends on factors
such as the perceived conservatism of each firm’s accounting practices, etc. (see
Bartholdy et al., 2003). Two of these book market models, for Toyota and Fuji Film,
146
also perform well in RMSE terms, relative to the two time series models used for
comparative performance tests. Itochu’s book value model does slightly worse in
RMSE terms than the time series models, however.
Table 6.2-3: Market to book value
ratio over the sample period Toyota 2.00
Fuji Film 1.53
Sony 2.00
Itochu 2.23
Sumitomo 2.57
Two of the firm book value models, Sony and Sumitomo, fail the interpretation test;
despite the fact that the Sony model provides F2 scenario RMSEs lower than the
comparable time series performance measures. The multiplier on Sony’s book value in
Table 6.2-1 is too large (35.23) to make the same empirical sense as in the case of the
three companies above. Similarly, β in this case is only 0.78 and significantly different
from 1 at the 5% level. None of the coefficients in Sumitomo’s book value model are
credible under the interpretation just given, despite the model’s marginally better RMSE
performance relative to the time series models (although this may simply reflect the fact
that book value is not a sufficient statistic in the case of this company). The estimate of
λ is very small and not significantly different from zero.
A form of sensitivity analysis of the book value interpretation can be undertaken by
resorting to data snooping. This is done by treating the entire sample as an estimation
basis for the coefficient λ and constructing book value models for each firm using a
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two-stage procedure similar to the original Engle-Granger approach to testing for co-
integration. First, market is regressed on book value, and the residuals from that
regression are lagged and used as regressors, with the change in market value as the
dependent variable. As before, all the variables are in logs, so the same multiplicative
interpretation of the variable in the raw data is implied.
The model is estimated in the manner just described (in two forms) and the results are
reported in Table 6.2-4 and 6.2-5. The first form is unrestricted in the sense that
constants are included in both the long run solution and in estimating the coefficient on
the ECT. In the second form, the coefficient β is restricted to unity in order to force an
estimation of the supposed multiplier effect on book value. Non-linear least squares is
used to estimate the long run solution in the latter instance, but the ECM is estimated
using OLS. Both tables also report λ re-estimated when the constant in the second stage
ECM is restricted to unity.
Table 6.2-4: Book value models estimated using entire sample
Including estimate of k1
Excluding estimate of k1
k1 k2 β λ R2 λ R2 Toyota 1.22 *** 0.35 *** 1.12 *** -0.38 *** 15% -0.37 *** 11% Fuji Film 1.17 *** 0.95 1.04 *** -0.29 ** 9% -0.28 ** 6% Sony 1.21 *** 4.99 *** 0.94 *** -0.31 ** 9% -0.31 ** 7% Itochu 1.15 ** 0.45 * 1.14 *** -0.14 1% -0.14 1% Sumitomo 1.18 *** 0.21 *** 1.18 *** -0.1 1% -0.1 0%
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Table 6.2-5: Book value models estimated using entire sample with the index on book value restricted to unity
Including estimate of k1 Excluding estimate of k1 k1 k2 λ R2 λ R2 Toyota 1.21 *** 1.66 *** -0.42 *** 32% -0.42 *** 25% Fuji Film 1.17 *** 1.53 *** -0.34 *** 15% -0.33 *** 11% Sony 1.21 *** 2.34 *** -0.18 1% -0.17 1% Itochu 1.15 ** 2 *** -0.22 ** 6% -0.22 ** 6% Sumitomo 1.19 *** 1.51 *** -0.18 ** 9% -0.17 ** 7%
From the various models of the market value, it can be seen that the book value
maintains a fairly robust pattern of estimated coefficients and explanatory power in the
case of both Toyota and Fuji Film. In the case of Fuji Film, the book value model is
actually the best performer in any case. Whilst a simple Engle-Granger type approach to
estimation using the whole of the sample data lessens the explanatory power of the
model, it leaves the estimated coefficients much the same as before. Forcing the index
on book value to unity restores much of the explanatory power to the model and
provides virtually identical estimates of the error correction coefficient as in the original
model.
The best Toyota ECM of market value includes the money supply as the explanatory
variable, but relacing money supply with book value produces an ECM very close in
performance. As with Fuji Film, although using the entire sample does not affect the
coefficient estimates to any great extent, it weakens the explanatory power of Toyota’s
book value model and its interpretation, since the multiplier on book value would be
expected to be greater than unity, rather than less. However, again, imposing the
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assumption of an index on book value being unity recovers the strength of the original
book value model and provides similar coefficient estimates.
