three essays on share price misvaluation
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University of Texas at El PasoDigitalCommons@UTEP
Open Access Theses & Dissertations
2012-01-01
Three Essays On Share Price MisvaluationMohammad Aminul KarimUniversity of Texas at El Paso, [email protected]
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Copyright ©
by
Mohammad Aminul Karim
2012
Dedication
To my wife, Fufrun Nahar and daughter Liana Karim. Thank you for your sacrifice, love and
support during my doctoral program years.
To my parents. Thank you for praying for me and supporting me during my difficult times.
To Erik Devos. I could not have done this without all your support and encouragement.
THREE ESSAYS ON SHARE PRICE MISVALUATION
BY
MOHAMMAD AMINUL KARIM, MBA
DISSERTATION
Presented to the Faculty of the Graduate School of
The University of Texas at El Paso
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
Department of Economics and Finance
THE UNIVERSITY OF TEXAS AT EL PASO
August 2012
v
Acknowledgements
I received extraordinary support from many people during different stages of my doctoral
program. I would like to thank my advisor, mentor, and doctoral dissertation chair Erik Devos
for his whole-hearted support. His guidance and encouragement helped me to be a better scholar.
I am indebted to my committee member William B. Elliott for guidance and inspiration. I would
like to thank my other committee members Feixue Xie and Gary Braun for their valuable and
timely feedback which substantially improved my dissertation. My heartfelt gratitude goes to
Zuobao (Eddie) Wei, Timothy Roth, and Patricia Eason for their encouragement. I am also
indebted to the Department of Economics and Finance and the College of Business
Administration of the University of Texas at El Paso.
I would like to thank my fellow Ph.D. student Syed Zaidi for his continuous
encouragement. Most importantly, I want to thank my wife, my parents, and my sister for their
love and support during this journey.
vi
Abstract
This doctoral dissertation examines the measurement and identification of share price
misvaluation using two prominent models. It also examines the role of equity misvaluation in the
context of a number of corporate events. The first essay examines abnormal returns surrounding
the announcement of stock splits and how this return is related to equity misvaluation and/or
growth options of the firm. I find that overvalued firms have lower abnormal returns than
undervalued firms and high-growth firms have higher abnormal returns than low-growth firms.
Moreover, undervalued firms perform better than overvalued firms in the long run. I conclude
that in the short run the market is able to distinguish, at least partially, between valid and false
split signals and eventually corrects itself in subsequent years. My second essay examines the
role of advertising and the method of payments in mergers or acquisitions. I find that stock-based
acquirers have a higher advertising intensity in the pre-merger year when compared with cash-
based acquirers and stock-based acquirers are more overvalued than cash-based acquirers prior to
the merger. Moreover, managerial ownership in the acquiring firm is positively related to pre-
merger advertising intensity where the method of payment is the acquirers’ own equity. My third
essay compares the Residual Income Model (RIM) by Ohlson [1995. Earnings, book values and
dividends in equity valuation. Contemporary Accounting Research 11, 661- 687] and the
Rhodes-Kropf, Robinson, and Viswanathan model [2005.Valuation waves and merger activity:
The empirical evidence. Journal of Financial Economics 77, 561–603]. These two models are
used to detect misvaluation. Using publicly traded US firms I find that the Rhodes-Kropf et al.
(2005) model is more consistent with theoretical predictions of misvaluation surrounding
corporate events such as seasoned equity offerings (SEOs) and open market share repurchases.
vii
Table of Contents Acknowledgements ..........................................................................................................................v Abstract .......................................................................................................................................... vi Table of Contents .......................................................................................................................... vii List of Tables ................................................................................................................................. ix List of Figures ................................................................................................................................ xi Chapter 1: Introduction ....................................................................................................................1 Chapter 2: Essay 1 - Stock split announcement returns, growth opportunities and mispricing ......3 2.1. Introduction .............................................................................................................3 2.2. Literature review .....................................................................................................6 2.3. Empirical approach and data ...................................................................................7 2.4. Results ...................................................................................................................16 2.4.1 Mispricing, growth options and the decision to split ..................................16 2.4.2 Mispricing, growth options and abnormal returns around the split announcement date .....................................................................................18 2.4.3 Mispricing, growth options and long term stock returns after the stock split ....................................................................................................21 2.4.4 Mispricing, growth options and accounting performance after the stock split ...................................................................................................26 2.5. Conclusions ...........................................................................................................28 Chapter 3: Essay 2 - Product Market Advertising Effects on the Method of Payment in M&A ...30 3.1. Introduction ...........................................................................................................30 3.2. Data .......................................................................................................................33 3.3. Advertising intensity and stock based acquisitions ..............................................38 3.4. Method of payments and misvaluation .................................................................49 3.5. Economic incentive for advertising ......................................................................51 3.6. Conclusions ...........................................................................................................55 Chapter 4: Essay 3 - An empirical assessment of earnings based valuation models in detecting equity mispricing and growth options..........................................................................56 4.1. Introduction ...........................................................................................................56 4.2. The models ............................................................................................................59 4.2.1. Residual Income Model (RIM) .................................................................59 4.2.2. Rhodes-Kropf et al. (2005) valuation model and M/B decomposition .....62 4.3. Events ....................................................................................................................66 4.3.1. Mergers and Acquisitions ..........................................................................66 4.3.2. Share repurchases .......................................................................................67 4.3.3. Financing activities ....................................................................................68
viii
4.3.4. Stock splits ..................................................................................................68 4.4. Sample...................................................................................................................69 4.5. Comparing RKRV and RIM .................................................................................73 4.5.1. Mergers and acquisitions ..........................................................................78 4.5.2. Open market share repurchases .................................................................89 4.5.3. Seasoned equity offering (SEO) ................................................................89 4.5.4. Stock splits .................................................................................................94 4.6. Conclusions .........................................................................................................102 Chapter 5: Conclusions and Summary .........................................................................................104 References ....................................................................................................................................106 Vita ............................................................................................................................................112
ix
List of Tables
Table 2.1: Time-Series Average Conditional Regression Coefficients .........................................10 Table 2.2: Distribution of stock splits by year and industry ..........................................................12 Table 2.3: Univariate Comparison of splitting and non-splitting firms .........................................14 Table 2.4: Abnormal returns around split announcement date ......................................................16 Table 2.5: Logistic regressions on the determinants of stock split ................................................17 Table 2.6: Abnormal returns across firm specific error and growth options quartiles ..................19 Table 2.7: Abnormal return for split samples across total error and growth option quartiles .......21 Table 2.8: Calendar-time factor regressions for firms with splits in the prior 3 and 5 years ........22 Table 2.9: Long run operating performance of split firms .............................................................27 Table 3.1: Distribution of deals by year and merger type ..............................................................36 Table 3.2: Advertising intensity of acquirers surrounding the merger ..........................................39 Table 3.3: Characteristics of acquiring firms .................................................................................42 Table 3.4: Regression results of advertising intensity of stock and cash-based acquirers ............46 Table 3.5: Robustness test using propensity score matching approach .........................................48 Table 3.6: Misvaluation of acquiring firms ...................................................................................51 Table 3.7: Summary statistics of economic incentive variables of stock-based acquiring firms ..52 Table 3.8: Economic incentive analyses of advertising by acquiring firms ..................................54 Table 4.1: Yearly and Industry distribution of RKRV (2005) and RIM sample ...........................71 Table 4.2: Summary statistics of relevant variables ......................................................................74 Table 4.3: Market-to-book components for all publicly traded US firms from Compustat ..........75 Table 4.4: 2X2 matrix showing combinations of misvaluation and growth options dummies .....77 Table 4.5: Summary statistics of relevant variables of stock and cash based acquirers ................80
x
Table 4.6: RKRV (2005) and RIM market-to-book components of stock and cash based acquirers .............................................................................................................81 Table 4.7: 2X2 matrix showing combinations of misvaluation and growth options dummies of stock and cash based acquirers .................................................................82 Table 4.8: RKRV (2005) and RIM market-to-book components of stock based acquirers only. ..............................................................................................................................83 Table 4.9: 2X2 matrix showing combinations of misvaluation and growth options dummies of stock based acquirers only ........................................................................84 Table 4.10: RKRV (2005) and RIM market-to-book components of cash based acquirers only .............................................................................................................................85 Table 4.11: 2X2 matrix showing combinations of misvaluation and growth options dummies of cash based acquirers only ........................................................................86 Table 4.12: Merger intensity regressions .......................................................................................88 Table 4.13: RKRV (2005) and RIM market-to-book components of open market share repurchase ...................................................................................................................90 Table 4.14: 2X2 matrix showing combinations of misvaluation and growth options dummies for open market share repurchase ................................................................91 Table 4.15: RKRV (2005) and RIM market-to-book components of Seasoned equity offerings (SEOs). ........................................................................................................92 Table 4.16: 2X2 matrix showing combinations of misvaluation and growth options dummies of seasoned equity offering (SEO) ..............................................................93 Table 4.17: RKRV (2005) and RIM market-to-book components of stock splits .........................96 Table 4.18: 2X2 matrix showing combinations of misvaluation and growth options dummies of stock splits ...............................................................................................97 Table 4.19: Logistic regressions on the determinants of stock split ..............................................99
Table 4.20: Summary of misvaluation and consistency of the methods. .....................................100 Table 4.21: Summary of misvaluation and consistency excluding middle 20% (3rd quintile) firm year observations. .......................................................................101
xi
List of Figures
Figure 3.1: Advertising intensity surrounding mergers .................................................................41
1
Chapter 1
Introduction - Three essays on share price misvaluation
1.1 Introduction
This dissertation contains three separate essays related to share price misvaluation. When
share prices deviate from their ‘intrinsic’ or ‘true’ value the result is over or undervaluation
which may motivate managers to undertake different corporate events. There are several
competing models to determine the ‘true’ value of equity. The most widely used valuation
models are discounted cash flow (DCF) approach, dividend discount model (DDM), and residual
income model (RIM). All of these models are based on the same principle that the value of a
firm is the summation of the present value of future cash flows. These cash flows could be
dividend, earnings, or residual income etc. A more recent approach by Rhodes-Kropf, Robinson,
and Viswanathan (RKRV, hereafter) (2005) estimates the intrinsic value by relating the market
value of equity with some accounting variables. I compare these models along a number of
dimensions in essay 3. I use the RKRV methodology in essay 1 in an attempt to explain the
announcement returns surrounding stock splits. In essay 2 I investigate whether advertising is
related to the method of payments in merger and acquisitions. In this essay I do use the RKRV
methodology but it is not a centerpiece of the analyses in this essay.
The first essay examines abnormal stock split announcement returns and their relation to
growth opportunities and share price misvaluation. Using the RKRV market-to-book
decomposition approach I find that over (under) valuation has a significant negative (positive)
relationship with abnormal announcement returns. I also find that firms’ future growth
opportunities have a positive relationship with the abnormal announcement return. Moreover,
overvalued firms perform poorly in the long run. Both long-run stock return and long-run
2
operating performance are negatively correlated with overvaluation and positively correlated
with future growth opportunities.
The second essay examines the role of product market advertising in corporate
restructuring. More specifically I examine how a firm’s advertising intensity relates to the
method of payments in mergers. Using publicly traded US firms, I find that firms increase their
advertising intensity in pre-merger year if the method of payment in the merger or acquisition is
stock. But this relationship is non-existent for cash based acquirers. I also find some evidence
that advertising intensity increases with the managerial ownership in the acquisition firm.
Moreover, stock-based acquirers have relatively higher overvaluation than cash-based acquirers
in the pre-merger announcement year which is consistent with findings of RKRV (2005).
My final essay compares RKRV and RIM in detecting equity misvaluation and growth
opportunities of the firm. Using Center for Research in Security Prices (CRSP) and Compustat
data I find that RKRV market-to-book decomposition is possible for a significantly larger
number of firms than when using the RIM model. Although both methods identify misvaluation
and growth options for a similar number of firms on average, at least one third of the time these
two methods fail to reach the same conclusion on whether a firm is over or undervalued. In the
case of growth opportunities both models provide more consistent results. I also examine four
corporate events where misvaluation is hypothesized or documented in earlier literature. I find
that, the RKRV model performs better than RIM in detecting misvaluation and growth options
and the conclusions from RKRV model are more consistent with theoretical predictions than the
results of the RIM model. Specifically, in cases of open market share repurchase and seasoned
equity offerings RKRV seems to perform better.
3
Chapter 2
Essay 1 - Stock split announcement returns, growth opportunities and mispricing
2.1 Introduction
A number of studies suggest that one possible explanation for stock splits is that they act
as a signal to the market.1 However, the split announcement may only be an initial step in the
signaling process. The announcement is likely to cause investors to more closely examine the
firm. If the result of this examination reveals misvaluation, then the market price of the stock
should adjust toward a more precise estimate of the firm’s fundamental value.
As in any costly signaling equilibrium, for a separating equilibrium to exist, there must be
a cost to those firms that attempt to mimic the signal. In the case of stock splits, there are both
what I will term direct and indirect costs. First, the direct costs include fees paid to an
investment banker, additional exchange listing fees, and the potential increase in trading costs
(both in terms of the spread and to some extent, the commission). Generally, these costs are
relatively minimal. The market reaction to the announcement may be viewed as an indirect cost.
As investors examine the firm more closely, they may be able to discriminate between firms that
are making valid versus false signals. If the market’s reaction is favorable, the subsequent
increase in stock price may easily offset the direct costs. However, if the market’s reaction is
less favorable or negative, the split announcement becomes even more costly.
A split announcement may be viewed by the market as favorable in the cases when the
firm’s equity is undervalued. The under-valuation may be the result of either irrational investor
behavior or asymmetric information. In either case, the split announcement serves as notice to
1 See Fama, Fisher, Jensen, and Roll (1969), Brennan and Copeland (1988), McNichols and Dravid (1990), Kadiyala and Vetsuypens (2002).
4
the market to reevaluate the position of the firm. Obviously, this should lead to a positive post-
split announcement price change. However, in the case of a firm whose equity is overvalued, the
split announcement may be an attempt to mimic the undervalued firm. In this case, if investors
are able to discover the deception, the split announcement returns should result in a negative
price change.
Grinblatt, Masulis, and Titman (1984) refer to the above concept as the ‘attention
hypothesis.’ In the summary of their findings they state that, “some of the information content of
stock distributions appears to be directly related with firms’ future cash flows.” However, they
caution that the evidence is not particularly robust. My study provides additional evidence that
signaling is one of the reasons that managers continue to engage in stock splits.
I use a method that allows me to disentangle the equity misvaluation effects and growth
opportunities from the traditional proxy for both; the market-to-book ratio (see Rhodes-Kropf,
Robinson, and Viswanathan [2005], RKRV [2005] hereafter). My study more clearly
demonstrates a correlation between split announcement returns and misvaluation effects. I find
evidence that is consistent with the premise that, conditioned upon a stock split announcement,
the market is able to partially, but not completely, distinguish between undervalued and
overvalued firms on the day of the announcement. More specifically, splitting firms with the
most undervalued equity have the highest abnormal split announcement returns. Conversely,
splitting firms with the most overvalued equity have the lowest abnormal split announcement
returns. Further, I find that the market continues to refine its valuation estimate of the firm
during the years following the split. Lastly, post-split firm performance is consistent with the
announcement day market reaction.
5
Using the RKRV (2005) decomposition, I divide splitting firms into two groups; those
whose equity is undervalued and those whose equity is overvalued. If splits are a signal, an
obvious question within that context is: why would a firm with overvalued equity (i.e. negative
information) attempt to fool the market with a stock split announcement? One possible
explanation lies in the uncertainty of the costs for false signaling. If managers believe that they
might ‘get away with’ sending a false signal (i.e. upon examination the market will not
determine that they are overvalued), they may choose to split their stock. Managers of
overvalued firms may believe that even though the market has been able to discriminate between
valid and false signals in the past, that they can somehow fool the market. One explanation for
this seemingly irrational behavior is as follows. Possibly there is a learning curve for managers,
and just as in the case of any learner, they learn best by doing (rather than from observing the
experience of other managers). To bluntly make the point, this is not unlike the child that
touches the hot stove top, despite prior warning by a parent that they will burn their hand. The
false signaling firm assumes that, unlike others in the past, the market will not see their firm as
an overvalued stock.
As a result, I should find that splitting firms are of two types. Type 1 firms are those that
use the split announcement to proclaim that their equity is truly undervalued, while Type 2 firms
use the split announcement in an effort to further increase the price of an already overvalued
stock. Previous studies have consistently found that the average adjusted split announcement
returns are significantly positive. However, a subsample of those firms have negative adjusted
announcement returns. This study is an attempt to account for the dispersion of announcement
returns, rather than simply focus upon the average effect.
6
The rest of the essay proceeds as follows: Section 2.2 provides a brief literature review
while Section 2.3 describes the RKRV (2005) decomposition and data. Section 2.4 contains my
results. Section 2.5 states my conclusions.
2.2 Literature review
The determinants of stock splits can generally be divided into several categories.
Following Easley, O’Hara, and Saar (2001), I separate them into the trading range hypothesis,
the reduction of information asymmetries hypothesis, and the optimal tick size hypothesis.2
Copeland (1979) first described the trading range hypothesis and suggested that firms
attempt to keep their shares in a particular price range, in order to attract certain clientele.
Maloney and Mulherin (1992), among others, find that institutional ownership increases after
splits, indicative of a clientele effect. Gompers and Metrick (2001) find that individuals tend to
hold lower priced stocks. Dyl and Elliott (2006) and Fernando, Krishnamurthy, and Spindt
(2004) provide more recent evidence that some groups of investors are more likely to desire
shares of a particular price. Like Gompers and Metrick, both studies also suggest that individual
investors prefer lower priced stocks. Finally, Baker, Greenwood, and Wurgler (2009) find that
managers seek a given share price in order to mimic share prices of firms that have high
valuations.
Numerous studies have hypothesized that splits are related to information asymmetry and
that the split is used to reduce the amount of information asymmetry between investors and
managers or simply as a means of attracting attention to the firm. Brennan and Copeland (1988)
find that splits signal an increase in firm performance. However, Lakonishok and Lev (1987) as
well as Asquith, Healy, and Palepu (1989) suggest that firm performance increases prior to rather 2 An alternative sub-grouping is provided by Weld, Michaely, Thaler, and Benartzi (2009), who distinguish between the marketability, pay to play, and signaling hypotheses. I include some of these theories in my bifurcation.
7
than after the split announcement. Dharan and Ikenberry (1995) and Ikenberry, Rankine, and
Stice (1996) find positive abnormal performance after the split.
Other evidence suggests that information asymmetry decreases after a split. For example,
Brennan and Copeland (1988) find that the number of shares outstanding is related to the
announcement return, and Brennan and Hughes (1991) find that changes in analyst coverage are
related to the split factor. Others conclude the opposite; for example, Desai, Nimalendran, and
Venkataraman (1998) use a spread decomposition approach and conclude that information
asymmetry does not decrease after a split.
Angel (1997) and Harris (1997) argue that split announcements are related to tick size.
Firms split to increase the ratio of the minimum tick relative to share price, so as to increase the
minimum bid-ask spread. With a larger minimum spread, dealers will be induced to provide
increased liquidity for the stock (also see Schultz, 2000 and Kadapakkam, Krishnamurthy, and
Tse, 2005).
A variety of studies have examined the effects of split factors. Nayar and Rozeff (2001)
find that the magnitude of the split factor affects the abnormal announcement returns. This
finding is consistent with earlier literature that posits that the motivations for splitting differ by
split factor. For example, McNichols and Dravid (1990) suggest that the split factor is related to
the amount of private information disclosed.
2.3 Empirical approach and data
My analyses depend critically on the proper identification of mispricing and growth
options. In order to do so I employ a methodology that was developed by RKRV (2005). This
approach decomposes market to book ratios into various components which represent mispricing
and growth options. Although RKRV (2005) employ their approach in a merger setting, it has
8
also been used to address various other research questions (e.g., Hertzel and Li (2010) study
SEOs and Hoberg and Phillips (2010) for industry wide valuation). RKRV (2005) decompose the
market to book ratio into a misvaluation and a growth option component as represented by
equation 2.1:
M/B = M/V * V/B (2.1)
Where M, V, and B represents market value, intrinsic value, and book value of equity
respectively, M/B represents the market to book ratio, M/V represents misvaluation, and V/B
represents the growth option component. This can be re-written in log form as follows:
m – b = (m – v) + (v – b) (2.2)
where the lower case letters denote the log form. RKRV argue that if the market anticipates
future growth rates, discount rates, and cash flows perfectly, there would be no place for pricing
errors and (m – v) would always be zero. In this case the term (v – b) would equal to the log of
M/B. However, if the market does make mistakes then the price-to-true value (m – v) captures
the misvaluation component of ln (M/B). RKRV (2005) then goes a step further and attributes
misvaluation not only to a firm specific component, but also to a sector specific component.
Hence, RKRV (2005) decomposes the log of market to book into three separate components: a
firm specific misvaluation component, a sector specific component, and a difference between
valuation based on long-run value and book value (labeled long-run value to book / growth
option component). To estimate these components they express v as a linear function of firms’
specific accounting information at a point in time, , and conditional accounting multiples, α
where i,j and t represents firm, industry and year respectively.
; ; ; ; (2.3)
9
Fundamental value );( jtitv is calculated using time-series average conditional
regression coefficients and long run value );( jitv is calculated using the industry average of
time series regression coefficients. Firm specific misvaluation component );( jtitit vm
captures the deviation from fundamental value, sector specific component );();( jitjtit vv
captures the time series deviation from long run value and final components itjit bv );(
captures deviation from book value. In most of my analyses I focus on the firm specific error and
label it as mispricing. The third component captures the growth options of the firm and I refer to
it as growth options interchangeably with long run value to book.
The estimation process critically depends on the estimation of market value. I follow
RKRV and estimate market value of equity in three ways. The first method simply links market
value of equity with book value of equity. The second method links market equity with book
equity, and also includes net income in explaining the cross sectional variation in market values.
The third model adds leverage to the previous model in order to model market values.3 I
implement their methodology in the following way. First, using firm-year observations from
CRSP and Compustat database between fiscal year 1970 and 2008 I estimate the parameters of
all three models (similar to table 4 in RKRV (2005)). I require that the necessary variables are
present in CRSP and Compustat databases to estimate the components of market-to-book. In
addition, Market equity, defined as share price (CRSP mnemonic: PRC) x number of shares
outstanding (CRSP mnemonic: SHROUT) needs to be at least 10 million dollar, book value of
3 For a detailed discussion see section 5 in RKRV (2005), although I report the coefficients of all three models in the Table, in my analysis I focus on the third model.
10
equity (Compustat mnemonic: CEQ) needs to be positive, and the market to book ratio cannot be
more than 100. I show the results of this analysis in my Table 2.1
Table 2.1 Time-Series Average Conditional Regression Coefficients.
Time-series average coefficients from three regressions. Each regression is estimated cross-sectionally at the industry-year level using Fama and French 12 industry classifications from fiscal years 1970 to 2009. The dependent variable is the natural log of market value (m) of equity and the independent variables are the natural log of book value of equity (b), the natural log of the absolute value of net income (ni)+, a dummy variable indicating negative net income (I(<0)), and market leverage (Lev). Firm, industry, and fiscal year are denoted using subscripts i, j, and t respectively. Fama-MacBeth (1973) standard errors are reported below the average estimated coefficients. R2 is the time series average of adjusted R2 for each industry.
