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131 : CADERNOS DO MERCADO DE VALORES MOBILIÁRIOS

1. INTRODUCTION

The year 2005 corresponds to the year when IASB normative came into effect in Portugal. As consequence the consolidated financial statements of the official stock market listed companies were obliged to follow the IAS/IFRS.

The adoption of the IASB normative, in the first stage, by these companies and financial state-ments, is the logical result of the global har-monization process which aims the stimulation of the comparability and the quality of financial information disclosed to users. This harmoniza-tion process contributes for better economical decisions and, as result, for more efficient eco-nomical resources affectation.

With the introduction of this new normative, beyond a significant change in proceedings and in techniques of recognition and measuring as-sets, liabilities and transactions, there is one important changing of the theoretical and con-ceptual support of accounting in Portugal.

In fact, the Portuguese accounting system is usually characterized by the strong binding be-tween accounting and fiscal matters and by the great influence of public institutions and disre-garding professionals’ organizations. In the Portuguese Economy the banking system is the principal financing source of companies and the

State is one of the major users of financial in-formation, while the IASB normative, under the strong Anglo-Saxon influence, is based on the idea of usefulness of financial information for the investor.

This new accounting paradigm the Portuguese actuality lives with raises the question of the analysis concerning the accounting information value relevance and changes the main objective of the financial reporting.

So, it seems important and opportune to ques-tion the situation of accounting information value relevance in Portugal in period exactly before the introduction of IASB normative, check the recent evolution and investigate the reasons for this evolution.

In this study was used a methodology based on Ohlson’s (1995) theoretical construction, apply-ing two linear regressions models of market performances about accounting information.

The main objectives of the study are, in the first stage: (i) identifying of the association level between the market indicators and the account-ing information and (ii) checking the evolution of this relationship, and in a second stage, (iii) identifying the determinants of the decrease of value relevance of Portuguese accounting numbers.

THE DETERMINANTS OF THE DECREASE IN VALUERELEVANCE OF ACCOUNTING NUMBERS IN PORTUGAL

PEDRO TRABUCHO


The remaining structure of this paper is organ-ized as follows: in the section 2 we firstly de-scribed the theoretical framing about value rele-vance, secondly we characterized the evolution of value relevance of accounting information identified in the previous research and after we described the Portuguese context about capital market and accounting system. Section 3 corre-sponds of the empirical study and in section 4 we point out the main conclusions of the study.

2. THEORETICAL FRAMING

2.1. Value relevance of accounting

information

Since the studies of Ball and Brown (1968) and Beaver (1968) the relationship between the ac-counting information and market performances has been presented the one of the most investi-gated subjects by the academic community and has changes the principles of the accounting theory. Financial accounting theory began to be based on the usefulness of information to the investor and became to support the major part of the conceptual frameworks of international accounting benchmarks.

The research of these authors is based in the logic of information content, were the main as-pect is to analyze the effect in market prices of the disclosure of earnings (“event studies”), working with narrow windows. Recently, and with particular excitement after the development of Ohlson Model (1995)1, a new research area appeared centred on the asso-ciation studies, that utilize the new way of measuring the impact of accounting information in the market performances but recognizing that there are firm genuine economical perform-

ances that drive the relationship between price and earnings, and.g., as Scott (2003) “prices lead earnings over the wide window”.

The value relevance studies methodology is based on the analyses of the determinant coeffi-cient (R2) of the regression of market values in function of accounting numbers, with the equa-tion: Pit = 0 + 1 Eit + 2 BVit + it (Price Model), were P is stock price (in the end of the period or 3 or 6 moths after the end of that pe-riod), E is Earnings per share in the period, BV is equity book value at the end of the period and represents the other information.

Alternatively, we can use de Return Model, where Return = (Price n – Price n-1 + Dividends) / Price n-1, and the regression is: Rt = 0 + 1 E/Pt-1 + 2 E/Pt-1 + it, where E is the change in earnings between year n-1 and year n. Some authors make the choice breaking up the equations in the two independents variables to identify the incremental explanatory power, as defined in accounting research by Biddle, Seow and Siegel (1995).

2.2. The evolution of accounting value

relevance

Once there is not a total consensus about this matter, the major part of the empirical studies have been suggesting that the capacity of the accounting information to explain the behaviour of stocks in the market has been declining in the course of time. The same type of suggestion is pointed out generally by market analysts, regu-lators and standard settings bodies.

In the United States of America (USA), Collins, Maydew and Weiss (1997) provide evidence

1- The Ohlson Model (1995) is based on the “clean surplus” assumption and on two fundamental buildings: (i) residual income valuation model (RIM) and (ii) linear information dynamics (LIM).


book values and earnings values did not decline and, in fact, appeared to have increased slightly and Francis and Schipper (1988) concluded that there was not the significant evidence of de-crease of relevance.

However, several authors, such as Brow, Lo, Lys (2000), defend that the result of the studies above referred and which point out to the con-clusion contrary to the reduction of the value relevance, is due to the bias of the metrics (adjusted R2) used, such studies suffering from “scale effect”. Brown, Lo, Lys (2000) justified the increase of value relevance seen in those studies with the rise of the scale variation coef-ficient revealed during the analyzed period. They concluded that since that effect is con-trolled the results point out to the reduction of accounting value relevance of both variables studied (earnings and equity book value).

The period of time used by Collins, Maydew and Weiss (1997), from 1953 to 1993, is also considered to justify the weaker consistence of the value relevance evolution tested in that study.

Lev and Zarowin (1999) made the time-trend regression of R2 level of the Collins, Maydew and Weiss’s study (1997) but only for the lower period, from 1977 to 1993, and identified a negative coefficient, expressing the reduction of the accounting value relevance in the most re-cent years. This conclusion is, otherwise, con-firmed by the Lev and Zarowin (1999) study, in

which they expand the observation period until 1996.

Other important studies such as Lo and Lys (2001), Dontoh et al. (2004) and Gu (2004)2,among others, also concluded that the explana-tory power of accounting decreased signifi-cantly during the last decade in the USA.

In an identical way, Ely and Waymire (1998) didn’t find evidence about the positive contrib-ute for value relevance of the standard setting bodies continuous reorganizations (with the creation of CAP, APB and FASB)3 .

Lev and Zarowin (1999) show that there is a trend towards the deterioration of financial in-formation usefulness, despite the growth in de-mand for information on behalf of investor and the persistent efforts of regulators and standard setting bodies to improve financial information quality and timeliness, as a consequence of “the change”.

Some studies have been concluded about the large explanatory power of financial informa-tion in the United Kingdom (UK), e.g., King and Langli (1998) which compares value rele-vance in UK, Norway and Germany4 in the pe-riod between 1982 and 1996. In this paper we don’t see evidence of decrease of value relevance in UK with statistical significance5.However, this empirical study suffers from the misrepresentation created by the elimination of observations related with firms with negative earnings6.

2- With different metrics, this author identified a significant reduction in value relevance since earliest 70’.

3- At this level we can do an analogy with what was expected to happen in Portugal with the introduction of the “Directrizes Contabilísticas” (Accounting 4– Directives - DC), because this rule turns the Portuguese normative closer to international accounting benchmarks.

4- The important conservatism of the German normative is noted also in some other studies, as Harris, Lang e Moller (1994), Bartov, Goldberg e Kim (2002) and Black e White (2003).

5- Arce and Mora (2002) also pointed out the higher value relevance of UK accounting information, when compared with other European countries.

6- About this point Hayn (1995) concluded by the weak value relevance of losses.

THE DETERMINANTS OF THE DECREASE IN VALUE RELEVANCE OF ACCOUNTING NUMBERS IN PORTUGAL: 133


Another empirical study, Cañibano, Garcia-Ayuso and Rueda (2000), compares value rele-vance among some European countries (Portugal isn’t included), from 1988 to 1998, and concludes that: (i) value relevance didn’t increase7 (UK is the only country with the posi-tive coefficient in time trend regressions, but with the small R2 value, 7%, and without statis-tical significance); (ii) there is the statistical significant decrease of explanatory power of book value; and (iii) the significant decrease of explanatory power of earnings doesn’t exist8.