Sony presents a different situation. Sony’s book value model of market value is almost
equal to the theoretically most superior model derived from the procedure adopted in
Chapter 4, although it is only marginally superior in its forecasts than the benchmark,
time series ECM. However, its explanatory power is diminished when estimated over
the entire sample by the simple two-stage procedure described above, and when the
book value index is restricted to one, it drops to virtually zero (see Table 6.2-5). In the
latter approach, the error correction term also falls away, from almost 40%, as in the
original model, down to 17%. Consequently, Sony’s book value model appears to be
empirically unsustainable, despite its initial promise. Once the interpretation is
questioned (i.e. the large multiplier coefficient on Bt-1 in the original book value model
from Chapter 4), re-estimation of the parameter coefficients of the supposed
relationship fails to bring forth anything of significance.
Sumitomo is an interesting contrast to Sony. The original book value model estimated
in Chapter 4 produced totally uninterpretable coefficients on lagged book value in the
ECT. Using the simple, two-stage estimation procedure over the entire sample period
does little to improve matters, but taking the a priori stance of setting the index on
lagged book value to one produces a model with some explanatory power (7-9%), and
coefficient estimates that test as significant at the 5% level and are capable of a sensible
interpretation. That is, it appears that the share market estimates the market value of
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Sumitomo at about 1.5 times book value and corrects about 17% of the imbalance
between market and book over the ensuing 12 months.
Finally, in the case of Itochu, there is a reasonably consistent picture of an estimated
book value model that is capable of a sensible interpretation but which, unlike Toyota
and Fuji Film, is significantly inferior to the best model from Chapter 4. Furthermore, it
does not perform in the neighbourhood of the performance of the time series ECM with
respect to RMSE or R2. Two-stage estimation over the entire sample reduces the overall
power of the model while restricting the long run parameters on logged book value in
the ECT more or less recovers the original book value model and its explanatory power
(compare Table 6.2-1 and 6.2-5). The explanatory power of this model is of the same
order as that of Sumitomo’s (Table 6.2-1), and can probably be attributed the same
degree of credibility: marginal but not totally without meaning.
Bearing in mind the various matters just discussed, it seems reasonable to rank the five
companies with respect to the degree of sufficiency of their book values. This ranking is
shown in Table 6.6, with a note of the average book value of each firm’s assets. Toyota
easily has the highest mean book value of assets, making it the firm with the most
sufficient book value. Its book value model has the highest error correction coefficient
of all the companies, suggesting that about 40% of any imbalance between market and
book value is corrected over a period of one year. It has an R2 in excess of 30% and a
robust interpretation. Similar claims can be made for Fuji Film’s book value model, but
the sufficiency of its book value is less than Toyota’s when judged by the size of its
error correction coefficient. This suggests that about one-third of a previous year’s
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imbalance between market and book is corrected by trading over the ensuing year.
Itochu and Sumitomo form a pair of marginal models and are probably not worth
separating in terms of their book value sufficiency ranking. Both have an error
correction coefficient that suggests about one-fifth of a market book value imbalance is
corrected by the share market over a one-year period. Finally, in the case of Sony, there
is no real evidence in the modelling process described above and in earlier chapters to
consider book value to be a sufficient statistic for the share market. In Sony’s case, real
GDP, defined as the ratio of nominal GDP to the CPI, seems to be a better candidate as
an attractor for market value than book value. There is no evidence that the size of mean
book value is associated with the ranking of the firms by sufficiency of book value,
other than the relative strength of the Toyota model.
Table 6.2-6: Ranking by sufficiency of book value
Rank Firm Mean book value 1 Toyota 2143522 2 Fuji Film 479316 3 Itochu 163392 3 Sumitomo 270341 5 Sony 688465
The question of why these differences exist between the five firms with respect to the
data generating processes for their market values, and with respect to the apparent
differences in the sufficiency of the book value of their net assets is left for future
research.
