Empirical Research in Accounting: Selected Studies 1971 || An Empirical Test of the Relevance of...

Accounting Research Center, Booth School of Business, University of Chicago

An Empirical Test of the Relevance of Accounting Information for Investment DecisionsAuthor(s): Alvin MartinSource: Journal of Accounting Research, Vol. 9, Empirical Research in Accounting: SelectedStudies 1971 (1971), pp. 1-31Published by: Wiley on behalf of Accounting Research Center, Booth School of Business, Universityof ChicagoStable URL: http://www.jstor.org/stable/2490082 .

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An Empirical Test of the Relevance of Accounting Information for

Investment Decisions

ALVIN MARTIN*

Statement of the Problem and Implications

In this paper we provide a test of the decision-relevance' of accounting information reported to holders (or prospective holders) of common stock equities through published financial statements (annual reports). It is important to establish the utility of accounting statements (1) to provide both users and preparers a perspective on the importance of the information they work with, (2) to establish a basis for accepting or rejecting normative changes in financial reports (i.e., new models should be more useful) and (3) to underscore the need to consider legislation related to change in financial reporting.

A regression model, the Accounting Model, was employed to test the decision-relevance of particular annual report accounting variables. The model results provide support for the utility of accounting information. The strength of the regression results is derived from a high degree of explanation of market yield differentials (R2 were generally greater than .6) and from the strong consistency of these results over time- and between the nonlagged and lagged formulation of the model. Based on these results, we conclude that a real and definite relationship exists between

* Assistant Professor, University of Washington. The author wishes to acknowledge the encouragement and critical support of Professors Haskel Benishay and Alfred Rappaport, and the financial support of the Ernst and Ernst Foundation during the writing of the doctoral dissertation on which this paper is based.

'Decision-relevance, usefulness and utility will be used interchangeably. Informa- tion affecting user decisions is considered decision-relevant. See Beaver (1969), p. 69, for definition of information in terms of decision-relevance and Shwayder (1968), for distinctions between levels of relevance.

1



2 EMPIRICAL RESEARCH IN ACCOUNTING: SELECTED STUDIES, 1971

annual report data and market rates of return. This conclusion is, on the whole, supported by previous empirical work (discussed below). This is an important conclusion because traditional accounting reports are not constructed explicitly to be useful but to satisfy fiduciary reporting responsi- bilities. Accountants do not characterize the financials as valuation statements, but as the objective results of past transactions.

The decision-relevance of annual report accounting information for investors has important implications for accounting theory. First, if existing accounting information has utility, any attempts to suggest improved information for users must be carefully considered. New bases for devel- oping investor information must have greater utility than existing data to offset changeover costs incurred to handle different information inputs. With expensive processing costs, information benefits must be clearly demonstrated. An important element of this demonstration is the empirical documentation of the decision-relevance of new information inputs to user decision models. The need to document utility suggests difficult problems such as how to develop research designs aimed at testing new information bases before these new bases are adopted. Obstacles such as these would indicate that change in accounting reports may be realized only over longer periods of time.

A second basic implication of this research arises from its methodology. We have sought to examine information in light of a particular decision objective. This is an appropriate direction for future studies in accounting to take. If accountants produce useful data while satisfying fiduciary reporting requirements, surely explicit attempts to provide decision-relevant data for specific resource allocation decisions can only result in increasingly beneficial information. For this reason, documenting the decision-relevance of existing annual report data should not be construed as rationalizing existing reports or fostering complacency with existing standards. On the contrary, the future of the accounting profession hinges on its ability to provide more relevant decision inputs.

Another basic implication of our research finding is the need to carefully legislate changes in reporting of financial information. Our position here is opposite to that of Benston (1969) who asserted that the lack of utility of existing information is one reason against increasing SEC disclosure requirements. We- assert that increasing disclosure of information that is in large part decision-relevant would be of benefit to investors.

Prior Studies In this section we compare several prior studies2 concerned with the

relationship between accounting information and share price changes to

2Staubus (1965), Nerlove (1968), Benston (1968), Ball and Brown (1968), Beaver (1969), Mlynarczyk (1970), and Gonedes (1970). There are not many empirical studies covering this topic, which was also Benston's (1968) conclusion. Benston stated that "the only other empirical studies of accounting data and stock prices



ACCOUNTING INFORMATION 3

our own. Our study is important as a direct test of the usefulness of reported accounting data (represented by our eight independent variable model) in contrast to tests aimed at examining the utility of a single variable or market reaction to a choice between accounting treatments. In addition, the model variables were basically generated from a questionnaire study of Chartered Financial Analysts.3 An attempt was made to provide a model based on variables perceived to be important by analysts and circumvent the criticism of a priori model construction. No effort was made to construct an optimal model. We are not attempting to provide a predictive stock valuation model in any sense. Our objectives are limited to examining the explanatory ability of accounting information. Before examining prior studies in relation to our own, we consider the expectational nature of our dependent variable in greater detail.

THE EARNINGS YIELD DEPENDENT VARIABLE

We have classified the earnings yield (weighted current earnings to price ratios) as an expectational dependent variable. This follows from the classical share price valuation model:

00

P Z (1 - bt)Eo(l + gt)t/(1 + kt)t or t=o

P =f(Eo I b I gt I kt) for all t

where share price, P, is a function of current earnings, Eo, and all future expected retention ratios, b, growth rates, g, and market discount rates, or cost of equity capital, k.4

Clearly share price is an expectational variable and, by itself, could be used as an empirical measure of market expectations.5 In order to avoid

[besides his 1968 study] of which I am aware, other than studies that seek to test whether retained earnings or dividends are superior determinants of stock prices are O'Donnell (1965), Staubus (1965), and Ashley (1962)." O'Donnell examines the effect of alternative accounting treatments (allocation of income taxes and acceler- ated depreciation) on PE multiples. Ashley (1962) created the null hypothesis that "good" and "bad" news- had equal effects on the percentage change in stock prices. By defining good and bad in terms of changes in dividends and reported earnings, he was able to reject the null hypothesis at the .01 level. Staubus' study is discussed below. O'Donnell's 1965 study and his follow-up study in 1968 are discussed by Mlynarczyk (1970), pp. 66-67.