2.3. The Portuguese context

The Portuguese accounting system before IASB was characterized by the existence of a base normative influenced by European Directive, strongly oriented for tax proposes and creditors and based on qualities and principles like his-torical costs, accruals and conservatism.

Confirming this situation, Callo and Jarne (1995) placed the Portuguese accounting sys-tem in the Continental European legal system, with political and economic French influence and the strong contribution of the banking sys-tem towards financing the Economy.

However, there has recently been the continu-ous approach of the Portuguese accounting sys-tem to the international benchmarks, through the emission of the “Directrizes Contabilísti-cas” (DC), which are strongly influenced by the IASB normative9.

For this reason we can say that Portuguese accounting system has the bases of the

European Continental system but has become increasingly permeable to the Anglo-Saxon in-fluence (whit the introduction of DC), as Jarne (1997) recognizes when he puts Portugal in the group of the countries that suffered, during the last decades, a big change of the accounting model from “an incipient model to a structured and consolidated system”.

So far, the Portuguese Stock Market represents a little weight in the business context, despite including a large part of the biggest companies. There is a regulator body “Comissão de

Mercado de Valores Mobiliários” (CMVM) which only recently began to define functional and operating regulations about supervision of companies, e.g., describing corporate govern-ance rules.

The majority of companies listed in the Portu-guese stock market has a small free-float which also contributes to titles short liquidy.

3. EMPIRICAL STUDY

This empirical study analyses the accounting information value relevance, under the share-holder's point of view and within the Portu-guese reality context.

The study is two folded: (i) during the first one we assess the current status of the accounting information value relevance, by means of a «static and evolutionary snapshot», by identify-ing the level and evolution of recent years (last 15); (ii) in the second one we attempt to ascer-tain the main determinant factors for the recent evolution of the accounting information value

7- Value relevance decreases significantly in Finland, Germany and Norway.

8- Except in France and Finland, where value relevance of earnings decrease with statistical significance.

9- The base Portuguese normative is the POC - Plano Oficial de Contabilidade (Government Chart of Accounts), which is based on the European Directive about Accounting (4ª Directive from 1978 and 7ªDirective from 1983) and the DC have a complementary function. However, there is a hierarchy of the norma-tive which defines that the POC is the first to apply in case of conflict between the POC and a DC.


relevance in Portugal.

3.1. Research questions

The research questions posed during the first phase of the empirical study are the follows:

(i) Due to the characteristics of the Portuguese accounting model and capital market «is the accounting information not too relevant for the market»?

(ii) Due to the successive implementation of new Accounting guidelines, mainly induced by the IASC/IASB normative and to the increasing demands at capital market level, the accounting information in Portugal is progressively pre-senting increasing value relevance?

In the second phase and subsequent to the per-ception of the significant reduction tendency in the capacity of the Portuguese accounting model to explain the evolution of the market shares we attempt to ascertain the main causes of this evolution.

Based on the existing theoretical support and also on the empirical works developed interna-tionally, pointing the generic cause for the re-duction in the accounting data value relevance the «alteration of the business environment and of the typology of the companies», we propose as an explanatory hypothesis for the reduction in the value relevance in Portugal, during the last years, the increasing weight undertaken (i) by the technological companies and (ii) by the deficit companies (companies showing losses).

3.2. Selected sample and processed data

The empirical study is based upon data collected from the database DataStream from

Worldscope Thomson Company relating to non-financial companies (the accounting practices, the entries and the evaluation parameters of the financial companies differ in such the way from those of the other companies that they cannot be the object of an aggregate analysis) quoted in the Portuguese market between 198810 and 2003.

In order to maximize the available information, the data was collected separately to be proc-essed in each of the two regression models, and therefore, without erasing, from the sample an observation (company-year), which available information for one model, would eventually be unavailable for use in another model.

For Price Model (described on the next point), we considered all observations (company-year) with available information relating to the value of the total stockholder equity and earnings11

(accounting data per share) and at stock price for the closing of 31 de March of the following year.

For the Return Model (described on the next point) we considered all the observations (company-year) with information available re-lating to (i) earnings in year n, (ii) earnings in the previous year (in order to evaluate the varia-tion of earnings, with the accounting data proc-essed per share), (iii) stock prices for the clos-ing of 31 December for the year n-1, (iv) stock price for the closing of 31 March for the year n+1 and (v) dividends relating to year n (paid in n+1).

In any of the regression models the inexistence of information relating to one of the items re-ferred for an observation (company-year) led to its exclusion from the sample.

10- From this year the referred database has enough information for this study.

11- Given the treatment carried out by the relevant database, the use of “Earnings Before Extraordinary Item”, “Earnings Reported” or “Earnings per Share”becomes practically irrelevant and the extraordinary earnings (in the definition of the Portuguese legislation) isolated generally from the cases. From here onwards, we use the expression «earnings» only to mean the variable net earnings used (earnings at the bottom line).



The information obtained allowed us to have access to observations relating to 16 years (from 1988 to 2003) for the Price Model and to 14 years (from 1990 to 2003) for the Return Model.

We excluded in both models the influential ob-servations (outliers), which could alter signifi-cantly the regression results.

The exclusion of this type of observations relied on econometric rules and on criteria resulting from the type of sample, but in the latter case, they were considered by analogy to the ones used in some reference studies of the empirical research in this field.

The statistical criterion, common to the two models, excluded the observations with abso-lute values of the standardized residuals of esti-mated regression higher than 3.

In the Price Model, we followed the Harris Lang and Moller (1994)12 methodology and we excluded the observations (company-year) with stock prices higher than EUR 30 and, in accor-dance with Ali and Hwang (2000), we ex-cluded the observations (company-year) with earnings for the year higher than one-half of the total stockholder equity at that year's final, in absolute values.

For the Return Model, we used as criteria for the identification of the influent observations (excluded observations) in accordance with Easton and Harris (1991), Ali and Hwang (2000) and others, (i) the observations (company-year) with earnings variations, be-tween the year under analysis and the preceding year, higher than 100% and (ii) the observations

(company-year) with the return (market ad-justed), of the relevant share, higher, in absolute value, to 150% (so the to avoid the pressure of extreme variations).

After the estimated models, we used the above-mentioned econometric criterion to exclude observations, which inclusion or exclusion will mean significant alterations to the results of the forecasting, either in terms of statistical signifi-cance of the estimated coefficients, or in terms of the goodness of fit.

After these corrective adjustments, the number of observations was set between 29 (1988) and 76 (1997) for the Price Model and between 29 (1993) and 58 (1998) for the Return Model.

In aggregate (total) terms (all the companies and all the years), the dimension of the samples, after adjusted for outliers, is 796 observations for the price model and of 602 observations for the return model.

We carried out many other regressions, as ex-plained in the next point, with the smaller an-nual sample, after adjustment for outliers, in the order of 18 observations (Return Model without losses in 1993)13 and in more than 90% of the situations, the sample exceeds 30 observations.

3.3. Applied Methodology

The methodology used is based on Ohlson's (1995) theoretical construction consolidated by the use of two linear regression models of the market performance about the accounting data, Price Model and Return Model, with measure-ments spread over windows comprising 15 months (association between the market

12- Harris, Lang & Moller (1994), in order to avoid econometric problems resulting of the too high share prices, exclude company-year with stock prices higher than DM 1000.

13- In the case of regressions with Price Model, the smallest sample was in the order of 28 observations.


performances relating to the period between the beginning of the year and three months after the end of that same year and the financial state-ments for that same year), following the sug-gestion of such studies the OTA (2001) and other empirical works developed on that basis, as Ali and Hwang (2000).