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Table 6.2-7: Summary of Error Correction Models, RMSE and R2
TOYOTA FUJIFILM SONY ITOCHU SUMITOMO TRUST BANK
ECM models Variables RMSE R2 Variables RMSE R2 Variables RMSE R2 Variables RMSE R2 Variables RMSE R2 Model 1 MS, D 0.30 0.53 E, B, I, X 0.34 0.73 G, I 0.22 0.48 E, MS, I 0.60 0.55 B, G, NIK 0.55 0.55 Model 2 MS 0.27 0.37 B, X 0.26 0.60 PR 0.29 0.38 B, NIK,X 0.27 0.38 B, E, PR,NIK 0.88 0.57 Model 3 MS, D 0.29 0.55 E, B, D 0.27 0.55 G, I 0.32 0.48 E, MS, G 0.61 0.56 B, G,NIK 0.52 0.59 Model 6 MS, D 0.44 0.17 E, B, I, X 0.49 0.23 G, I 0.24 0.35 E, MS, I 0.80 0.26 B, G, NIK 0.42 0.26 Model 7 MS 0.31 0.2 B, X 0.45 0.28 PR 0.25 0.32 B, NIK, X 0.24 0.25 B, E, PR,NIK, 0.43 0.31 Model 8 MS, D 0.20 0.14 E, B, D 0.37 0.25 G, I 0.28 0.23 E, MS, G 0.79 0.26 B, G,NIK 0.39 0.31 RW model constant 0.43 0.00 constant 0.24 0.00 constant 0.34 0.00 constant 0.33 0.00 constant 0.88 0.00 BEN model cons.& trend 0.43 0.28 cons.& trend 0.39 0.24 cons& trend 0.32 0.34 cons& trend 0.36 0.14 cons.& trend 0.84 0.16 OHL model A, B 0.47 0.28 A, B 0.41 0.28 A, B 0.23 0.22 A, B 0.36 0.07 A, B 0.87 0.13 BV model B 0.36 0.32 B 0.38 0.20 B 0.24 0.22 B 0.34 0.07 B 0.86 0.13 Model 6-10 MS, D 0.31 0.11 E, B, I, X 0.37 0.23 G, I 0.22 0.27 E, MS, I 0.90 0.34 B, G, NIK 0.66 0.24 Model 7-10 MS 0.22 0.34 B, X 0.35 0.23 PR 0.39 0.48 B, NIK ,X 0.34 0.23 B,E PR, NIK, 0.75 0.28 Model 8-10 MS, D 0.23 0.22 E, B, D 0.33 0.25 G, I 0.34 0.35 E, MS, G 0.44 0.24 B, G,NIK 0.86 0.25 RW -10 constant 0.31 0.00 constant 0.22 0.00 constant 0.43 0.00 constant 0.43 0.00 constant 0.69 0.00 BEN-10 cons.& trend 0.31 0.27 cons.& trend 0.31 0.28 cons& trend 0.38 0.38 cons& trend 0.38 0.13 cons.& trend 0.83 0.17 OHL-10 A, B 0.33 0.28 A, B 0.18 0.20 A, B 0.36 0.29 A, B 0.50 0.09 A, B 0.64 0.09 BV-10 B 0.24 0.32 B 0.17 0.18 B 0.34 0.24 B 0.48 0.06 B 0.64 0.09 Model 6-101 MS, D 0.32 E, B, I, X 0.27 G, I 0.33 E, MS, I 0.65 B, G, NIK 0.63 Model 7-101 MS 0.22 B, X 0.31 PR 0.39 B, NIK, X 0.36 B, E, PR, NIK, 0.75 Model 8-101 MS, D 0.32 E, B, D 0.23 G, I 0.36 E, MS, G 0.41 B, G,NIK 0.68 RW-101 constant 0.31 constant 0.21 constant 0.42 constant 0.42 constant 0.68 BEN-101 cons.& trend 0.28 cons.& trend 0.39 cons& trend 0.38 cons& trend 0.38 cons.& trend 0.67 OHL-101 A, B 0.24 A, B 0.22 A, B 0.39 A, B 0.49 A, B 0.64 BV-101 B 0.24 B 0.15 B 0.37 B 0.47 B 0.64 B: Book Value of net assets; E; Net Income; D: Dividend; A: Abnormal Earnings; MS; Money Supply; G; GDP;I: CPI; NIK: Nikkei Index; PR: Productivity Index Model 1: constructed model; use the entire information set as the starting point for the reduction sequence. RMSE: root mean square error Model 2: results from dropping series in dividends, GDP and the CPI, indicated as possibly being I(2) by ADF tests (see Chapter 4 EQ3) Model 3: based on ‘real’ data, where nominal financial data is divided by the CPI (and multiplied by 100). Model: 4 year hold data, 2001-2004 forecast RW model: Random walk model; Mt/Mt-1 = k Model -10: A) hold 1995-2004 to forecast 10 years BEN model: with constant and trend model: Mt/Mt-1 =k1*{exp(k2+.at) / Mt-1}λ Model -101:B) one year hold-out at a time to forecast 10 years OHL model: based on Ohlson model: Mt/Mt-1 =k1*{(A t-1) α( Bt-1) / /Mt-1 } λ BV model: Regression Market Value on Book value of net assets model: Mt/Mt-1 = k1 *{( Bt-1) / Mt-1 }λ
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6.3 Summary of thesis findings
The research question asked at the beginning of this thesis was essentially descriptive in
nature: ‘What was the nature of the relationship between market values and reported
accounting information for five selected Japanese firms during the period 1950 to
2004?’ This question was embedded in the context of capital market research in the
accounting literature, which was reviewed in Chapter 2. The literature review
highlighted the fact that most prior studies in this field have taken a cross-sectional
approach to regression modelling of the relationship between market and accounting
values. One of the issues discussed in previous research is whether earnings and book
value have declined in their significance over the period covered by this study and,
related to this, how much relevance such well known accounting numbers have for the
valuation of the shares of quoted firms, and therefore for the market value of those firms.