'Martin (1971), chap. 2. 'Bower and Bower (1969) make a similar statement about share price valuation

and cite Gordon and Shapiro, Walter, Modigliani and Miller, Malkiel, Molodovsky, Bauman and Wendt as examples of academic and practitioner (security analyst) theoretical support. Gonedes (1970) uses the same starting point, and an essentially similar but more elegant formulation was used by the AAA Committee on External Reporting (Committee Reports, The Accounting Review Supplement, 1969, p. 81).

'See the Mlynarczyk (1970) and Staubus (1965) studies, discussed below, for examples. The link between investor estimates of value and market prices of stocks is an assertion commonly made in the literature of finance and generally supported by



4 ALVIN MARTIN

the problem of interfirm comparison of market values being dominated by scale factors, we can divide both sides of the share valuation model by the constant E0 and invert the P/Eo result to provide an expectational measure in terms of a rate of return or yield:

Eo/P = f(bt, gt, kt) for all t.

This ratio is essentially the dependent variable used in this study except we calculate a five-year (declining) weighted average earnings for the E0 numerator under the assumption that investors look beyond earnings of the current period only in evaluating the appropriateness of market price.

Recent studies provide some empirical evidence that past earnings is a good predictor of future earnings.6 If weighted Eo approximates future earnings, it will incorporate some of the effects of future retention and growth rates and the weighted earnings yield may be considered a measure of the cost of equity capital, k. This dependent variable is of interest in examining statements in the literature of finance about the constancy of k within a risk class or the relationship between k and independent variables such as leverage. In this study, the dependent variable measure is only of interest as it is a representation of market expectations. We are concerned more narrowly with the relationship of annual report accounting data to these market expectations. For this purpose, we consider weighted Eo/P an appropriate dependent variable.7

COMPARISON TO PRIOR STUDIES

Table 1 provides a summary of the basic differences between previous research and the present study. The first difference discussed in that table is related to sampling procedures. All the studies, except for Staubus and Mlynarczyk's, use Compustat data. Nerlove, Ball and Brown, Beaver and Benston use large samples based on the criterion of completeness of Com- pustat data. The Gonedes study and the Accounting Model segregate

empirical evidence. We subsequently use market expectations and individual investor expectations interchangeably.

'Cragg and Malkiel (1968) found past growth rates of earnings predicted future earnings as well as the involved earnings forecasts of five large institutional investors (based partially on inside information) and past earnings growth rates predicted earnings better than a naive zero growth model (p. 77). In another recent article, Whitehurst (1970) found a Spearman rank correlation coefficient of +.42 (for a 198 company sample) in comparing projected earnings per share (based on a three year trend period) to actual. Whitehurst concluded that "the data cast a strong doubt on the ability of a prospective shareholder to predict the rankings of future earnings" (p. 557). While this is true for individual equities, the strong positive rank correlation coefficient strongly supports the idea that on the average reported earnings adequately represent future (expected) earnings.

'An identically formulated variable was used by Benishay (1968) and a similar dependent variable was used in the multiple regression models of Bower and Bower (1969) and Gonedes (1970, ex ante model).




Compustat data by industry and use firms with complete data in selected (larger) Compustat industry categories (sample selection criteria for the Accounting Model of this study are discussed below). The accounting model data is based on a purposive sample of 98 companies in four industries which will limit the generalizability of the results.

Another difference among the studies is related to the periods tested. Benston and Ball and Brown essentially used a single cross-section period. The other studies cited used examination relationships over several years. Statistical relationships that are evident over longer periods of time provide stronger results than those obtained by a one-year analysis. The three cross-section years used in the present study (dictated by data limitations) are better than a cross-section study for only one year, but can be expanded to advantage in subsequent work to cover more years.

The list of independent variables reflects another important difference between the Table 1 models. Except for the Accounting, Gonedes, Mlynar- czyk and Nerlove models, all the studies are essentially explorations of the relationship between market price changes and a single accounting variable. There is no single unit of accounting information capable of representing the complex set of financial transactions, although some accounting variables may be more useful for investors than others. The accounting model and the Nerlove model each assemble a series of possi- bly useful accounting variables in efforts to test the relationship between the entire series of variables and market movements. Gonedes and Mly- narczyk use multiple variable models to hold market effects constant while examining the relationships between market variables and the use of alternative accounting procedures.

A final difference between the accounting model and the other studies, the basis for selection of the set of independent variables, is not included in Table 1. In the other studies variables were chosen on a priori theoretical considerations. The accounting model variables of this study, on the other hand, were selected, primarily, to represent financial analysts' opinion as to what accounting information was important in explaining market movements. An attempt was made to base the model variables on a questionnaire to Chartered Financial Analysts in the Chicago area.8

In terms of empirical results, Staubus, Beaver and Ball and Brown found strong statistical substantiation of the relationship between earnings numbers and market movements (price and volume). Nerlove attributes failure of his model (to adequately explain capital gain rates of return) to capital market imperfections and Benston (1968) uses a capital gains rate of return and essentially one accounting variable: earnings or sales. Benston's results consisted of a series of small partial correlation coefficients between earnings (and alternatively sales) and market price

8The questionnaire and results are described in Martin (1971), chap. 2; the process of selecting questionnaire variables for inclusion in the accounting regression model is described on pages 95-98, Martin (1971).



6 ALVIN MARTIN

rates of return. He concluded that "the effects (as measured here) of published accounting data on stock prices are not very great." (p. 22) Benston asserts that a relatively small portion of the information used by investors is contained in published accounting annual report data:

The finding that it does not make a great deal of difference which accounting construct of net income or model is used may be interpreted to mean that accounting data are used only to confirm information, etc., and that the arguments within the accounting profession of which concept of income is "best" are beside the point. (p. 28)

Benston calculated monthly market price rates of return (his dependent variable) at earnings announcement dates and for several dates immedi- ately preceding and ending with the earnings announcement date.9 Based on the Ball and Brown results, Benston's specification of timing for his dependent variables may not be sensitive to the time at which the market reacts to changes in accounting information. In other words, the market may decide on earnings changes well before announcement dates and Benston may have chosen the wrong series of months to compute his dependent variable.10

As we noted above, in a subsequent article Benston (1969) relied partially on the results of his 1968 study to conclude that the Securities and Exchange Commission should not require additional disclosure of financial information because his findings indicate that "the data required by the SEC do not seem to be useful to investors." (p. 53) We would argue that based on the questions raised above with regard to timing and the use of a single variable model, the extension of Benston's results to the area of SEC policy may be unwarranted.