Fama and French industry classification (12 Industry) Parameter 1 2 3 4 5 6 7 8 9 10 11 12
Model 1: 0.88
0.07 1.79 0.12
1.07 0.07
1.61 0.08
1.39 0.09
1.60 0.07
2.00 0.14
0.83 0.11
1.03 0.07
1.77 0.09
1.22 0.07
1.58 0.06
0.89 0.01
0.68 0.02
0.84 0.01
0.78 0.01
0.84 0.01
0.79 0.01
0.73 0.02
0.90 0.01
0.86 0.01
0.82 0.02
0.80 0.01
0.75 0.01
0.70 0.62 0.74 0.78 0.75 0.69 0.72 0.84 0.70 0.75 0.73 0.66 Model 2:
1.86 0.07
2.45 0.11
1.75 0.07
2.06 0.08
2.29 0.07
2.22 0.06
2.33 0.12
1.44 0.13
1.92 0.05
2.42 0.06
2.02 0.05
2.13 0.06
0.43 0.02
0.32 0.02
0.50 0.02
0.57 0.02
0.34 0.03
0.46 0.03
0.50 0.03
0.59 0.06
0.43 0.02
0.45 0.02
0.41 0.01
0.46 0.01
0.47 0.02
0.42 0.03
0.39 0.02
0.23 0.02
0.55 0.04
0.41 0.02
0.26 0.03
0.31 0.06
0.49 0.02
0.43 0.03
0.43 0.01
0.35 0.02
-0.27 0.02
-0.17 0.03
-0.19 0.02
-0.14 0.02
-0.26 0.06
-0.28 0.02
-0.07 0.04
-0.15 0.11
-0.32 0.02
-0.34 0.07
-0.25 0.02
-0.21 0.01
0.77 0.69 0.79 0.80 0.81 0.75 0.75 0.86 0.78 0.80 0.80 0.72 Model 3:
2.33 0.07
2.66 0.10
2.18 0.07
2.47 0.07
2.43 0.07
2.57 0.05
2.91 0.12
2.25 0.10
2.46 0.05
2.66 0.05
2.48 0.05
2.56 0.05
0.60 0.02
0.54 0.02
0.63 0.01
0.64 0.02
0.58 0.03
0.55 0.02
0.58 0.03
0.82 0.03
0.58 0.01
0.57 0.02
0.46 0.01
0.56 0.01
0.32 0.01
0.30 0.02
0.29 0.01
0.24 0.01
0.38 0.03
0.35 0.01
0.28 0.02
0.16 0.03
0.33 0.01
0.35 0.02
0.42 0.01
0.31 0.01
-0.04 0.02
-0.02 0.02
-0.02 0.01
-0.04 0.02
-0.05 0.03
-0.11 0.01
0.02 0.02
-0.07 0.07
-0.11 0.01
-0.21 0.04
-0.22 0.03
-0.08 0.01
-2.41 0.05
-2.38 0.08
-2.14 0.08
-2.21 0.09
-2.48 0.14
-2.50 0.09
-2.46 0.18
-2.59 0.17
-2.13 0.05
-2.44 0.08
-1.10 0.06
-1.93 0.07
0.85 0.79 0.86 0.87 0.89 0.83 0.85 0.94 0.87 0.87 0.81 0.81
11
The table shows the summary statistics for all three models, and the results are very
similar to those obtained by RKRV (2005). The contrasts among three models between their
analysis and mine are generally similar in magnitude and the differences between their analysis
and mine are most likely explained by the fact that my sample includes an additional eight years,
when compared to their sample period.
I am able to decompose the market to book ratio for 183,187 firm years. I then proceed
by identifying which of these 183,187 firm years split their shares by a factor of at least 1, during
the 1970-2008 period. I then apply Lin, Singh, and Wu (2009) filters to both splitting and non-
splitting samples. To be included in the sample, share code (CRSP mnemonic: SHRCD) has to
be 10 or 11, pre-split price has to be at least $10, split factor (CRSP mnemonic: FACPR) has to
be 1 or higher and CRSP Factor to Adjust Price (CRSP mnemonic: FACPR) has to equal to the
CRSP Factor to Adjust Shares Outstanding (CRSP: FACSHR). I identify almost 2,933 unique
firms and 4,837 years in which firms split at least once, 82,859 firm-years are labeled as non-
splitters. Table 2.2, Panel A presents a distribution of the sample firms by year. During my
sample period the number of splitting firm varies widely, from 6 in fiscal year 1970 (or 0.5
percent of the sample firms in that year) to 309 (or 14.3 percent of the sample firms) in 1983.
However, splits occur in all of my sample years. In Panel B I report the industry distribution of
my sample firm years, based on the 12 Fama-French industry classification. All 12 industries are
well represented in both the splitting and the non-splitting samples. There also does not seem to
be a tremendous variation in the incidence of splitting when I investigate the proportion of
splitting by industry. Firms in Business Equipment split 8.5 percent of the time whereas utilities
seem to be least likely to split, although they still have a split proportion of three percent.
12
Table 2.2 Distribution of stock splits by year and industry.
Panel A reports the distribution of splits by fiscal year and panel B reports the distribution of splits based on Fama and French 12 industry classification from 1970 to 2008. All split information is from the CRSP database. To be included in the split sample pre-split stock price has to be at least $10, a stock must have a share code of 10 or 11 with split factor of 1 or higher, with the CRSP Factor to Adjust Price (FACPR) equal to the CRSP Factor to adjust Shares Outstanding (FACSHR). Non-splitting observations are all Compustat samples for which M/B decompositions are possible. Panel A: Distribution by fiscal year
Fiscal year Splitting Non-splitting % splitting1970 6 1106 0.5%1971 48 1231 3.9%1972 60 1734 3.5%1973 68 1462 4.7%1974 22 1020 2.2%1975 38 1268 3.0%1976 80 1508 5.3%1977 75 1576 4.8%1978 103 1692 6.1%1979 70 1715 4.1%1980 175 1782 9.8%1981 154 1888 8.2%1982 75 1670 4.5%1983 309 2154 14.3%1984 111 1893 5.9%1985 143 1859 7.7%1986 238 1889 12.6%1987 198 1877 10.5%1988 60 1793 3.3%1989 105 1870 5.6%1990 81 1601 5.1%1991 99 1834 5.4%1992 165 2085 7.9%1993 181 2830 6.4%1994 154 2881 5.3%1995 211 3198 6.6%1996 232 3502 6.6%1997 224 3523 6.4%1998 210 3141 6.7%1999 195 3007 6.5%2000 225 2768 8.1%2001 60 2613 2.3%2002 83 2358 3.5%2003 88 2645 3.3%2004 133 2837 4.7%2005 145 2851 5.1%2006 108 2915 3.7%2007 79 2734 2.9%2008 26 549 4.7%Total 4837 82859
13
Table 2.2 (continued)
Panel B: Distribution by Fama-French 12 industry classification
Industry Splitting Non-splitting % splitting
Consumer Non-Durables 367 5540 6.6%Consumer Durables 130 2352 5.5%Manufacturing 633 12008 5.3%Energy 211 3069 6.9%Chemicals and Allied Products 151 2501 6.0%Business Equipment (Computers, software etc.) 902 10650 8.5%Telephone and Television Transmission 123 1929 6.4%Utilities 152 4840 3.1%Wholesale, Retail and some services 528 8140 6.5%Healthcare, Medical equipment and drugs 342 5434 6.3%Finance 798 17624 4.5%Others (Mines, Construction, Entertainment etc.) 500 8772 5.7%
Total 4837 82859
In Table 2.3 I show the univariate characteristics of both my split firms as well as the
non-splitting firms. In addition to several firm characteristics I also show the three market-to-
book components of the splitting and non-splitting firms. First, the mean total assets (Compustat
mnemonic: AT) are not different when I compare the two subsamples. The mean is $3.7 bln. for
firms that split compared to $4.1 bln. for non-splitting firms but median total asset is higher for
non-splitters compared to splitters ($386 mln. vs $345 mln.). However, when I compare total
market equity (CRSP mnemonic: PRC times CRSP mnemonic: SHROUT) I do find that splitting
firms are substantially larger than non-splitting firms (statistically significant at the 1 percent
level for both the mean and median). Similarly, I find that splitting firms have higher prices
(CRSP mnemonic: PRC) and perform better when I look at differences in ROA (defined as
Compustat mnemonic: NI / Compustat mnemonic: AT). My performance inferences are similar
when I investigate differences in ROE (defined as [Compustat mnemonic: CSHO times
Compustat mnemonic: EPSPX] / Compustat mnemonic: CEQ).
14
Table 2.3 Univariate Comparison of splitting and non-splitting firms.
This table reports the mean/median of log of market to book ratio and its three components along with some characteristics for split samples and for a comparison sample of CRSP/Compustat firms (non-splitters) for the fiscal period of 1970 to 2008. The components are estimated following RKRV (2005). t/z (diff) column reports the t/z value of the difference between splitting and non-splitting firms. ***, **, * represents significance level at the 1%, 5%, 10% level, respectively.
Characteristics Split firms Non-split firms Difference
score t / Wilcoxon Z
Mean Median
N Mean Median
N
Total assets (in millions) 3,698.80 345.33
4,812 4,124.95 386.83
82,703 1.34-3.99
***
Total market equity (in millions) 3,643.58 435.13
4,813 2,069.11 259.63
82,703 6.3717.59
*** ***
Common equity (in millions) 835.85 134.64
4,813 794.04 133.46
82,703 0.80-0.25
Common shares outstanding (in millions) 45.83 10.15
4,811 56.50 12.26
82,659 -3.99-8.40
*** ***
Price per share 45.94 39.63
4,782 26.42 21.41
82,703 40.4659.98
*** ***
Return on assets (ROA) 7.19% 7.25%
4,811 4.41% 4.58%
82,654 19.9928.23
*** ***
Return on Equity (ROE) 15.24% 15.92%
4,812 9.66% 12.40%
82,654 4.4032.70
*** ***
1.1725 1.0822
4,813 0.6700 0.5856
82,703 40.1941.70
*** ***
Firm Specific error, ; 0.3042 0.2681
4,147 0.1210 0.0769
68,449 20.9422.43
*** ***
Time-series sector error, ; ;
0.0882 0.0853
4,147 0.0636 0.0771
68,449 5.753.37
*** ***
Growth options ;
0.8395 0.8836
4,147 0.5397 0.5564
68,449 33.3930.29
*** ***
Leverage (market) 0.2642 0.2192
4,148 0.3588 0.3332
68,492 -28.33-26.15
*** ***
Leverage (book) 0.5266 0.5147
4,812 0.5573 0.5573
82,703 -8.84-8.83
*** ***
M/B (price/book value per share) 4.7907 2.7736
4,808 3.2023 1.8072
82,485 4.6635.41
*** ***
M/B – RKRV (Market value of TA/ TA) 3.3468 1.99902
4,148 1.9827 1.3601
68,492 17.9136.48
*** ***
15
Finally, and perhaps most importantly I find that the market to book of splitting firms is
much higher than the market to book of non-splitting firms. The average (median) market to
book equity for splitting firms is 1.17 (1.08) whereas non-splitting firms have a mean (median)
market to book of 0.67 (0.58). Not surprisingly, the differences in mean and median between the
two sub samples are highly significant. When I compare the three components of the market to
book I find that mispricing components (the firm specific error and the time series error) are both
significantly higher for splitting firms. Interestingly, the difference in growth options
components exhibits an even larger difference between my subsamples. In short, these results
suggest that splitting firms do have not only a higher market to book, but that they seem to be
more mispriced (overvalued) and seem to have more growth options.
An important part of my analysis focuses on abnormal returns around the split
announcement. I report the abnormal return from the event study in Table 2.4. Using a simple
market adjusted return, I calculate the abnormal return by subtracting the value weighted index,
equal weighted index and S&P composite index returns from the daily return. Mean and median
cumulative abnormal returns are reported in table 2.4 for 2 days (split announcement day and 1
day after split announcement day) and 3 days ( -1, 0 and +1 days) surrounding split
announcements. I am able to calculate abnormal returns for 4,812 announcements. I use value
weighted index adjusted abnormal return in subsequent abnormal return analysis. Consistent with
earlier split literature the splits in my sample have abnormal announcement returns between
1.8% and 3.2%.
16
Table 2.4 Abnormal returns around split announcement date.
This table reports average compounded market adjusted returns (equally weighted index, value weighted index and S&P composite index adjusted) over different event windows for stock splits with a split factor at least 1 between 1970 and 2008. ***, **, * represents significance level at the 1%, 5%, 10% level, respectively.
Event window
Split sample N = 4,812
Abnormal return
Equal Weighted Value Weighted S&P composite index
adjusted
Mean Median Mean Median Mean Median
Announcement date (0 to +1)
0.0269*** 0.0184*** 0.0279*** 0.0188*** 0.0281*** 0.0194***
Announcement date (-1 to +1)
0.0305*** 0.0207*** 0.0318*** 0.0215*** 0.0321*** 0.0220***
2.4 Results
2.4.1 Mispricing, growth options and the decision to split.
First, I investigate how the different components of market to book are related to the split
decision. In Table 2.3 I showed that splitting firms have not only a higher market to book, but
also that all components were higher for splitting firms. In addition, splitting firms were larger
(by market value) and performed better in pre-split years. To investigate whether some of these
patterns hold when I control for a number of other explanations that previous literature has
identified to be associated with the decision to split I perform a multivariate analysis. The results
of this analysis are shown in Table 2.5. I perform a logit analysis where the decision to split the
stock is the dependent variable equal to 1, 0 elsewise. I control for size using total assets. I
include two dummies (labeled pre1997 and post2000) to control for microstructure effects (i.e.
minimum tick size changes, see Angel (1997) and Harris (1997)). These two dummies have a
value of 1 when the firm year is before 1997 (pre1997) or post 2000 (post2000), and are equal to
17
0 elsewise. I further include a dummy labeled traderange (stock appreciation) in model 3 and 4
(model 1 and 2). Both these variables are designed to capture pre-split price appreciation as
researchers suggest that firms desire to maintain a target share price level to attract particular
clienteles (Copeland (1979), Dyl and Elliott (2006) and Fernando, Krishnamurthy, and Spindt
(2004)).
Table 2.5 Logistic regressions on the determinants of stock split.
This table reports the logit regressions to analyze the split decision. The dependent variable is a binary variable coded 1 if the firm splits and 0 for non-splitting firms. Three components of market-to-book ratio; Firm Specific error, Time-series sector error and Long-run value to book are used as explanatory variables (estimated using RKRV (2005) model 3 of table 1) along with other split explanatory control variables. Pre1997 and post2000 are control variables for minimum tick changes, traderange is a dummy variable that takes a value of 1 if the actual share price is 50% greater than the predicted price (estimated following Dyl and Elliott, 2006) and 0 otherwise, stock appreciation is the ratio of the t-1 year-end share price over the t-3 year-end share price to capture the price appreciation in pre-split period. P values are reported below estimates.
Variables Model 1 Model 2 Model 3 Model 4
Intercept -4.93
<0.001 -4.95
<0.001 -4.54
<0.001 -4.56
<0.001
Firm Specific error 0.52
<0.001 0.51
<0.001 0.72
<0.001 0.72
<0.001
Time-series sector error 0.89
<0.001 0.81
<0.001 0.81
<0.001 0.81
<0.001
Growth options 1.09
<0.001 1.09
<0.001 1.10
<0.001 1.10
<0.001
Log (Total assets) 0.16
<0.001 0.17
<0.001 0.09
<0.001 0.10
<0.001
pre1997 0.64
<0.001 0.66
<0.001 0.75
<0.001 0.76
<0.001
post2000 -0.49
<0.001 -0.50
<0.001 -0.44
<0.001 -0.45
<0.001
Traderange -1.57
<0.001 -1.57
<0.001
Stock appreciation <0.01
0.83 <0.01
0.75
Number of common shareholders <-0.01
0.02
<-0.01 0.22
18
Traderange and stock appreciation are calculated following Dyl and Elliott (2006).
Traderange is a binary variable which takes a value of 1 if the ratio of actual and predicted share
price is more than 1.50 and takes a value of 0 if the ratio is less than 1.50. Predicted share price
is estimated using:
E(sharepricej,t-1│etc) = δ0 + δ1 BVEquityj,t-1 + δ2 AvgHldgj,t-1, + 3EPSj,t-1. (2.4)
Stock appreciation is calculated as a ratio of share price at (t-1) to share price at (t-3), where
share price is the fiscal year end price. Finally, I include number of common shareholders
(Compustat mnemonic: CSHR) in my regression to capture information asymmetry explanation
of stock splits.
In all models I find that all three components are significantly positively related to the
decision to split, although I am controlling for other split determinants. Interestingly, I find that
other than common shareholders in model 4 all other variables are also significantly positively
related to the decision to split. In short, the results of this analysis are consistent with my
univariate findings that splitting firms do not only have a higher market to book, but that all
components are larger as well.
2.4.2 Mispricing, growth options and abnormal returns around the split announcement
date
I now turn to the analysis of the announcement returns that accompany the announcement
of a stock split to test my main hypotheses. I showed in Table 2.4 that split announcements on
average (and in the median) exhibit positive abnormal returns. However, when I bifurcate my
sample by firm specific errors and growth options I find substantial cross sectional variation. The
results pertaining to this analysis are provided in Table 2.6. In order to bifurcate the sample I
19
create a simple four-by-four matrix in which I assign quartiles for firm specific error and growth
options for each observation. For example, the upper left cell contains the mean and median
abnormal return for the 176 observations which are in the most undervalued (low firm specific
error) quartile and have the lowest growth options.
Table 2.6 Abnormal returns across firm specific error and growth options quartiles.
This table reports mean/median abnormal return and number of observations of split samples across firm specific error and growth option quartiles. Firm specific error and growth options are calculated following RKRV (2005) model 3 and quartiles are based on splitting firms.
Abnormal Return Distribution (Mean / Median / N)
Growth options
Firm specific error Low Quartile 2 Quartile 3 High Total
Low 4.10% 2.89%
176
3.47% 2.78%
288
3.46% 2.47%
329
5.59% 3.93%
243
4.07% 2.80%
1036
Quartile 2 2.42% 1.86%
265
2.28% 1.96%
286
2.38% 1.75%
255
4.39% 3.32%
231
2.81% 2.09%
1037
Quartile 3 1.71% 1.41%
304
2.23% 2.04%
267
3.73% 2.90%
230
3.73% 2.64%
236
2.75% 2.02%
1037
High 1.62% 1.04%
291
3.50% 2.18%
196
3.30% 1.68%
223
4.78% 3.08%
326
3.33% 1.78%
1036
Total 2.27% 1.64%
1036
2.83% 2.18%
1037
3.22% 2.24%
1037
4.64% 3.20%
1036
3.24% 2.16%
4146
The mean abnormal return is 4.10 percent whereas the median abnormal return is 2.89
percent. In contrast, the mean (median) abnormal return for split announcement of firms who are
the most mispriced (high firm specific error quartile) and have the most growth options (high
growth options quartile) is 4.78 percent (3.08 percent). The Table also shows the abnormal
returns when I only group by firm specific error quartiles in the most right column and when I
group by growth options quartiles in the bottom row. Several important patterns emerge. First,
20
conditional on the amount of growth options a firm has I find that splitting firms with the most
overvalued equity (the row labeled high firm specific error) have relative low announcement
returns whereas firm that have undervalued equity (the row labeled low firm specific error) have
relatively high announcement returns. For example, for firms that have low growth options,
undervalued firms exhibit mean (median) announcement returns of 4.10 percent (2.89 percent)
vis-à-vis a mean (median) of 1.62 percent (1.04 percent) for firms that are in the most overvalued
quartile. This trend seems to be consistent for the other three growth option quartiles.
Interestingly, I also find that the market rewards splitting firms that have a large amount of
growth options with a larger announcement return. For all quartiles (and the total) of firm
specific error I find an almost monotonic increase in abnormal returns as the amount of growth
options increases.
I replicate this analysis using total error (the sum of the firm specific error and the time
series trend) in Table 2.7. Although the magnitude of the abnormal returns in all of the cells is
slightly different from those in the previous table, my overall inferences are similar. Undervalued
firms are rewarded with higher abnormal returns on the announcement date whereas firms that
are relatively overvalued exhibit lower (albeit positive) abnormal returns. And, secondly,
splitting firms with relatively more growth options exhibit larger abnormal returns as well.
21
Table 2.7 Abnormal return for split samples across total error and growth option quartiles.
This table reports mean/median abnormal return and number of observations of split samples across total error (firm + sector) and growth option quartiles. Total error and growth options are calculated following RKRV (2005) model 3 and quartiles are based on splitting firms.
Abnormal Return Distribution (Mean / Median / N)
Growth options
Total error Low Quartile 2 Quartile 3 High Total
Low 3.67% 2.74%
225
3.67% 2.87%
283
3.15% 2.32%
298
5.20% 3.83%
230
3.86% 2.74%
1036
Quartile 2 2.31% 1.84%
245
2.07% 1.64%
297
3.21% 2.57%
277
4.30% 3.34%
218
2.90% 2.15%
1037
Quartile 3 1.79% 1.36%
275
2.24% 2.07%
259
2.72% 1.93%
237
3.91% 3.02 %
266
2.66% 1.97%
1037
High 1.62% 1.05%
291
3.54% 2.21%
198
3.84% 1.78%
225
5.08% 3.06%
322
3.54% 1.79%
1036
Total 2.27% 1.64%
1036
2.83% 2.18%
1037
3.22% 2.24%
1037
4.64% 3.20%
1036
3.24% 2.16%
4146
2.4.3 Mispricing, growth options and long term stock returns after the stock split
In order to see whether the stock market impounds the signal provided by the split
announcement immediately or refines its informational value at a later date I investigate long run
returns after the stock split. I do so using a Fama French 3 factor model (Fama and French
(1993)) and a Carhart 4 factor model (Carhart (1997)). In both factor models alpha, which is
generated by running monthly regressions of monthly stock returns on a number of factors
represents the monthly abnormal return. I run these regressions for both a 3 year (36 month)
interval and a 5 year (60 month) interval. In addition, I do so using equal weighted and value
weighted returns. To shed light on whether firm specific error matters I split my sample into four
quartiles (similar to my earlier analysis). The top part of Panel A, Table 2.8 shows that when I
22
Table 2.8 Calendar-time factor regressions for firms with splits in the prior three and five years.