This thesis has investigated these issues by adopting a time series approach to modelling
the relationship between market and book values. This approach, described in Chapters
3 and 4, is empirically-based and was designed to reduce, as much as possible, any
‘theory dependence’ in the construction of variables, so that whether the models seemed
to explain the data or not is more or less unequivocal. The particular style of time series
modelling implemented was based upon error correction principles and relied on the
existence of co-integration between the relevant variables for validity of the models.
Implementation of this approach using the data from the five Japanese firms studied
lead to the identification of a number of models of market value, some of which showed
evidence of firm market value being influenced with a lag by accounting numbers. The
estimates of the specified models were reported in Chapter 5. A summary of the
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specifications of all the models derived by the standard testing down procedure is
shown in Table 6.2-7. The specification of the ‘best’ final models selected from those
in Table 6.2-7 is shown in Table 6.2-2. Table 6.2-5 provides details of the
comparable, ‘interpreted’ book value models of market value.
Several aspects of these models are interesting and make a contribution to knowledge in
CMR. One noteworthy feature is that the well-specified models are log linear; i.e.
implying a multiplicative relationship between the proportionate growth of market value
and the regressors. All linear additive models in the raw data proved to be poorly
specified, with unstable parameters and poor forecasting ability. This result is consistent
with the results reported in Willett (2003; 2004) and other similar research currently
being undertaken at QUT. Other interesting features of the statistical models are their
relative simplicity and apparent stability of their coefficient estimates over time. Cross-
sectional work in CMR tends to produce coefficients on key variables that behave
erratically over time, and time series work on aggregated time series data such as
indices often fails to reveal connections (especially error correction ones) of market
values with accounting values. These findings therefore suggest that lagged accounting
data is important in estimating market value and that aggregation may obscure rather
than highlight such relationships.
With respect to the value relevance issues raised in CMR, models based on the book
value of net assets were found to either come a close second to the best statistical model
(Chapter 5) or to sometimes outperform the often more complex, empirically-derived
models, using the procedure described in Chapter 4. This matter was analysed in the
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previous subsection, leading to the conclusion that book value appeared to be sufficient
for market value in the case of four of the five Japanese firms studied. This does not
demonstrate that other kinds of accounting information may not also be value relevant.
Many accounting numbers not considered in this thesis could be candidates for value
relevance, and there are an infinite number of possible functional forms for their
relationship with book value. However, it does demonstrate the possibility that book
value may have a systematic long run relationship with market value and that the
approach taken in the thesis could be used to investigate this possibility in a larger
sample of companies. In this regard, the findings of this thesis are consistent with the
belief that the reported accounting information is relevant for market value in the long
run, and there has been no increase or decrease in the extent of this importance over the
fifty-year-plus period of the study.
6.4 Limitations of the study
As noted above, there are many possible alternative functional forms and many
candidates for best explanatory variable among the different accounting numbers
published in annual reports. This study has considered only three accounting variables:
book value of net assets, earnings and dividends. It has considered only the log
transform of the raw data. Log transforms are difficult to interpret when it is necessary
to deal with negative values of some of the variables. Treating negative values as
missing is unsatisfactory in time series analysis, and adding an arbitrary constant to the
entire series to ensure positivity leaves open the question of how sensitive the results are
to choice of the constant. While the latter approach was undertaken in the modelling
performed in this thesis, earnings and dividends only rarely appeared as statistically
156
significant regressors. Furthermore, both variables failed to survive the additional filters
(forecasting and interpretability) to find their way into the ‘best’ chosen models.
Consequently, the approach adopted in this research could have downplayed the long
run value relevance of earnings and dividends for market value, relative to the book
value of net assets. Similarly, it may bias the results against the significance of
accounting information generally, as to its relevance for market value. This is because
so many other accounting numbers are not considered in the initial information set used
for model construction (sales, earnings excluding unusual items, intangible assets, ratios
of accounting numbers, etc.).
The key limitations to the generalisability of the results, however, are the small size of
the sample and the need to delve more deeply into the interpretation of the models to
see what, if anything, they may tell us about the behaviour of the market value of the
companies over the sample period, which would not have been evident in the absence of
the models. Both of these limitations result purely from time constraints on the research.
6.5 Future research
Extending the sample of firms and the analysis of the models in the broader context of
economic background events is the most obvious next step in developing the research
reported in this thesis. Incorporating another 45 firms into the analysis would allow
greater generalisability of the findings and would also permit the important question of
how such time series analysis relates to the cross-sectional analysis typically undertaken
in CMR. Apparently little is known about this matter, even in the econometric literature.