Both Mlynarczyk and Gonedes were concerned with the effect of alternative accounting procedures on investors' market perceptions. It is clearly a difficult task to examine this question, and although their results are far from unambiguous, both authors conclude essentially that accounting practices do matter to investors. The studies by Gonedes and Mlynarczyk do provide, indirectly, evidence about the total or overall usefulness of accounting data and market expectations. Gonedes' results, in particular, will be discussed again with the accounting model results as his was the only other study reviewed using a similar dependent variable.

Taken as a whole, we believe this series of empirical results represents

Benston selected (1) earnings announcement date, (2) month of preliminary announcement of earnings (PAD), (3) PAD minus one month, (4) PAD minus 2 months and (5) sum of (2), (3) and (4). (See Benston, 1968, p. 13.)

1 Parker (1968) states essentially the same criticism in his critique of Benston's paper. Beaver's (1969) finding that price and volume movements are abnormal at earnings announcement dates does not force the conclusion that this is the relevant period for study because, as Beaver states: based on the Ball and Brown (1968) study, we can conclude that investors form unbiased forecasts of reported earnings. "They are not very efficient, for if they were, there would be no volume or price reaction when earnings reports are released." (p. 85)




an important accumulation of evidence towards the documentation of the importance of financial report accounting output in the equity investment process. We will return to this conclusion later.

Description of the Accounting Model

The use of multiple regression models in this study provides the advantage of examining the information content of a series of accounting variables taken together. As market movements are complex phenomena, a multivariate statistical technique would constitute an appropriate test of the hypothesized relationship between accounting information and a measure of these market movements. The multiple regression model does not deny the existence of other information, it merely provides a quanti- fied estimate as to what proportion of the differences in market rates of return between firms (measured by earnings yields) can be explained by the independent (accounting) variables.

Regressions of earnings yields in 1965, 1966 and 1967 are run on independent variables for 1965, 1966 and 1967. This set of regressions is described as nonlagged. In addition, the earnings yields in 1965, 1966 and 1967 are regressed on the independent variables in 1964, 1965 and 1966, in other words, lagged one year. We have called these regressions lagged regressions. The lagged variables will enable us to examine the ability of the model to predict future market evaluations of ex ante performance. As this task is considerably more difficult than explaining current market yields with current financial results, we would expect the lagged models to possess less explanatory power. We also provide a series of industry dummy variables to hold industry effects constant. The final model is of the following form:

8 11

log (Eo/P) = ao + E ajXi + E ajXj i=1 i=9

where X1 through X8 are the specific accounting annual report variables and X9 through Xi, are industry dummy variables where Xi = 1 if the company is in a given industry."

We provide variable definitions below for all accounting model variables :12

Earnings yield. The market yield measure used for the dependent variable in this study is represented by the ratio of exponentially declining weighted reported earnings for the five years up to and including the cross-section year to the arithmetic mean of the high and low of the

11 The following values of X9, XKo and Xn respectively, represented the four industry groupings in terms of Compustat codes comprising the sample: 1311 (1, 0, 0), 2800 (0, 1, 0), 3310 (0, 0, 1) and 3714 (0, 0, 0). Companies selected are discussed below.

'See Appendix for mathematical formulation and Compustat item number reference.



8 ALVIN MARTIN

equity market value in the cross-section year. The weighting scheme em- ploys (.8) as a smoothing coefficient13 and may be illustrated with the following example for 1965:

weight for cross-section year: ( 6 (1965 earnings)

weight for year preceding cross-section year: = S)l (1964 earnings)

weight for first year in average (cross-section (.8) ( year minus four years): ( e=1a(.8

This weighting scheme will be used for all variables requiring exponentially declining weights. The five years included in all averages for all independent variables discussed below are the five years up to and including the cross-section year for the nonlagged model and the five years up to and including the year before the cross-section year for the lagged model.

OCFGRO. The growth of operating cash flow over time was defined as the slope of the simple linear regression of operating cash flow on time, divided by the arithmetic mean of the operating cash flows. Operating cash flows were defined as income plus depreciation and amortization. It was expected that this variable would incorporate much of the information contained in a growth of earnings variable. Many analysts place great emphasis on the operating cash flow quantity and per share statistics are common.14 Staubus (1965) in discussing this quantity indicated that adding back depreciation eliminated the intercompany difference caused by depreciation method. Based on the above, we feel that rate of growth in earnings before depreciation provides an adequate measure of investor preference for (or aversion to) growth in earnings. Nerlove, Ben- ston and Mlynarczyk included a similar independent variable in their regression models.

Captos. The exponentially declining weighted ratio of capital expenditures to sales was also included as a growth measure. The proportion of the sales dollar a company is able to channel into capital expenditures (other things equal) is posited to be a variable of interest to the market. Capital expenditures were important in each industry included in the sample data.

Salests. The stability of sales over time is represented by arithmetic mean sales divided by the standard deviation of the deviations from the simple linear regression of sales on time.'5 This variable will test for stockholder preference for stable or fluctuating sales.

"This weighting scheme is adopted from Benishay (1968). He experimented with various types of weighting systems and concluded that .8 performed best (p. 326).

'4 See, for example, Mason (1961). 1 Nerlove (1968) found a similar variable to be important in his regressions, "The




Payout. The payout ratio is an exponentially declining weighted ratio of dividends to net earnings available to common shareholders during the cross-section year and the previous four years.

Assets. The exponentially declining weighted summation of total assets was used as a size variable. Several writers have suggested that investors' risk assessments vary inversely with firm size.'6 The assets variable will attempt to assess stockholder preferences for larger firms.

CFTODT. This variable was represented by the geometrically weighted ratio of income plus depreciation (used for the operating cash flow variable) divided by debt. As a measure used to test investor aversion to financial risk, cash flow to debt has the advantage of relating earnings flows to financial obligations.17 Nerlove, Mlynarczyk and Gonedes used variants of the debt to equity ratio for financial risk variables.