This table reports the calendar-time factor regression results of portfolio of firms that split stocks in prior three and five years. Firms are also grouped based on firm-specific error quartile, total error quartile and long-run value to book quartiles (panel A. B, and C respectively). Every month from 1970 to 2008 I formed both value weighted (VW) and equally weighted (EW) portfolios of split firms that split their stocks three and five years prior. Dependent variable is risk free rate adjusted return of the portfolio and independent variables are Fama and French (1993) three factor and Carhart (1997) four factors. Portfolio performance is measured using the intercept of the regressions (α). *,** and *** represents significance level at 5%, 1% and 0.1% respectively. Panel A: Calendar-time factor regressions for split firms by firm-specific error quartiles
Firm-specific error FF 3 Factor Model CARHART 4 Factor Model Quartile Portfolio α (%) MKT SMB HML Adj. R2 α (%) MKT SMB HML UMD Adj. R2
Low EW 3 yr 0.54*** 0.97 0.59 0.01 0.76 0.55*** 0.97 0.59 0.01 -0.01 0.762 3 yr 0.50*** 1.07 0.50 0.06 0.83 0.61*** 1.05 0.49 0.02 -0.11 0.843 3 yr 0.26*** 1.09 0.38 0.04 0.88 0.37*** 1.07 0.37 -0.00 -0.11 0.89High 3 yr 0.35*** 1.22 0.40 -0.27 0.89 0.54*** 1.17 0.39 -0.34 -0.21 0.90 Low EW 5 yr 0.39*** 0.96 0.60 0.12 0.81 0.42*** 0.95 0.59 0.11 -0.03 0.812 5 yr 0.35*** 1.07 0.53 0.20 0.85 0.46*** 1.04 0.53 0.16 -0.11 0.863 5 yr 0.20** 1.04 0.42 0.09 0.90 0.29*** 1.02 0.41 0.06 -0.09 0.90High 5 yr 0.23** 1.20 0.48 -0.13 0.90 0.40*** 1.16 0.47 -0.19 -0.18 0.92 Low VW 3 yr 1.29*** 1.05 0.45 -0.16 0.67 1.26*** 1.06 0.45 -0.15 0.04 0.672 3 yr 1.29*** 1.04 0.26 -0.28 0.71 1.31*** 1.04 0.26 -0.29 -0.03 0.713 3 yr 0.79*** 1.03 -0.10 -0.33 0.83 0.80*** 1.03 -0.10 -0.33 -0.02 0.83High 3 yr 0.88*** 1.08 -0.17 -0.33 0.87 0.89*** 1.07 -0.17 -0.34 -0.02 0.87 Low VW 5 yr 1.23*** 1.03 0.51 -0.22 0.67 1.15*** 1.05 0.51 -0.20 0.08 0.682 5 yr 1.04*** 1.06 0.14 -0.09 0.74 1.05*** 1.06 0.14 -0.10 -0.01 0.743 5 yr 0.80*** 0.98 -0.04 -0.24 0.84 0.75*** 0.99 -0.04 -0.22 0.05 0.84High 5 yr 0.79*** 1.07 -0.18 -0.26 0.89 0.81*** 1.07 -0.18 -0.26 -0.02 0.89
23
Table 8 (continued) Panel B: Calendar-time factor regressions for split firms by total (firm + sector) error quartiles Total (firm + sector) error FF 3 Factor Model CARHART 4 Factor Model
Quartile Portfolio α (%) MKT SMB HML Adj. R2 α (%) MKT SMB HML UMD Adj. R2
Low EW 3 yr 0.69*** 0.94 0.59 0.12 0.69 0.67*** 0.94 0.59 0.12 0.02 0.692 3 yr 0.47*** 1.05 0.53 0.18 0.87 0.56*** 1.03 0.53 0.15 -0.09 0.873 3 yr 0.33*** 1.11 0.37 0.01 0.84 0.42*** 1.09 0.37 -0.02 -0.09 0.84High 3 yr 0.27** 1.22 0.41 -0.27 0.87 0.48*** 1.17 0.40 -0.34 -0.21 0.90 Low EW 5 yr 0.51*** 0.97 0.59 0.21 0.76 0.52*** 0.97 0.59 0.21 -0.01 0.762 5 yr 0.32*** 1.05 0.56 0.27 0.89 0.44*** 1.02 0.56 0.23 -0.12 0.903 5 yr 0.26*** 1.08 0.44 0.08 0.85 0.33*** 1.06 0.44 0.06 -0.08 0.85High 5 yr 0.17*** 1.19 0.47 -0.15 0.90 0.35*** 1.14 0.46 -0.21 -0.19 0.92 Low VW 3 yr 1.50*** 1.08 0.41 0.10 0.64 1.45*** 1.10 0.41 0.12 0.05 0.642 3 yr 1.01*** 1.07 0.26 0.13 0.77 0.97*** 1.08 0.26 0.15 0.04 0.773 3 yr 1.10*** 1.05 -0.09 -0.16 0.76 1.04*** 1.06 -0.08 -0.14 0.06 0.76High 3 yr 0.81*** 1.08 -0.40 -0.40 0.87 0.84* 1.07 -0.16 -0.41 -0.03 0.87 Low VW 5 yr 1.41*** 1.09 0.50 -0.06 0.68 1.50*** 1.13 0.50 -0.01 0.14 0.682 5 yr 0.87*** 1.05 0.21 0.10 0.81 0.85*** 1.06 0.21 0.11 0.02 0.813 5 yr 1.01*** 1.03 -0.07 -0.16 0.78 0.93*** 1.05 -0.07 -0.13 0.09 0.78High 5 yr 0.76*** 1.07 -0.15 -0.29 0.88 0.79*** 1.06 -0.15 -0.30 -0.04 0.88
24
Table 8 (continued) Panel C: Calendar-time factor regressions for split firms by growth options quartiles
Growth options FF 3 Factor Model CARHART 4 Factor Model Quartile Portfolio α (%) MKT SMB HML Adj. R2 α (%) MKT SMB HML UMD Adj. R2
Low EW 3 yr 0.43*** 1.16 0.30 0.39 0.78 0.53*** 1.14 0.29 0.35 -0.10 0.782 3 yr 0.12 1.03 0.20 0.33 0.86 0.18* 1.01 0.20 0.31 -0.06 0.863 3 yr 0.38*** 1.12 0.42 -0.06 0.85 0.51*** 1.09 0.41 -0.11 -0.14 0.86High 3 yr 0.56*** 1.18 0.58 -0.46 0.90 0.72*** 1.14 0.58 -0.52 -0.17 0.91 Low EW 5 yr 0.33*** 1.13 0.34 0.46 0.81 0.45*** 1.11 0.34 0.42 -0.11 0.822 5 yr 0.03 1.01 0.24 0.41 0.86 0.11 0.99 0.24 0.39 -0.08 0.873 5 yr 0.26*** 1.11 0.48 0.07 0.88 0.36*** 1.08 0.47 0.03 -0.10 0.88High 5 yr 0.44*** 1.15 0.64 -0.37 0.91 0.60*** 1.12 0.63 -0.43 -0.17 0.92 Low VW 3 yr 0.83*** 1.28 -0.16 0.03 0.69 0.86*** 1.27 -0.16 0.03 -0.03 0.692 3 yr 0.54*** 1.05 -0.27 0.13 0.77 0.58*** 1.04 -0.27 0.11 -0.04 0.773 3 yr 0.87*** 1.05 -0.07 -0.26 0.79 0.80*** 1.07 -0.07 -0.23 0.06 0.79High 3 yr 1.20*** 1.06 -0.11 -0.62 0.87 1.23*** 1.06 -0.11 -0.63 -0.03 0.87 Low VW 5 yr 0.68*** 1.22 -0.21 0.08 0.71 0.66*** 1.23 -0.21 0.09 0.02 0.712 5 yr 0.47*** 1.03 -0.27 0.19 0.81 0.51*** 1.02 -0.27 0.18 -0.05 0.813 5 yr 0.88*** 1.05 -0.06 -0.22 0.81 0.81*** 1.07 -0.06 -0.20 0.07 0.82High 5 yr 1.12*** 1.03 -0.08 -0.55 0.87 1.13*** 1.03 -0.08 -0.55 -0.01 0.87
25
use equal weighted returns and a 3 year window that the alphas decrease monotonically when
firm specific errors increase. Specifically, using the Fama French 3 factor model for the most
undervalued firms that split I find an alpha of 0.54 and for the most overvalued firms I find an
alpha of 0.35. The corresponding alphas for the 5 year window are 0.39 and 0.23. Interestingly,
when I use the Carhart model I find a similar trend. In the bottom half of the Panel I replicate
these analyses using value weighted returns and the insights are similar. Undervalued firms that
split do relatively better than splitting firms that are overvalued. This is consistent with the
notion that the market keeps refining its valuation estimates following the split announcement for
a substantial amount of time after the split. In Panel B I replicate the previous Panel but now
using total error, defined as the sum of firm specific error and time trend. Similar to my
announcement return analysis in the previous section I find that using total error instead of firm
specific error does not change the inferences of my analysis much. Again, as firms move
quartiles from relatively undervalued to relatively overvalued alpha continue to decrease,
irrespective of time horizon used and indifferent to the use of equal versus value weighted
returns.
In Panel C I provide more insights into whether a similar continued valuation refinement
occurs when I split the sample of splitting firms by growth options. Interestingly, the market also
keeps refining its valuation estimates in a manner that is consistent with the abnormal returns
surrounding the announcement dates. For example, for the splitting sample firms who have the
lowest growth options (the row labeled Low) I find an alpha of 0.43 (using the Fama French 3
factor model with equal weighted returns and a three year horizon) whereas splitting firms that
have relative more growth options (the row labeled High) have an alpha of 0.56. Again, these
results are similar when I use the Carhart 4 factor model, value weighted returns, or a 5 year
26
horizon and my inferences do not change. Overall, the findings in this Panel show that splitting
firms with relatively higher growth options have relatively higher long term returns.
2.4.4 Mispricing, growth options and accounting performance after the stock split
In the last part of my analysis I investigate whether operating performance of splitting
firms exhibit similar patterns as those I found in the earlier analyses. To do so I calculate long
run abnormal performance of my splitting firms using a matching algorithm, similar to the one
used by Loughran and Ritter (1997). For all splitting firms I find a matching non-splitting firm
with similar size (closest one within the range of 70% - 130% of total assets), industry (Fama-
French 12 industry classification), and pre-split year operating performance (closest one within
the range of 70% - 130% of pre-split performance). I use ROA (Net income over total assets) as
my performance measure and winsorize it at 1 and 99 percent to limit the effect of outliers.
Similar to my analysis of announcement returns I split my sample into various firm specific error
and growth option quartiles. Moreover, I show the abnormal performance for a period up to 4
years after the split. The results of my analysis are shown in Table 2.9.
Not unlike the previous analyses, two patterns emerge from the long run operating
performance results. First, as growth options increase the abnormal performance increases. For
example, conditional on low firm specific errors (undervalued firms) I find that when growth
options are low the mean (median) abnormal performance in year 1 is 0.3 percent (0.0 percent)
while the corresponding mean (median) for the high growth option quartile is 2.8 percent (2.5
percent). Similarly, for the high firm specific error (overvalued firms) quartile I find a mean
(median) abnormal operating ROA of 0.9 percent (0.2 percent) for the low growth option
subsample and a mean (median) of 3.7 percent (2.8 percent). Interestingly this trend seems to be
27
Table 2.9 Long run operating performance of split firms
This table reports the long run operating performance (ROA) of split firms from 1970-2008 for 1 to 4 years. Abnormal operating performance is calculated using Loughran and Ritter (1997) matching algorithm based on size, industry and pre-split year operating performance. ROA is winsorized at 1 and 99 percent level. Mean and median abnormal operating performance is reported in combinations of firm-specific error quartile and growth option quartiles. Two highlighted rows report two extreme combinations: low firm specific error and high growth option quartiles and high firm specific error and low growth option quartiles.
Quartiles Mean (median) abnormal operating performance
N Firm-specific error Growth options Year 1 Year 2 Year 3 Year 4 Low Low 0.3% (0.0%) 0.3% (0.0%) 0.1% (0.0%) 0.1%(0.0%) 209 190 178 161Low 2 0.9% (0.4%) 1.9% (0.4%) 1.7% (0.4%) 3.2%(0.8%) 291 276 266 254Low 3 2.8% (1.4%) 1.9% (1.3%) 3.2% (2.2%) 3.9% (2.6%) 382 364 341 315Low High 2.8% (2.5%) 5.0% (3.7%) 7.4% (3.3%) 4.6% (3.5%) 240 229 218 2012 Low 0.4% (0.0%) 0.5% (0.0%) -0.1% (0.1%) -1.6% (0.0%) 323 301 285 2692 2 1.3% (0.4%) 1.2% (0.5%) 1.9% (0.7%) 1.6% (0.7%) 304 286 271 2542 3 3.0% (1.4%) 3.2% (1.8%) 2.6% (1.1%) 2.7% (1.4%) 288 277 264 2472 High 1.8% (2.7%) 4.2% (2.8%) 6.1% (3.7%) 4.1% (3.4%) 212 201 191 1873 Low 0.2% (0.1%) -0.5% (0.0%) 0.5% (0.0%) 1.5% (0.0%) 320 300 278 2573 2 2.4% (0.6%) 0.9% (0.5%) 2.9% (0.4%) 1.5% (0.6%) 292 280 260 2483 3 2.0% (1.0%) 3.0% (1.5%) 5.0% (2.7%) 3.9% (2.6%) 244 237 227 2143 High 4.3% (3.0%) 4.2% (3.1%) 6.4% (3.8%) 7.5% (4.7%) 272 260 249 238High Low 0.9% (0.2%) 1.0% (0.2%) 1.3% (0.3%) 0.7% (0.1%) 271 260 243 236High 2 1.4% (0.3%) 1.7% (0.2%) 1.7% (0.2%) 3.4% (0.4%) 224 214 199 182High 3 -0.8% (0.7%) -0.8% (1.4%) -2.1% (1.1%) 5.8% (1.8%) 195 184 168 160High High 3.7% (2.8%) 2.2% (3.2%) 4.6% (2.8%) 5.7% (2.8%) 337 324 309 295
28
consistent for all four firm specific error quartiles and seems to hold for almost all the post-split
years I investigate. A second pattern that emerges (albeit less clear) from this analysis is that for
most of my years and subsamples abnormal operating performance is substantially lower for
firms that split while being relatively overvalued and substantially higher for splitting firms that
are relatively undervalued. These findings are consistent with the analyses provided in the
previous sections.
2.5 Conclusions
My paper contributes to the literature that investigates the role that signaling plays when
firms decide to split their shares. Using a market to book decomposition approach, pioneered by
RKRV (2005) I find that there are substantial differences in the way stock prices absorb the
information embedded in stock split announcements. I find that, although on average stock splits
are seen as a positive signal by the market, there are predictable cross-sectional differences in the
abnormal returns generated around the stock split announcement. Firms that announce a stock
split while relatively overvalued have substantially lower abnormal returns than firms that
announce a stock split while being relatively undervalued. I also find that firms that have
relatively more growth opportunities exhibit higher abnormal stock returns.
When I investigate long term stock returns, using a number of different methodologies
and time frames, I find that the market does not embed all information that is incorporated in the
announcements immediately. Abnormal returns (as measured by alphas) are higher (lower) for
firms that are relatively undervalued (overvalued). Also, alphas are larger for firms that have
more growth options. My final analysis, which employs a matched sample abnormal accounting
performance methodology, suggests that these patterns are not limited to short and long term
stock price performance. Again, abnormal operating performance is higher for firms with more
29
growth options and firms that undervalued while firms with overvalued equity tend to be
performing less, for a period up to four years. Combined, I interpret my findings as consistent
with the idea that the market is able to discriminate between valid and false signals.
30
Chapter 3
Essay 2 - Product Market Advertising Effects on the Method of Payment in M&A
3.1 Introduction
The economic significance of advertising in the US economy is substantial. For, example
in 2010, the top ten firms in the US spent $9.6 billion (about 1.2% of their combined revenues)
on advertising.4 The academic literature has long suggested that advertising affects product
market demand (e.g., early work by Borden (1942), Stigler (1961), Telser (1964), Nelson (1974),
Grossman and Shapiro (1984), Bagwell and Ramey (1994), and, more recently Rao, Agarwal,
and Dahlhoff (2004), Braun-LaTour, LaTour, Pickrell, and Loftus (2004), and Erdem, Keane,
and Sun (2008)). However, a related literature, primarily based on insights by Merton (1987),
suggests that there exists another role for advertising. To wit, advertising may influence the
demand for the firm’s equity. As an illustration, researchers often point to commercials aired in
the popular media by firms that do not directly provide services to the target audience, but who
may be potential investors. For example Lockheed Martin advertises its dedicated workforce,
growing technology portfolio, and innovative spirit in its commercial “Visions of Tomorrow”.
Similarly, its “How” series of commercials depict the company as a leader in technological
innovations and its assurance that the company is working to ensure the technological superiority
of United States.5
While there exists a substantial literature that investigates the relation between advertising
and product market demand6, there is a relative paucity investigating the link between
advertising and financial markets. Recent papers that investigate the link between advertising
4Based on the ten largest (total assets) of all Compustat firms with available advertising data. 5 See: http://www.lockheedmartin.com/ and http://www.lockheedmartin.com/how. 6 For a recent survey see Rao et al. (2004).
31
and ownership and liquidity (e.g., Grullon, Kanatas, and Weston (2004)) as well as advertising
and stock returns (Chemmanur and Yan (2009b)) suggest that such a link exists. Chemmanur
and Yan (2009a) document a link between product market advertising and new equity issues.
They find that firms that need to raise external capital are more likely to increase their
advertising efforts, presumably to increase investors’ awareness of their firm’s quality and thus
increase the stock price. My paper is related to and complements their findings. While their
paper focuses on firms that issue equity in either an IPO or SEO setting, I investigate advertising
expenditure patterns of acquirers prior to engaging in a merger. However, a relatively high stock
price is most beneficial when a firm undertakes a merger or acquisition using its stock as a
currency of exchange. Hence, I investigate whether there are differences in advertising
expenditures prior to merger, conditional on the method of payment.
My findings are consistent with the findings of Chemmanur and Yan (2009a). In both
univariate and multivariate analyses I find that advertising intensity is significantly related to the
method of payment used in a merger. Using a large sample of US mergers, from 1990 to 2010, I
find that stock-based acquiring firms have advertising intensity of 3.7% and 6.1% in the two
years prior to the merger announcement and 4.5% in the merger year, but I do not find a similar
trend for cash based acquirers. To be specific, advertising intensity for cash based acquirers
remains unchanged at 1.9% in the two years prior to the merger and declines to 1.8% during the
merger year. To mitigate concerns that my findings may be driven by selection bias unrelated to
advertising differences between cash-based and stock-based acquirers, I replicate my
multivariate analyses using a propensity score matching approach. The results of this analysis
confirm that stock-based acquirers have relatively high advertising expenditures prior to the
merger, whereas cash based acquirers do not. Finally, although I am largely agnostic about the
32
means by which advertising may increase stock prices, and therefore benefit the pre-merger
owners, I do attempt to provide some preliminary evidence that may shed some light on this
issue.7 First, I attempt to relate insider stock ownership with advertising activities prior to the
stock based acquisition. If managers attempt to increase stock prices through increased
advertising for selfish reasons, they are more likely to do so when they have more ownership in
their firm.8 The evidence I present in the paper is indeed consistent with this notion. Surprisingly,
when I attempt to link the deal ratio (defined as the ratio of the deal value [price paid to the target
for the acquisition] divided by the acquirer’s immediate past fiscal-year-end market value of the
equity) with advertising expenses prior to the acquisition I do not find any evidence that suggests
that increased advertising is linked with deal ratios that are beneficial to the managers of the
acquiring firms.
My paper contributes to at least two streams of literature. First, it adds to the literature that
investigates the financial effects of advertising. Examples in this line of inquiry are Comanor and
Wilson (1967, 1974), Grullon, et al. (2004), Rao, et al. (2004), and Chemmanur and Yan (2009a,
2009b). The basic tenet in this literature is that advertising has an effect on returns and/or
financial decisions of firms. My paper shows that advertising can also be linked to merger
activities in general, but to the method of payment used in the transactions, in particular.
Secondly, my paper adds to the literature that looks at different methods of payment in
mergers and the determinants thereof. There exists a large literature that attempts to explain the
choice of method of payment and the relation with a host of firm variables (e.g., DeAngelo
7 The literature suggests that there exist at least 2 mechanisms through which this may occur: spillovers and signaling (e.g., Nelson (1974), Chemmanur and Yan (2009a)). 8 Erickson and Wang (1999) test this notion in the context of earnings management prior to mergers. Note that a number of researchers (e.g., Shleifer and Vishny (2003), D’Avolio, Gildor, and Shleifer (2001), Erickson and Wang (1999), Louis (2004)) also argue that managers of acquiring firms have strong incentives to increase the stock price before stock based acquisitions. Earnings management could be one way to do so. In this article I argue that advertising is another way of influencing the stock price positively prior to stock based acquisitions.
33
(1986, 1990), Amihud, Lev, and Travlos (1990), Erickson and Wang (1999), and Facio and
Masulis (2005))9. I add to this literature by investigating the role that advertising plays in this
choice. I provide evidence that there is indeed a predictable link between advertising intensity
and the choice of method of payment in a subsequent merger.
The remainder of the paper is organized as follows. Section 3.2 describes the data. In
Section 3.3 I report my findings on the relationship between method of payment and advertising
of the acquirer. This Section will also contain the results of my analysis that employs the
propensity score matching approach. In section 3.4 I document differences in acquirer’s stock
price misvaluation based on the methods of payments. In Section 3.5 I investigate the economic
incentives for stock-based acquirers to spend relatively more on advertising. Finally, Section 3.6
concludes.
3.2 Data
My initial sample of mergers is from Thomson Financial’s Securities Data Corporation
(SDC) U.S. Mergers and Acquisition database. I include firms that announced and completed at
least one merger between 1990 and 2010 and require that both acquirer and target are US firms. I
only consider completed deals and deals with transaction values over $1 million. I also require
that the acquirer owns 100% of the target after completion of the deal. My initial sample has
4,425 mergers by 2,524 unique acquirers. I also require that my sample firms have advertising
data available in the Compustat database which reduces the sample to 2,351 deals by 1,179
unique acquirers.
There is contradictory evidence about the durability of the effects of advertising. Some
argue that advertising creates intangible capital and the effect of advertising is long lived (e.g.,
9 Examples of theoretical papers that investigate the choice of method of payment in M&A are: Hansen (1987), Fishman (1989), and Eckbo, Giammarino, and Heinkel (1990)
34
Hirshey (1982), Hirshey and Weygandt (1985)). On the other hand, Clark (1976) and Landes and
Rosenfield (1994) find that the duration of the advertising effect on sales is short lived and Clark
(1976) estimates that the duration is between 3-15 months. In order to avoid confounding effects
of deals that are undertaken chronologically near a given deal but are paid for using different
methods of payment, I consider only merger announcements where all deals undertaken are paid
for using the same method of payment during the timeframe from the original announcement
minus two years (year (-2)) to one year after the merger announcement (year (+1)). For example,
if an acquirer announces a merger in a year and uses stock as the payment method then in order
to be included in my sample it cannot have different payment types (e.g., cash) in the
surrounding year (-2) to year (+1) of that announcement. This requirement further reduces my
sample to 1,470 merger announcements by 1,118 unique firms. When I filter in a more
conservative manner, by considering merger announcements where the acquirer undertakes only
one deal during the entire sample period (1990-2010) the sample reduces to 734 unique
announcements of 734 unique acquirers. For the sake of robustness, I use both of these samples
in my analyses.
I classify merger types in two ways. In both classification schemes, if the acquirer pays
100% with cash I classify the merger as a ‘cash’ merger and 100% with stock is classified as a
‘stock’ merger. In classification Scheme 1 (labeled ‘merger type 1”), if some portion is paid
with cash (stock) and the balance is paid with stock (cash), the merger is classified as ‘mixed’
and if some percentage is paid with a combination of cash and stock and the balance with other
liabilities, the deal is classified as ‘other’. My second classification scheme (‘merger type
definition 2’) allocates a portion of the ‘mixed’ and ‘other’ categories from Scheme 1, to either
the pure cash or pure stock categories. If the ‘mixed (other)’ deal is paid for with at least 75%
35
cash (stock), then it is classified as a pure cash (stock) deal. Otherwise, the categories remain the
same as Scheme 1.
Table 3.1 reports the distribution of my sample (by merger definition type) over time,
across various industries (industry is defined using the Fama-French 12 industry classification)
and the methods of payment. Panel A shows a significant increase in merger activity in the later
part of the 1990s and early 2000s, although merger activity seems to taper off after 2001.
Specifically, using merger type 1, I find a steady increase from 26 total deals in 1990 to a
maximum of 129 in 1998 and then a steady decrease to about 40 during the last few sample
years. These findings are similar to Moeller, Schlingemann, and Stulz (2004), Moeller,
Schlingemann, and Stulz (2005), and Bargeron, Schlingemann, Stulz, and Zutter (2008) who find
a similar trend. Table 1, Panel A also shows that stock-based acquisitions are more common
than other methods of payment. For example, for merger type 1, nearly 39% (568 of 1,470)
mergers are stock-based and 27% (394 of 1,470) are cash-based. The corresponding percentages
for merger type 2 are 45% and 30% for stock- and cash-based, respectively. Panel B shows that
acquisition activity is mostly concentrated in the Finance (563 deals, or 38%), Business
Equipment, and (324 deals, or 22%), Healthcare, Medical Equipment, and Drugs industries (127
deals, or 9%).
I use the Compustat advertising variable (mnemonic: XAD) which represents the cost of
advertising in media such as radio, television, periodicals, and any other promotional expenses.
Following the industrial organization literature and Chemmanur and Yan (2009a), I use a
measure labeled ‘advertising intensity’ which is defined as advertising expenditure in year t
scaled by total sales (Compustat mnemonic: SALE) at the end of year t. In my multivariate
analyses I use pre-merger announcement advertising intensity of the acquirer as the dependent
36
Table 3.1 Distribution of deals by year, merger type, and industry.
The Table shows the yearly distribution of deals during the sample period of 1990-2010 by merger type (Panel A) and across Fama-French twelve industries (Panel B). In merger type 1, if the acquirer pays 100% through cash the merger is classified as a ‘cash’ mergers, 100% with stock is classified as a ‘stock’ merger, some portion through cash (stock) and rest through stock (cash) is classified as a ‘mixed’ deal, and if some percentage (<100%) through cash and/or stock and rest through other liabilities the deal is classified as ‘other’. In merger type 2, if the acquirer pays at least 75% of the deal with cash (stock) the merger is classified as a cash (stock) merger, if less than 75% is paid with cash (stock) and rest with stock (cash) then the deal is labeled as ‘mixed’ and if less than 75% is paid with cash/stock and rest with assumption of other liabilities then the deal is classified as ‘other’.
Panel A: Distribution across year
Year
Merger Type definition 1 100% cash = Cash
100% stock = Stock X% cash and X% stock = Mixed
Cash/stock <100% and rest other type = other
Merger Type definition 2 Greater than 75% cash = Cash
Greater than 75% stock = Stock Cash <75% and stock <75% = Mixed
Cash/stock <75% and rest other type = other Stock Cash Mixed Other Total Stock Cash Mixed Other Total
1990 15 7 1 3 26 15 9 1 1 26 1991 11 7 7 6 31 13 7 5 6 31 1992 20 4 5 4 33 22 4 4 3 33 1993 18 10 5 6 39 22 10 4 3 39 1994 34 9 7 4 54 36 10 5 3 54 1995 48 22 8 10 88 49 25 5 9 88 1996 41 17 4 25 87 45 19 3 20 87 1997 48 17 8 19 92 53 22 7 10 92 1998 77 23 4 25 129 82 27 4 16 129 1999 60 32 10 16 118 62 35 8 13 118 2000 51 30 18 15 114 59 33 12 10 114 2001 41 21 16 15 93 45 22 14 12 93 2002 18 20 16 13 67 20 23 15 9 67 2003 16 15 21 7 59 21 16 16 6 59 2004 18 26 31 7 82 22 28 26 6 82 2005 20 16 25 7 68 23 18 22 5 68 2006 18 35 21 11 85 30 39 11 5 85 2007 7 30 39 3 79 17 32 28 2 79 2008 8 18 12 10 48 10 19 10 9 48 2009 10 11 7 8 36 13 12 4 7 36 2010 7 24 7 4 42 8 24 6 4 42
Total 586 394 272 218 1470 667 434 210 159 1470
37
Table 3.1 continued.