A detailed qualitative examination of the models in the context of the raw data and
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background economic events is also important in giving meaning to the models. Again,
such a qualitative, post-modelling interpretation of regression models does not seem to
be undertaken in CMR, or at least not reported in publicly available sources.
The research method is designed to be rigorous and scientific in its approach to model
building and to be sufficiently well-described as to be open to replication on the same
initial information set. Consequently, implementing this method on other data obtained
from different countries should provide a valid basis for making international
comparisons of value relevance questions. There is also a definite need for a more
general approach to transforming data, including the log transformation. This would
enable the relative strengths of book values, earnings and dividends to be more
confidently assessed with respect to their value relevance.
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APPENDICES
Appendix 1: Standard procedure
Standard procedure (SP) for deriving a basic statistical single equation conditional equilibrium correction model (ECM) from an information set (the following SP should be applied to the IS). Step 1: After choosing the dependent variable from the IS, formulate an unrestricted autoregressive distributed lag (ADL) using two lags on all variables in the IS. Include a constant and trend. Use a hold-out sample of four periods. Step 2: Test the ADL for correct functional form21 and the residuals for homoscedasticity22, non-autocorrelation23 and normality24 . Continue the SP only if these tests are statistically insignificant at the 5% level. Step 3: Eliminate the trend term if its ordinary least squares (OLS) estimate proves statistically insignificant. Step 4: Repeat step 2. Step 5: Compute the long run solution for the ADL and test the significance of lags25. Step 6: If the maximum lag length remaining tests jointly insignificant for all variables26, eliminate that lag length. Note any significant coefficients on eliminated variables at the eliminated lag length in the estimated OLS model or at any lag length27 for later inclusion in the short run dynamics (SRD) of the ECM. Repeat from step 4 until all insignificant lags have been eliminated. Step 7: Eliminate insignificant explanatory variables one by one, starting with the least significant t statistic in the long run solution28. Repeat steps 4 to 6 in each case. Note any significant coefficients on eliminated variables in the estimated OLS model or at any lag length (as in Step 6 above) for later inclusion in the SRD of the ECM. Repeat until all variables in the model test significant in the long run dynamics. Step 8: Calculate the equilibrium correction term (ECT) implied by the long run solution at the completion of step 7, using the exact estimates computed for that solution. Continue the SP only if Augmented Dickey Fuller (ADF) unit root tests indicate that the ECT is stationary. Step 9: Construct SRD terms by differencing all variables in the ECT and all variables previously eliminated but noted as significant in earlier steps. Construct the ECM by combining the ECT calculated in Step 9 with the SRD terms. Step 10: Eliminate any insignificant lag lengths as in Step 6. Then eliminate the SRD variables one by one, beginning with the least significant in the estimated OLS model; repeating this step until all insignificant SRD variables have been eliminated. When this step is completed, the resulting model is a basic statistical ECM for the chosen dependent variable, given the IS. Note: If the SP halts by reason of the application of a rule defined in the SP but model construction nevertheless continues through subsequent steps to derive an ECM with significant coefficients, all deviations from the SP must be fully documented and referenced to the resulting non-standard ECM, so that it may be objectively replicated from the same IS.
21 RESET test (Ramsey, 1969) 22 Based on White (1980) 23 F test form for unconditional autocorrelation and ARCH (Harvey, 1990) 24 Doornik and Hansen (1994). 25 See Hendry and Doornik (2001, pp. 255-257) 26 See Hendry and Doornik (2001, p. 257, Sections 18.3.2.2 and 18.3.3). 27 See Hendry and Doornik (2001, p. 257, Section 18.3.2.1). 28 Using tests on the ‘static long run parameters’ defined in Hendry and Doornik (2001, p. 255-6, Section 18.3.1).
159
Appendix 2: Definitions and sources of data in information set
Earnings, dividends and book value of net assets
Company Annual Report. Sources: Tokyo Sock Exchange (TSE) Information Centre 1949 - 1999; DataStream; 1980-2003; Company web site; Toyota; 2000-2004; Fuji Photo Film; 1999-2004; Itochu 1997-2004; Sony 1995- 2004; Sumitomo Trust Bank; 1999-2004. http://www.toyota.co.jp/en/ir/reports/ http://home.fujifilm.com/info/profile/factsheet.html http://www.sony.net/SonyInfo/IR/ http://www.itocu.co.jp/main/ http://www.sumitomotrust.co.jp/IR/company/index_en.html
Market value Defined as share price at fiscal year end (A199) multiplied by the number of shares outstanding. Sources: Tokyo Sock Exchange (TSE) Information Centre; 1949 – 2004.