OPMARG. The exponentially declining weighted ratio of net operating income to sales was used to represent the operating margin. Operating income is defined by the Compustat manual as "net sales less cost of sales and operating expenses before deducting depreciation, amortization, etc." (p. 5-10) Operating expenses include most recurring business expenses as selling, general and administrative, and research and development, and we expect to test market preference for a high operating margin.

BKROR. This was the most frequently mentioned variable in the questionnaire used to generate model variables. More attention in the literature had been paid to this variable than any of the other independent variables included in the accounting model. Book rate or return was empirically defined as the exponentially declining weighted ratio of net income available for common stockholders divided by net worth (at book value). Although frequently mentioned as a management target, this ratio suffers from complete reliance on recorded book values and recorded book income. The compustat common equity variable used is normal equity less intangibles and other items (for example, unamortized debt discount and expense).

Where possible (lack of zero or negative values), the logarithm of each variable was taken for the regressions (OCFGRO, payout and CFTODT were the only variables not converted to logs). This procedure provides

most important variables explaining differences among rates of return over both short and long periods are sales growth and retention of earnings." (p. 313) Benston also tested a rate of change in sales independent variable.

'6Benishay's (1968) size variable (in terms of market value) provided his strongest and most consistent results. Gonedes (1970) used a size variable similar to ours.

'Beaver (1967) empirically explored the ability of a series of risk ratios to predict failure. He concluded that "the ability to predict failure is strongest in the cash-flow to total debt ratio" (p. 85). Beaver's theoretical debt was to an article in the Journal of Business by James Walter (1957) entitled "The Determination of Technical Solvency." Walter first explored the problem of solvency in terms of liquid asset inflows as related to debt levels.



10 ALVIN MARTIN

variables with more nearly normal distributions appropriate for linear multiple regression models.'8

COLLINEARITY OF MODEL VARIABLES

We consider the collinearity of the accounting model variables before examining the regression results. Table 2 presents the series of arithmetic mean simple correlation coefficients for all model variables. In this table we find no cases of very high collinearity. The ratio of capital expenditures to sales (Captos) is strongly related (average correlation .74) to the operating income to sales ratio (OPMARG) probably due to the identical denominators. OPMARG is also correlated with rate of return on net worth (BKROR) due to similar numerators in both ratios. The relatively low correlations between all other pairs of model variables indicate that the selected variables each provide information about reported accounting values.

Data and Sampling Procedures

In this section we discuss the data base used for the regression model and the companies and periods selected for test.

COMPUSTAT DATA

The data used as a basis for testing the regression model was the Compustat annual industrial tape available for research purposes at Northwestern University. Compustat data has been the basis for an apparently increasing number of empirical studies.19 The method of compil- ing company statistics may lose substantial amounts of information especially when mergers have occurred.20 However, the advantages of an accu- rately compiled and relatively complete data base are easily seen.2'

A total of 60 items of data for a 20-year period can be included for each company on the tape. The tape was studied for the availability of complete data over the 20-year period for each company (1711 companies were on Northwestern's tape during the summer of 1968). After 1960, data availability sharply increased, many companies apparently having been added by Standard Statistics starting in 1960.22 The data included in this

"All regression models reviewed above used logarithms of variables except Ner- love. All of Nerlove's variables were in ratio form.

"9See, for example, the prior research studies reviewed above. Beaver (1969) states: "the Compustat population represents over 90 per cent of the total market value of the common stocks of publicly held corporations and hence is a relevant population for study." (p. 70)

' For some discussion of these limitations see Nerlove (1968), pp. 315-16. Advantages of the tape are explained in the following sources: Tuttle (1967),

Feuerstein (1968). ' Tapes are supposed to be continually updated.




study was taken from 1960-196723 with the final data input taken from Northwestern University's Compustat tape in December, 1968.

COMPANIES SELECTED

The four industries with the largest number of companies with relatively complete 1960-67 data were selected for the study. The final screen- ing was done by a computer program requiring complete information for each variable of interest for the entire 1960-67 period.24 The resultant sample of 98 companies, by industry,25 was as follows:

Com~pustat Number of cor- Accepted for code panies on tape test26

1311 Oil, Crude Producers 44 14 2800 Chemicals 41 36 3310 Steel 43 30 3714 Auto Parts and Accessories 45 18

98

The small number of industry27 groups selected resulted in the decision to handle industry effects on a dummy variable basis. The sample is a complete count of companies with the necessary data. No attempt was made to construct a representative group of companies (if this is possible for listed stocks). The sample of companies is derived from diverse industry groupings and is of sufficient size to impart some generality to the statistical findings. Interpretation of the results must always be done with caution and consideration of the basis of sample selections.

Analysis of Regression Results OVERVIEW

We set out to examine the hypothesis that accounting information reported to holders or prospective holders of residual market equities (com-

23To compute a regression on 1965 where the independent variables are based on 5-year averages lagged one year, data from 1960 to 1964 are needed. The total of eight years (1960-1967) was sufficient, therefore, to compute all lagged and nonlagged regressions for the 1965, 1966 and 1967 cross-section years.

' Lack of information on capital expenditures was the constraint that eliminated more companies from the study than any other.

2 These are all capital intensive industries. This provides another compelling reason to limit our discussions of the generalizability of results. We would not expect regression coefficients of the capital expenditures to sales ratio to be significant for service industries such as financial institutions, for example. In addition, the sample industries are obviously not free of interdependence (in an input-output sense); however, the industries selected do not appear to be closely related enough to destroy the generalizability of the research result. The sample is represented as a purposive one, and the conclusions sought are indications of general relationships-not final answers.

26Company names are included in the Appendix. Mautz (1968) discusses the inadequacy of SIC coding (used by Compustat) on

pp. 131-32. Nerlove (1968) raises the possibility of Compustat misclassification on p. 322.



12 ALVIN MARTIN

mon stock shareholders) through published financial statements (accounting information in annual reports) constitutes decision-relevant data for equity investment decisions.

We conclude that the results of our regressions28 provide substantive support for the above hypothesis. This conclusion is derived from the consistent relationships of predicted direction between the independent variables and market yield and ability of the accounting model to provide an overall explanation of changes in market yields that is statistically significant in each case.