Panel B: Industry (Fama French 12 industry classification) distribution of samples of acquiring samples across different merger types.
Fama French 12 Industry classification
Merger Type definition 1 100% cash = Cash
100% stock = Stock X% cash and X% stock = Mixed
Cash/stock <100% and rest other type = other
Merger Type definition 2 Greater than 75% cash = Cash
Greater than 75% stock = Stock Cash <75% and stock <75% = Mixed
Cash/stock <75% and rest other type = other Stock Cash Mixed Other Total Stock Cash Mixed Other Total Consumer Nondurables 19 31 13 15 78 23 33 11 11 78 Consumer Durables 5 14 2 6 27 5 16 2 4 27 Manufacturing 22 42 10 16 90 26 45 7 12 90 Energy 3 1 3 3 10 4 1 3 2 10 Chemicals and Allied Products 4 9 2 5 20 5 12 2 1 20 Business Equipment 149 99 38 38 324 164 107 21 32 324 Telephone and Television Transmission 13 10 11 12 46 16 13 9 8 46 Utilities 0 0 0 1 1 1 0 0 0 1 Wholesale, Retail, and Some Services 32 25 10 26 93 38 28 9 18 93 Healthcare, Medical Equipment, and Drugs 50 39 17 21 127 60 42 12 13 127 Finance 261 104 150 48 563 291 112 121 39 563 Other 28 20 16 27 91 34 25 13 19 91
Total 586 394 272 218 1470 667 434 210 159 1470
38
variable and other product market related control variables such as sales, changes in sales
relative to the prior year, pre-merger announcement market competition (defined as fraction of
firm sales relative to the total industry sales, where industry is based on the 3-digit SIC code),
and the advertising intensity in the prior year as independent variables.
3.3 Advertising intensity and stock based acquisitions
I hypothesize that stock-based acquirers will have relatively high advertising intensity, in
an effort to increase their stock price in the pre-merger period, relative to cash-based acquirers.
Therefore, I examine the advertising intensity surrounding a merger announcement year for the
different payment methods. Table 3.2 reports the advertising intensity for the different payment
methods for the 2 different merger type definitions for the period ranging from year (-3) to year
(+3) around the merger announcement.
In Panel A I show these results for the sample of mergers where I restrict the sample to
those mergers in which there was only one merger undertaken by the acquirer during the whole
sample period (1990-2010), whereas in Panel B I restrict the sample to those mergers in which
there was only one merger undertaken by the acquirer during the period from year (-2) to (+1).
Because the results in the Panels are similar I will focus the discussion on the results reported in
Panel A. For stock deals (as defined by merger type 1) I find that the mean advertising intensity
increases from 2.3% in year (-3) to 3.7% in year (-2) and peaks in year (-1) at 6.1%. During the
merger year the mean advertising intensity stays relatively high at 4.5%, but it decreases
drastically after the merger year (from 2.5% in year (+1) to 1.6% in year (+3)). When I
investigate the corresponding medians I find a less clear trend. Advertising seems to be relatively
high at about 1.4% in the years prior to (and including) the merger year. However, afterwards
there clearly is a decrease in the median with a low of 0.9% in year (+3).
39
Table 3.2: Advertising intensity of acquirers surrounding the merger
This Table shows the mean and median advertising intensity of acquiring firms from -3 to +3 years surrounding merger announcement year. Advertising intensity is defined as advertising expenditure in year t scaled by total sales at the end of year t. In merger type 1, if the acquirer pays 100% through cash the merger is classified as a ‘cash’ mergers, 100% with stock is classified as a ‘stock’ merger, some portion through cash (stock) and rest through stock (cash) is classified as a ‘mixed’ deal, and if some percentage (<100%) through cash and/or stock and rest through other liabilities the deal is classified as ‘other’. In merger type 2, if the acquirer pays at least 75% of the deal with cash (stock) the merger is classified as a cash (stock) merger, if less than 75% is paid with cash (stock) and rest with stock (cash) then the deal is labeled as ‘mixed’ and if less than 75% is paid with cash/stock and rest with assumption of other liabilities then the deal is classified as ‘other’. Panel A includes the sample of acquiring firms which completed only one merger during the sample period from 1990-2010 and Panel B includes the samples of acquiring firms which completed one merger in 3 years (+1 to -2 years) surrounding a merger announcement.
Years from merger
announcement
Advertising Intensity Merger type definition 1
100% cash = Cash 100% stock = Stock
X% cash and X% stock = Mixed
Merger type definition 2 Greater than 75% cash = Cash
Greater than 75% stock = Stock Cash <75% and stock <75% = Mixed
Stock Cash Mixed Stock Cash Mixed Mean Median N Mean Median N Mean Median N Mean Median N Mean Median N Mean Median N
Panel A: Sample firms have only 1 completed merger during the sample period (1990-2010)
-3 0.023 0.014 126 0.019 0.011 93 0.024 0.013 92 0.024 0.014 154 0.020 0.012 105 0.024 0.013 68-2 0.037 0.014 149 0.019 0.012 114 0.027 0.012 97 0.037 0.014 175 0.020 0.012 125 0.024 0.012 75-1 0.061 0.014 158 0.019 0.011 118 0.022 0.012 100 0.057 0.014 184 0.020 0.012 129 0.020 0.012 790 0.045 0.013 159 0.018 0.009 109 0.018 0.012 101 0.042 0.013 186 0.019 0.009 120 0.017 0.011 791 0.025 0.012 138 0.018 0.011 104 0.016 0.011 91 0.024 0.012 159 0.018 0.011 114 0.016 0.011 732 0.017 0.010 127 0.018 0.011 89 0.012 0.010 76 0.017 0.010 144 0.0190 0.011 97 0.013 0.010 623 0.016 0.009 118 0.020 0.011 74 0.014 0.011 59 0.016 0.009 132 0.021 0.011 79 0.015 0.011 48
Panel B: Sample firms have only 1 completed merger surrounding the merger (year (-2) - year (+ 1))
-3 0.022 0.011 261 0.023 0.012 193 0.030 0.012 175 0.028 0.012 316 0.023 0.013 214 0.019 0.012 133-2 0.028 0.012 299 0.021 0.012 221 0.021 0.012 181 0.028 0.012 349 0.022 0.012 241 0.012 0.012 143-1 0.039 0.012 327 0.021 0.012 230 0.020 0.012 192 0.038 0.012 379 0.022 0.012 250 0.018 0.012 1530 0.033 0.012 330 0.022 0.012 215 0.016 0.011 197 0.031 0.012 382 0.022 0.012 235 0.015 0.011 1561 0.022 0.011 307 0.021 0.013 202 0.017 0.011 183 0.023 0.011 355 0.022 0.013 220 0.016 0.011 1442 0.019 0.010 297 0.021 0.013 177 0.014 0.010 166 0.019 0.010 342 0.021 0.013 193 0.013 0.010 1313 0.018 0.010 291 0.021 0.012 151 0.016 0.011 131 0.019 0.010 325 0.022 0.012 164 0.015 0.011 106
40
Cash based acquirers exhibit a very different trend. The levels of advertising intensity
stay more or less constant during the period from year (-3) to year (+3). For example, for merger
type 1 the mean advertising intensity for cash based acquirers is 1.9%, 1.9% and 1.9% for year (-
3), year (-2) and year (-1), respectively. During the merger year (0) this mean remains at 1.8%
and barely changes in the post-merger years. Similarly, the medians do not exhibit much
variation.
I graphically illustrate these results in Figure 3.1. Panel A shows figures that graph the
mean advertising intensity surrounding the merger announcement year for merger types 1 and 2
where acquirers have only one merger announcement during the (1990-2010) period. Panel B
shows the mean advertising intensity surrounding the merger announcement year when acquirers
completed only one merger during the three years (year (-2) to year (+1)) surrounding a merger
year. All figures clearly exhibit the same patterns. Advertising intensity is substantially higher in
the year prior to and the year of the merger, but this trend only occurs for stock deals and is not
exhibited by cash deals.
Before I move to my multivariate analyses I first show the characteristics of my sample
firms by reporting the mean and median of the variables I use in my analyses. Table 3.3 also
reports the results of my univariate tests (t-test for differences in means and Wilcoxon z-test for
differences in medians) when I compare cash and stock deals along these dimensions. Panel A
reports the mean, median and univariate test results of my variables of interest for stock and cash
based acquirers where sample firms have only mergers during the (1990-2010) period. The
results show that stock based acquirers are smaller in terms of total assets and sales at the
beginning of the merger year than cash based acquirers (albeit only significantly different for the
medians) and that this finding does not depend on the merger type definition. For example, for
41
Figure 3.1 Advertising intensity surrounding mergers.
Panel A: Sample firms have only one completed merger during the sample period (1990-2010)
Panel B: Sample firms have only one completed merger surrounding the merger (year (-2) - year (+ 1))
merger type definition 1, the mean (median) sales are $2,350.57 million ($211.65 million) for
cash based acquirers and $651.38 million ($82.58 million) for stock based acquirers. Although
the advertising expenditure is higher for cash based acquirers than stock based acquirers, the
adverting intensity (advertising expenditure divided by sales) is significantly higher for stock
based acquirers in pre-merger announcement year. The mean advertising intensity is more than
three times higher (6.1% versus 1.9%) for stock based acquirers than cash based acquirers in pre-
merger year. Finally, transaction value (total value paid by the acquirer excluding fees and
expenses) is higher (but not significantly) for stock based acquirer than cash based acquirers in
0%
2%
4%
6%
‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5Mea
n A
dver
tisi
ng I
nten
sity
Relative year from announcement
Merger Type 2
Stock Cash
0%
1%
2%
3%
4%
‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5
Mea
n A
dver
tisi
ng I
nten
sity
Relative year from announcement
Merger Type 2
Stock Cash
0%
2%
4%
6%
8%
‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5
Mea
n A
dver
tisi
ng I
nten
sity
Relative year from announcement
Merger type 1
Stock Cash
0%
1%
2%
3%
4%
5%
‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5Mea
n A
dver
tisi
ng I
nten
sity
Relative year from announcement
Merger type 1
Stock Cash
42
Table 3.3 Characteristics of acquiring firms
This Table shows the summary statistics of various variables for stock and cash based acquiring firms. Total assets is Compustat mnemonic AT, Sales is Compustat mnemonic SALE, which represents gross sales reduced by discounts and allowances, advertising expenditure is Compustat mnemonic XAD which represents the cost of advertising in media and promotional expenses, advertising intensity is defined as advertising expenditure in a fiscal year scaled by total sales of that year, transaction value (in $mil) is from SDC which represents total value of consideration paid by the acquirer, excluding fees and expenses, merger premium is from SDC which represents offer Price to target stock price premium four weeks prior to announcement, and t-1 represents the immediate prior year of merger announcement. Merger type definition 1 and 2 are defined as in Table 3.1. Panel A includes the samples of acquiring firms which completed only one merger during the sample period from 1990-2010. Panel B includes the samples of acquiring firms which completed one merger in three years (-2 to +1 years) surrounding a merger announcement. t/Z Diff. column shows the mean and median test statistic between stock and cash based acquirers. ***, **, * represents significance level at the 1%, 5%, 10% level, respectively.
Variables Merger type definition 1
100% cash = Cash 100% stock = Stock
Merger type definition 2
Greater than 75% cash = Cash Greater than 75% stock = Stock
Stock Mean
(Median)
Cash Mean
(Median)
Diff. t
(Z)
Stock Mean
(Median)
Cash Mean
(Median)
Diff. t
(Z)
Panel A: Sample firms have only one completed merger during the sample period (1990-2010)
Total Assetst-1 1990.55 (356.93)
4320.55(672.78)
-1.08(-2.67)
***
3031.21(379.31)
4436.64 (671.17)
-0.64(-2.68)
***
Sales t-1 651.38 (82.58)
2350.57(211.65)
-1.36(-4.86)
***
1283.43(81.80)
3131.78 (236.10)
-1.34(-5.21)
***
Advertising expenditure t-1 18.47 (1.21)
65.08(2.06)
-1.77(-2.19)
* **
37.79(1.17)
70.80 (2.30)
-1.16(-2.45)
***
Advertising intensity t-1 0.0614 (0.0136)
0.0186(0.0113)
2.15(1.46)
** *
0.0567
(0.0136)0.0196
(0.0116) 2.16
(1.21)**
Transaction Value ($mil) 344.10 (58.45)
290.84(72.37)
0.59(0.73)
446.20(61.77)
344.27 (76.98)
0.82(-0.85)
Merger Premium 39.58% (29.97%)
44.50%(33.89%)
-0.79(-1.04)
40.44%
(30.86%)43.91%
(34.45%) -0.60
(0.61)
N 158 118 184 129
43
Table 3.3 continued.
Panel B: Sample firms have only one completed merger surrounding the merger (year (-2) - year (+ 1))
Total Assetst-1 14125.00 (909.93)
7614.57(1473.67)
1.13(-2.78)
***
13717.62(971.29)
7499.45 (1400.13)
1.24(-2.47)
***
Sales t-1 2004.17 (115.15)
5299.16(598.44)
-2.76(-6.74)
*** ***
2482.22(122.79)
5629.72 (598.44)
-2.69(-6.87)
*** ***
Advertising expenditure t-1 80.90 (1.41)
145.63(6.04)
-1.73(-5.02)
* ***
87.85(1.54)
155.25 (6.21)
-1.84(-5.15)
* ***
Advertising intensity t-1 0.039 (0.012)
0.021(0.012)
1.86(0.04)
*
0.038(0.012)
0.022 (0.012)
1.87(0.20)
*
Transaction Value ($mil) 1334.22 (74.82)
430.93(108.25)
2.14(-2.27)
** **
1545.01(83.16)
462.83 (113.08)
2.58(-1.82)
** **
Merger Premium 39.80% (33.25%)
47.88%(38.76%)
-1.98(-1.06)
**
41.98%(33.33%)
48.23% (38.76%)
-1.45(-1.14)
N 327 230 379 250
most cases, and the merger premium (defined as [acquirer’s offer price for target share – target’s
closing share price four weeks before announcement]/ target’s closing share price four weeks
before announcement, expressed as percentage) is higher for cash based acquirers than stock
based acquirers.10 Most of these patterns are similar, irrespective of merger type and sample
(Panel B shows the results when I restrict the sample to mergers in which only one merger was
completed during the year (-2) – year (+1) period).
To examine whether my univariate results hold in a multivariate setting, I use the
following regression specification:
(3.1)
where Product is a vector of product market variables (similar to Chemmanur and Yan
(2009a)). To be specific, I use: 1/SLS, where SLS is the sales revenue in the current year,
10 Transaction value and merger premium are obtained from SDC.
44
LOGSLS, the change in the log of sales revenue from past year to the current year, Lag_FSLS
is the fraction of sales revenue to the industry sales revenue of the past year, where industry is
based on the 3 digit SIC code, Lag_Advint is the past year advertising intensity. The variable Tt-j
is a year dummy which takes a value of 1 if the year is t-j or else 0 where j is the relative past
year from the merger announcement date (for example j = 1 when that observation is in pre-
merger announcement year). I run separate regressions for stock-based, cash-based, and mixed
acquirers. My variable of interest is T-1. It is the time dummy for the most recent prior year to the
merger announcement year. If stock based acquirers advertise more in this pre-merger
announcement year this dummy should be positive and significant while I control for other
product market variables that may explain advertising intensity of the firm.
In Table 3.4, Panel A I report the regression results for the sample where I only include
mergers where sample firms have only one merger in the (1990-2010) period. For merger type 1
and 2 I find that the pre-merger announcement year time dummy coefficient is positive and
significant at 5% level for stock based acquirers. When I run similar regressions for cash based
and mixed acquirers none of the time dummies have significant coefficients. I repeat these
regressions for the sample in which sample firms have only one completed merger in the three
years (year (-2) to year (+1)) surrounding a merger in Panel B. Again, I find that the pre-merger
announcement year dummy has a positive and significant coefficient for stock based acquirers
but it is insignificant for cash based and mixed acquirers. These results are robust to using 2 digit
industry (SIC) codes to define industry when I calculate market competition (Lag_FSLS). The
results in this part of the paper are consistent with my hypothesis that managers of stock based
acquiring firms advertise more in pre-merger period. One may argue that the method of payment
is clustered across industries (i.e., in some industries stock-based acquisitions are more prevalent
45
and hence higher advertising is due to industry characteristics rather than the method of payment.
Table 3.1 does not show any patterns that suggest that certain industries are more likely to
exhibit a particular method of payment. To further investigate this issue I use industry fixed
effects in my regressions which are also reported in table 3.4, where industry is defined using the
Fama-French twelve industry classification. The main results are even stronger, supporting my
major hypothesis that acquirers advertise more in the pre-merger announcement year when the
method of payment is stock based. So, I conclude that industry clustering does not seem to be
driving the relationship between the method of payment and advertising intensity. Finally, to
mitigate concerns that my findings may be driven by the fact that cash-based and stock-based
acquirers are different I replicate my multivariate analyses using a propensity score matching
(PSM) approach. This approach was first developed by Rosenbaum and Rubin, in 1983. This
method is used in observational studies to reduce bias due to incomplete and/or inexact
matching. Instead of matching based on several criterion this method uses a logistic regression to
calculate a propensity score based on selected matching criterion. Then, each observation is
matched with a pair using the closest propensity score.11 The propensity score is calculated using
immediate pre-announcement year market-to-book and size (log of total assets). Then based on a
Greedy matching technique (where once a match firm is selected it is not replaced in the
matching pool of observations) I find a cash-based merger for all of the stock mergers and run a
similar regression as the OLS regressions in Table 3.4. The only difference is that instead of
using two separate regressions where I am interested in the time dummies, I now run one
regression and use a stock dummy which takes a value of ‘1’ if the merger is stock based and
takes a value of ‘0’ if the merger is cash based. If stock based acquirers advertise more in
11 Recently, this approach has gained popularity when matching may be problematic (e.g., Villalonga (2004), Hochberg, Sapienza, and Vissing-Jørgensen (2009), Mikkelson and Partch (2003)).
46
Table 3.4 Regression results of advertising intensity of stock and cash based acquirers
This Table shows the regression results where dependent variable is advertising intensity of the acquiring firms and explanatory variables are year dummies relative to merger announcement year and other product market related variables. The product market variables 1/SLS, where SLS is the sales revenue in the current year (Compustat mnemonic SALE), LOGSLS, the change in the log of sales revenue from past year to the current year, Lag_FSLS is the fraction of sales revenue to the industry sales revenue of prior year where industry is based on 3 digit SIC code, LagAdvint is the past year advertising intensity where advertising intensity is defined as advertising expenditure in a fiscal year scaled by total sales of that year. Ti is the year dummy variable which takes a value of 1 if the year is i or else 0 where i is the relative past year from the merger announcement date (for example T-1 is the immediate past year of merger announcement year). Panel A includes the samples of acquiring firms which completed only one merger during the sample period and Panel B includes the samples of acquiring firms which completed one merger in the three years (-2 to +1 years) surrounding a merger announcement. ***, **, * represents significance level at the 1%, 5%, and 10% level, respectively.
Panel A: Sample firms have only one completed merger during the sample period (1990-2010)
Dependent Variable: Advertising intensity
Merger type definition 1 100% cash = Cash
100% stock = Stock X% cash and X% stock = Mixed
Merger type definition 2 Greater than 75% cash = Cash
Greater than 75% stock = Stock Cash <75% and stock <75% = Mixed
Stock Cash Stock Cash
Intercept 0.0168 *** 0.0394 *** 0.0122 *** 0.1077 *** 0.0153 *** 0.0350 *** 0.0117 *** 0.0106 ***1/SLS 0.0001 0.0003 0.0401 *** 0.0417 *** 0.0002 0.0003 0.0391 *** 0.0405 ***
LOGSLS 0.0079 *** 0.0066 *** -0.0213 *** -0.0203 *** 0.0091 *** 0.0078 *** -0.0197 *** -0.0193 ***
Lag_FSLS 0.0257 ** -0.0039 0.0087 *** -0.0035 0.0406 * 0.0035 0.0041 * -0.0056 **
Lag_Advint 0.2201 *** 0.2065 *** 0.4787 *** 0.4474 *** 0.2302 *** 0.2177 *** 0.5041 *** 0.4750 ***
T-5 0.0053 0.0053 -0.0005 -0.0011 0.0041 0.0041 -0.0004 -0.0008
T-4 -0.0008 0.0012 0.0012 0.0008 -0.0008 0.0004 0.0025 0.0022
T-3 -0.0023 -0.0007 -0.0005 0.0002 -0.0022 -0.0011 0.0002 0.0008
T-2 -0.0007 0.0011 -0.0003 0.0007 0.0024 0.0034 0.0002 0.0009
T-1 0.0084 ** 0.0105 *** -0.0005 0.0026 0.0081 ** 0.0031 *** -0.0017 0.0010
T0 -0.0018 0.0017 0.0017 -0.0013 0.0009 0.0018 0.0028
Industry fixed effects No Yes No Yes No Yes No Yes N 2191 2191 2073 2073 2581 2581 2313 2313 Adjusted R2 0.24 0.28 0.72 0.74 0.25 0.29 0.71 0.73
47
Table 3.4 continued.
Panel B: Sample firms have only one completed merger surrounding the merger (year (-2) - year (+ 1))
Dependent Variable: Advertising intensity
Merger type definition 1 100% cash = Cash
100% stock = Stock X% cash and X% stock = Mixed
Merger type definition 2 Greater than 75% cash = Cash
Greater than 75% stock = Stock Cash <75% and stock <75% = Mixed
Stock Cash Stock Cash
Intercept 0.0127 *** 0.0305 *** 0.0084 *** 0.0098 *** 0.0124 *** 0.0258 *** 0.0080 *** 0.0089 ***
1/SLS 0.0026 ** 0.0028 *** 0.0281 *** 0.0294 *** 0.0024 ** 0.0028 *** 0.0257 *** 0.0267 ***
LOGSLS 0.0036 *** 0.0031 ** -0.0133 *** -0.0129 *** 0.0027 ** 0.0026 ** -0.0134 *** -0.0128 ***
Lag_FSLS 0.0712 *** 0.0485 *** 0.0061 *** -0.0001 0.0571 *** 0.0341 *** 0.0056 *** 0.0001
Lag_Advint 0.3120 *** 0.2806 *** 0.6814 *** 0.6394 *** 0.3408 *** 0.3085 *** 0.6952 *** 0.6565 ***
T-5 0.0042 0.0047 * -0.0002 -0.0003 0.0029 0.0034 -0.0003 -0.0001
T-4 0.0001 0.0019 0.0010 0.0009 -0.0005 0.0009 0.0011 0.0013
T-3 -0.0005 0.0015 0.0001 0.0004 -0.0008 0.0008 0.0002 0.0007
T-2 0.0004 0.0022 -0.0006 -0.0003 0.0016 0.0030 * -0.0003 0.0001
T-1 0.0045 ** 0.0067 *** -0.0011 -0.0006 0.0037 ** 0.0056 *** -0.0008 -0.0002
T0 -0.0011 0.0021 0.0007 0.0012 -0.0011 0.0016 0.0010 0.0016
Industry fixed effects No Yes No Yes No Yes No Yes N 5573 5573 4626 4626 6531 6531 5157 5157 Adjusted R2 0.34 0.39 0.73 0.74 0.36 0.41 0.73 0.75
48
pre-merger announcement period then I should observe a significant positive coefficient for the
stock dummy. The results of this analysis are reported in Table 3.5.
Table 3. 5 Robustness test using propensity score matching approach
This Table shows the regression results of advertising intensity of stock and cash based acquirers using propensity score matching approach (Greedy matching technique). For every stock-based acquirer a cash-based acquirer is selected using propensity score. Propensity score is calculated using immediate pre-announcement year market-to-book and size (log of total assets). The product market variables 1/SLS, where SLS is the sales revenue in the current year (Compustat mnemonic: SALE), LOGSLS, the change in the log of sales revenue from past year to the current year, Lag_FSLS is the fraction of sales revenue to the industry sales revenue of prior year where industry is based on 3 digit SIC code, Lag_Advint is the past year advertising intensity where advertising intensity is defined as advertising expenditure in a fiscal year scaled by total sales of that year. Stock dummy is a binary variable that takes a value of 1 if the method of payment is stock and 0 if the method of payment is cash. ***, **, * represents significance level at the 1%, 5%, and 10% level, respectively.