GDP Source: Economic and Social Research Institute, Cabinet Office, Government of Japan/ Statistics Information Site, 1946-2002 File downloaded 10/12/2003; 2003 data downloaded 30/5/2004; 2004 data downloaded 10/1/2005. http://www.esri.cao.go.jp/jp/sna/qe011-68/gaku-mg01168.csv
Interest rate Data from 1947-1965, “Meiji Iko Honpo Shuyo Keizai Tokei 1965” published by Bank of Japan Statistic Bureau; Data from 1955-2004, Official interest/discount rate in December each year, Source: Official site of Bank of Japan http://www.boj.or.jp/stat/stat_f.htmhttp://www.federalreserve.gov/releases/h15/data/a/prime.txt Files downloaded 7/19/2004. 2004 data downloaded 10/1/2005.
CPI Statistics Bureau, Ministry of Internal Affairs and Communication http://www.stat.go.jp/data/cpi/200107/zuhyou/a001hh.xls 1947-2003 CPI monthly data downloaded 11/4/2004; 2004 data downloaded 11/1/2005.
Productivity index Data from 1950-1955; Data from 1955-1975 “Meiji Iko Honpo Shuyo Keizai Tokei, 1965” published by Bank of Japan Statistic Bureau;"Bukka Shisu Nenpo" 1970, published by Shakai Keizai Seisan-sei Honbu; Data from 1965-1975,” Rodo seisan sei no Jittai”, published in 1978 by Shakai Seisansei Honbu; Data from 1975-1989 “Rodo Seisansei no Kokusai Hikaku”, published in 1990 by Shakai Keizai Seisan-sei Honbu; Data1990-1997, “ Keizai Tokei Nenpo” published in 1997 by Bank of Japan Bureau; Data from 1998 -2003, Japan Productivity Centre for Socio-Economic Development web site www.jpc_sed.or.jp Files downloaded 11/5/2004.
Foreign exchange rate Foreign currency units per 1 US Dollar, 1948-2004 Bank of Japan, Bureau, Historical data; http://www.boj.or.jp/stat/dlong_f.htm 1948-2004 data downloaded 10/4/2004.
Money supply Data from 1947-1995 “Meiji Iko Honpo Shuyo Keizai Tokei”, published by Bank of Japan in 1965; “Honpo Keizai Tokei”, published by Bank of Japan in 1960 and 1965; “Keizai Tokei Nenpo” published by Bank of Japan in 1997; Data from 1980-2004 Bank of Japan web site, http://www.boj.or.jp/stat/money/money.htm Files downloaded 11/5/2004.
Nikkei index The Nikkei 225 Index Performance from 1914-2004 http://www.finfacts.com/Private/curency/nikkei225performance.htmFiles downloaded 10/4/2004 and 11/1/2005.
160
Appendix 3: Financial variables: level, first difference, Logged and the first difference logged variables for four firms
Panel A: Fuji Photo Film:
Fuji Film Market value
1950 1960 1970 1980 1990 2000
500000
1e6
1.5e6
2e6
2.5e6Fuj i Film Market value
0 5 10 15 20
0
1Fuj i film ACF-market value
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5
15.0Fuj i Film Logged market value
0 5 10 15 20
0
1Fuj i Film ACF-Logged market value
1950 1960 1970 1980 1990 2000
0
500000Fuj i Film First difference of market value
0 5 10 15 20
0
1Fuj i Film ACF-first difference of market value
1950 1960 1970 1980 1990 2000
0.0
0.5
1.0
1.5Fuj i Film First difference of logged market value
0 5 10 15 20
0
1Fuj i Film ACF-First difference of logged market value
Fuji Film Book value,
1950 1960 1970 1980 1990 2000
500000
1e6
1.5e6 Fuji Film Book value of net assets
0 5 10 15 20
0
1Fuji Film ACF-Book value of net assets
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5
15.0Fuji Film logged of book value of net assets
0 5 10 15 20
0
1Fuji Film ACF-Logged book value of net assets
1950 1960 1970 1980 1990 2000
0
50000
100000 Fuji Film First difference of book value of net assets
0 5 10 15 20
0
1Fuji Film ACF-First difference of book value of net assets
1950 1960 1970 1980 1990 2000
0.00
0.25
0.50 Fuji Film First difference of logged book value of net assets
0 5 10 15 20
0
1Fuji Film ACF-First difference of logged book value of net assets
161
Fuji Film Dividends
1950 1960 1970 1980 1990 2000
5000
10000
15000Fuji Film Dividends
0 5 10 15 20
0
1Fuj;i Film ACF-Dividend
1950 1960 1970 1980 1990 2000