We, thus, estimate, multiple regressions having the dependable variables, respectively, the market price, and the share return,

For the sake of language simplicity, we use for the Price Model references “EPS” and “BV” to designate, respectively, the variables Total Earnings and Stockholder Equity, whereas for the Return Model we use “EPS” and “VAR.EPS” to indicate, respectively, the vari-ables Earnings dividing into the Share Price at the previous year end and the Earnings Varia-tion between two consecutive years, dividing into the Share Price at the final of the first of the two years.

The coefficients are calculated using the ordi-nary less square (OLS) method.

For the Return Model, we used the share return (variation of the stock price for the period plus the dividends relating to the relevant year di-vided by the stock price in the beginning of the first year) adjusted to the portfolio's average return of the companies in the sample, with all the companies weighted in the fashion.

The accounting and market values, per share, were adjusted for possible stock splits.

In the end and in order to ascertain the account-ing information value relevance in Portugal, we

calculated some two hundred regressions based on the present models.

3.3.1. Methodology for the first research

question of the first phase of the study

In order to answer to the first research question, about the level of the value relevance, the basic methodology regressions, as above, are calcu-lated based on total of the sample (all the com-panies and all the years, after adjustments for outliers), as well as based on the samples for each year (after adjusting the influent observa-tions, usually known as “outliers”)14.

The equation is decomposed for both Models (Price and Return), carrying out regressions with each of the explanatory variables, in order to assess the incremental explanatory power of each one of them and of the common explana-tory power.

3.3.2. Methodology for the second research

question of the first phase of the study

In the search for an answer to the second re-search question, relating to the evolution of the accounting data value relevance in Portugal, besides dividing the period of analysis in vari-ous chronological sub-periods and establishing for each of these the mean value relevance, we also carried out the regression analysis of the time series, made up of the adjusted R2, of both models, about the time trend variable: Rt2

= 0 + 1 t + i.

3.3.3. Methodology for second phase of the

study

During the second phase of the empirical study, in order to identify the effects of certain factors

14- We realized, in this last case, the level of value relevance in each year of the sample period.


10 2it ititP EPS BV

0 1 1 2 1Ret. / . /t ittit EPS P VAR EPS P


about the accounting information value rele-vance (media and technological companies and companies with losses), we assessed in both cases, the following regressions:

i) The same regressions, annual and aggre-gate (total), presented under 3.3.1., ex-cluding from the sample the observations under testing;

(ii) Explanatory regressions of the temporal evolution of those factors;

(iii) Regressions to assess the explanatory power of these factors (both separate and jointly) about the evolution of the R2 in the basic models.

3.4. Results

3.4.1. Level of the value relevance of the

accounting information in Portugal

As to the first research question, the study bears results, in a certain way, astonishing, not be-cause of their inconsistency or reduced expres-sion, but exactly because of the strong adher-ence obtained, both to the research in progress in this domain at an international level and to the underlying theoretical support, despite the background of a not very sophisticated and of a reduced dimension capital market and of a basic accounting normative geared to the protection of the creditor and to the lenience of the taxa-tion.

The study leads us to conclude that the account-ing data in Portugal, taking into account the adjusted determination coefficient (R2) in the two models used, presents an interesting level of value relevance, i.e., the financial informa-tion is found useful for the stakeholders.

In the Price Model, taking into account all of

the observations (company-year) the value of Adjusted R2 is in the order of 42,8% (Table 1A) with a significance level at 0,0515 putting the accounting information value relevance in Por-tugal at a level not very far from the one ob-tained in more developed markets and with An-glo-Saxon accounting legislation, as can be verified in Table 2A, for indicative purposes only, about the value of Adjusted R2 for the Price Model obtained in some empirical studies at international level supported in the North-American legislation (FASB and previous), of the IASB, of the ASB (United Kingdom) or of countries in Continental Europe.

Also for the Return Model, the value of R2 Ad-justed for the regression of the aggregate (total) sample (all the companies-year), in the order of 9,9%, statistically significant (Table 1B), was found to be interesting and not very far from the reality of other markets, see Table 2B.

In the annual regressions for the Price Model, the values of Adjusted R2 obtained vary be-tween 72,8% (1988) and 21,6% (2002), always statistically significant and in fourteen of the sixteen years both the independent variables have an explanatory power that is statistically relevant (only in two of the years does the BV show explanatory power), see Table 3A.

Under the annual regressions for the Return Model, the values obtained for Adjusted R2

vary between 44,2% (1990) and 5,6% (1997), statistically significant in eleven of the fourteen cases (with a 0,1 significance in the other three years) and with the estimated coefficient of the variable EPS statistically significant in ten of the fourteen years; the estimated coefficient of the variable Variation EPS was found to be sta-tistically relevant in three years (in fact, in two

15- By defect, throughout the study, it was considered a level of significance in the order of 0,05. We identified the cases where the regression is not statisti-cally significant at 0,05 but is at 0,1.


of these, it is the only variable with explanatory power), see Table 3B.

In both the models, we seek to identify the in-cremental explanatory power of the independ-ent variables, but we did not arrive at very ex-pressive results.

In the Price Model (Table 4A), we concluded that the common factor to the two independent variables (BV and EPS) is the one with the higher incremental explanatory power (common explanatory power), wit the higher explanatory power in ten of the sixteen years, i.e., in 62,5% of the cases (with BV showing the higher incremental explanatory power in five years and EPS in one year only).

For the Return Model (Table 4B), we conclude that the independent variable EPS (level) pre-sents an incremental explanatory power gener-ally higher than the other variable (Earnings Variation); this is the one that better explains the model, in incremental terms, in nine of the fourteen years under analysis (64,3%), against three years of the independent variable Earn-ings Variation and two years of the common explanatory power.

3.4.2. The evolution of value relevance of the

accounting information in Portugal

In Portugal, the results established as far as the evolution of value relevance of the accounting information is concerned, reveal a tendency towards decrease.

Fragmenting the period of observation (16 years) applied in the Price Model into four peri-ods of the same number of years, or into two

eight year periods, the evolution of value rele-vance downwards becomes evident (Table 5A).

A similar conclusion has been reached by ap-plying the Return Model, and using the same logic of sub-periods of construction for the 14-year interval analysed (Table 5B).

A regression time-trend with values of adjusted R2 for the Price Model was carried out, which points, with statistical significance, to a de-crease of the explanatory power of accounting data, obtaining a negative value for the estimate of the “time” coefficient, with 44,7% Adjusted R2 and significance (Table 6A16).

The estimates of regression time-trend for ad-justed R2 calculated on annual regressions for the Return Model, also lead to the same conclu-sions, obtained with the price model, although the results are less robust (28,5% Adjusted R2)but maintaining its significance (Table 6B).

In order to gauge the evolution of the explana-tory power of the two variables separately, each model was subjected to a simple linear estimate of regression models, on just one of the variables, which seems to clearly indicate that in both models, both variables have been loosing their capabilities to explain either the share price or their return (Table 7A/B).

An evolution of the individual explanatory power of the independent variables for both models can also be assessed by means of time-trend regressions (Tables 8A/B and 9A/B), which indicate that only the R2 evolution in the “VAR.EPS” variable for the return model is not explained by time and that furthermore, on the Price Model EPS evolution is not statistically significant.

16- Whenever relevant, on the tables presenting the results for simple linear regressions, the respective scattergram was shown.



Regarding incremental explanatory power, in the Price Model, both the variables and the common factor show negative estimates for the coefficients, indicating a decreasing tendency, (Graphics 1,2 and 3), although regressions are not statistically significant; the same is valid for the Return Model, excepting the variable “Variation EPS” (Graphs 4,5 and 6).

3.4.3. Justification for the reduction of the

explanatory power of the Portuguese

accounting model

The results obtained show a significant input brought by the TMT´s (designation usually ap-plied to companies acting in the areas of tech-nology, media and telecommunications17) and by companies with losses for a decrease in the accounting data value relevance in Portugal, in the last 15 years, approximately.