In addition, we interpret our results with some confidence because the predicted directions and magnitudes of relationships for comparable variables and for the regressions overall are similar to an existing regression model used as a point of comparison in our previous work. In our original study (Martin, 1971), an identical dependent variable was regressed on a Benishay model identical to Benishay's (1968) model except five-year weighted averages were used to Benishay's nine-year weighted averages.29 The same data was used for both the accounting and Benishay models. These results were also consistent with the work of Bower and Bower (1969) who used a price-earnings ratio dependent variable.30

Table 3 summarizes the complete results for all regressions run in this study. These regressions are all statistically significant at less than or equal to the one percent level (in terms of F ratio) in each cross-section year for both the nonlagged and lagged formulations of the model. The regressions explain a medium to high proportion of the variance in earnings yields. The regression coefficients of the independent variables (to be discussed in detail below) are not highly significant in -most instances. We interpret our results with some confidence, however, because these regression coefficients and the partial correlation coefficients of the independent variables present consistent relationships in terms of both magnitude and direction, over the three cross-section years and in both the nonlagged and lagged formulation of the models. In the years tested (1965, 1966 and 1967) major market indicators showed movements both up (1965 and

2'The regressions were run using the UCLA Bio-Medical multiple regression program, BMD03R. In addition, residuals were plotted and tested for normality with a chi-square test. Residuals were also plotted against individual independent variables in each cross-section. These plots appeared normal in almost all cases.

" Benishay's model independent variables were (1) growth in equity values, (2) payout (identical to the accounting model variable), (3) stability of earnings (similar to sales stability in the accounting model), (4) stability of stock market equity values, (5) size (in market value terms), (6) debt to equity ratio, (7) skewness of equity values (in terms of the third moment) and (8) conformity of equity values to S & P 425 industrial stock index.

'3The Bower and Bower model (1969) is a five variable multiple regression using a similar ex ante dependent variable. Their variables are growth in earnings, variability of stock prices, payout, marketability, and conformity to market. Their results are similar to and confirm the findings of the Benishay model although their R2 were much lower.




1967) and down (1966), which add strength to the consistency of the statistical findings. As might be expected, the lagged regressions do not provide as much explanation of investor anticipations as the nonlagged. It is more difficult to explain current market yields with past information than with more current information.

We did not set out to investigate the use of accounting information as the sole basis for predictive models. Market yields are too complex to be described by changes in accounting information, no matter how sophisti- cated the statistical model used. Our objectives were limited to the exami- nationi of accounting information as one of the potential inputs to possible predictive models. On this basis, consistently significant results are adequate to provide support for the hypothesis that accounting data have utility to the investor.

It is clear that these overall conclusions must be interpreted with full recognition of the limited nature of the study. First, we used a multiple regression model and we have some doubts concerning the effectiveness of this type of statistical model.3' Second, our data were collected for a relatively short time span (1960 to 1967) which may have some atypical attributes. Finally, the sample of 98 companies in our capital-intensive industries demands a careful interpretation of the study results. With these limitations in mind, we turn to the analysis of regression results in terms of individual model variables.

ACCOUNTING MODEL RESULTS IN TERMS OF SPECIFIC INDEPENDENT

VARIABLES

Salests. This variable measures the stability of sales over time. We would predict that the more stable the reported sales figures, the greater the perceived accuracy of the future predicted sales by investors and the higher the price per dollar of expected earnings. In other words, we antici- pate a market premium for the reduced risk of predicting future sales (smoother sales curves). This prediction is based on acceptance of the sales figure as reflecting "the fundamental, dynamic activity of a business" (Kennedy and McMullen, 1968, p. 401) and the belief that the variability of sales is a factor determining investor perceptions of total business risk.82 Interfirm sales comparisons also offer an advantage over earnings comparisons because no cost allocations obscure the comparison and few alternative accounting techniques can affect sales reporting.

Interest in investigating management attempts to present smooth finan-

See Benston (1968, p. 3) for limitations of this type of analysis. 82Analysis of corporate annual reports supports the importance of sales figures over

time. Every historical statistical summary includes sales as well as earnings statistics. Information on market penetration and charts showing sales growth and composition frequently appear (see Boise Cascade 1968 report, for example). Sales variables were the most significant independent variables in the studies of both Nerlove (1968) and Benston (1968).



14 ALVIN MARTIN

cial variables has centered on tests of the smoothing of reported earnings.33 Lev (1969) stated "despite the fact that net income is not the only item of interest to financial statement users, no attempt has yet been made to generalize the smoothing question to financial ratios." (p. 291)

The regression results (in Table 3) indicate the predicted preference for stability of sales only in two of three nonlagged cross-section years and no regression coefficient is significant at the five percent level or less. No preference for stability is indicated by our results.34

If we hypothesize that accountants' methods allow management to attempt to smooth financial results and smooth results misrepresent economic reality enough to cause suboptimal capital allocation decisions by investors, it becomes an important question to document market preferences for smoother results. The stronger the market preference for smooth sales or earnings, the more important the choice of methods to effect this smoothing becomes.35 We consider the exploration of the market effect of smoothing an interesting and important direction for future research.

OCFGRO. Despite efforts of the accounting community to limit the use of cash flow per share statistics,36 a recent study by Fess and Weygandt (1969) concluded that "Opinion No. 3 did not have a significant effect on reporting practices in respect to the manner in which cash-flow data are presented in annual reports." (p. 56) Drebin (1964) examined the basis for the perceived usefulness of operating cash flow data:

By adding the depreciation deduction back to income it is often possible to obtain

See, for example, M. J. Gordon, et al., "Accounting Measurements and Normal Growth of the Firm," in Jaedicke, et al., eds., Research in Accounting Measurement (AAA, 1966), pp. 221-31; S. Hepworth, "Smoothing Periodic Income," in Zeff and Keller, eds., Financial Accounting Theory, 1964; R. Sprouse, "Accounting for What- You-May-Call-It," Journal of Accountancy (October, 1966); Gary White, "Alternative Accounting Procedures and Trends in Reported Earnings: An Empirical Investiga- tion of Income Normalization," unpublished Ph.D. dissertation, University of Wash- ington, 1969; and R. M. Copeland, "Income Smoothing," in Empirical Research in Accounting: Selected Studies, 1968, Supplement to Vol. 6, Journal of Accounting Re- search, pp. 101-12.