Dependent Variable: Advertising intensity
Sample firms have only one completed merger during the sample period (1990-2010)
Sample firms have only one completed merger surrounding the merger
(year (-2) to year (+ 1))
Merger type definition 1
Merger type definition 2
Merger type definition 1
Merger type definition 2
Intercept 0.0161 *** 0.0162*** 0.0112 *** 0.0096 ***
1/SLS -0.0831 *** -0.0655*** 0.0176 *** 0.0165 ***
LOGSLS -0.0142 *** -0.0109*** -0.0156 *** -0.0110 ***
Lag_FSLS 0.0153 *** 0.0175*** 0.0221 *** 0.0230 ***
Lag_Advint 0.3387 *** 0.2932*** 0.5929 *** 0.6205 ***
Stock dummy 0.0044 *** 0.0044*** 0.0012 * 0.0014 **
Adjusted R2 0.34 0.33 0.60 0.63
My PSM approach also confirms that stock based acquirers have relatively higher
advertising intensity in the pre-merger period compared with cash based acquirers. For both
samples and both merger types I find that the coefficient on the stock dummy is positive and
significant. To be specific, it is significant at 1% level for both merger type definitions, when I
restrict the sample to mergers where there was only one completed merger, for a given acquirer,
in the 1990-2010 period. For the less restrictive sample, the coefficient is significant at 10% and
5% level for merger type definition 1 and 2, respectively.
49
3.4 Method of payments and misvaluation
Prior literature documents that merger activity occurs in waves and that high market to
book ratios are a common phenomenon during these waves, which may explain why the
predominant method of payment during these waves is stock (Rhodes-Kropf, Robinson, and
Viswanathan (2005), Maksimovic and Phillips (2001), Jovanovic and Rousseau (2001)). Several
theoretical explanations exist that suggest that valuation plays a role during these merger waves.
Shleifer and Vishny (2003) propose a theory based on irrational investor behavior and Rhodes-
Kropf and Viswanathan (2004) propose a rational theory where over-estimated synergy
influences merger activities. In the previous section, I documented that acquirers advertise more
in pre-merger period especially when the method of payment is stock. If advertising affects the
stock price of the acquirer then I would expect that acquirers with high advertising intensity (in
this case stock-based acquirers) have higher misvaluation than acquirers with relatively low
advertising intensity (in this case cash-based acquirers). I calculate misvaluation following
Rhodes-Kropf et al. (2005). They decompose market-to-book ratios into firm and sector specific
misvaluation and growth option components by calculating fundamental values and long run
values of firm. These fundamental values are calculated by linking market equity to book equity,
net income, and leverage12. The following equation describes the decomposition.
(3.2)
Here m and b are the log of market and book equity, respectively and i, j, and t represent
firm, industry, and year respectively. The expression m Fundamentalvalue represents
firm specific misvaluation, Fundamentalvalue Longrunvalue represents sector specific
misvaluation and finally Longrunvalue b represents growth options of the firm.
12 See Rhodes-Kropf et al (2005) for a detail discussion of the method.
50
Table 3.6 shows firm, sector specific, and total misvaluation of the acquiring firms in the
pre-merger announcement year for different methods of payment. Panel A shows the univariate
results for the sample where sample firms have only one merger during the sample period. I find
that both mean and median firm and total misvaluation is significantly higher for stock-based
acquirers than cash-based acquirers. Similar results are reported in panel B where I the show the
results for the less restrictive sample. Overall, my findings are consistent with Rhodes-Kropf et
al. (2005) and may provide some link between advertising, misvaluation, and the method of
payment used in mergers and acquisitions.
3.5 Economic incentive for advertising
If managers of stock based acquiring firms have an incentive to increase advertising
intensity in pre-merger announcement years then one would expect that advertising intensity in
that year is positively related to the economic benefits generated from increasing advertising.
Following Erickson and Wang (1999) I use two proxies for an economic benefit analysis: deal
ratio (DR) and managerial ownership (OWN) in the pre-merger year. Deal Ratio considers the
relative size of the transaction and managerial ownership represents the portion of outstanding
shares of the acquirer held by the top executives and directors of the firm. In my regression
analysis I use the following specification:
(3.3)
Deal ratio (DR) is defined as the ratio of deal value (price paid to the target for
acquisition) over the acquirer’s immediate past fiscal year end market value of equity.
Managerial ownership (OWN) is defined as the percentage of outstanding stock of the acquiring
51
Table 3.6 Misvaluation of acquiring firms
This table shows the univariate statistics of related misvaluation measures based on Rhodes-Kropf et al (2005) for stock- and cash-based acquiring firms. All values are at the immediate past fiscal year before the merger announcement. Panel A includes the samples of acquiring firms which completed only one merger during the sample period from 1990-2010. Panel B includes the samples of acquiring firms which completed one merger in three years (+1 to -2 years) surrounding a merger announcement. t/z Diff. column shows the mean and median test statistic between stock- and cash-based acquirers. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables Merger type definition 1
100% cash = Cash 100% stock = Stock
Merger type definition 2
Greater than 75% cash = Cash Greater than 75% stock = Stock
Mean / Median Mean / Median
Stock Cash t/z
Diff. Stock Cash
t/z Diff.
Panel A: Sample firms have only one completed merger during the sample period
Firm specific misvaluation 0.18 0.04 1.74 * 0.04 0.02 1.55 0.16 -0.04 2.24 ** 0.01 0.01 1.98 ** Sector specific misvaluation 0.27 0.23 1.25 0.16 0.07 1.06 0.24 0.19 1.00 0.15 -0.03 0.78 Total misvaluation 0.44 0.26 2.10 ** 0.26 0.22 1.55 (firm + sector) 0.42 0.21 2.30 ** 0.23 0.19 2.02 ** N 121 96 140 105
Panel B: Sample firms have only 1 completed merger in three years (+1 to -2 years) surrounding a merger
Firm specific misvaluation 0.25 0.10 3.13 *** 0.22 0.11 2.41 ** 0.19 0.04 3.85 *** 0.18 0.05 3.47 *** Sector specific misvaluation 0.26 0.22 1.76 * 0.25 0.22 1.44 0.25 0.21 1.30 0.24 0.22 1.24 Total misvaluation 0.51 0.32 3.49 *** 0.47 0.33 2.76 *** (firm + sector) 0.45 0.27 3.73 *** 0.43 0.29 3.37 *** N 261 194 302 211 firm owned by the top executives and directors. In similar spirit as Erickson and Wang (1999), I
argue that if the relative size of the target is small compared to the acquirer then managers of
acquiring firms may have little incentive to increase advertising because the gain from such
action is small. Relatively larger sizes of targets will induce the managers to inflate the stock
price through advertising because of the possibility of significant economic gains. Hence, in
equation (3.3) I hypothesize a positive coefficient on the deal ratio variable. Similarly, higher
managerial ownership may induce managers to increase advertising intensity due to relatively
52
large personal gain from doing so. I also hypothesize a positive coefficient for the managerial
ownership variable. However, it is important to note that in both cases increasing the stock price
through increased advertising does not conflict with the existing acquiring firm’s shareholders’
interests.
First, Table 3.7 reports the summary statistics for the deal ratio and managerial ownership
variables. I report the summary statistics for stock-based acquirers, using both merger type
definitions. The mean (median) deal ratio is around 52% (28%) of the acquiring firms and
insiders of acquiring firms (managers and directors) own on average 13% of the acquiring firms’
shares. However, there is considerable dispersion in these variables as the deal ratio ranges from
0.4% to 484% of the market value of the acquiring firm and insider ownership ranges from 0% to
99.9% of the outstanding shares of the acquirer.
Table 3.7 Summary statistics of economic incentive variables of stock based acquiring firms.
This Table shows summary statistics of economic incentive variables (Deal ratio, DR and Management ownership, OWN) of stock-based acquiring firms. Deal ratio is defined as a ratio of deal value (price paid to the target for acquisition, from SDC) over acquirer’s immediate past fiscal year-end market value of the equity. Managerial ownership (OWN) is defined as the percentage of outstanding stock of the acquiring firm owned by the top executives and directors. Ownership data is from proxy statements. All values are calculated using pre-merger announcement year data. Stock based acquisition is defined as merger type definition 1 and 2 as described in Table 1.
Merger type definition 1 100% stock = Stock
Merger type definition 2 Greater than 75% stock = Stock
Deal ratio (DR)
Management Ownership (OWN)
Deal ratio (DR)
Management Ownership (OWN)
Mean 0.525 0.128 0.521 0.126
Median 0.279 0.075 0.279 0.074
Standard deviation 1.543 0.156 1.480 0.156
Minimum 0.004 0.000 0.004 0.000
Maximum 4.84 0.999 4.84 0.999
N 241 242 265 266
53
Table 3.8 reports the regression results based on equation (3.3) for merger type 1 and 2.
In Panel A I report the results when I restrict the sample to those acquisitions where stock is used
as method of payment and in Panel B I report the regression results when cash is used as method
of payment. In Panel A, model (1) shows the regression results for only the variables of interest
and model (2) shows the results for the full model where all product market variables are added.
For merger type 1, the deal ratio coefficient is not significant but the regression coefficient on the
managerial ownership variable is positive and significant at the 5% and 10% level for model (1)
and model (2), respectively. Similarly, for merger type 2, the deal ratio has an insignificant
coefficient but managerial ownership has a regression coefficient that is positive and significant
at 5% level for both models. Hence, it seems that higher insider ownership induces acquiring
firms to increase advertising intensity in pre-merger announcement period. The relative size of
the acquisition has no significant impact on the acquirer’s pre-merger advertising intensity. Also,
when I run these regressions for the sample of cash-based acquirers (Panel B) I find that both the
deal ratio and ownership variable do not load significantly (the only exception being deal ratio,
which has a significantly positive coefficient for merger type 2).
54
Table 3.8 Economic incentive analyses of advertising by acquiring firms
This Table shows the regression results of economic incentive analysis of advertisement of acquirers before acquisitions. Deal ratio, DR is defined as a ratio of deal value (price paid to the target for acquisition) over acquirer’s immediate past fiscal year end market value of the equity. Managerial ownership, OWN is defined as the percentage of outstanding stock of the acquiring firm owned by the top executives and directors. The product market variables 1/SLS, where SLS is the sales revenue in the current year (Compustat mnemonic: SALE), LOGSLS, the change in the log of sales revenue from past year to the current year, Lag_FSLS is the fraction of sales revenue to the industry sales revenue of prior year where industry is based on 3 digit SIC code, LagAdvint is the past year advertising intensity where advertising intensity is defined as advertising expenditure in a fiscal year scaled by total sales of that year. Stock based acquisition is defined as merger type definition 1 and 2 as described in Table 1. Panel A and B show the results where the acquisition is stock and cash based respectively. ***, **, * represents significance level at the 1%, 5%, and 10% level, respectively.
Panel A: The method of payment is stock
Dependent Variable: Advertising intensity
Merger type definition 1 100% cash = Cash
100% stock = Stock
Merger type definition 2 Greater than 75% cash = Cash
Greater than 75% stock = Stock Model 1 Model 2 Model 1 Model 2
Intercept 0.0182 0.0121 *** 0.0192 * 0.0120 **
DR -0.0035 -0.0023 -0.0037 -0.0024
OWN 0.0012 ** 0.0004 * 0.0012 ** 0.0005 **
1/SLS -0.1028 -0.1056
LOGSLS 0.0176 0.0176
Lag_FSLS 0.0838 * 0.0975 **
Lag_Advint 0.1545 *** 0.1723 ***
Adjusted R2 0.01 0.17 0.01 0.21
Panel B: The method of payment is cash
Intercept 0.0232 0.0020 * 0.0197 *** 0.0014
DR -0.0106 -0.0014 -0.0004 0.0012 **
OWN -0.0001 <-0.0001 0.0001 <-0.0001
1/SLS -0.0192 -0.0234
LOGSLS -0.0084 *** -0.0083 ***
Lag_FSLS (by 3 digit SIC) 0.0030 0.0034
Lag_Advint 0.9586 *** 0.9518 ***
Adjusted R2 0.02 0.94 <0.01 0.96
55
3.6 Conclusions
Stock-based acquisitions are more common than any other forms of payment in (large)
acquisitions. When the acquirer pays the target shareholders with its own shares, a relatively
higher share price of the acquirer may provide economic gain for the acquirer shareholders.
Using a sample of US mergers for the period of 1990 to 2010 I show that stock-based acquirers
advertise more in pre-merger period to inflate the stock price. I do not find that cash-based
acquirers have relatively higher advertising intensity prior to a merger. This may not be
surprising, given that previous literature has found that advertising is related to higher stock price
(and better performance). However, to my knowledge I am the first to document the link
between advertising prior to a merger and the method of payment used in that merger. Finally, I
find that higher managerial ownership in acquiring firms leads to higher advertising intensity in
the pre-merger year but that the relative size of the target has no significant relationship with
such strategic actions. Finally, this paper examines whether stock based acquirers increase their
advertising intensity in pre-merger period to gain economic benefit. It provides evidence
consistent with this hypothesis. It also provides some evidence of the existence of an economic
incentive for such strategic actions by the stock-based acquirers. But this paper does not discuss
through which mechanism the advertising affects stock price. Does advertising signal the higher
quality of the product and higher quality of the firm or do increases in advertising intensity create
short term misvaluation of the acquirer’s stock price? Although we show that stock based
acquirers tend to be overvalued (Table 3.6), future research may address these questions in more
detail.
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Chapter 4
Essay 3 - An empirical assessment of earnings based valuation models in detecting equity mispricing and growth options
4.1 Introduction
Share price misvaluation is a central issue in the finance and accounting literature. To
quantify misvaluation one needs to establish a benchmark (or intrinsic value) to which one can
compare the market price and determine whether the stock is overvalued or undervalued.
Calculating this benchmark is a challenge for empirical researchers because it is not apparent on
what factors the ‘fair’ or ‘intrinsic’ value of the stock depends. A common approach is to use
earnings based valuation models to determine this benchmark. Earnings based valuation models
depend on accounting variables (e.g., earnings, dividends) to estimate the value of the firm.
Although the neoclassical approach states that the value of the firm equals the present value of
expected dividends, Ohlson shows in a series of papers (Ohlson [1995], Feltham and Ohlson
[1995], Ohlson [2001]) that a simple clean surplus assumption (i.e., the change in book value is
equal to earnings minus dividends) modifies this model to a residual income based valuation
model (RIM). Residual income which is also referred to as abnormal earnings is equal to
earnings minus a charge for the use of capital. In a spirit similar to Miller and Modigliani (1961)
which concludes that firm value is independent of dividend policy, Ohlson (1995) argues that
dividends reduce the book value but do not affect the expected earnings sequence. The empirical
implementation of this model requires finite horizon estimation and terminal value calculation.
Residual incomes carry less weight than dividends in the terminal value calculation (D’Mello
and Shroff (2000)). Consequently residual earnings based valuation models provide lower mean
57
valuation errors (Bernard (1995) and Penman and Sougiannis (1998)) and better fair value
estimation than dividend discount models (DDM) or a discounted cash flow approach (DCF).
Empirical implementation of RIM is not without problems. Under the perfect foresight
assumption, the model uses ex-post realized earnings as proxies for expected earnings which
may lead to endogeneity (Elliott, Koeter-Kant, and Warr (2007)). An alternative approach is to
use analysts’ forecasted earnings as a proxy for future earnings. But, this approach makes it
difficult to separate the mispricing and growth components (Ritter and Warr (2002)). RIM is
used in a number of different corporate event studies to detect misvaluation. For example,
mergers and acquisitions (e.g. Ang and Chen (2004), Dong, Hirshleifer, Richardson, and Teoh
(2006)), share repurchases (D’Mello and Shroff (2000)), funding financing deficits (Elliott,
Koeter-Kant, and Warr (2007)), and leverage adjustments (Elliott, Koeter-Kant, Oztekin, and
Warr (2011)).
Rhodes-Kropf, Robinson, and Viswanathan (RKRV, hereafter) (2005) propose another
method to calculate the intrinsic value. Although their method originates from the same
neoclassical approach as RIM, instead of estimating an appropriate discount factor, it assumes
that market equity maintains a constant relationship with book equity, net income, and leverage.
Estimating separate regression equations for each industry and year, they then calculate
fundamental and long run value of the firm. Although the RKRV decomposition and valuation
method is originally developed to measure misvaluation surrounding merger activities, their
method is used in other financial studies as well. For example, Hertzel and Li (2010) study
misvaluation surrounding SEOs and Hoberg and Phillips (2010) use it for industry wide
valuation.
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Although the RIM and RKRV methods originated from the same basic valuation
principle and both attempt to calculate ‘fair’ value of the firm and hence attempt to detect share
price misvaluation across different corporate events, the main underlying assumption for each
model is different. RKRV estimate fundamental value of the firm using historical time-series
average of firm specific and industry specific book equity, net income and leverage whereas
RIM estimates fundamental values using forward looking estimates of earnings.
I first compare if there exist any differences in the samples for which I am able to
calculate each measure and find that valuation measures for substantially more firm-years when I
use the RKRV method vis-à-vis the RIM method. I also find that the RKRV firms tend to be
smaller. Interestingly, I also find that the market to book ratio is larger for RKRV firms. I then
investigate whether there are differences between the two measures around events where extant
literature suggests an important role for misvaluation (i.e., mergers and acquisitions, open market
share repurchases, stock splits, and seasoned equity offerings (SEOs)). Finally, I find that RIM
and RKRV disagree in 30% to 40% of all cases whether a firm is over- or undervalued. This
finding holds for the whole sample as well as surrounding all the events I investigate. My
findings also suggest that RKRV performs better at finding over- or undervaluation in SEOs and
share repurchase events where theory predicts that misvaluation is a prelude to these events.
In the next section I will describe the RIM and RKRV models in more detail, I follow this
with a short literature review of the events that I study in relation to misvaluation. In section four
I describe the data. In section five I present my results of my comparison of the two methods.
Finally, in section six I conclude.
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4.2 The models
In this section I will briefly discuss the RKRV (2005) and Residual Income Model
(RIM).
4.2.1 Residual Income Model (RIM)
RIM is derived from the basic valuation principle that the value of a firm equals the
present value of expected dividends. Using three simple assumptions, Ohlson (1995) shows that
the dividend based valuation model can be transformed into an accounting/information based
valuation model. The first assumption is that the market value of a firm is the sum of the present
value of expected dividends (PVED):
∑ (4.1)
Here, MVt is the market value of the firm’s equity at time t, dt is the net dividend paid at
time t, r is the discount rate (assumed to be equal to the risk free rate), and Et[.] is the expected
value operator conditioned on the information available at date t.
The second assumption is that, accounting data and dividends follow a clean surplus
relationship (CSR) i.e., the change in the book value is equal to earnings minus dividends:
(4.2)
Here BVt is the book value at time t and Xt is the earnings for the period t. Peasnell
(1982), Ohlson (1995), and Feltham and Ohlson (1995) show that the CSR assumption makes the
RIM model theoretically equivalent to the PVED and the discounted cash flow (DCF) model. It
is important to note that dividends reduce current book value; they do not reduce current
earnings but reduce the subsequent period’s expected earnings. If one replaces dividends from
equation (4.1) with equation (4.2), and after some simple algebraic manipulations equation (4.1)
can be rewritten as follows:
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∑ . (4.3)
The numerator of the second term in eq. (4.3) is current earnings minus risk free rate (r)
times the beginning book value (i.e., earnings minus the cost of capital assuming the risk free
rate is the cost of capital). This is labeled as ‘residual income’ or ‘abnormal earnings’ and is
defined as
.
So equation (4.3) can be rewritten as
∑ (4.4)
The second component of equation (4.4) represents goodwill which is a function of
abnormal earnings. It is assumed that the residual income has a linear relationship with past
period residual income and a scalar variable that represents ‘other information’ that is
independent of residual income. Mathematically,
,
Where, t is the information content other than abnormal earnings.
The final assumption is about the time-series behavior of residual income. The model
assumes that residual income satisfies an autoregressive process. This restriction eliminates the
need for ‘other information’ content of the valuation equation (4.4) and states that current
abnormal earnings determine the goodwill.
Equation (4.1) assumes the risk free rate as the discount factor. Ohlson (1995) suggests
that one can incorporate risk by replacing risk free rate with a risk adjusted discount factor. For
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example, one could use the firms’ cost of equity or the expected rate of return using CAPM.
Alternatively, a Fama-French (1993) three factor model could be used.
Bernard (1995) and Penman and Sougiannis (1998) state that the empirical
implementation of equation (4.4) requires a finite horizon assumption. They also mention that
although dividend discount model (DDM), discounted cash flow approach (DCF), and RIM
require finite horizon implementation. RIM is less affected by this assumption. DDM involves
forecasting an uncertain stream of finite dividends, DCF involves estimation of liquidating cash
flow or terminal value, and RIM involves calculation of abnormal earnings after the finite time
horizon. Abnormal earnings estimation after the finite time horizon represents a relatively
smaller number than a DDM or DCF model. Bernard (1995), and Penman and Sougiannis (1998)
also find that residual income model dominates a DCF approach in terms of lower mean
valuation error. D’Mello and Shroff (2000) find that that the terminal values using RIM, DCF,
and DDM models are 11%, 33% and 58% of market value respectively for their repurchasing
firm samples.
Following D’Mello and Shroff (2000) empirical implementation of RIM model is as
follows:
∑ . (4.5)
Where T equals 2 years (fiscal) and TV is the terminal value calculated as
∗ ∗ (4.6)
D’Mello and Shroff (2000) consider the last two years residual incomes as a perpetuity
for their TV calculation and they use the average of these residual incomes to reduce the effect of
any extreme values. Following Bernard (1995) and Penman and Sougiannis (1998), D’Mello and
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Shroff (2000) restricts TV to be non-negative. They argue that this restriction results in a greater
TV and hence biases against detecting undervaluation for their share repurchase sample. Ohlson
(1995) mentions that although the goodwill (second term in equation [4.4]) exhibits positive
serial correlation, over the long run it is mean reverting. Hence, considering the average residual
income as a perpetuity this model then assumes mean reversion of the residual income over time.
Because managers do not observe the earnings ex-ante, the earnings used in equation (4.5) can
be measured using a perfect foresight assumption (D’Mello and Shroff (2000)) i.e., ex-post
realized earnings as proxies for managers’ expected earnings. Perfect foresight may have an
endogeneity issue which may bias valuation calculations (Elliott et al (2007)). One possible
solution is to use analysts’ forecasted earnings instead of realized earnings.
D’Mello and Shroff (2000) suggest three different ways to calculate cost of equity (r) of
equation (4.5); (a) A constant discount rate (e.g., Dechow, Hutton and Sloan (1999) suggests
12% which is approximately the long-run average return on US equities), (b) Firm specific r
using Capital Asset Pricing Model and, (c) Fama and French (1993) three factor model. When
using CAPM or the three factor model, short term T-Bill rate can be used as proxy for risk free
rate. Book value of equity (Compustat mnemonic: CEQ) and Income before extraordinary items
(Compustat mnemonic: IB) are used for BVt and Xt respectively in equation (4.5).
4.2.2 Rhodes-Kropf et al. (2005) valuation model and M/B decomposition
Rhodes-Kropf et al. (2005) decomposes market to book ratios into various components
which represent mispricing and growth options. Although RKRV (2005) used their approach in a
merger setting, it has also been used to address various other valuation settings (e.g., Hertzel and
Li (2010) study SEOs and Hoberg and Phillips (2010) for industry wide valuation). RKRV
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(2005) decompose the market to book ratio into two components as represented by equation
(4.7):
M/B = M/V * V/B (4.7)
Here M/B represents the market to book ratio, M/V represents misvaluation, and V/B
represents the growth options component. This can be re-written in log form as follows:
m – b = (m – v) + (v - b) (4.8)
where the lower case letters denote the log form of the corresponding upper case
variables. If the market perfectly anticipates future growth rates, discount rates, and cash flows,
there would be no place for pricing errors and (m – v) would always be zero. Hence, the term (v
– b) would equal to (m-b). However, if the market does make mistakes in anticipating these
factors then the price-to-true value (m – v) captures the misvaluation component of (m-b).
RKRV (2005) further attributes misvaluation not only to a firm specific component, but also to a
sector specific component. Hence, RKRV (2005) decomposes the (m-b) into three separate
components: a firm specific misvaluation component, a sector specific component, and a
difference between valuation based on long-run value and book value (labeled long-run value to
book / growth option component). Their decomposition equation is as follows:
; ; ; ; (4.9)
They estimate these components by expressing v as a linear function of firm specific
accounting information at a point in time it and conditional accounting multiples jt , where
i,j, and t represent firm, industry and fiscal year respectively. m ‐v θ ; α represents the firm
specific deviation, ; ; represents the sector specific deviation and finally
; represents the growth options component.