At this stage, the study analyses in the first place what the evolution of the market weight of the factors under consideration is, and then, goes on to assess the influence of these factors on the accounting information value relevance.

3.4.3.1. Tmt’s contribution

An assessment of both models (there are differ-ences arising from the sample selection crite-ria), leads us to conclude that the TMT´s evolu-tion during the period under study showed an upward trend (Tables 10A/B)18.

The regression time-trend of TMT’s percentage within the sample context leads us to conclude as well about a TMT´s significant increase dur-ing this period, with statistical significance

(Tables 11A/B).

The difference between the basic yearly ad-justed R2 and the adjusted R2 of the regressions where every year the TMT’s are excluded from the sample, for each model, allows us to reach the first conclusion as to the TMT´s negative contribution for value relevance.

For the Price Model, in 62,5% of the years, the TMT’s presented a negative contribution to value relevance (however, if the TMT’s weight per year is taken into consideration, the percent-age rises to 88,3%), as, on average, the TMT´s have led to a 4,3 percentage point decrease in the yearly adjusted R2 explanatory regressions of the accounting data value relevance (Table 12).

In the Return Model, in 71,4% of the years, the TMT’s had a negative contribution to value relevance, as in this case the TMT´s has caused on average, a 1,1% percentage point decrease in the adjusted R2 (Table 13).

The TMT’s negative contribution to value rele-vance is confirmed by the regression of the Ad-justed R2 established in the yearly regressions for each basic model, in the light of the TMT´s percentage, concluding that the TMT´s percent-age explains the evolution of the adjusted R2,significantly for both models (10% significance on the Return Model), see Tables 14 and 15.

In aggregate (total) terms (all companies-year, after the outliers exclusion), for both models, a regression where the TMT´s were excluded from the sample, shows a multiple regression correlation coefficient above basic regression,

17-The selection criteria applied to companies as proxies of the technological effect is not conclusive in international literature, although this is the more widely applied procedure. Some authors do not include media companies but do include, in this type of analysis, for instance, pharmaceutical companies, in view of the relevance of intangibles, namely R&D. In this study, considering that the Portuguese companies in the media sector have a tendency to perform within the technological/internet areas as well, it was deemed relevant to include all companies having as main activity TMT (Technology, Media and Tele-communications), regardless of the economic activity code assigned by the database.

18- “OUT” indicates that the TMT is an outlier.


going from 42,8% to 48,0% in the Price Model (Table 27) and from 9,9% to 10,5% in the Return Model (Table 28).

By excluding from the samples all observations related to TMT´s, the time-trend regression of the accounting information value relevance, for the period under study for the Return Model, only shows statistical significance at 0,1, main-taining the significance level in the Price Model (with lower R2 and F Test).

3.4.3.2. Contribution of losses

Analyzing both models (there are differences, resulting from the sample selection criteria), it was found that the evolution of losses, through-out the period under study, showed an upward trend (Tables 16A/B); it was also recognized not only the percentage of losses but also the absolute weight of losses for total earnings for each19.

The time-trend regressions of percentage of losses and weight of losses in absolute earnings also lead to conclude about a significant growth of losses as dependable variables throughout the period (Tables 17 to 20); they are more ex-pressive in the case of percentage losses in the Price Model and absolute weight in value of losses in the Return Model (significance at 0,05 for both cases and at 0,1 for the two other cases).

The difference between the adjusted R2 of basic yearly regressions for each model and the R2

regressions where every year the companies presenting losses are excluded from the sample, allows us to draw the first conclusion about the negative impact of losses on the accounting data value relevance.

In the Price Model, in 87,5% of the years tested, losses have shown a negative contribu-tion to value relevance (if the percentage of losses in each year is taken into account the percentage rises to 98,9%); on average, the losses have led to a decrease in the yearly Ad-justed R2 of regressions explaining the account-ing information value relevance in 10,1 percent-age points (Table 21).

In the Return Model, in 57,1% of the years, losses had a negative impact on value rele-vance; in this case the losses led to a decrease in the yearly adjusted R2 of regressions explain-ing the accounting information value relevance in 4,9 percentage points (Table 22).

The negative contribution of losses to value relevance is confirmed by the regression be-tween the Adjusted R2 established in the yearly regressions and (i) the percentage of losses and (ii) the absolute weight of losses for total the earnings, for both models

It was found that in the four regressions, the estimated coefficient of the explanatory vari-able (percentage of losses or absolute weight of losses for the total earnings) is negative and statistically significant in the cases of percent-age of losses in the Price Model (with a 0,1 sig-nificance) and absolute weight of losses in the total earnings in the Return Model (with a 0,05 significance), see Tables 23 to 26.

In aggregate (total) terms (all companies-year, after the correction of outliers), for both mod-els, the regression where all companies present-ing losses are excluded from the sample, shows adjusted R2 above basic regression, in the Price Model going from 42,8% to 49,3% and in the Return Model from 9,9% to 18,9%.

19- The clear high value found for the absolute weight of the losses in the total earnings of yearly samples, in comparison with the percentage of the number of companies showing losses, illustrate possible situations of earning management, i.e. these findings supports the premise that, in the case of near zero losses, the leadership of Portuguese companies show a tendency to manipulate Financial Statements so that the earnings are maintained near zero, but positive.



By removing from the samples all observations relating to losses, in the Price Model the time-trend regression for the accounting data value relevance during the period under study, points to a reduction in the value relevance loses sta-tistical significance20 and in the Return Model the coefficient for the Time variable itself be-come positive, even though without statistical significance.

3.4.3.3. Total contribution of both factors

(tmt’s + losses)

Excluding from the samples all observations relating to losses and to the TMT’s, in aggre-gate (total) terms, (all observations) the Ad-justed R2 increases as a percentage of the basic model, from 42,8% in the Price Model and from 9,9% in the Return Model, to 53,8% and 18,9%, respectively, according to Tables 27 and 28.

By removing from the samples all observations relating to TMT’s and to losses, for both mod-els, time-trend regressions no longer point to-wards a reduction in the accounting information value relevance.

4. CONCLUSIONS

In this study we evaluated the accounting data value relevance in Portugal and its recent evolu-tion, making use of two methodological con-structions derived from the Ohlson Model (1995).

The results show that the accounting data in Portugal is relevant for the market, i.e., is asso-ciated to the price and to the performance of the

shares.

Albeit a very undeveloped capital market, the level of the financial information value rele-vance shows expressiveness, although placed at a lower level in relation to that shown by other more developed markets and with a more market oriented accounting system; this favours the perception of the Portuguese accounting normative as of a continental basis, with an interesting level of organization and influenced by the international accounting benchmarks of Anglo-Saxon influence.

The level of association of the BV and of the Earnings with the market price reflects mainly the common explanatory power to both these two, although, BV presents an incremental ex-planatory power higher than the Earnings.

The level of association between Earnings (level) and Variation of Earnings and the share return, in incremental terms, is explained in a significant manner by the level of the Earnings; the variation of the Earnings did not prove to be a relevant contributory, to which is probably related with a reduced degree of persistency of the earnings and warrants further investigation.

The contribution for the value relevance that the entity responsible for the national accounting legislation might have, implicitly or explicitly, expected with the introduction of successive “Directrizes Contabilísticas” (DC), to a great extent influenced by legislation from IASC/IASB, it is not clear enough to neutralize the reduction tendency in value relevance during the last 15 years, evolution, in fact, consistent with what has been generically and empirically shown at an international level.

20- Results without total adherence to Collins, Pincus & Xie (1999), which concluded that the losses present an explanatory power of the level of association if in the presence of a model including BV.


Without ignoring the existence of evident inco-herencies and inconsistencies in the accounting legislation, which the inclusion of some DC highlights, we identified empirically, supported on the theoretical derivation, the strong shift of its own «business environment» as a factor for the decline of the accounting data value rele-vance in Portugal.