'This is consistent with the mixed results of the similarly formulated earnings stability variable in the Benishay model and in Benishay (1968). A preference for stability is indicated by a negative partial correlation coefficient of the Salests variable. The greater the stability of sales, the greater the share price per dollar of earnings (if investors prefer stability) or the lower the earnings yield.

I This may raise ethical questions as to the propriety of smoothing, or of the manipulation possible with existing rules. This question is, however, beyond the scope of this study.

' Accounting Principles Board Opinion No. 3, 1963, attempted to expand the use of source and application of funds statements and restrict the use of out-of-context cash flow statistics. On December 18, 1970, a new exposure draft was circulated by the APB calling for the funds statement to become one of the "basic" financial statements. Cash flow is normally considered net earnings plus depreciation (see for example, Merril Lynch, Pierce, Fenner & Smith Inc., How to Read a Financial Report, 1968, p. 35). We have called this quantity operating cash flow and will subsequently use cash flow and operating cash flow interchangeably.




a more clear indication of the economic progress of a company. This is particularly useful when year-to-year or firm-to-firm comparisons are to be made.... this figure does not represent cash flow in the literal sense of the term; nor does it indicate the real income of a particular company for a given period. However, it does provide a method of reducing the effects of a major inconsistency in accounting practice: discretionary variations in the depreciation charge. (p. 27)

Staubus (1965) provided empirical results indicating that divergent depreciation practices limited the usefulness of net earnings figures. Mason's (1961) examination of cash flow analyses concluded, in part, that analysts are concerned with (1) forecasting individual company future earnings and (2) intercompany comparisons of forecasted earnings and "the cash- flow concept appears to be widely if not universally used in both of these areas" (Mason, 1961, p. 14, emphasis added). Finally, Fess and Weygandt report the following 1965 Forbes editorial comment:

Forbes feels that, increasingly, cash flow is at least as important as reported earnings. Hence, in our Annual Report on American Industry we use cash flow-to- equity capital as one of our three yardsticks of profitability. (p. 56)

Based on the perceived importance of operating cash flow to investors we would expect a market preference for rate of growth in this variable.

The regressions provide the expected result that investors pay a premium for high historical growth rates in earnings plus depreciation (OCFGRO). This result is strongest in the nonlagged model where the regression coefficients are statistically significant at the .01 or less level in all cross-section years. This result implies that the market relies on the accounting variable operating cash flow to formulate expectations.

Payout. Financial theorists have focused a great deal of attention on the relationship between dividends and share prices. Substantial theoretical support can be found for the idea that investors prefer dividends8 or that investors are indifferent to dividends (dividends have no effect on share prices).38

We might also discuss the possibility that investors prefer lower payout, based on (1) differential tax treatment afforded capital gains versus dividends, (2) the use of low dividends as a market signal indicating favorable perceptions of future prospects by management, and (3) the existence

37For example, Graham and Dodd in their 1934 book Security Analysis stated: "given two companies in the same general position and with the same earning power, the one paying the larger dividend will always sell at higher prices" [quoted in Mao (1969), p. 479]. For other arguments supporting investor preference for high payout see Arditti (1967, p. 22), Bower and Bower (1969, footnote 6, p. 356) and Beaver, Kettler and Scholes (1970, p. 660). Benishay (1962) provides an explanation for empirical results showing preference for payout on technical grounds (p. 213). For an excellent summary of the question "do dividends count" see Mao (1969, pp. 477-93).

' The "no-effect" school is supported by Modigliani and Miller (1961) who state: "We may conclude that given a firm's investment policy, the dividend payout policy it chooses to follow will affect neither the current price of its shares nor the total return to its shareholders." (p. 343)



16 ALVIN MARTIN

of market gamblers who would prefer low payout for portions of their wealth.

As indicated in Table 3, the regression coefficients of the payout ratio are not significant. The sign of the regression coefficients is negative in 4 of 6 regressions. These results would appear to support the dividends- don't-count theorists. However, based on the large amount of conflicting literature on the question of preference for payout and the exploratory nature of our own study, we do not interpret our mixed findings as supporting any particular theory relating dividend payout and share prices.

OPMARG. Typically, the operating margin (or operating ratio) is calculated by deducting recurring operating expenses including depreciation charges from net sales to arrive at net operating income89 and dividing net operating income by net sales. We used the Compustat definition of net operating income which excludes depreciation and amortization charges. Considering the problems involved in calculating a meaningful depreciation charge (discussed in the section on the OCFGRO variable above), we believe that the following comments about the typical operating margin would apply equally to our OPMARG variable.

Kennedy and McMullen (1968) state that operating income component of the operating margin "which is one of the most important figures shown on the income statement, reveals the profitableness of the sales, i.e., the profitableness of the regular buying, manufacturing, and selling operations of the business." (p. 416) D'Ambrosio (1970) explains that "costs of inputs need to be related to revenues derived from outputsFthat is, NOI [net operating income]-to determine the efficiency of the production process. The operating ratio can be used as an index of that overall efficiency." (p. 149) In the Merril Lynch, Pierce, Fenner and Smith Inc. monograph How to Read a Financial Report, one of the few income statement ratios suggested is the operating ratio: "Before you select a company for investment, you will want to know something of its Operating Margin of Profit and how this figure has changed over the years." (p. 28) This leads us to predict an investor preference for high operating income to sales margins.

The regression results (Table 3) indicate a consistent and statistically significant investor preference for stocks exhibiting a high margin of operating income to sales. The regression coefficients are significant in each cross-section except 1965 lagged. These results provide substantial support for investor preference for high operating margin.

BKROR. Rate of return on net worth or book rate of return was the most frequently mentioned variable of investment-decision importance by the financial analyst respondents to the questionnaire used to generate variables for this study. This variable, or some variant of BKROR, is accorded the lion's share of space in most discussions of accounting statement analysis. For example, in the New York Stock Exchange Pamphlet

89See, for example, Kennedy and McMullen (1968), p. 416.