6
8
10Fuji Film Logged dividends
0 5 10 15 20
0.5
1.0Fuji Film ACF-Logged dividends
1950 1960 1970 1980 1990 2000
-5000
0
5000 Fuji Film First difference of dividends
0 5 10 15 20
0
1Fuji Film ACF-First difference of dividends
1950 1960 1970 1980 1990 2000
-0.5
0.0
0.5
1.0Fuji Film First difference of logged dividends
0 5 10 15 20
0
1Fuji Film ACF-First difference of logged dividends
Fuji Film Net Incomes
1950 1960 1970 1980 1990 2000
50000
100000Fuji Film Net incomes
0 5 10 15 20
0
1Fuji Film ACF-Net incomes
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5Fuji Film logged net incomes
0 5 10 15 20
0
1Fuji Film ACF-Logged net incomes
1950 1960 1970 1980 1990 2000
-25000
0
25000 Fuji Film First difference of net incomes
0 5 10 15 20
0
1Fuji Film ACF-First difference of net incomes
1950 1960 1970 1980 1990 2000
-0.5
0.0
0.5 Fuji Film First difference of logged net incomes
0 5 10 15 20
0
1Fuji Film ACF-First difference of logged net incomes
162
Panel B: Sony Corporation:
Sony Market value
1960 1970 1980 1990 2000500000
2.5e6
4.5e6
6.5e6 Sony Market value
0 5 10 15 20
0
1Sony ACF-Market value
1960 1970 1980 1990 2000
10
15Sonly Logged market value
0 5 10 15 20
0
1Sony ACF-Logged market value
1960 1970 1980 1990 2000
5000002.5e64.5e66.5e6 Sony Difference of market value
0 5 10 15 20
0
1Sony ACF-Difference of market value
1960 1970 1980 1990 2000
0
1
2Sony First difference of logged market value
0 5 10 15 20
0
1Sony ACF-First difference of logged market value
Sony Book value
1960 1970 1980 1990 2000
500000
1.5e6
2.5e6Sony Book value of net assets
0 5 10 15 20
0
1Sony ACF-Book value of net assets
1960 1970 1980 1990 2000
7.5
10.0
12.5
15.0Sony Logged book value of net assets
0 5 10 15 20
0
1Sony ACF-Logged book value of net assets
1960 1970 1980 1990 2000
250000
0
250000
500000 Sony First difference of book value of net assets
0 5 10 15 20
0
1Sony ACF-First difference of book value of net assets
1960 1970 1980 1990 2000
0.0
0.5
1.0Sony First difference of logged book value of net assets
0 5 10 15 20
0
1Sony ACF-First difference of logged book value of net assets
163
Sony Dividends
1960 1970 1980 1990 2000
10000
20000Sony Dividends
0 5 10 15 20
0
1Sony ACF-Dividends
1960 1970 1980 1990 2000
5.0
7.5
10.0 Sony Logged dividends
0 5 10 15 20
0
1Sony ACF-Logged dividends
1960 1970 1980 1990 2000
-5000
0
5000 Sony First difference of dividends
0 5 10 15 20
0
1Sony ACF-First difference of dividends
1960 1970 1980 1990 2000
-0.5
0.0
0.5
1.0Sony First difference of logged dividends
0 5 10 15 20
0
1Sony ACF-First difference of logged dividends
Sony Net Incomes
1960 1970 1980 1990 2000
-200000
0
200000 Sony Net incomes
0 5 10 15 20
0
1Sony ACF-Net Incomes
1960 1970 1980 1990 2000
-10
0
10 Sony Logged net incomes
0 5 10 15 20
0
1Sony ACF-Logged net incomes
1960 1970 1980 1990 2000
-100000
0
100000
200000Sony First difference of net incomes
0 5 10 15 20
0
1Sony ACF-First difference of net incomes
1960 1970 1980 1990 2000
-20
0
20 Sony First difference of logged net incomes
0 5 10 15 20
0
1Sony ACF-First difference of logged net incomes
164
Panel C: Itochu Corporation:
Itochu Market value
1950 1960 1970 1980 1990 2000
500000
1e6
1.5e6 Itochu Market value
0 5 10 15 20
0
1Itochu ACF-Market value
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5
15.0Itochu Logged market value
0 5 10 15 20
0
1Itochu ACF-Logged market value
1950 1960 1970 1980 1990 2000
-500000
0
500000 Itochu First difference of market value
0 5 10 15 20
0
1Itochu ACF- First difference of market value
1950 1960 1970 1980 1990 2000
0
1Itochu First difference of logged market value
0 5 10 15 20
0
1Itochu ACF- First difference of logged market value
Itochu Book value
1950 1960 1970 1980 1990 2000
200000
400000
600000Itochu Book value of net assets
0 5 10 15 20
0
1Itochu ACF-Book value of net assets