Using as a proxy of the «business change», the weight increase of the technological companies in the market, where a great part of the assets are of an intangible nature and valued by the market but are not recognized on the financial statements, we showed its contribution for the reduction of the accounting data value rele-vance in Portugal.

Further to the technological effect, we identi-fied that the increasing number of companies with losses represents an important factor for the reduction tendency in the value relevance of the Portuguese market; if we ignore this effect, the level of association remains, greatly, unchanged.

Through the introduction in practice and in the whole of the universe of the listed companies of the IASB normative, less subject to the conservatism and the bias of the financial information,21 we can expect that it contributes to offset, in an evident fashion, the negative effects of the technological and losses firms for the value relevance, which might be a possible future field of continuous research.

21- Cases, for instance, of the non-redemption of the JV usage goodwill, to the capital appreciation, of the lesser “recognition lag” in the variations of the financial instruments and in other situations.



TABELS AND GRAPHS

145 : CADERNOS DO MERCADO DE VALORES MOBILIÁRIOS THE DETERMINANTS OF THE DECREASE IN VALUE RELEVANCE OF ACCOUNTING NUMBERS IN PORTUGAL: 145

TABLE 1 A – VALUE RELEVANCE - PRICE MODEL – TOTAL SAMPLE (1988 - 2003)

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,6550 R Square 0,4291 Adjusted R Square 0,4276 Standard Error 4,4845 Observations 796

ANOVAdf SS MS F Significance F

Regression 2 11.984,12 5.992,06 297,96 0,0000 Residual 793 15.947,50 20,11 Total 795 27.931,62

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 3,3862 0,2320 14,5931 0,0000 2,9307 3,8417 2,9307 3,8417BV 0,4470 0,0294 15,1920 0,0000 0,3893 0,5048 0,3893 0,5048EPS 1,9930 0,1617 12,3220 0,0000 1,6755 2,3105 1,6755 2,3105

TABLE 1 B – VALUE RELEVANCE - RETURN MODEL – TOTAL SAMPLE (1990 - 2003)

SUMMARY OUTPUT

Regression Statistics

Multiple R 0,3190

R Square 0,1018Adjusted R Square 0,0988Standard Error 0,4441Observations 602


Regression 2 13,3886 6,6943 33,9362 0,0000Residual 599 118,1599 0,1973Total 601 131,5485

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept -0,0825 0,0182 -4,5382 0,0000 -0,1183 -0,0468 -0,1183 -0,0468EPS 0,6598 0,0898 7,3466 0,0000 0,4834 0,8362 0,4834 0,8362VAR. EPS 0,2123 0,1299 1,6343 0,1027 -0,0428 0,4675 -0,0428 0,4675


AUTHORS YEAR SAMPLE (NORMATIVE) ADJUST R2 . AUTHORS YEAR SAMPLE (NORMATIVE)

ADJUST R2 .

1994

1998 44,1%

Australia 20,7%France 16,0%

Italy 31,5%1999 75,8% Holand 38,0%

United Kingdom 43,3%

45,3% 1998

Germany 40,2%Norway 64,6%

United Kingdom 66,2%

42,8%

Firms from several countries/normtives,

1986-19952000

Ali and Hwang (3)

(3) In this paper the independent variables are deflated by BV. We don’t show all the sixteen countries in the study.

(1) This paper doesn’t show the R2 from the pooled regression, so we determined the average R2 from annual regressions.

(2) This paper doesn’t show the R2 from the pooled regression, so we show the average R2 from annual regressions in the three periods.

Dontoh, Randharkrishnan

and Ronen (2)

USA 1983-1998King and Langli

(4) Firms from 3 countries(Norway, UK andGermany), 1982-1996

Harris, Lang and Moller

Germany Firms (German

Normative), 1982-1991

A) PRICE MODEL

14,0%Collins, Maydew

and Weis1997 USA 1953-1993 53,6%

Ely and Waymire USA 1927-1993

Francis and Schipper

1999 USA 1952-1994 62,0%

Lev and Zarowin (1)

USA 1977-1996

2004

Portugal 1988-2003

(4) In this paper the losses observations are excluded from the sample.

TABLE 2 – VALUE RELEVANCE – SOME EMPIRICAL STUDIES

AUTHORS YEAR SAMPLE (NORMATIVE) ADJUST R2 .

1998 18,5%

1999 7,4%

2004

Harris, Lang and Moller

1994 7,0%

1990-2003 9,9%

B) RETURN MODEL

Easton and Harris1991 EUA 1969-1986 7,7%

(1) This paper doesn’t show the R2 form the pooled regression, so we determined the average

R2 from annual regressions.

Dumontier and Raffournier

13,0%German, Swiss andAustrian firms, 1999 -2002, with IASBnormative

Portugal

German firms (with German normative),

1982-1991

22,0%

Lev and Zarowin (1)

EUA 1978-1996

Ely and Waymire EUA 1939-1993

Francis and Schipper

1999 EUA 1952-1994


TABLE 3 – VALUE RELEVANCE – RELATIVE EXPLANATORY POWER

YEAR NºOBS. ADJUSTED R2

1988 29 72,8%1989 29 56,2%1990 33 49,5%1991 33 60,2%1992 40 65,7%1993 43 42,2%1994 48 49,1%1995 49 51,9%1996 72 63,2%1997 76 29,5%1998 66 57,9%1999 61 42,2%2000 61 24,1%2001 57 46,8%2002 46 21,6%2003 46 37,2%

A) ANNUAL REGRESSIONS - PRICE MODEL

YEAR NºOBS. ADJUSTED R2

1990 32 44,2%1991 30 42,6%1992 35 42,1%1993 29 15,7%1994 41 19,1%1995 44 19,3%1996 44 33,6%1997 58 5,6%1998 58 12,5%1999 48 30,9%2000 50 16,3%2001 49 36,2%2002 45 8,7%2003 38 8,0%

B) ANNUAL REGRESSIONS - RETURN MODEL


TABLE 4 – VALUE RELEVANCE – INCREMENTAL EXPLANATORY POWER

YEAR NºOBS. BV EPS COMMONLARGER

EXPLANATORY POWER

1988 28 9,2% 31,6% 32,0% COMMON1989 29 13,7% 9,4% 33,2% COMMON1990 32 27,3% -0,5% 22,6% BV1991 33 12,3% 10,0% 37,9% COMMON1992 39 16,7% 23,4% 25,6% COMMON1993 46 23,0% 22,3% -3,1% BV1994 49 36,5% 1,5% 11,1% BV1995 50 25,5% 5,1% 21,3% BV1996 76 21,8% 10,8% 30,6% COMMON1997 79 11,0% 8,9% 9,7% BV1998 68 8,3% 7,7% 41,9% COMMON1999 66 8,9% 15,8% 17,5% COMMON2000 65 -13,1% -0,7% 37,9% COMMON2001 59 14,2% 7,9% 24,7% COMMON2002 50 1,9% 8,3% 11,4% COMMON2003 48 13,8% 16,2% 7,2% EPS

A) PIRCE MODEL - ANNUAL REGRESSIONS

YEAR EPS VAR EPS COMMONLARGER

EXPLANATORY POWER

1990 12,9% -1,6% 32,9% COMMON1991 42,6% 0,3% -0,2% EPS1992 37,0% 6,1% -1,0% EPS1993 13,6% -1,0% 3,0% EPS1994 4,2% 16,0% -1,0% VAR EPS1995 18,8% -1,9% 2,4% EPS1996 34,7% -1,5% 0,4% EPS1997 4,0% 0,7% 1,0% EPS1998 5,6% 0,9% 6,0% COMMON1999 3,3% 18,9% 8,6% VAR EPS2000 16,7% 3,2% -3,6% EPS2001 35,5% 4,8% -4,0% EPS2002 10,7% -2,0% 0,0% EPS2003 -2,3% 5,4% 4,9% VAR EPS