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In equation (4.9), there are two value components that require estimation; fundamental
value components ; and long run value component ; . To estimate these two
values RKRV starts with the fundamental valuation concept where firm value is the present
value of expected free cash flows. Considering the present value of free cash flows as the value
of assets in place plus economic value added the valuation equation transforms into a similar
equation as RIM, which is as follows:
∑ (4.10)
Where Mt is the market value at time t, Bt is the book value at time t, RI is the residual
income and r is the discount factor. By considering the RI as a difference between Return on
Equity (ROE) and the charge for using capital the equation (4.10) can be written as:
∑ (4.11)
RKRV (2005) argue that the perpetuity calculation of residual income i.e. the second
term of equation (4.11) is based on some assumptions which makes it difficult to identify
misvaluation and distinguish between misvaluation and growth options components. To avoid
these issues, they took a different approach to calculate the fundamental values. They propose
three different models to calculate fundamental and long-run value. Model 1 relates market
equity to book equity only. It assumes that expected future ROE is a constant multiple of
expected future discount rates and book equity grows at a constant rate over time. The equation
for model 1 is as follows:
(4.12)
Model 2 adds another variable, net income (NI) with book equity and assumes both book
equity and Net income grow at a constant rate. The model 2 regression equation is as follows:
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ln ln (4.13)
Here NI+ is absolute value of net income and I is an indicator variable for negative net
income observations. The third model adds leverage with book equity and net income. The
equation for model 3 is as follows:
ln ln (4.14)
Fundamental value is calculated using time-Series average conditional regression
coefficients and long run value is calculated using the industry average of time series regression
coefficients. The value components are calculated as follows:
Fundamental value ),( jtitv ; , ln
Long run value ),( jitv , , ln
where 1/ ∑
Here ACC represents the accounting variables book equity, net income and leverage.
RKRV argue that estimating separate equation for each industry (by Fama French 12 industry
classification) and year incorporates the time varying risk premia and expected growth
opportunities into the models. The firm specific misvaluation component, ;
captures the deviation from fundamental value, sector specific component, ; ;
captures the time series deviation from long run value and final component, ;
captures deviation from book value. In most of my analyses I focus on the firm specific error and
label it as mispricing. The third component captures the growth options of the firm and I refer to
it as growth options interchangeably with long run value to book.
I implement their methodology in the following way. First, using firm-year observations
from CRSP and Compustat database for available fiscal years I estimate the parameters for all
66
three models (similar to table 4 in RKRV (2005)). I require that the necessary variables are
present in CRSP and Compustat databases to estimate the components of market-to-book. In
addition, Market equity (defined as share price (CRSP mnemonic: PRC) x number of shares
outstanding (CRSP mnemonic: SHROUT) needs to be at least 10 million dollars, book value of
equity (Compustat mnemonic: CEQ) needs to be positive, and the market to book ratio cannot be
more than 100.
4.3 Events
I identify four corporate events where the existing literature suggests that there is an
important role for misvaluation. These are mergers and acquisitions, share repurchases, SEO’s,
and stock splits.
4.3.1 Mergers and Acquisitions
Shleifer and Vishny (2003) and Rhodes-Kropf and Viswanathan (2004) provide
theoretical models in which merger activities are driven by misvaluation. Shleifer and Vishny
(2003) argue that share price misvaluation determines the role of the firm as potential acquirer or
target and the medium of payment (cash or stock) of the acquisition. They propose that
overvalued firms are more likely to be an acquirer and undervalued or less overvalued firms will
more likely be targets. Rhodes-Kropf and Viswanathan (2004) provides a similar argument and
suggest that overvaluation triggers merger waves even if there is no underlying economic reason
for mergers. RKRV (2005) provides empirical evidence for this proposition. Dong et al. (2006)
compares Q theories of takeover (growth opportunities drive merger activities) and misvaluation
hypotheses and finds support for both. They also find that highly overvalued bidders are more
likely to use stock than cash as medium of payment and willing to pay larger merger premium. In
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short, these theories suggest that firms that undertake mergers or acquisitions tend to be
overvalued, especially when they use stock as a method of payment.
4.3.2 Share repurchases
The undervaluation hypothesis of share repurchase states that repurchase announcements
reduce the information asymmetry between insiders and outside shareholders. The positive
reaction after repurchase announcement is a way to correct that misvaluation (Vermaelen (1981),
and Comment and Jarrell (1991)). But the increase in price after repurchase announcement may
not be sufficient to correct the undervaluation and consequently these firms earn abnormal
returns in the long run (Ikenberry, Lakonishok, and Vermaelen (1995)). Dittmar (2000) also
states that undervaluation may be a reason for share repurchases. Ikenberry and Vermaelen
(1996) propose an exchange option model for open market share repurchases. They argue that
repurchase programs are not commitments and hence when managers find their stocks
undervalued, they tend to buy shares and otherwise forgo the repurchase. Deviation from ‘true
value’ motivates managers to maximize wealth of the long term shareholders. Isagawa (2002)
proposes a theoretical model assuming market inefficiency. He argues that although open market
repurchase announcements provide a signal to the market about the undervaluation of the stock,
the market underreacts, and, hence managers buy back outstanding shares until the misperception
disappears. D’Mello and Shroff (2000) investigate repurchase via tender offers and find that a
greater number of repurchasing firms are undervalued. Moreover undervaluation and tender
premium are strongly positively correlated. Hence, the overall tenet of this literature is that
particularly undervalued firms would tend to repurchase shares.
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4.3.3 Financing activities
Hertzel and Li (2010) decompose market-to-book ratios into misvaluation and growth
option components in the pre-seasoned equity offering (SEO) period and finds that issuers have
higher misvaluation and growth opportunities compared to non-issuers. Moreover they find that
higher growth issuers do not experience post-issue underperformance but more highly mispriced
firms observe lower returns in the post-issue period which is consistent with the behavioral
explanation of post-issue under-performance. Elliott, Koeter-Kant, Oztekin, and Warr (2011)
examine the rate of adjustment toward target debt ratios and finds that when firms are above the
target debt level, overvalued firms adjust more rapidly toward the target debt ratio than
undervalued firms. But when they are below the target debt level, overvalued firms adjust more
slowly than the undervalued ones. Elliot et al. (2007) examine market timing, mispricing, and
capital structure. They find that overvalued firms finance their deficit with equity more often
than undervalued firms. I compare RIM and RKRV prior to SEO’s and expect that overvalued
firms tend to take additional equity through an offering.
4.3.4 Stock splits
A number of studies (e.g., Brennan and Copeland (1988) and Brennan and Hughes
(1991)) suggest that one possible explanation for stock splits is the signaling role of this event.
However, the split announcement may only be an initial step in the signaling process. The
announcement is likely to attract investors’ attention to the firm. If the increased attention and
closer examination reveal misvaluation (i.e., undervaluation) then the market price of the stock
should adjust toward a more precise estimate of the firms’ fundamental value.
The market may perceive split announcements to be favorable in cases where the firm’s
equity is undervalued. Either irrational investor behavior or asymmetric information can cause
69
this under-valuation. In either case, the split announcement serves as a signal to the market to
reevaluate the position of the firm.
4.4 Sample
I calculate RIM and RKRV (2005) misvaluation components using CRSP and Compustat
data from 1971 to 2005. RIM requires five consecutive years of accounting data to calculate the
fundamental value of the firm. Although RKRV (2005) is not limited by this restriction, for
comparability between these two methods I restrict my sample to 2005. I require that necessary
variables for RIM and RKRV calculation are available in CRSP and Compustat.
For RIM I calculate intrinsic value and misvaluation following D’Mello and Shroff
(2000). I estimate industry cost of equity using Fama and French’s (1997) three factor model
where three factors and risk free rate are from Kenneth French’s website13 and return data is
from CRSP (see D’Mello and Shroff (2000) for a detailed discussion of this method). I use Fama
and French’s 48 industry classification to calculate annual industry cost of equity. I consider
only publicly traded US firms’ ordinary common shares (CRSP share class code of 10 and 11)
where book value of equity (Compustat mnemonic: CEQ) is positive. I exclude foreign
incorporated firms and firms with missing book value of equity and income before extraordinary
item (Compustat mnemonic: IB). My final RIM sample includes intrinsic values of 106,569
firm-year observations.
I consider 35 years data (1971-2005) for calculating RKRV (2005) market-to-book
decomposition components. Similar to RIM, I consider only publicly traded US firms’ ordinary
common shares (CRSP share class code of 10 and 11) and exclude foreign incorporated firms.
13 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
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RKRV decompose market-to-book ratios by relating market equity with book equity, net income
and market leverage. Market equity is calculated by multiplying share price (CRSP mnemonic:
PRC) with number of shares outstanding from CRSP (CRSP mnemonic: SHROUT). Market
leverage is calculated as [1- (market equity/market value of total assets)] where market value of
total assets = Market equity + Total assets (Compustat mnemonic: AT) – Book equity
(Compustat mnemonic: CEQ) – Deferred tax (Compustat mnemonic: TXDB). I exclude any
observation with negative book value of equity from my calculation. My final RKRV sample
includes 127,551 firm-year observations where all three components of market-to-book
decomposition are available. The RIM model has 20% less observations than the number of
firm-years generated by the RKRV model. RIM’s five consecutive years of data requirement
probably explains this difference. When one requires that both RKRV and RIM components are
available for sample firm-year observations the sample size reduces to 92,203 firm-year
observations.
Table 4.1 shows the yearly and Fama-French 48 industry distribution of the RKRV
(2005) and RIM sample. Panel A shows that sample firms are more or less evenly distributed
across years Although, both RIM and RKRV show that there are fewer observations during the
first few years. Both seem to exhibit the same time trends. Panel B show that the three most
frequently represented industries are Banking, Business Services, and Electronic Equipment for
both the RKRV and the RIM sample. Banking contains 10.5% and 9.9% of the observations,
Business Services contains 8.7% and 8% of the observations, and electronic equipment has 5.5%
and 5.7% of firm-year observations for RKRV and RIM, respectively.
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Table 4.1 Yearly and Industry distribution of RKRV (2005) and RIM sample.
Samples are publicly traded US firm for the period of 1971-2005. Relevant data for calculating Residual Income Model (RIM) and Rhodes-Kropf et al, 2005 (RKRV) components need to be available in both Center for Research in Security Prices (CRSP) and Compustat databases. Panel A shows the fiscal year distribution of the sample whereas panel B shows the industry distribution. Industry is classified using Fama-French 48 industry classification. Panel A: Yearly distribution of the sample
Fiscal Year RKRV RIM N % N %
1971 1666 1.3 2489 2.31972 2491 2.0 2649 2.51973 2888 2.3 2637 2.51974 3064 2.4 3122 2.91975 3037 2.4 3030 2.81976 3094 2.4 2932 2.81977 3074 2.4 2835 2.71978 3056 2.4 2727 2.61979 3007 2.4 2708 2.51980 3038 2.4 2682 2.51981 3135 2.5 2602 2.41982 3163 2.5 2629 2.51983 3355 2.6 2684 2.51984 3456 2.7 2655 2.51985 3364 2.6 2715 2.51986 3472 2.7 2806 2.61987 3608 2.8 2867 2.71988 3572 2.8 2924 2.71989 3457 2.7 2976 2.81990 3434 2.7 3032 2.81991 3536 2.8 3117 2.91992 3688 2.9 3235 3.01993 4537 3.6 3600 3.41994 4747 3.7 3561 3.31995 4830 3.8 3625 3.41996 5056 4.0 3533 3.31997 4975 3.9 3330 3.11998 4758 3.7 3429 3.21999 4675 3.7 3548 3.32000 4506 3.5 3506 3.32001 4122 3.2 3413 3.22002 3980 3.1 3324 3.12003 3900 3.1 3225 3.02004 3921 3.1 3191 3.02005 3889 3.0 3231 3.0
Total 127,551 100 106,569 100
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Panel B: Fama-French 48 industry distribution of the sample
Fama French 48 industry RKRV RIM
Fama French 48 industry RKRV RIM
N % N % N % N %
Agriculture 379 0.3 329 0.3 Shipbuilding, Railroad Equipment 274 0.2 210 0.2Food Products 2599 2.0 2185 2.1 Defense 269 0.2 206 0.2Candy & Soda 251 0.2 199 0.2 Precious Metals 325 0.3 244 0.2Beer & Liquor 469 0.4 404 0.4 Metallic and Industrial Metal Mining 432 0.3 380 0.4Tobacco Products 153 0.1 103 0.1 Coal 206 0.2 138 0.1Recreation 981 0.8 718 0.7 Petroleum and Natural Gas 5470 4.3 4577 4.3Entertainment 1742 1.4 1373 1.3 Utilities 5147 4.0 5065 4.8Printing and Publishing 1218 1.0 1069 1.0 Communication 2641 2.1 2219 2.1Consumer Goods 2418 1.9 2050 1.9 Personal Services 1286 1.0 1100 1.0Apparel 2005 1.6 1638 1.5 Business Services 11105 8.7 8531 8.0Healthcare 1939 1.5 1575 1.5 Computers 4559 3.6 3461 3.2Medical Equipment 3820 3.0 3032 2.8 Electronic Equipment 6938 5.5 6053 5.7Pharmaceutical Products 4645 3.7 3919 3.7 Measuring and Control Equipment 3354 2.6 2976 2.8Chemicals 2438 1.9 2196 2.1 Business Supplies 2047 1.6 1846 1.7Rubber and Plastic Products 1564 1.2 1242 1.2 Shipping Containers 519 0.4 424 0.4Textiles 1050 0.8 832 0.8 Transportation 2802 2.2 2490 2.3Construction Materials 3392 2.7 2916 2.7 Wholesale 4800 3.8 3982 3.7Construction 1450 1.1 1348 1.3 Retail 6337 5.0 5367 5.0Steel Works 1911 1.5 1645 1.5 Restaurants, Hotels, Motels 2339 1.8 1981 1.9Fabricated Products 537 0.4 426 0.4 Banking 13414 10.5 10519 9.9Machinery 4881 3.8 4223 4.0 Insurance 3990 3.1 3372 3.2Electrical Equipment 2123 1.7 1975 1.9 Real Estate 944 0.7 770 0.7Automobiles and Trucks 1889 1.5 1658 1.6 Trading 2129 1.7 1953 1.8Aircraft 854 0.7 753 0.7 Almost Nothing 1181 0.9 897 0.8 Total 127216 100 106569 100
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4.5 Comparing RKRV and RIM
Researchers use both RKRV and RIM models to detect firm specific misvaluation. As
mentioned in the previous section, I am able to calculate under/over valuation for a substantially
larger number of firm years, using RKRV when compared with RIM. Table 4.2 shows that I am
able to calculate RKRV for over 125,000 firm years and for RIM this number is about 22,000
lower. When I compare the two samples, I find that RKRV firms are smaller in virtually all size
related measures. For example, the mean total assets for RKRV is $2,344mln. compared to
$2,496 mln. for RIM, and these are significantly different. Another notable observation is that
RKRV sample firms have higher market to book ratios and higher market leverage than RIM
sample firms. Finally, the cross-section of the both RIM and RKRV samples consists of over
90,000 firm-years. Hence, researchers who are choosing between these two methodologies need
to be aware that there are consequences in terms of sample characteristics and sample size.
In table 4.3 I calculate valuation components and growth options using both
methodologies and investigate whether they provide similar inferences. First, I calculate all
components using RKRV, and I find that firm specific misvaluation is 0 in the mean, sector
misspecification is 0.03, and growth options have a mean of 0.53. These numbers are generally
consistent with those reported by RKRV (2005). I define over (under) valued firms in the RKRV
sense as those firm years with firm specific misvaluation is higher (lower) than zero. I find that
49% of the firms-years are overvalued. I also investigate whether firms have growth options (i.e.,
the growth options component is larger than zero). I find 83% of the firm years where this is the
case.
For the firm years where I am able to calculate misvaluation using RIM I find the
ln(M/V) is -0.07 and the growth option components is 0.62. Again, these values are generally
74
Table 4.2 Summary statistics of relevant variables. The table provides mean, median and number of observations for key variables for all observations used to calculate RKRV and RIM market-to-book decomposition from Center for Research in Security Prices (CRSP) and Compustat. All values are based on fiscal year end values for the period 1971-2005 except share price and number of shares outstanding. Share price and number of shares outstanding are three month later values from Compustat fiscal year end month. Total assets (Compustat mnemonic: AT), total revenue (Compustat mnemonic: REVT), book value of equity (Compustat mnemonic: CEQ), income before extraordinary item (Compustat mnemonic: IB), deferred taxes (Compustat mnemonic: TXDB), net income (Compustat mnemonic: NI) are from Compustat annual file, share price (CRSP mnemonic: PRC), number of shares outstanding (CRSP mnemonic: SHROUT) are from CRSP monthly file. Return on assets (ROA) is calculated as (NI/AT), Market value of equity (MKTEQ) is calculated as (PRC*SHROUT), Market value of assets (MKTAT) is calculated as (MKTEQ + AT - CEQ – TXDB), market-to-book ratio (equity) is calculated as share price (PRC)/ book value per share (Compustat mnemonic: BKVLPS), Market-to-book (total assets) is calculated as (MKTAT / AT) and leverage (market) is calculated as [1-(MKTEQ/ MKTAT). Difference columns shows the t and Wilcoxon z statistics and ***, **, * represent significance at the 1%, 5%, 10% level, respectively.
Variables RKRV (1)
RIM (2)
Both RKRV and RIM
Difference (RKRV-RIM) (1-2)
Mean Median N Mean Median N Mean Median N t Z Total assets (book value) 2,344.2 129.8 127,551 2,496.2 134.7 106,567 2,740.9 155.4 92,203 -1.74 -1.24Total revenue 1,034.2 104.8 127,107 1,150.6 110.9 106,246 1,241.3 127.3 91,908 -5.22 *** -4.80 *** Book value of equity 468.7 52.4 127,551 518.3 54.8 106,569 564.4 64.6 92,203 -4.62 *** -3.24 *** Income before extraordinary item 57.3 3.9 127,551 65.4 4.5 106,569 72.8 5.3 92,203 -3.92 *** -16.44 *** Deferred taxes 66.2 0.4 112,917 73.0 0.4 95,133 80.6 0.6 81,817 -2.85 *** -9.36 *** Net income 56.1 3.8 127,551 63.7 4.5 106,565 70.9 5.3 92,203 -3.19 *** -16.64 ***
Share price 18.7 13.6 125,803 20.4 15.1 91,086 20.4 15.2 90,993-
11.78 *** -23.47 *** Number of shares outstanding 36,191.0 7,746.0 126,001 41,122.1 8,145.0 92,446 41,622.9 8,279.0 91,112 -5.42 *** -7.83 *** Return on assets 0.5% 3.7% 127,551 1.9% 4.2% 106,561 0.0 0.0 92,203 -6.62 *** -23.31 *** Market value of equity 1,232.1 84.7 125,803 1,494.4 104.4 91,086 1,500.6 105.1 90,993 -6.46 *** -20.64 *** Market value of assets 2,511.4 140.7 111,304 3,031.4 172.1 80,857 3,045.3 173.0 80,713 -4.63 *** -18.48 *** Market to book (equity) 1.8 1.2 111,304 1.8 1.2 80,857 1.8 1.2 80,713 2.284 ** 0.71 Market to book (total assets) 3.4 1.6 125,406 2.7 1.6 90,917 2.7 1.6 90,827 4.737 *** 1.24 Leverage (market) 0.4 0.4 111,304 0.4 0.4 80,857 0.4 0.4 80,713 4.30 *** 3.91 ***
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Table 4.3 Market-to-book components for all publicly traded US firms from Compustat.
This table shows the market to book decomposition based on RKRV (2005) and RIM method. For RKRV model, overvaluation dummy takes a value of 1 if firm specific misvaluation is positive and 0 otherwise, high growth dummy takes a value of 1 if growth option is positive and 0 otherwise. For RIM model, overvaluation dummy takes a value of 1 if misvaluation (log of market value/intrinsic value) is greater than 1 and 0 otherwise, high growth dummy takes a value of 1 if growth option (log of intrinsic value/ book value) is greater than 1 and 0 otherwise. First three columns shows mean, median and number of observations for RKRV model, next three columns shows the same for RIM and last three column shows the same for common samples where both RKRV and RIM values are available. Final two rows show the t value of mean difference between RKRV and RIM overvaluation and growth option dummies for samples where both RKRV and Rim data are available and. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1) RIM (2) Both RKRV and RIM Mean Median N Mean Median N Mean Median N
RKRV (2005) measures Firm specific misvaluation (a) 0.00 -0.02 127,551 0.00 -0.01 92,203 Sector specific misvaluation (b) 0.03 0.06 127,551 0.01 0.05 92,203 Total misvaluation (a + b) 0.03 -0.01 127,551 0.02 -0.02 92,203 Growth option 0.53 0.50 127,551 0.53 0.50 92,203
Overvaluation dummy 48.50% 127,551 48.88% 92,203 High growth dummy 82.85% 127,551 83.53% 92,203 RIM measures Intrinsic value of firm 988.24 98.71 106,569 1,078.92 111.91 92,203 Misvaluation (ln M/V) -0.07 -0.05 90,941 -0.06 -0.05 90,849 Growth option (ln V/B) 0.62 0.43 106,335 0.56 0.41 92,047
Overvaluation dummy 47.27% 91,086 47.37% 90,993 High growth dummy 92.19% 106,560 92.04% 92,203 Difference in dummies of RKRV and RIM common sample
Overvaluation t value 5.93 *** Growth option t value -59.16 ***
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consistent with earlier literature as well. Similar to RKRV I define RIM over (under) valuation as
having M/V higher (lower) than one and growth options based on V/B greater or smaller than
one. I find that 47% (92%) of the RIM sample is overvalued (has high growth options). Because
my goal is to compare the two methods I will focus most of my ensuing analyses on the third
sample (i.e., the sample for which I am able to calculate both RKRV and RIM). The mean
(median) firm-specific misvaluation calculated using RKRV is 0.00 (-0.01) whereas mean
(median) total misvaluation is 0.03 (-0.01). The mean (median) total misvaluation calculated for
RKRV model is 0.02 (-0.02). The mean (median) misvaluation component calculated using RIM
is -0.06 (-0.05). So misvaluation numbers are slightly higher in RKRV model than RIM model.
If I compare the growth option components between these two models, I find that the median
growth option is higher for the RKRV model but mean growth option is higher for the RIM
model (0.56 of RIM compared to 0.53 of RKRV).
According to the RKRV sample, 48.9% of the observations are overvalued whereas this
number is 47.4% for the RIM sample. This difference is statistically significant at 1% level. For
growth options, 83.5% of the RKRV sample has high growth compared to 92.0% of the RIM
sample. These differences are also statistically significant at 1% level. From these results I can
conclude that the RKRV method has a slightly higher rate of ‘labeling’ overvaluation than the
RIM model but the RIM model has a higher tendency of ‘labeling’ higher growth option.
Although there is a statistically significant difference between RKRV and RIM in
identifying overvaluation (undervaluation), an important artifact is hidden. In the next analysis, I
examine whether these two methods conclude the same (under or overvaluation, high or low
growth options) for a given firm-year observation.
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Table 4.4 Two by two matrix showing combinations of misvaluation and growth options dummies.
This table compares the consistency of RKRV and RIM models in identifying misvaluation and growth options. Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy for both methods. Each cell of the matrix represents a combination of the number (percentage) of observations labeled as over (under) valuation (in panel A) and high (low) growth options (in panel B) under both methods. In panel A, over (under) valuation-over (under)valuation combination represents consistency of both methods in identifying misvaluation whereas over (under) valuation – under (over) valuation cells represents contradictory conclusion of the methods in identifying misvaluation. In panel B, high (low) growth -high (low) growth combination represents consistency of both methods in identifying growth options whereas high (low) growth – low (high) growth cells represents contradictory conclusion of the methods in identifying growth options.
Panel A: Misvaluation matrix. Missing misvaluation dummies = 1210 (1.3%)
RIM
Overvaluation Undervaluation
RKRV
Overvaluation 26371 (28.6%) 17835 (19.3%)
Undervaluation 16733 (18.1%) 30054 (32.6%)
Panel B: Growth option matrix.
RIM
High growth Low growth
RKRV
High growth 71,800 (77.9%) 5216 (5.7 %)
Low growth 13068 (14.2 %) 2119 (2.3%)
To examine this issue I use my common sample where both RKRV and RIM data are
available. I use a 2x2 matrix where in one axis I list the RKRV misvaluation (growth options)
dummy counts and in another axis I list the same for RIM misvaluation dummy counts (growth
options). Table 4.4 panel A shows the misvaluation matrix. For example first left cell of the
matrix shows in how many cases both RKRV and RIM agree that the firm year observation is
overvalued. On the other hand, the RIM overvaluation and RKRV undervaluation cell lists the
number of firm-year observations RIM considered overvalued whereas RKRV considered
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undervalued. I find that both RIM and RKRV agree on misvaluation 61.2% (28.6% + 32.6%)
whereas 37.4% of the time these two methods disagree whether the firm if over or undervalued
in that year. For growth options, reported in panel B, these two methods seem to agree
substantially more often. In 80.2% of all firm-years RKRV and RIM reach the same conclusion.
In brief, these findings suggest that there is substantial disagreement between the two methods
when it comes to valuation, but there seems to be a considerate amount of agreement when it
comes to the growth options potential of the firms.