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5 Itochu Logged book value of net assets
0 5 10 15 20
0
1Itochu ACF-Logged book value of net assets
1950 1960 1970 1980 1990 2000
0
100000
200000 Itochu First difference of book value of net asstes
0 5 10 15 20
0
1Itochu ACF-First difference of book value of net assets
1950 1960 1970 1980 1990 2000
-1
0
1 Itochu First difference of logged book value of net assets
0 5 10 15 20
0
1Itochu ACF-First difference of logged book value of net assets
165
Itochu Dividends
1950 1960 1970 1980 1990 2000
5000
10000Itochu Dividends
0 5 10 15 20
0
1Itochu ACF-Dividends
1950 1960 1970 1980 1990 2000
2.5
5.0
7.5
10.0Itochu Logged dividends
0 5 10 15 20
0
1Itochu ACF-Logged dividends
1950 1960 1970 1980 1990 2000
0
5000
10000 Itochu First difference of dividends
0 5 10 15 20
0
1Itochu ACF-First difference of dividends
1950 1960 1970 1980 1990 2000
-5
0
5
10Itochu First difference of logged dividends
0 5 10 15 20
0
1Itochu ACF-First difference of logged dividends
Itochu Net Incomes
1950 1960 1970 1980 1990 2000
-50000
0
50000 Itochu Net incomes
0 5 10 15 20
0
1Itochu ACF-Net incomes
1950 1960 1970 1980 1990 2000
-10
0
10 Itochu Logged net incomes
0 5 10 15 20
0
1Itochu ACF-Logged net incomes
1950 1960 1970 1980 1990 2000
-50000
0
50000
100000Itochu First difference of net incomes
0 5 10 15 20
0
1Itochu ACF-First difference of net incomes
1950 1960 1970 1980 1990 2000
-20
0
20 Itochu First difference of logged net incomes
0 5 10 15 20
0
1Itochu ACF-First difference of logged net incomes
166
Panel D: Sumitomo Trust & Banking:
Sumitomo Trust & Banking Market value
1950 1960 1970 1980 1990 2000500000
2.5e6
4.5e6 STB Market value
0 5 10 15 20
0
1STB ACF-Market value
1950 1960 1970 1980 1990 2000
5
10
15 STB Logged Market value
0 5 10 15 20
0
1STB ACF-Logged market value
1950 1960 1970 1980 1990 2000
-1
0
1 STB First difference of logged market value
0 5 10 15 20
0
1STB ACF-First difference of logged market value
1950 1960 1970 1980 1990 2000
500000
1.5e6
2.5e6 STB First difference of market value
0 5 10 15 20
0
1STB ACF-First difference of market value
Sumitomo Trust & Banking Book value
1950 1960 1970 1980 1990 2000
250000
500000
750000 STB Book value of net assets
0 5 10 15 20
0
1STB ACF-Book value of net assets
1950 1960 1970 1980 1990 2000
7.5
10.0
12.5 STB Logged book value of net assets
0 5 10 15 20
0
1STB ACF-Logged book value of net assets
1950 1960 1970 1980 1990 2000
0
200000STB First difference of book value of net assets
0 5 10 15 20
0
1STB ACF-First difference of book value of net assets
1950 1960 1970 1980 1990 2000
-0.5
0.0
0.5
1.0 STB First difference of logged book value of net assets
0 5 10 15 20
0
1STB ACF-First difference of logged book value of net assets
167
Sumitomo Trust & Banking Dividends
1950 1960 1970 1980 1990 2000
5000
10000STB Dividends
0 5 10 15 20
0
1STB ACF-Dividends
0 5 10 15 20
0
1STB ACF-First differece of dividends
1950 1960 1970 1980 1990 2000
5.0
7.5
10.0STB Logged dividends
0 5 10 15 20
0
1STB ACF-Logged dividends
1950 1960 1970 1980 1990 2000
-2500
0
2500 STB First differece of dividends
1950 1960 1970 1980 1990 2000
-0.5
0.0
0.5 STB First differece of logged dividends
0 5 10 15 20
0
1STB ACF-First differece of logged dividends
Sumitomo Trust & Banking Net Incomes
1950 1960 1970 1980 1990 2000
-100000
0
100000STB Net incomes
0 5 10 15 20
0
1ACF-NI
1950 1960 1970 1980 1990 2000
-100000
0
100000
200000STB First difference of net incomes
0 5 10 15 20
0
1ACF-First differece of net t incomes
1950 1960 1970 1980 1990 2000
10
12STB Logged net incomes
0 5 10 15 20
0
1STB ACF-Logged net incomes
1950 1960 1970 1980 1990 2000
-2
0STB First differece of logged net incomes
0 5 10 15 20
0
1STB ACF-First differece of logged net incomes
168
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