B) RETURN MODEL - ANNUAL REGRESSIONS

PRICE MODEL


TABLE 5A – VALUE RELEVANCE – PRICE MODEL – TIME EVOLUTION

PERIOD 1988-1991 59,7%PERIOD 1988-1995 55,9%

PERIOD 1992-1995 52,2%

PERIOD 1996-1999 48,2%PERIOD 1986-1993 40,3%

PERIOD 2000-2003 32,4%

PRICE MODEL - ADJUSTED R2 TIME EVOLUTION

4 YEAR PERIODS AVERAGE 8 YEAR PERIODS AVERAGE

TABLE 5B – VALUE RELEVANCE – RETURN MODEL – TIME EVOLUTION

PERIOD 1990-1995 32,7%

PERIOD 1996-1999 20,4%

PERIOD 2000-2003 17,3% PERIOD 1997-2003 16,9%

(1) 6 years in the first períod

PERIOD 1990-1996 30,9%

RETURN MODEL - ADJUSTED R2 TIME EVOLUTION

4 YEAR PERIODS AVERAGE (1) 7 YEAR PERIODS AVERAGE


TABLE 6A – VALUE RELEVANCE – PRICE MODEL – TIME TREND REGRESSION

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,6957R Square 0,4840Adjusted R Square 0,4472Standard Error 0,1095Observations 16



Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,6646 0,0574 11,5729 0,0000 0,5414 0,7877 0,5414 0,7877Time -0,0215 0,0059 -3,6241 0,0028 -0,0343 -0,0088 -0,0343 -0,0088

VALUE RELEVANCE EVOLUTIONPRICE MODEL(BV+EPS)

0

0,2

0,4

0,6

0,8

1988 1990 1992 1994 1996 1998 2000 2002

YEAR

AD

JUS

TE

D R

2

\

TABLE 6B – VALUE RELEVANCE – RETURN MODEL – TIME TREND REGRESSION

SUMMARY OUTPUT





VALUE RELEVANCE EVOLUTION RETURN MODEL (EPS+VAR.EPS)

0

0,1

0,2

0,3

0,4

0,5

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

YEAR

AD

JUS

TE

D R

2


TABLE 7A – VALUE RELEVANCE – PRICE MODEL – ONE INDEPENDENT VARIABLE

PERIOD 1988-1991 47,0%

PERIOD 1988-1995 43,1%PERIOD 1992-1995 39,2%

PERIOD 1996-1999 37,4%PERIOD 1986-1993 30,9%

PERIOD 2000-2003 24,5%

PERIOD 1988-1991 44,1%PERIOD 1988-1995 35,4%

PERIOD 1992-1995 26,8%

PERIOD 1996-1999 35,7%PERIOD 1986-1993 32,0%

PERIOD 2000-2003 28,2%


PRICE MODEL - ONE INDEPENDENT VARIABLE: "BV"


PRICE MODEL - ONE INDEPENDENT VARIABLE: "EPS"


TABLE 7B – VALUE RELEVANCE – RETURN MODEL – ONE INDEPENDENT VARIABLE

PERIOD 1990-1995 28,8%PERIOD 1990-1996 28,6%

PERIOD 1996-1999 17,0%

PERIOD 2000-2003 14,4% PERIOD 1997-2003 12,3%

PERIOD 1990-1995 10,7%

PERIOD 1990-1996 7,6%

PERIOD 1996-1999 7,1%

PERIOD 2000-2003 2,2% PERIOD 1997-2003 6,4%


(1) 6 YEARS IN THE FIRST PERIOD.

RETURN MODEL - ONE INDEPENDENT VARIABLE: "EPS"


RETURN MODEL - ONE INDEPENDENT VARIABLE: "VAR EPS"


TABLE 8A – PRICE MODEL – ONLY “BV” – TIME TREND REGRESSIONSUMMARY OUTPUT





VALUE RELEVANCE BV - EVOLUTION

00,10,20,30,40,50,6

1988

1990

1992

1994

1996

1998

2000

2002

YEAR

AD

JUS

TE

D R

2

TABLE 8B – PRICE MODEL – ONLY “EPS” – TIME TREND REGRESSION

SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,4378 0,0719 6,0863 0,0000 0,2835 0,5920 0,2835 0,5920Time -0,0119 0,0074 -1,5957 0,1329 -0,0278 0,0041 -0,0278 0,0041

VALUE RELEVANCE EPS - EVOLUTION

00,20,40,60,8

1988

1990

1992

1994

1996

1998

2000

2002

YEAR

AD

JUS

TE

D R

2


TABLE 9A – RETURN MODEL – ONLY “EPS” – TIME TREND REGRESSION

SUMMARY OUTPUT





VALUE RELEVANCE EPS - EVOLUTION

0

0,1

0,2

0,3

0,4

1990 1992 1994 1996 1998 2000 2002

YEAR

AD

JUS

TE

D R

2

TABLE 9B – RETURN MODEL – ONLY “VAR.EPS” – TIME TREND REGRESSION

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,2299R Square 0,0529Adjusted R Square -0,0261Standard Error 0,1079Observations 14



Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,1137 0,0609 1,8678 0,0864 -0,0189 0,2464 -0,0189 0,2464Tempo -0,0059 0,0072 -0,8184 0,4291 -0,0214 0,0097 -0,0214 0,0097

VALUE RELEVANCE VAR.EPS EVOLUTION

-0,08

0,02

0,12

0,22

0,32

1990 1992 1994 1996 1998 2000 2002

YEAR

AD

JUS

TED

R2


GRAPH 1, 2 AND 3 - PRICE MODEL - INCREMENTAL EXPLANATORY POWER - EVOLUTION

GRAPH 1 - BV - INCREMENTAL EXPLANATORY POWER -

EVOLUTION

-20%-10%

0%10%20%30%40%

1988 1990 1992 1994 1996 1998 2000 2002

YEAR

R2

GRAPH 2 - EPS - INCREMENTAL EXPLANATORY POWER - EVOLUTION

-1%

4%

9%

14%

19%

24%

29%

34%

1988 1990 1992 1994 1996 1998 2000 2002

YEAR

R2

GRAPH 3 - COMMON - EVOLUTION

-10%

0%

10%

20%

30%

40%

50%

1988 1990 1992 1994 1996 1998 2000 2002

YEAR

R2


GRAPH 4, 5 AND 6 - RETURN MODEL - INCREMENTAL EXPLANATORY POWER

- EVOLUTION

GRAPH 4 - EPS - INCREMENTAL EXPLANATORY POWER - EVOLUTION

-5%5%

15%25%35%45%

1990 1992 1994 1996 1998 2000 2002

YEAR

R2

GRAPH 5 - VAR EPS - INCREMENTAL EXPLANATORY POWER - EVOLUTION

-5%

0%

5%

10%

15%

20%

1990 1992 1994 1996 1998 2000 2002

YEAR

R2

GRAPH 6 - COMMON - EVOLUTION

-5%

5%

15%

25%

35%

1990 1992 1994 1996 1998 2000 2002

YEAR

R2


TABLE 10 (A AND B) – NUMBER OF TMT’S - ANNUAL SAMPLES

YEAR NºTMT %

1988 1 3,4%1989 1 (1 OUT.) 0,0%1990 1 (1 OUT.) 0,0%1991 1 3,0%1992 1 2,5%1993 2 (1 OUT) 2,3%1994 2 (1 OUT) 2,1%1995 3 6,1%1996 6 (1 OUT) 6,9%1997 7 (1 OUT) 7,9%1998 7 10,6%1999 7 (2 OUT) 8,2%2000 11 18,0%2001 11 (1 OUT) 17,5%2002 10 (1 OUT) 19,6%2003 10 (1 OUT) 19,6%