In the next part of the paper, I will perform a similar analysis to investigate whether the
two methods reach similar conclusions when misvaluation is deemed to be even more important
(i.e., mergers and acquisitions, share repurchase, seasoned equity offerings (SEOs) and stock
splits).
4.5.1 Mergers and acquisitions
In my merger and acquisition analysis, I consider 27 years (1979-2005). Mergers and
acquisition data from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and
Acquisition database. I consider merger data where both acquirers and targets are US firms. I
confine my analysis to acquirer’s only because RIM requires five consecutive post-merger year
data which is not available for target firms. I consider completed deals where the value of the
transaction is greater than $1 million, only. I classify deals based on the method of payments. If
the acquirer pays 100% with cash I classify the merger as a ‘cash’ merger and 100% with stock
is classified as a ‘stock’ merger. For my analysis I consider only ‘stock’ and ‘cash’ based
acquisitions. My final sample contains of 3,978 mergers where 1,529 are cash-based acquisitions
and 2,449 are stock-based acquisitions. When I require that RKRV decomposition components
are available, my sample size reduces to 1,735 deals out of which 590 deals are cash-based and
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1,145 are stock-based. For RIM I have a smaller sample of 1,374 total deals where 492 deals are
cash-based and 882 deals are stock based acquisitions. When I impose both RKRV and RIM data
availability requirement, the sample size further reduces to 1,349 total deals where 483 deals are
cash-based and 866 deals are stock based acquisitions.
Table 4.5 shows the summary statistics of some key variables for my sample (irrespective
of the method of payment). There is no significant difference in mean in any of the variables
although the median return on assets, market equity, and market value of assets are higher for the
RIM sample. There are a few more statistical differences in medians between the samples, but
they are only significant at marginal levels.
In Table 4.6 I show misvaluation and growth options components in a manner similar to
Table 4.3. The misvaluation numbers are higher for RIM than for RKRV (0.33 versus 0.22).
When I consider the common sample where both RKRV and RIM data are available it appears
that both methods identify misvaluation fairly evenly. RKRV identifies 70.50% of the M&A
sample as overvalued whereas RIM considers 70.54% of as overvalued. Although RIM has a
slightly higher tendency of classifying firms with high growth options, the difference is only
2.15% (93.92% - 91.77%). To examine whether these two methods identify the same firm-year
observations as over (or under) valued or with high (or low) growth options, I use the same 2X2
matrix as in Table 4.4. In Panel A of Table 4.7 I report that 68.2% of the time both methods
conclude the same about misvaluation but 31.2% (15.6% + 15.6%) of the time they disagree. In
case of growth options (Panel B) I find that in the vast majority (86.2% + 0.5%) both methods
agree.
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Table 4.5 Summary statistics of relevant variables of stock and cash based acquirers. The table provides mean, median and number of observations for key variables of 100% stock and 100% cash based US acquirers for the period 1979 -2005 for which RKRV and RIM market-to-book decomposition is possible. Merger data is from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and Acquisition database. All values are based on fiscal year end values except share price and number of shares outstanding. Share price and number of shares outstanding are three month later values from Compustat fiscal year end month. Total assets (Compustat mnemonic: AT), total revenue (Compustat mnemonic: REVT), book value of equity (Compustat mnemonic: CEQ), income before extraordinary item (Compustat mnemonic: IB), deferred taxes (Compustat mnemonic: TXDB), net income (Compustat mnemonic: NI) are from Compustat annual file, share price (CRSP mnemonic: PRC), number of shares outstanding (CRSP mnemonic: SHROUT) are from CRSP monthly file. Return on assets (ROA) is calculated as (NI/AT), Market value of equity (MKTEQ) is calculated as (PRC*SHROUT), Market value of assets (MKTAT) is calculated as (MKTEQ + AT - CEQ – TXDB), market -to-book ratio (equity) is calculated as share price (PRC)/ book value per share (Compustat mnemonic: BKVLPS), Market-to-book (total assets) is calculated as (MKTAT / AT) and leverage (market) is calculated as [1-(MKTEQ/ MKTAT). Difference columns shows the t and Wilcoxon z statistics and ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1)
RIM (2)
Both RKRV and RIM
Difference (RKRV-RIM) (1-2)
Mean Median N Mean Median N Mean Median N t Z
Total assets (book value) 12,257.4 1,951.2 1735 13,020.4 2,052.2 1374 13,236.5 2,122.8 1349 -0.40 -0.80 Total revenue 4,091.5 545.5 1722 4,695.5 600.4 1361 4,755.7 610.2 1336 -1.37 -1.87 * Book value of equity 2,232.1 411.1 1735 2,529.3 448.9 1374 2,568.4 457.5 1349 -1.35 -1.53 Income before extraordinary item 381.3 43.6 1735 449.5 49.2 1374 456.7 51.1 1349 -1.35 -1.87 * Deferred taxes 190.7 2.4 1000 218.4 3.5 819 222.1 3.4 801 -0.73 -1.18 Net income 383.4 42.7 1735 451.4 48.9 1374 458.6 50.8 1349 -1.34 -1.88 * Share price 35.0 28.7 1722 36.2 29.6 1342 36.3 29.6 1341 -1.27 -1.37 Number of shares outstanding 212,114.3 34,724 1722 247,260 39,156 1342 247,441 39,206 1341 -1.39 -1.79 * Return on assets 0.02 0.02 1735 0.03 0.02 1374 0.032 0.019 1349 -1.47 -2.54 ***Market value of equity 9,840.0 917.0 1722 11,419.5 1,077.5 1342 11,428.0 1,077.5 1341 -1.28 -2.03 ** Market value of assets 17,807.9 2,060.4 992 20,557.4 2,368.0 797 20,582.8 2,371.1 796 -1.06 -2.00 ** Market to book (equity) 3.8 2.3 1721 3.8 2.4 1341 3.8 2.4 1340 -0.21 -1.16 Market to book (total assets) 3.0 1.8 992 3.1 1.8 797 3.1 1.8 796 -0.02 -0.50 Leverage (market) 0.3 0.2 992 0.3 0.2 797 0.3 0.2 796 0.38 0.47
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Table 4.6 RKRV (2005) and RIM market-to-book components of stock and cash based acquirers.
This table shows the market to book decomposition based on RKRV (2005) and RIM method for 100% stock and 100% cash based US acquirers for the period 1979 -2005. Merger data is from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and Acquisition database. For RKRV model, overvaluation dummy takes a value of 1 if firm specific misvaluation is positive and 0 otherwise, high growth dummy takes a value of 1 if growth option is positive and 0 otherwise. For RIM model, overvaluation dummy takes a value of 1 if misvaluation (log of market value/intrinsic value) is greater than 1 and 0 otherwise, high growth dummy takes a value of 1 if growth option (log of intrinsic value/ book value) is greater than 1 and 0 otherwise. First three columns shows mean, median, and number of observations for RKRV model, next three columns shows the same for RIM and last three column shows the same for common samples where both RKRV and RIM values are available. Final two rows show the t value of mean difference between RKRV and RIM overvaluation and growth option dummies and. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1) RIM (2) Both RKRV and RIM
Mean Median N Mean Median N Mean Median N RKRV (2005) measures Firm specific misvaluation (a) 0.22 0.17 1735 0.22 0.18 1349Sector specific misvaluation (b) 0.20 0.19 1735 0.20 0.18 1349Total misvaluation (a + b) 0.42 0.37 1735 0.43 0.38 1349Growth option 0.56 0.44 1735 0.59 0.50 1349
Overvaluation dummy 70.43% 1735 70.50% 1349High growth dummy 91.18% 1735 91.77% 1349 RIM measures 5713.37 758.33 1374 5796.63 780.32 1349Intrinsic value of firm 0.33 0.35 1333 0.33 0.35 1332Misvaluation (ln M/V) 0.62 0.44 1364 0.61 0.44 1340Growth option (ln V/B)
Overvaluation dummy 70.49% 1342 70.54% 1341High growth dummy 93.89% 1374 93.92% 1349 Difference in dummies of RKRV and RIM common sample
Overvaluation t value -0.05 Growth option t value -2.17 **
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Table 4.7 Two by two matrix showing combinations of misvaluation and growth options dummies of stock and cash based acquirers.
This table compares the consistency of RKRV and RIM models in identifying misvaluation and growth option for 100% stock and 100% cash based US acquirers for the period 1979 -2005. Merger data is from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and Acquisition database. Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy using both methods. Each cell of the matrix represents a combination of the number (percentage) of observations labeled as over (under) valued (in panel A) and high (low) growth options (in panel B) under both methods. In panel A, over (under) valuation-over (under) valuation combination represents consistency of both methods in identifying misvaluation whereas over (under) valuation – under (over) valuation cells represents contradictory conclusion of the methods in identifying misvaluation. In panel B, high (low) growth -high (low) growth combination represents consistency of both methods in identifying growth options whereas high (low) growth – low (high) growth cells represents contradictory conclusion of the methods in identifying growth options.
Panel A: Misvaluation matrix. Missing misvaluation dummies = 1210 (1.3%)
RIM
Overvaluation Undervaluation
RKRV
Overvaluation 735 (54.5%) 210 (15.6%)
Undervaluation 211 (15.6%) 185 (13.7%)
Panel B: Growth option matrix.
RIM
High growth Low growth
RKRV
High growth 1163 (86.2%) 75 (5.6%)
Low growth 104 (7.7%) 7 (0.5%)
I proceed by dividing my sample into stock and cash-based acquirers because previous
literature suggests that stock based acquisitions are more driven by overvaluation. In the case of
stock-based acquirers (Table 4.8), the RKRV method identifies overvaluation 76.9% of the time,
whereas RIM does so in 72.2% of the cases. Not surprisingly, these numbers are higher than
those reported in Table 4.6, which included cash acquisitions. The difference (4.7%) between the
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Table 4.8 RKRV (2005) and RIM market-to-book components of stock based acquirers only.
This table shows the market to book decomposition based on RKRV (2005) and RIM method for 100% stock based US acquirers for the period 1979 -2005. Merger data is from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and Acquisition database. For RKRV model, overvaluation dummy takes a value of 1 if firm specific misvaluation is positive and 0 otherwise, high growth dummy takes a value of 1 if growth option is positive and 0 otherwise. For RIM model, overvaluation dummy takes a value of 1 if misvaluation (log of market value/intrinsic value) is greater than 1 and 0 otherwise, high growth dummy takes a value of 1 if growth option (log of intrinsic value/ book value) is greater than 1 and 0 otherwise. First three columns shows mean, median and number of observations for RKRV model, next three columns shows the same for RIM and last three column shows the same for common samples where both RKRV and RIM values are available. Final two rows show the t value of mean difference between RKRV and RIM overvaluation and growth option dummies and. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1) RIM (2) Both RKRV and RIM Mean Median N Mean Median N Mean Median N
RKRV (2005) measures Firm specific misvaluation (a) 0.28 0.22 1145 0.29 0.22 866Sector specific misvaluation (b) 0.21 0.19 1145 0.21 0.18 866Total misvaluation (a + b) 0.49 0.42 1145 0.49 0.42 866Growth option 0.56 0.39 1145 0.57 0.40 866Overvaluation dummy 76.94% 1145 76.91% 866High growth dummy 92.75% 1145 93.53% 866 RIM measures Intrinsic value of firm 3304.34 621.84 882 3355.13 634.73 866Misvaluation (ln M/V) 0.35 0.38 852 0.35 0.38 851Growth option (ln V/B) 0.64 0.44 872 0.63 0.44 857Overvaluation dummy 72.13% 861 72.21% 860High growth dummy 93.88% 882 94.00% 866 Difference in dummies of RKRV and RIM common sample
Overvaluation t value 2.55 ** Growth option t value -0.40
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Table 4.9 Two by two matrix showing combinations of misvaluation and growth options dummies of stock based acquirers only. This table compares the consistency of RKRV and RIM models in identifying misvaluation and growth option for 100% stock based US acquirers for the period 1979 -2005. Merger data is from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and Acquisition database. Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy using both methods. Each cell of the matrix represents a combination of the number (percentage) of observations labeled as over (under) valuation (in panel A) and high (low) growth options (in panel B) under both methods. In panel A, over (under) valuation-over (under)valuation combination represents consistency of both methods in identifying misvaluation whereas over (under) valuation – under (over) valuation cells represents contradictory conclusion of the methods in identifying misvaluation. In panel B, high (low) growth -high (low) growth combination represents consistency of both methods in identifying growth options whereas high (low) growth – low (high) growth cells represents contradictory conclusion of the methods in identifying growth options. Panel A: Misvaluation matrix. Missing misvaluation dummies = 6 (0.7%)
RIM Overvaluation Undervaluation
RKRV
Overvaluation 523 (60.4%) 137 (15.8%)
Undervaluation 98 (11.3%) 102 (11.8%)
Panel B: Growth option matrix.
RIMHigh growth Low growth
RKRV
High growth 762 (88.0%) 48 (5.5%)
Low growth 52 (6.0%) 4 (0.5%)
two methods is significant at 5% level. In the 2X2 matrix for stock based acquirers which I
report in Table 4.9, I find (Panel A) that 72.2% of the time both methods conclude the same
about misvaluation but 27.1% of the time they disagree. Similar as Table 4.7, both methods are
similar about the high or low growth options. Disagreement occurs in only 11.5% (Panel B). For
cash based acquirers the differences between two methods in identifying misvaluation and
growth options are more pronounced. Table 4.10 shows that for both the misvaluation and
growth options components RIM has a higher tendency of showing overvaluation and high
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Table 4.10 RKRV (2005) and RIM market-to-book components of cash based acquirers only. This table shows the market to book decomposition based on RKRV (2005) and RIM method for 100% cash based US acquirers for the period 1979 -2005. Merger data is from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and Acquisition database. For RKRV model, overvaluation dummy takes a value of 1 if firm specific misvaluation is positive and 0 otherwise, high growth dummy takes a value of 1 if growth option is positive and 0 otherwise. For RIM model, overvaluation dummy takes a value of 1 if misvaluation (log of market value/intrinsic value) is greater than 1 and 0 otherwise, high growth dummy takes a value of 1 if growth option (log of intrinsic value/ book value) is greater than 1 and 0 otherwise. First three columns shows mean, median and number of observations for RKRV model, next three columns shows the same for RIM and last three column shows the same for common samples where both RKRV and RIM values are available. Final two rows show the t value of mean difference between RKRV and RIM overvaluation and growth option dummies and. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1) RIM (2) Both RKRV and RIM
Mean Median N Mean Median N Mean Median N RKRV (2005) measures Firm specific misvaluation (a) 0.10 0.08 590 0.12 0.10 483Sector specific misvaluation (b) 0.19 0.19 590 0.20 0.19 483Total misvaluation (a + b) 0.29 0.25 590 0.31 0.27 483Growth option 0.57 0.58 590 0.61 0.63 483Overvaluation dummy 57.80% 590 59.01% 483High growth dummy 88.14% 590 88.61% 483 RIM measures Intrinsic value of firm 10032.00 1281.36 492 10174.13 1,286.05 483Misvaluation (ln M/V) 0.31 0.30 481 0.31 0.30 481Growth option (ln V/B) 0.58 0.44 492 0.57 0.44 483Overvaluation dummy 67.57% 481 67.57% 481High growth dummy 93.90% 492 93.79% 483 Difference in dummies of RKRV and RIM common sample
Overvaluation t value -2.96 *** Growth option t value -2.83 ***
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Table 4.11 Two by two matrix showing combinations of misvaluation and growth options dummies of cash based acquirers only. This table compares the consistency of RKRV and RIM models in identifying misvaluation and growth option for 100% stock based US acquirers for the period 1979 -2005. Merger data is from Thomson Financial’s Securities Data Corporation (SDC) U.S. Mergers and Acquisition database. Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy using both methods. Each cell of the matrix represents a combination of the number (percentage) of observations labeled as over (under) valuation (in panel A) and high (low) growth options (in panel B) under both methods. In panel A, over (under) valuation-over (under) valuation combination represents consistency of both methods in identifying misvaluation whereas over (under) valuation – under (over) valuation cells represents contradictory conclusion of the methods in identifying misvaluation. In panel B, high (low) growth - high (low) growth combination represents consistency of both methods in identifying growth options whereas high (low) growth – low (high) growth cells represents contradictory conclusion of the methods in identifying growth options. Panel A: Misvaluation matrix. Missing misvaluation dummies = 2(0.4%)
RIM Overvaluation Undervaluation
RKRV
Overvaluation 212 (43.9%) 73 (15.1%)
Undervaluation 113 (23.4%) 83 (17.2%)
Panel B: Growth option matrix.
RIMHigh growth Low growth
RKRV
High growth 401 (83.0%) 27 (5.6%)
Low growth 52 (10.8%) 3 (0.6%)
growth options than the RKRV method. Moreover, the differences in misvaluation and growth
options are larger for cash-based acquirers than stock-based acquirers. The two methods disagree
38.5% of the time about misvaluation and 16.4% of the time about high or low growth options
(Table 4.11, Panels A and B, respectively).
Next, I examine whether misvaluation measured by these two models predict merger
intensity differently, which is well documented in the literature. To do so I use probit regressions
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with misvaluation and growth options calculated using either the RKRV or RIM method and
attempt to determine whether they determine firm-level merger intensity, differently. I follow a
similar setting as RKRV (2005) where each component of market-to-book is used as a
determinant of merger intensity. I also control for year effects as it is documented that merger
activities happen in waves (e.g., Shleifer and Visney (2003) and RKRV (2005)). In Table 4.12,
Panel A the dependent variable is coded as 1 if the firm engages in merger activity in that year, 0
otherwise. Panel B shows the results of similar regressions where only cash and stock based
acquirers are included. In panel B the dependent variable takes a value of 1 if the acquisition is
100% stock based and 0 if it is 100% cash based. For the full sample of firms (Panel A) I find
that misvaluation components are positively and growth options are negatively related to merger
propensity. This finding is similar to RKRV (2005). But, when I use misvaluation and growth
options components calculated using the RIM method, both misvaluation and growth options are
positively related to merger propensity. In panel B with the cash- and stock-only acquirer
sample, misvaluation is positively related to stock based acquisition propensity for both models.
But, growth options are insignificant in the RKRV model. Hence, I conclude that both methods
are consistent in identifying the role of misvaluation in merger propensity although they have
different predictions for the role of growth options.
In brief, in the event of mergers and acquisitions, both methods identify misvaluation on
average in almost a similar manner. But in about one third of all cases the methods disagree
whether the firm is under or overvalued. For growth options this disagreement is less pronounced
(ranges from 11.5% to 16.4%). Moreover, both methods conclude that misvaluation is the
driving force of merger activities although both methods are not consistent about the role of
growth options in acquisition propensity.
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Table 4.12 Merger intensity regressions. This table reports the probit regressions to analyze the merger intensity. Panel A shows all Compustat firms whose RKRV and RIM decomposition are available for the period 1979-2005. In panel A the dependent variable is a binary variable coded 1 if the firm engages in merger in that year and 0 otherwise. In Panel B dependent variable takes a value of 1 if the acquisition is 100% stock based and 0 if it is 100% cash-based. Three components of market-to-book ratio; Firm specific misvaluation - RKRV, Sector specific misvaluation - RKRV and Growth options - RKRV are used as explanatory variables (estimated using Rhodes-Kropf et al (2005) model 3 of table 1). Misvaluation-RIM and growth option – RIM is calculated using residual income model (following D’Mello and Shroff, 2000). ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Valuation components RKRV RIM
Model 1 Model 2 Model 1 Model 2
Panel A: merger = 1, non-merger = 0
Firm specific misvaluation - RKRV 0.229 *** 0.230 ***
Sector specific misvaluation - RKRV 0.386 *** 0.128 ***
Growth options - RKRV -0.032 ** -0.039 **
Misvaluation - RIM 0.162 *** 0.124***
Growth options - RIM 0.088 *** 0.055***
Log likelihood 18743 18743 18520 18520
2 306.57 647.30 155.94 520.55
N 76150 76150 75058 75058
Year fixed effects No Yes No Yes
Panel B: stock = 1, cash = 0
Firm specific misvaluation - RKRV 0.448 *** 0.372 ***
Sector specific misvaluation - RKRV 0.763 *** 0.136
Growth options - RKRV 0.068 0.060
Misvaluation - RIM 0.303 *** 0.150***
Growth options - RIM 0.293 *** 0.241***
Log likelihood 2663 2210 2627 2627
2 113.11 453.13 60.78 428.27
N 2017 2017 1994 1994
Year fixed effects No Yes No Yes
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4.5.2 Open market share repurchases
I collect open market share repurchase data from Securities Data Corporation (SDC) for
the period of 1983-2005. My initial sample has 10,381 open market share repurchase
observations. When I impose the RKRV decomposition data availability restriction the sample
reduces to 6,567 repurchase observations. For RIM data availability restriction the sample size
equals 5,005 repurchase observations. If I require the availability of market-to-book components
for both methods the sample reduces to 4,920 observations.
Table 4.13 shows the misvaluation and growth option components of both methods. Both
mean and median firm-specific misvaluations (RKRV) are negative. Moreover, RKRV shows
that majority (53%) of the repurchase observations are undervalued. These results are consistent
with the idea that firms undertake open market share repurchase programs when their equity is
undervalued. But RIM fails to identify undervaluation. According to RIM, 65.1% of the
repurchase firms are overvalued. This difference is highly significant at 1% level. RIM also
identifies higher growth options for repurchase firms than RKRV. In Table 4.14 I examine the
consistency of both methods. I find that both methods provide consistent results about
misvaluation 62.9% of the time whereas 37.1% of the time the conclusions from the methods are
contradictory. In case of growth options 16.8% of the time the conclusions are contradictory.
4.5.3 Seasoned equity offering (SEO)
I collect 35 years of seasoned equity offerings (SEO’s) (1971-2005) data from Securities
Data Corporation (SDC). Following Hertzel and Li (2010) I exclude utility companies (SIC
4910-4949), closed end funds (SIC 6720-6739), and real estate investment trusts (REITs; SIC
6798). I exclude firms that only issue secondary shares. If a firm issues primary shares more than
once in three years I only consider the first issue. I end up with 2,665 SEOs where RKRV
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Table 4.13 RKRV (2005) and RIM market-to-book components of open market share repurchase.
This table shows the market to book decomposition based on RKRV (2005) and RIM method for open market share repurchase firms collected from Thomson Financial’s Securities Data Corporation (SDC) database for the period 1983-2005. For RKRV model, overvaluation dummy takes a value of 1 if firm specific misvaluation is positive and 0 otherwise, high growth dummy takes a value of 1 if growth option is positive and 0 otherwise. For RIM model, overvaluation dummy takes a value of 1 if misvaluation (log of market value/intrinsic value) is greater than 1 and 0 otherwise, high growth dummy takes a value of 1 if growth option (log of intrinsic value/ book value) is greater than 1 and 0 otherwise. First three columns shows mean, median and number of observations for RKRV model, next three columns shows the same for RIM and last three column shows the same for common samples where both RKRV and RIM values are available. Final two rows show the t value of mean difference between RKRV and RIM overvaluation and growth option dummies and. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1) RIM (2) Both RKRV and RIM
Mean Median N Mean Median N Mean Median N RKRV (2005) measures Firm specific misvaluation (a) -0.03 -0.05 6567 -0.02 -0.03 4920Sector specific misvaluation (b) 0.17 0.17 6567 0.16 0.17 4920Total misvaluation (a + b) 0.14 0.13 6567 0.14 0.13 4920Growth option 0.55 0.53 6567 0.59 0.57 4920Overvaluation dummy 45.62% 6567 47.03% 4920High growth dummy 84.79% 6567 86.24% 4920 RIM measures Intrinsic value of firm 2681.15 305.19 5005 2704.65 307.56 4920Misvaluation (ln M/V) 0.22 0.24 4877 0.22 0.24 4871Growth option (ln V/B) 0.50 0.37 5004 0.50 0.37 4919Overvaluation dummy 65.07% 4878 65.09% 4872High growth dummy 93.93% 5005 93.86% 4920 Difference in dummies of RKRV and RIM common sample
Overvaluation t value -21.92 *** Growth option t value -13.25 ***
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Table 4.14 Two by two matrix showing combinations of misvaluation and growth options dummies for open market share repurchase. This table compares the consistency of RKRV and RIM models in identifying misvaluation and growth option for open market share repurchase firms from Thomson Financial’s Securities Data Corporation (SDC) database for the period 1983-2005. Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy using both methods. Each cell of the matrix represents a combination of the number (percentage) of observations labeled as over (under) valuation (in panel A) and high (low) growth options (in panel B) under both methods. In panel A, over (under) valuation-over (under) valuation combination represents consistency of both methods in identifying misvaluation whereas over (under) valuation – under (over) valuation cells represents contradictory conclusion of the methods in identifying misvaluation. In panel B, high (low) growth -high (low) growth combination represents consistency of both methods in identifying growth options whereas high (low) growth – low (high) growth cells represents contradictory conclusion of the methods in identifying growth options. Panel A: Misvaluation matrix. Missing misvaluation dummies = 48(1.0%)
RIM Overvaluation Undervaluation
RKRV
Overvaluation 1841 (37.4%) 448 (9.1%)
Undervaluation 1330 (27.0%) 1253 (25.5%)
Panel B: Growth option matrix.