Understanding Financial Statements: 7 Keys to Value (1969) the net income to net worth ratio is listed as one of the seven "keys" and described as "one of the most significant of all financial ratios." (p. 24) Moore and Jaedicke (1967) also discuss BKROR as an investment criterion:

Financial analysts and potential investors will evaluate a company and its management by looking at the profits that are earned in relation to the assets employed and in relation to the stockholders' equity. A company that can earn a better-than-average rate of return without taking unwarranted risks will ordinarily be able to attract more investment funds and to obtain them at a lower cost than its less fortunate competitors. (p. 126)

Moore and Jaedicke imply an investor premium for high BKROR. Helfert (1963) also indicates that BKROR "is especially crucial to the financial analyst interested in investing equity funds." (p. 63)40

While BKROR may be accepted as a relevant practical measure of business effectiveness, its limitations must also be considered. Lerner and Carlton (1966) mention the following:

1. The ratio does not provide a consistent gauge of effectiveness. Because the BKROR value is affected by profitability and asset turnover the value of either could change with changing asset composition.

2. Cannot be used in making intercompany comparisons. 3. The analyst cannot tell how a change in sales affects profits. 4. BKROR gives no indication of the effects of adopting different capital struc-

tures. (pp. 15-17)

Ezra Solomon (1966, in Rappaport, 1970) raises other serious questions about BKROR. Exploring the difference between time adjusted internal yields and a book yield similar to BKROR, Solomon concludes that financial analysis is presented witl a serious dilemma:

On the one hand, the ratio of net income to net book assets is not a reliable measure of return on investment. On the other hand, analysis definitely requires some measure of return on investment and there appears to be no other way in which this concept can be measured for an ongoing division or a company. The pragmatic answer is that book yield will continue to be used, but that its use must be tempered by a far greater degree of judgment and adjustment than we have employed in the past. (p. 325)

Solomon indicates that a company that is growing

acquires new investments at a rising pace, the overall company yield (BKROR) in any year will be more heavily weighted with investment projects which are in their early phase of development and for which net book values are high relative

40 Kennedy and McMullen (1968) consider BKROR to be "one of the most important relationships in financial statement analysis." (p. 393) Bevis (1965) goes so far as to state: "Some analysts make a computation, which might be called 'return on invested capital,' by dividing net income by the amount of stockholders' equity. For the intended purpose, no better statistics are available, and probably no better ones could be devised." (p. 57)



18 ALVIN MARTIN

to net income. Thus, the observable book yield for a growing company will be smaller than the observable yield for a non-growing company, even though both hold essentially identical investments. (p. 323)

If we can assert that growing companies have more favorable (lower) earnings yields then we would not expect a market preference for high BKROR (characteristic of slower growing companies). Investors may prefer companies with low BKROR which may be higher growth companies.

In addition to the above comments questioning the utility of BKROR we can consider the possible lack of preference for BKROR with the following two-period example:

Ai A2

NW (book value of net worth) 100 100 MV (total market value of equity shares) 100 200 NI (net income) 10 20 BKROR .10 .20 NI/MV (earnings to price ratio) .10 .10

By employing the assumption of a constant earnings to price ratio (or PE Multiple) we examine the effect on BKROR of a change in earnings. In period 2, Company A increases earnings without increasing net worth. This could have resulted from an increased asset base financed by debt or more efficient utilization of existing assets. This change results in the doubling of BKROR and a constant earnings yield. Due to changes in net worth which need not result in higher earnings levels and change in market capitalization rates which are a priori independent of changes in book value of net worth (i.e., changes related to future expectations) we conclude that market yields and BKROR need not be related.

The regression results document a strong market aversion to the net income to net worth ratio. The higher this ratio, the lower the price to earnings ratio. Based on the above criticisms of BKROR, market aversion to BKROR is not unexpected even though this is contrary to our strong questionnaire results.

Assets. This variable was included to test for market preference for firms with larger book value of assets. The statistical results appear on the surface to indicate an aversion to high asset book values as reflected by the positive partial correlation coefficient in 4 of 6 regressions; however, as the regression coefficients are only significant for the 1967 cross- sections at more than the .05 level, we cannot make a strong statement in this regard.

Gonedes (1970) also found disappointing results for his identical (log of total assets) variable. Although his model results indicated a market preference for larger firms in 3 of 4 cases (his ex ante Model I, p. 105), the regression coefficients were not significant.

One hypothesis to explain the lack of preference for high asset book values may be the existence of an inverse relationship between book values and economic values. In our regression model the positive regres-




sion coefficient may not represent aversion to larger firms. Accounting measurement techniques for balance sheet assets may actually misrepresent economic values and are, therefore, not decision-relevant results for investors. More specifically companies with high market price to earnings ratios (low yields) may be companies where earnings and asset book values are less than projected future earnings based economic asset values. The larger the price in relation to reported earnings, the greater the gap between the market (economic) value and book value of assets is likely to be. Therefore, the lower the yield, the lower the asset book value in relation to economic asset value. This results in the positive relationship between assets and earning to price ratios reflected in our regression re- suits. This reasoning may also be used to explain the preference for low BKROR discussed above.

In contrast to the mixed results for size in terms of book values found in our study and by Gonedes, Benishay (1968) found strong, consistent market preference for larger firms (in terms of market values). One conclusion that can be drawn from these results is that the effect of firm size or return on assets is best measured in market value terms. This conclusion can be interpreted as support for the theoretical arguments of many accountants who suggest that the cost basis of reporting must be aban- doned or supplemented by market value reporting.4'

Captos. The ratio of capital expenditures to sales (captos) provides important results for this study. First, in the empirical studies reviewed above, capital expenditures were not used as an independent variable. This study therefore provides a first look at the possible significance of this variable in the context of examining the utility of annual report data. Second, the industries included in the sample were all capital intensive and should provide an important population on which to test the significance of captos.

Results for this variable are mixed and nowhere significant. Both the numerator and denominator of this ratio are stated very nearly in current dollar terms and the amount of a firm's capital investment is related to the expected future dividend generating capacity of the firm. We expected, therefore, more significant results for the captos variable. We are hesitant to dismiss this key expenditure variable as decision-relevant on a priori grounds and look for more sensitive specifications of this information in future studies.