YEAR NºTMT %

1990 1 3,13%1991 1 3,33%1992 1 2,86%1993 1 3,45%1994 2 4,88%1995 2 4,55%1996 3 6,82%1997 7 (1 OUT) 10,34%1998 5 8,62%1999 5 (3 OUT) 4,17%2000 5 10,00%2001 9 18,37%2002 10 22,22%2003 9 (1 OUT) 21,05%

OUT - Means Outlier

PRICE MODEL

A)

B)

RETURN MODEL

OUT - Means Outlier


TABLE 11 (A AND B) – NUMBER OF TMT’S – EVOLUTION - TIME TREND REGRESSIONS

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,9202

R Square 0,8468 %T

MT

Adjusted R Square 0,8359Standard Error 0,0283Observations 16,0000

ANOVA YEAR

df SS MS F Significance F

Regression 1,0000 0,0620 0,0620 77,3799 0,0000Residual 14,0000 0,0112 0,0008Total 15,0000 0,0732

Coeficientes Erro-padrão Stat t valor P 95% inferior 95% superior Inferior 95,0% Superior 95,0%

Intercept -0,0351 0,0148 -2,3665 0,0329 -0,0670 -0,0033 -0,0670 -0,0033Time 0,0135 0,0015 8,7966 0,0000 0,0102 0,0168 0,0102 0,0168

PERCENTAGE OF TMT EVOLUTION

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

1988 1990 1992 1994 1996 1998 2000 2002

A) PRICE MODEL

B) RETURN MODEL

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,8544R Square 0,7301

%T

MT

Adjusted R Square 0,7076Standard Error 0,0375Observations 14



Coeficientes Erro-padrão Stat t valor P 95% inferior 95% superior Inferior 95,0% Superior 95,0%

Intercept -0,0174 0,0212 -0,8218 0,4272 -0,0635 0,0287 -0,0635 0,0287Time 0,0142 0,0025 5,6972 0,0001 0,0087 0,0196 0,0087 0,0196

%T

MT

YEAR

PERCENTAGE OF TMTEVOLUTION

0%2%4%6%8%

10%12%14%16%18%20%

1990 1992 1994 1996 1998 2000 2002


TABLE 12– PRICE MODEL – CONTRIBUTION OF TMT’S FOR VALUE RELEVANCE

WITH TMT

WITHOUT TMT

1988 72,8% 72,4% Positive 0,4%1989 56,2% 56,2% Neutral 0,0%1990 49,5% 49,5% Neutral 0,0%1991 60,2% 59,8% Positive 0,4%1992 65,7% 74,8% Negative -9,1%1993 42,2% 41,9% Positive 0,3%1994 49,1% 57,2% Negative -8,1%1995 51,9% 50,5% Positive 1,4%1996 63,2% 63,9% Negative -0,7%1997 29,5% 42,0% Negative -12,5%1998 57,9% 62,5% Negative -4,6%1999 42,2% 42,8% Negative -0,6%2000 24,1% 46,1% Negative -21,9%2001 46,8% 48,6% Negative -1,8%2002 21,6% 22,9% Negative -1,3%2003 37,2% 47,4% Negative -10,2%

1610

62,5% -4,3%

PRICE MODEL

Average

Total Negative Years %Total Negative Contribution

Total Years

DIFFERENCE R2

TMT CONTRIBUTION

ADJUSTED R2

YEAR

TABLE 13– RETURN MODEL – CONTRIBUTION OF TMT’S FOR VALUE RELEVANCE

WITH TMT

WITHOUT TMT

1990 44,2% 44,5% Negative -0,4%1991 42,6% 44,7% Negative -2,1%1992 42,1% 42,3% Negative -0,1%1993 15,7% 13,7% Positive 2,0%1994 19,1% 21,5% Negative -2,4%1995 19,3% 19,6% Negative -0,3%1996 33,6% 36,4% Negative -2,8%1997 5,6% 7,1% Negative -1,4%1998 12,5% 12,4% Positive 0,1%1999 30,9% 22,9% Positive 8,0%2000 16,3% 15,8% Positive 0,5%2001 36,2% 38,3% Negative -2,1%2002 8,7% 11,0% Negative -2,3%2003 8,0% 19,8% Negative -11,9%

14

1071,4% -1,1%

RETURN MODEL

TMT CONTRIBUTION

DIFFERENCE R2

Total Years

ADJUSTED R2

Total Negative Years %

YEAR

AverageTotal Negative Contribution


TABLE 14 – PRICE MODEL – EVOLUTION - TMT’S PERCENTAGE AS INDEPENDENT VARIABLE OF R2 OF VALUE RELEVANCE IN PORTUGAL

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,6363R Square 0,4049Adjusted R Square 0,3624Standard Error 0,1176Observations 16,0000



Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,5885 0,0454 12,9566 0,0000 0,4911 0,6859 0,4911 0,6859% TMT -1,3414 0,4346 -3,0863 0,0080 -2,2736 -0,4092 -2,2736 -0,4092

% TMT EXPLAINS THE EVOLUTION OF ADJUSTED R2

0,20,25

0,30,35

0,40,45

0,50,55

0,60,65

0,70,75

0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 0,18 0,2

% TMT

AD

JUS

TE

D R

2TABLE 15 – RETURN MODEL – EVOLUTION - TMT’S PERCENTAGE AS INDEPENDENT

VARIABLE OF R2 OF VALUE RELEVANCE IN PORTUGAL

SUMMARY OUTPUT % TMT EXPLAINS THE EVOLUTION OF ADJUSTED R2




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,3271 0,0558 5,8594 0,0001 0,2055 0,4487 0,2055 0,4487% TMT -0,9906 0,5025 -1,9714 0,0722 -2,0855 0,1043 -2,0855 0,1043

00,05

0,10,15

0,20,25

0,30,35

0,40,45

0,03 0,06 0,09 0,12 0,15 0,18 0,21

% TMT

AD

JUS

TE

D R

2


TABLE 16 (A AND B) – LOSSES - ANNUAL SAMPLES

YEAR PERC. LOSSES. ABSOLUTE VALUE (1)

1988 3,4% 2,2%1989 0,0% 0,0%1990 3,0% 2,6%1991 3,0% 4,0%1992 27,5% 40,1%1993 34,9% 64,2%1994 22,9% 55,9%1995 24,5% 36,7%1996 20,8% 34,5%1997 15,8% 27,3%1998 13,6% 18,6%1999 14,8% 18,6%2000 19,7% 32,5%2001 36,8% 49,5%2002 31,9% 42,6%2003 37,0% 35,4%

YEAR PERC. LOSSES. ABSOLUTE VALUE (1)

1990 3,1% 3,3%1991 3,3% 2,9%1992 30,6% 24,2%1993 31,0% 60,3%1994 24,4% 64,6%1995 26,1% 69,0%1996 15,9% 24,7%1997 16,4% 54,1%1998 13,3% 59,1%1999 17,3% 30,4%2000 17,0% 62,7%2001 33,3% 66,6%2002 32,7% 60,8%2003 34,1% 75,1%

B - RETURN MODEL - EVOLUTION - LOSSES

(1) Absloute weight of Losses in the sample (total of absolute value of Earnings).

A - PRICE MODEL - EVOLUTION - LOSSES

(1) Absloute wheight of Losses in the sample (total of absolute value of Earnings).