RIMHigh growth Low growth
RKRV
High growth 4016 (81.6%) 227 (4.6%)
Low growth 602 (12.2%) 75 (1.5%)
decomposition is available and 2,321 SEOS where RIM data are available and 2,126 SEOs
where both RKRV and RIM misvaluation and growth options values are available.
The literature (e.g. Elliott et al. (2007), Hertzel and Li (2010)) suggests that if equity is
overvalued in the market, firms issue SEOs to finance their projects or to accumulate funds for
future projects. So, I expect to observe overvaluation in the pre-SEO year. Table 4.15 shows that
the misvaluation (firm specific) is positive using RKRV whereas RIM suggests undervaluation.
Moreover, RKRV identifies 66.8% of the firms as overvalued whereas RIM suggests
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Table 4.15 RKRV (2005) and RIM market-to-book components of seasoned equity offerings (SEOs). This table shows the market to book decomposition based on RKRV (2005) and RIM method for seasoned equity offering firms from Thomson Financial’s Securities Data Corporation (SDC) database for the period 1971-2005. For RKRV model, overvaluation dummy takes a value of 1 if firm specific misvaluation is positive and 0 otherwise, high growth dummy takes a value of 1 if growth option is positive and 0 otherwise. For RIM model, overvaluation dummy takes a value of 1 if misvaluation (log of market value/intrinsic value) is greater than 1 and 0 otherwise, high growth dummy takes a value of 1 if growth option (log of intrinsic value/ book value) is greater than 1 and 0 otherwise. First three columns shows mean, median and number of observations for RKRV model, next three columns shows the same for RIM and last three column shows the same for common samples where both RKRV and RIM values are available. Final two rows show the t value of mean difference between RKRV and RIM overvaluation and growth option dummies and. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1) RIM (2) Both RKRV and RIM
Mean Median N Mean Median N Mean Median N RKRV (2005) measures Firm specific misvaluation (a) 0.25 0.20 2665 0.24 0.19 2126Sector specific misvaluation (b) 0.09 0.10 2665 0.08 0.10 2126Total misvaluation (a + b) 0.34 0.28 2665 0.31 0.27 2126Growth option 0.65 0.65 2665 0.65 0.65 2126Overvaluation dummy 67.09% 2665 66.84% 2126High growth dummy 87.35% 2665 88.01% 2126 RIM measures Intrinsic value of firm 773.68 139.61 2321 807.84 156.89 2126Misvaluation (ln M/V) -0.08 -0.07 2100 -0.08 -0.07 2094Growth option (ln V/B) 0.91 0.69 2315 0.90 0.68 2122Overvaluation dummy 46.34% 2104 46.43% 2098High growth dummy 95.61% 2321 95.67% 2126 Difference in dummies of RKRV and RIM common sample
Overvaluation t value 15.19 *** Growth option t value -9.53 ***
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Table 4.16 Two by two matrix showing combinations of misvaluation and growth options dummies of seasoned equity offering (SEO). This table compares the consistency of RKRV and RIM models in identifying misvaluation and growth option for seasoned equity offering firms from Thomson Financial’s Securities Data Corporation (SDC) database for the period 1971-2005. Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy using both methods. Each cell of the matrix represents a combination of the number (percentage) of observations labeled as over (under) valuation (in panel A) and high (low) growth options (in panel B) under both methods. In panel A, over (under) valuation-over (under) valuation combination represents consistency of both methods in identifying misvaluation whereas over (under) valuation – under (over) valuation cells represents contradictory conclusion of the methods in identifying misvaluation. In panel B, high (low) growth - high (low) growth combination represents consistency of both methods in identifying growth options whereas high (low) growth – low (high) growth cells represents contradictory conclusion of the methods in identifying growth options. Panel A: Misvaluation matrix. Missing misvaluation dummies = 28(1.3%)
RIM Overvaluation Undervaluation
RKRV
Overvaluation 752 (35.4%) 647 (30.4%)
Undervaluation 222 (10.4%) 477 (22.4%)
Panel B: Growth option matrix.
RIMHigh growth Low growth
RKRV
High growth 1800(84.7%) 71 (3.3%)
Low growth 234 (11.0%) 21 (1.0%)
that 46.4% of the firms are overvalued in the pre-SEO year. So, RKRV findings are consistent
with the theoretical predictions. These findings are similar to the findings of Hertzel and Li
(2010), but contradictory to Elliott et al. (2007), Hertzel and Li (2010) use the RKRV method
and find overvaluation of SEOs during 1970 to 2004. Elliott et al. (2007) use a RIM based model
to identify overvaluation for the period 1980 to 1999. They find that 82.62% of the SEO firms
are overvalued, compared to my findings of 46.4% of overvalued firms. According to my
calculation using RIM, 49.6% of the firms are overvalued for that same period. However, there
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are substantial differences between my RIM methodology and their methodology. For example, I
use five years data to calculate the RIM whereas Elliott et al. (2007) use four years data and I
estimate industry cost of equity using a Fama-French three factor model whereas they use a firm-
specific single factor cost of equity. Moreover, their sample size is larger (3,781 equity issues)
compared to my SEO sample (1,741 SEOs). This could be due to my requirement that if there
are multiple issues within a three year period only the first issue is considered. Similar to their
findings I also find that firm specific cost of equity estimated using Fama-French three factor
model is a noisy measure.
When I check for consistency of both models in Table 4.16, I find that in 40.8% of the
firm years the two methods contradict on misvaluation. Importantly, only in 35.4% of the cases
both models results in overvaluation. In the case of growth options, disagreement happens only
in 14.3% of the firm years. Again, my findings suggest that RKRV identifies misvaluation more
consistently than RIM and is more consistent with the theoretical prediction.
4.5.4 Stock splits
Stock split data is collected from CRSP for the period 1971 – 2005. To be included in the
sample, the share code (CRSP mnemonic: SHRCD) has to be 10 or 11, the pre-split (5 trading
days earlier) price has to be at least $10, the split factor (CRSP mnemonic: FACPR) has to be 1
or higher and the CRSP Factor to Adjust Price (CRSP mnemonic: FACPR) has to equal to the
CRSP Factor to Adjust Shares Outstanding (CRSP mnemonic: FACSHR). I find 4,586 stock
splits where the RKRV decomposition is possible, 3,948 stock splits where the RIM calculation
is possible and 3,915 stock splits where both RKRV and RIM misvaluation and growth options
components are available.
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Table 4.17 shows that RKRV has a slightly higher tendency (3.4% = 69.5% - 66.0%) of
showing overvaluation than RIM. Moreover, in the raw misvaluation measures both methods
consistently show that split firms are overvalued in pre-split year. In case of growth options, both
methods show very high growth opportunities for split firms. RKRV shows that 96.9% of the
split firms have high growth opportunities and RIM identifies 97.7% of the split firms as having
high growth opportunities. This finding is consistent with the idea that splits work as a signal to
the market about future growth opportunities of the firm. In Table 4.18 I check the consistency of
both methods in identifying misvaluation and growth options. In the case of misvaluation, 33.8%
of the time RKRV and RIM fail to reach the same conclusion about misvaluation whereas this
disagreement occurs only in 5% in case of growth options. These results indicate that growth
options may be the driving force of stock splits and firms attempt to signal growth options of the
firm to the market using splits rather than signaling undervaluation of the firm.
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Table 4.17 RKRV (2005) and RIM market-to-book components of stock splits. This table shows the market to book decomposition based on RKRV (2005) and RIM method for stock split firms from Center for Research in Security Prices (CRSP) for the period 1971-2005. For RKRV model, overvaluation dummy takes a value of 1 if firm specific misvaluation is positive and 0 otherwise, high growth dummy takes a value of 1 if growth option is positive and 0 otherwise. For RIM model, overvaluation dummy takes a value of 1 if misvaluation (log of market value/intrinsic value) is greater than 1 and 0 otherwise, high growth dummy takes a value of 1 if growth option (log of intrinsic value/ book value) is greater than 1 and 0 otherwise. First three columns shows mean, median and number of observations for RKRV model, next three columns shows the same for RIM and last three column shows the same for common samples where both RKRV and RIM values are available. Final two rows show the t value of mean difference between RKRV and RIM overvaluation and growth option dummies and. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Variables RKRV (1) RIM (2) Both RKRV and RIM
Mean Median N Mean Median N Mean Median N RKRV (2005) measures Firm specific misvaluation (a) 0.26 0.18 4,586 0.25 0.18 3915 Sector specific misvaluation (b) 0.10 0.13 4,586 0.10 0.12 3915 Total misvaluation (a + b) 0.36 0.28 4,586 0.35 0.27 3915 Growth option 0.81 0.84 4,586 0.82 0.85 3915 Overvaluation dummy 69.39% 4,586 69.45% 3915 High growth dummy 96.62% 4,586 96.93% 3915 RIM measures Intrinsic value of firm 2283.29 305.65 3,948 2297.79 309.85 3915 Misvaluation (ln M/V) 0.25 0.27 3,879 0.25 0.27 3878 Growth option (ln V/B) 0.77 0.59 3,941 0.76 0.59 3908 Overvaluation dummy 66.03% 3,886 66.02% 3885 High growth dummy 97.75% 3,948 97.73% 3915 Difference in dummies of RKRV and RIM common sample
Overvaluation t value 3.63 *** Growth option t value -2.20 **
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Table 4.18 Two by two matrix showing combinations of misvaluation and growth options dummies of stock splits. This table compares the consistency of RKRV and RIM models in identifying misvaluation and growth option of stock splits firms from Center for Research in Security Prices (CRSP) database for the period 1971-2005. Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy using both methods. Each cell of the matrix represents a combination of the number (percentage) of observations labeled as over (under) valuation (in panel A) and high (low) growth options (in panel B) under both methods. In panel A, over (under) valuation-over (under) valuation combination represents consistency of both methods in identifying misvaluation whereas over (under) valuation – under (over) valuation cells represents contradictory conclusion of the methods in identifying misvaluation. In panel B, high (low) growth -high (low) growth combination represents consistency of both methods in identifying growth options whereas high (low) growth – low (high) growth cells represents contradictory conclusion of the methods in identifying growth options. Panel A: Misvaluation matrix. Missing misvaluation dummies = 30 (0.8%)
RIM Overvaluation Undervaluation
RKRV
Overvaluation 1969 (50.3%) 728 (18.6%)
Undervaluation 596 (15.2%) 592 (15.1%)
Panel B: Growth option matrix.
RIMHigh growth Low growth
RKRV
High growth 3711 (94.8%) 84 (2.1%)
Low growth 115 (2.9%) 5 (0.1%)
Finally, I attempt to identify which component of the market-to-book explains the stock
split propensity of the firm in a multivariate setting. I run a logistic regression where the
dependent variable takes a value of 1 if the firm splits in that year or it takes a value of 0
otherwise. I control for size using total assets. I include two dummies (labeled pre1997 and
post2000) to control for minimum tick size changes (see Angel (1997) and Harris (1997)). These
two dummies have a value of 1 when the firm year is before 1997 (pre1997) or post 2000
(post2000), and are equal to 0 elsewise. I further include two dummy variables labeled
traderange and stock appreciation. Both these variables are designed to capture pre-split price
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appreciation as researchers suggest that firms desire to maintain a target share price level to
attract particular clienteles (Copeland (1979), Dyl and Elliott (2006) and Fernando,
Krishnamurthy, and Spindt (2004)). Minimum tick size change variables Traderange and stock
appreciation are calculated following Dyl and Elliott (2006). Traderange is a binary variable
which takes a value of 1 if the ratio of actual and predicted share price is more than 1.50 and
takes a value of 0 if the ratio is less than 1.50. Predicted share price is estimated using
E(sharepricej,t-1│etc) = δ0 + δ1 BVEquityj,t-1 + δ2 AvgHldgj,t-1, + 3EPSj,t-1. Stock appreciation
is calculated as a ratio of share price at (t-1) to share price at (t-3), where share price is the fiscal
year end price. Finally, I include the number of common shareholders (CRSP mnemonic: CSHR)
in my regression to capture information asymmetry explanation of stock splits. Table 4.19 shows
that all of the misvaluation and growth options components of both RIM and RKRV methods
load positively and significantly in the regressions. It appears that for splitting both methods
reach similar conclusions.
I summarize the misvaluation and growth options results in table 4.20. Panel A
summarizes the percentage of the firms that are identified as overvalued or have high growth
options for the full sample and for the four events examined. It shows that both methods
correctly identify misvaluation (consistent with theoretical predictions) for stock splits and
mergers. But, for SEOs and open market share repurchases RIM identifies misvaluation in the
opposite direction of the theoretical predictions, whereas RKRV method correctly identifies
misvaluation. Panel B shows the consistency of both methods in identifying misvaluation and
growth options. The disagreement on whether a firm is overvalued ranges from 27.1% to 40.8%.
In identifying high growth options both methods are more consistent (disagreement ranges from
5.0% to 16.8%).
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Table 4.19 Logistic regressions on the determinants of stock split. This table reports the logit regressions to analyze the split decision. The split data is from Center for Research in Security Prices (CRSP) database for the period 1971-2005.The dependent variable is a binary variable coded 1 if the firm splits and 0 for non-splitting firms. Three components of market-to-book ratio; Firm specific misvaluation - RKRV, Sector specific misvaluation - RKRV and Growth options - RKRV are used as explanatory variables (estimated using Rhodes-Kropf et al (2005) model 3 of table 1). Misvaluation-RIM and growth option – RIM is calculated using residual income model (following D’Mello and Shroff 2000). along with other split explanatory control variables. Pre1997 and post2000 are control variables for minimum tick changes, traderange is a dummy variable that takes a value of 1 if the actual share price is 50% greater than the predicted price (estimated following Dyl and Elliott, 2006) and 0 otherwise, stock appreciation is the ratio of the t-1 year-end share price over the t-3 year-end share price to capture the price appreciation in pre-split period. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively. Variables RKRV RIM
Firm specific misvaluation - RKRV 0.789 ***
Sector specific misvaluation - RKRV 0.815 ***
Growth options - RKRV 2.271 ***
Misvaluation - RIM 1.333 ***
Growth options - RIM 1.319 ***
Log (Total assets) 0.671 *** 0.535 ***
pre1997 1.143 *** 1.065 ***
post2000 -0.653 *** -0.581 ***
Traderange -0.354 *** -0.443 ***
Stock appreciation -0.002 -0.002
Number of common shareholders -0.003 *** -0.003 ***
Log likelihood 17588 17563
2 4809 3947
N 27452 27404
To explore whether these results are driven by marginal misvaluation or growth options
estimation, I rank the firm-year observations into five quintiles based on misvaluation and
growth options values. Then, I exclude the third quintile (middle 20%) observations from my
analysis and replicate table 4.20 with the smaller sample. Table 4.21 shows these results. There
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are a couple of notable changes. For open market share repurchase the number of overvalued
firms changes from 47.0% to 51.1% using the RKRV method (i.e., on average repurchase firms
are slightly overvalued). For the SEO sample, the RIM results are now more consistent with
Table 4.20 Summary of misvaluation and consistency of the methods. In this table panel A summarizes the overvaluation and high growth option results from table 4.3, 4.6, 4.8, 4.10, 4.13, 4.15, 4.17. Panel B summarizes consistency results from table 4.4, 4.7, 4.9, 4.11, 4.14, 4.16, 4.18.Each observation is labeled as overvalued or undervalued using overvaluation dummy and high growth or low growth firm using high growth dummy using both methods. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively.
Panel A: Summary of overvaluation and high growth option identified by RKRV and RIM 4.4, 4.7, 4.9, 4.11, 4.14, 4.16, 4.18
Sample Overvaluation High growth options
RKRV RIM t diff. RKRV RIM t diff.
Full sample 48.9% 47.4% 1.5% *** 83.5% 92.0% -8.5% ***
M&A
Stock & Cash 70.5% 70.5% 0.0% 91.8% 93.9% -2.1% ***
Stock only 76.9% 72.2% 4.7% ** 93.5% 94.0% -0.5%
Cash only 59.0% 67.6% -8.6%*** 88.6% 93.8% -5.2% ***
Open market share repurchase 47.0% 65.1% -18.1%*** 86.2% 93.9% -7.7% ***
SEO 66.8% 46.4% 15.2%*** 88.0% 95.7% -7.7% ***
Stock splits 69.5% 66.0% 3.5%*** 96.9% 97.7% -0.8% **
Panel B: Summary of Consistency of RKRV and RIM in identifying overvaluation and high growth option.
Events Disagreement on
Overvaluation High growth options
Full Sample 37.4% 19.9%
M&A
Stock & Cash 31.2% 13.3%
Stock only 27.1% 11.5%
Cash only 38.5% 16.4%
Open market share repurchase 37.1% 16.8%
SEO 40.8% 14.3%
Stock splits 33.8% 5.0%
Range 27.1 % - 40.8% 5.0% - 16.8%
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theoretical predictions (52.1% firms are overvalued, compared to 46.4% overvaluation for full
sample). Except for these changes the rest of the results are similar to those presented in table
4.20.
Table 4.21 Summary of misvaluation and consistency excluding middle 20% (3rd quintile) firm year observations.
This table replicates table 4.21 excluding 3rd quintile of the observations ranked based on misvaluation or growth option. ***, **, * represent significance level at the 1%, 5%, 10% level, respectively. Panel A: Summary of overvaluation and high growth option identified by RKRV and RIM
Sample & Events Overvaluation High growth options
RKRV RIM t diff. RKRV RIM t diff.
Full sample 50.4% 50.3% 0.1% 79.7% 89.% -10.1%***
M&A
Stock & Cash 76.4% 77.5% -1.1% 90.4% 92.4% -1.9%
Stock only 83.0% 79.6% 3.4%* 92.4% 91.8% 0.6%
Cash only 64.4% 73.8% -9.5%*** 86.5% 93.3% -6.7%***
Open market share repurchase 51.1% 69.9% -18.8%*** 82.3% 92.3% -10.0%***
SEO 72.5% 52.1% 20.4%*** 87.5% 95.5% -7.9%***
Stock splits 75.8% 73.4% 2.4%** 96.2% 97.5% 1.3%*** Panel B: Summary of Consistency of RKRV and RIM in identifying overvaluation and high growth option.
Sample & Events Disagreement on
Overvaluation High growth options
Full Sample 33.1% 23.3%
M&A
Stock & Cash 22.6% 15.6%
Stock only 19.1% 14.3%
Cash only 29.0% 18.2%
Open market share repurchase 32.2% 20.6%
SEO 35.5% 14.6%
Stock splits 26.9% 6.0%
Range 19.1% - 35.5% 6.0% - 20.6%
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4.6 Conclusions
Share price misvaluation has received significant attention in the finance and accounting
literature. Two methods commonly used to identify misvaluation are the RKRV (2005) model
and the RIM. Although both models identify misvaluation, their approach is significantly
different. This paper examines if there are any significant sample biases when calculating
misvaluation using each method. I also examine if both models provide consistent inferences
about misvaluation. Using a sample of publicly traded US firms for the period of 1971-2005, I
find there are some significant differences in both models in terms of samples and misvaluation
inferences. Specifically, I find that the RKRV method allows for calculation of about 20% more
firm years than the RIM model. A possible explanation for this is that RIM requires five
consecutive years of data to calculate intrinsic values, which in turn imposes a survivorship bias.
Also, RIM firms are relatively larger in size, relatively more profitable and have higher stock
prices. On the other hand, RKRV firms have relatively higher market-to-book ratios and higher
leverage than RIM sample firms. When I use both models to detect equity misvaluation I find
that RKRV has a slightly higher tendency (1.5%) of showing overvaluation and significantly
lower (8.5%) tendency of identifying high-growth options. However, when I compare both
models on a firm-year basis, I find that in around one third of the cases the two methods disagree
on whether the firm is over or undervalued. For growth options this disagreement is found in
about one out of five cases. When I use four events M&A, open market share repurchase, SEO’s,
and stock splits, this pattern remains more or less consistent across events. Particularly in years
prior to M&A both methods are consistent, on average. In the case of open market share
repurchases, RKRV correctly identifies undervaluation which is consistent with theoretical
predictions whereas RIM shows overvaluation which contradicts with the idea that firms make
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repurchase decisions when the share is undervalued. For SEO’s, RKRV correctly identifies
overvaluation which is consistent with the theoretical prediction that firms issue equity when
equity is overvalued. But, RIM seems to provide opposite findings. Finally, for stock splits, both
methods perform similarly and I show that both overvaluation and high growth options increases
the propensity to split. Moreover, both methods consistently show that split firms have higher
growth options (around 97% on average) which is consistent with the idea that stock splits act as
a signal to the market to reduce information asymmetry about the future growth potential of the
firm. Overall, RKRV model performs better in identifying misvaluation than RIM model.
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Chapter 5
5.1 Conclusions and Summary
The three essays of this dissertation broadly discuss the role misvaluation plays in
corporate financing, restructuring, and investment decisions. Further, this dissertation provides a
comparative analysis of two prominent models (RKRV and RIM) that are used to detect equity
misvaluation and growth options of the firm. Both models are frequently used in finance
literature to identify misvaluation. Hence this dissertation contributes by showing relative
performance of both models in different corporate events. My dissertation also provides some
insights into the role that advertising plays in merger and acquisitions.
In the first essay I find a significant correlation between the abnormal stock split
announcement return and the level of growth opportunities and equity misvaluation. Growth
opportunities and equity misvaluation are measured by decomposing market-to-book into a
growth component and an industry and firm misvaluation component (Rhodes-Kropf et al.
(2005)). Splitting firms with high growth opportunities and undervalued equity have the highest
abnormal returns while firms with overvalued equity and low growth opportunities have the
lowest abnormal announcement returns. I interpret this as evidence that the market is able to
discriminate between valid and false signals. Further, those firms with high growth opportunities
also have significantly higher operating performance in the years subsequent to the split. It
appears that firms that use stock splits to mimic higher value firms are unable to entirely fool the
market.
Prior literature suggests that managers have an incentive to increase stock prices prior to
stock based acquisitions. In the second essay I postulate that managers of firms that use stock to
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finance bids increase advertising intensity in the pre-merger period and find that advertising is
higher prior to stock-based mergers, relative to that of cash-based acquirers. These results hold in
OLS regressions and in regressions based on a propensity score matching approach, which
controls for the possibility that differences in firm characteristics of stock and cash acquirers are
driving the results. Consistent with the conjecture that managers knowingly employ advertising
to inflate stock prices, we find that managerial ownership in stock based acquiring firms is
positively related to pre-merger advertising intensity.
Share price misvaluation is a central issue in the corporate restructuring, financing, and
investment literature. In the third essay I compare two methods that identify misvaluation:
Residual Income Model by Ohlson (1995) and Rhodes-Kropf et al. (2005). I first compare if
there exist any differences in the samples for which I am able to calculate each measure and find
that I am able to calculate valuation measures for substantially more firm-years when I use the
RKRV method vis-à-vis the RIM method. I also find that the RKRV firms tend to be smaller.
Interestingly, I also find that the market to book ratio is larger for RKRV firms. I then investigate
whether there are differences between the two measures around events where extant literature
suggests an important role for misvaluation (i.e., mergers and acquisitions, open market share
repurchases, seasoned equity offerings (SEOs), and stock splits). Finally, I find that RIM and
RKRV disagree in 30% to 40% of all cases whether a firm is over- or undervalued. This finding
holds for the whole sample as well as surrounding all the events we investigate. Interestingly, my
findings also suggest that RKRV is better at finding over or under valuation in events where
theory predicts that misvaluation is a prelude to these events (specifically, SEOs and share
repurchases).
106
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Vita
Mohammad Aminul Karim, the second child of Mohammad Serazul Karim and Hamida
Begum, was born and raised in Chittagong, Bangladesh. He graduated from Chittagong College,
Bangladesh with Higher Secondary Certificate (HSC) (equivalent to US High School Diploma).
He earned his BBA and MBA in Finance from The University of Chittagong, Bangladesh. He
joined the same university as a lecturer and served there for more than five years. Then he came
to United States to study. He earned his MBA in Finance from Ball State University, Indiana,
USA in 2008. He was recognized as Outstanding MBA Graduate and Dean’s Citation for
Academic Excellence at Ball State University. He started Ph.D. in International Business with a
concentration in Finance at The University of Texas at El Paso in 2008. He has taught several
Finance and Economics courses during his career. He also presented paper in Financial
Management Association (FMA) meetings and Eastern Finance Association (EFA) meetings.
Permanent Address: 300 W Nevada Ave, Apt 6
El Paso, TX 79902
This dissertation was typed by Mohammad Aminul Karim (author).