TABLE 17 – PRICE MODEL – EVOLUTION – PERCENTAGE OF LOSSES

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,6818

R Square 0,4649Adjusted R Square 0,4266Standard Error 0,0948Observations 16

ANOVA YEAR

df SS MS F Significance FRegression 1 0,1092 0,1092 12,1611 0,0036Residual 14 0,1257 0,0090Total 15 0,2349

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,0412 0,0497 0,8295 0,4208 -0,0654 0,1478 -0,0654 0,1478Time 0,0179 0,0051 3,4873 0,0036 0,0069 0,0289 0,0069 0,0289

%L

OS

SE

S

PPERCENTAGE OF LOSSES - EVOLUTION

0

0,1

0,2

0,3

0,4

1988 1990 1992 1994 1996 1998 2000 2002

TABLE 18 – PRICE MODEL – EVOLUTION – ABSOLUTE WEIGHT OF LOSSES SUMMARY OUTPUT




CoefficientsStandard Erro t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,1206 0,0949 1,2719 0,2241 -0,0828 0,3241 -0,0828 0,3241Time 0,0200 0,0098 2,0361 0,0611 -0,0011 0,0410 -0,0011 0,0410

WEIGHT OF LOSSES IN TOTAL ABSOLUTE EARNINGS

0,0%

20,0%

40,0%

60,0%

1988 1990 1992 1994 1996 1998 2000 2002

YEARA

BS

.WE

IGH

T O

F L

OS

SE

S

TABLE 19 – RETURN MODEL – EVOLUTION – PERCENTAGE OF LOSSES SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,1179 0,0538 2,1931 0,0487 0,0008 0,2350 0,0008 0,2350Time 0,0127 0,0063 2,0157 0,0668 -0,0010 0,0265 -0,0010 0,0265

PERCENTAGE OF LOSSES - EVOLUTION

0,0%

10,0%

20,0%

30,0%

40,0%

1990 1992 1994 1996 1998 2000 2002

YEAR

% L

OS

SE

S


TABLE 20 – RETURN MODEL – EVOLUTION – ABSOLUTE WEIGHT OF LOSSES

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,6699R Square 0,4487Adjusted R Square 0,4028Standard Error 0,1908Observations 14,0000



Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,1731 0,1077 1,6068 0,1341 -0,0616 0,4079 -0,0616 0,4079Time 0,0395 0,0127 3,1254 0,0088 0,0120 0,0671 0,0120 0,0671

ABSOLUTE WEIGHT OF LOSSES IN TOTAL EARNING - EVOLUTION

0,0%20,0%40,0%60,0%80,0%

1990 1992 1994 1996 1998 2000 2002

YEAR

AB

S.W

EIG

HT

O

F L

OS

SE

S

TABLE 21 – PRICE MODEL – CONTRIBUTION OF LOSSES TO VALUE RELEVANCE

WITH LOSSES

WITHOUT LOSSES

1988 72,8% 71,9% Positive 0,9%1989 56,2% 56,2% Neutral 0,0%1990 49,5% 49,8% Negative -0,4%1991 60,2% 63,8% Negative -3,6%1992 65,7% 67,0% Negative -1,3%1993 42,2% 51,7% Negative -9,5%1994 49,1% 49,9% Negative -0,7%1995 51,9% 81,1% Negative -29,2%1996 63,2% 65,8% Negative -2,6%1997 29,5% 30,6% Negative -1,1%1998 57,9% 70,3% Negative -12,4%1999 42,2% 45,8% Negative -3,6%2000 24,1% 50,8% Negative -26,7%2001 46,8% 54,0% Negative -7,2%2002 21,6% 58,5% Negative -36,9%2003 37,2% 63,7% Negative -26,5%

16

14

87,5% -10,1%Total Negative Years %

DIFFERENCE R2

LOSSES CONTRIBUTION

PRICE MODEL

Total YearsAverage

Total Negative Contribution

YEARADJUSTED R2


TABLE 22 – RETURN MODEL – CONTRIBUTION OF LOSSES TO VALUE RELEVANCE

WITH LOSSES

WITHOUT LOSSES

1990 44,2% 43,9% Positive 0,3%1991 42,6% 40,5% Positive 2,1%1992 42,1% 17,8% Positive 24,4%1993 15,7% -5,7% Positive 21,4%1994 19,1% 40,4% Negative -21,3%1995 19,3% 56,7% Negative -37,4%1996 33,6% 20,6% Positive 13,0%1997 5,6% 6,6% Negative -1,0%1998 12,5% 5,4% Positive 7,1%1999 30,9% 41,5% Negative -10,6%2000 16,3% 34,4% Negative -18,2%2001 36,2% 45,5% Negative -9,2%2002 8,7% 15,2% Negative -6,5%2003 8,0% 40,8% Negative -32,8%

14

8

57,1% -4,9%

RETURN MODEL

YEARADJUSTED R2

LOSSES CONTRIBUTION DIFFERENCE R2

Total YearsAverage

Total Negative Contribution

Total Negative Years %

TABLE 23 – PRICE MODEL – PERCENTAGE OF LOSSES AS INDEPENDENT VARIABLE

OF R2 OF VALUE RELEVANCE IN PORTUGAL SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,579 0,065 8,921 0,000 0,440 0,718 0,440 0,718PERC.LOSSES -0,504 0,284 -1,771 0,098 -1,113 0,106 -1,113 0,106


TABLE 24 – RETURN MODEL – PERCENTAGE OF LOSSES AS INDEPENDENT VARIABLE

OF R2 OF VALUE RELEVANCE IN PORTUGAL

SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,349 0,082 4,235 0,001 0,169 0,528 0,169 0,528PERC.LOSSES -0,514 0,348 -1,476 0,166 -1,273 0,245 -1,273 0,245

TABLE 25 – PRICE MODEL – ABSOLUTE WEIGHT OF LOSSES IN TOTAL EARNINGS

AS INDEPENDENT VARIABLE OF R2 OF VALUE RELEVANCE IN PORTUGAL

SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,5625 0,0639 8,8066 0,0000 0,4255 0,6996 0,4255 0,6996ABS.WEIGHT LOSSES -0,2786 0,1833 -1,5202 0,1507 -0,6717 0,1145 -0,6717 0,1145


TABLE 26 – RETURN MODEL – ABSOLUTE WEIGHT OF LOSSES IN TOTAL EARNINGS

AS INDEPENDENT VARIABLE OF R2 OF VALUE RELEVANCE IN PORTUGAL

SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0%Intercept 0,4498 0,0515 8,7370 0,0000 0,3377 0,5620 0,3377 0,5620ABS.WEIGHT LOSSES -0,4486 0,0978 -4,5874 0,0006 -0,6616 -0,2355 -0,6616 -0,2355

TABLE 27 – PRICE MODEL – EFFECT ON THE EXPLANATORY POWER OF THE TOTAL

REGRESSION OF THE ELIMINATION OF OBSERVATIONS WITH LOSSES AND/OR TMT’S

SAMPLE R2 ADJUST. R2 SIGNIF. SAMPLE R2 ADJUST. R2 SIGNIF. SAMPLE R2 ADJUST. R2 SIGNIF. SAMPLE R2 ADJUST. R SIGNIF.

796 42,9% 42,8% 0,0000 631 49,5% 49,3% 0,0000 719 48,2% 48,0% 0 580 54,0% 53,8% 0

6,6% 5,3% 11,1%INCREASEINCREASE INCREASE

WITHOUT TMT'S WITHOUT LOSSES AND TMT'S

A - PRICE MODEL - TOTAL

WITH LOSSES AND TMT'S WITHOUT LOSSES

TABLE 28 – RETURN MODEL – EFFECT ON THE EXPLANATORY POWER OF THE TOTAL

REGRESSION OF THE ELIMINATION OF OBSERVATIONS WITH LOSSES AND/OR TMT’S

SAMPLE R2 ADJUST. R2 SIGNIF. SAMPLE R2 ADJUST. R2 SIGNIF. SAMPLE R2 ADJUST. R2 SIGNIF. SAMPLE R2 ADJUST. R SIGNIF.

602 10,2% 9,9% 0,0000 480 18,9% 18,5% 0,0000 546 10,8% 10,5% 0,0000 439 19,3% 18,9% 0,0000

8,6% 0,6% 9,1%

B - RETURN MODEL - TOTAL

WITH LOSSES AND TMT'S WITHOUT LOSSES

INCREASEINCREASE INCREASE

WITHOUT TMT'S WITHOUT LOSSES AND TMT'S


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c m v m the eterminants of the decrease in value relevance …€¦ · 6- about this point hayn...

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