CFTODT. As indicated above, the operating cash flow to debt variable

4The arguments to abandon the cost basis generally prescribe a market-related value for the tangible assets [see AAA (1966) or Chambers (1966) for example]. If we do not approach the problem of valuation of intangible factors also, accounting measures may never provide economically relevant information. P. L. Defliese, the current APB Chairman, indicates that even the APB is studying departures from the historical cost basis for certain balance sheet items. Also, draft opinions on intercor- porate investments and marketable securities involve significant departures from the cost basis "The APB and Its Recent and Pending Opinions," Journal of Accountancy, 131 (February, 1971), 66-69.



20 ALVIN MARTIN

provides a measure of the relationship between net earnings inflows and fixed financial obligations. This variable was successful in predicting bankruptcy in a previous empirical study (Beaver, 1967) and provides an appropriate measure of financial risk for this study. Walter (1957) examined the inadequacy of traditional risk-measures (such as current ratios) and provided empirical support for the importance of net cash flows in relation to fixed outflows. Donaldson (1961) developed a new approach to the determination of corporate debt capacity levels in terms of variations of cash flows and the possible amounts of adverse cash flows in recession periods. Donaldson's framework for debt risk is developed in terms of debt relationships to cash flows:

In order to examine the problem of the risk of debt in a simple and familiar framework, this study focuses on the limits of adversity-the possibility that debt could lead to cash insolvency. This over-simplification of reality is justified on two grounds. The first is that it is common practice for businessmen to think of debt limits in terms of the ultimate hazard of running out of cash under recession conditions. The second is that the risk of cash insolvency may be viewed as the extreme case of a whole family of risks herein described as the risk of cash inadequacy. It can be argued that an assessment of the risk of cash insolvency will provide a basic reference point to which all risks associated with debt may be related. (p. 7, emphasis added)

The operating cash flow and debt ratio should reflect investor preference based on these arguments.

The regressions (Table 3) indicate a consistent preference for a high operating cash flow to debt ratio. The sign of the regression coefficient is in the predicted direction in each case and is statistically significant (at the .05 level) in three of the six regressions. These results support the theory that investors perceive that the risk of a debt level is meaningfully measured in relation to the magnitude of cash inflows needed to service the debt. This conclusion is only tentative, based on the limitations of this study, but this result is uniformly supported by financial literature which leads to some confidence in this interpretation.

Industry dummy variables. We found the industry dummy variables to significantly affect our regression results in this study.42 The industry effect was strong because we used data from only four industries and the bulk of our sample from steels and chemicals-industries with, apparently, important homogeneous attributes. We do not report specific results for these dummy variables as we were uninterested in industry effects except to hold them constant in examining our other variables.

Conclusion

We have presented empirical evidence in support of the decision-relevance of accounting annual report data for investment decisions.43 In our

4aR' on regressions run without industry dummies (not reported here) were at least .20 lower than regressions with dummies.

'4 The notion of accounting decision-relevance is not lacking in authoritative




view, this evidence is complementary to evidence provided by existing studies examining various aspects of accounting information utility. This study uniquely provides an explicit test of the usefulness of a series of accounting variables taken together.

Based on this conclusion, we assert that the foundation for change inr accounting rules must be carefully constructed in view of the utility of the existing set of annual report data. Finally, we consider legislation to, increase the scope and amount of reported data as potentially beneficial to investors, based on the ability of current information to explain investor expectations. We are aware of our study limitations (mentioned above) and do not advertise universality in our results.

By using a multivariate statistical model we explicitly recognize the interrelatedness of all aspects of corporate activity. Management may purposely force the corporation into an ostensibly weak position in one aspect of performance in trade for success in another (for example, exhibit strong growth and poor liquidity). Concurrent presentation of variable interrelationships, therefore, may be an important reporting improvement by allowing the analyst to more rigorously consider variable interrelationships. Table 2 presents a possible basis for analyzing pertinent financial measures in that the matrix format provides a comprehensive perspective on variable interrelationships. This type of empirical data would seem to provide a relevant perspective (in terms of generating expectations about variable interrelationships) for the analysis of an individual company's reported results.

Other possible areas of future research emphasis are highlighted by our study. The results of our study, in conjunction with the results of similar statistical efforts, provide bases of comparison for future attempts to examine (or predict) stock price yields. Also, little investigation of the use of accounting information in other decision contexts has been performed.44 Data availability45 has focused empirical effort on stock market decisions. While these decisions are undeniably significant, we assert that accounting information is no less of an important element in other economic decisions.

support. A recent American Accounting Association Committee consisting of David Solomons, Chairman, and N. Bedford, E. Hendriksen, T. Keller, E. Nelson, A. Rappa- port and G. Sorter concluded in their January 8, 1971 report that the importance of accounting is hardly a matter of dispute. They state: "Accounting reports provide the information by which millions of investors judge corporate investment performance and by reference to which they make investment decisions. Every day, decisions concerning the allocation of resources of vast magnitude are made on the basis of accounting information." (p. 3)

"Beaver's (1967) work on business failure is a notable example. '4 Compustat and CRSP data primarily.



22 ALVIN MAR~TIN

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26 ALVIN MARTIN

TABLE 3 ACCOUNTING MODEL

Regression Coefficients, T Ratios, and Partial Correlation Coefficients, Respectively

Nonlagged Lagged

1965 1966 1967 1965 1966 1967

R2 .6249 .6177 .7008 .4688 .6292 .6503 (Log) Salests-Sales stability -.012 .038 -.048 .017 .029 .029

-.319 .801 -1.269 .396 .717 .607 -.035 .087 -.136 .043 .078 .066

OCFGRO-Growth in -.4992 - .5172 -.7092 -.182 -.3652 - .336t earnings plus deprecia- -4.164 -3.405 -4.544 -1.189 -2.859 -2,194 tion -.412 -.346 -.442 -.128 -.295 -.232

Payout-Dividend pay- .0002 -.001 .00005 -.0003 -.0003 -.0001 out ratio .281 -1.040 1.274 -.578 -.450 -.135

.030 -.112 .137 -.063 -.049 -.015 (Log) OPMARG-operating -.3111 -.4712 -.3622 -.091 - .4372 - .4292

income to sales -2.386 -3.560 -3.306 -.678 -3.151 -3.220 -.251 .360 -.338 -.073 -.323 -.330

(Log) BKROR-Net income .3862 .4632 .4102 .2271 .4272 .4162 to net worth 3.447 3.880 4.358 2.259 3.576 3.461

.350 .388 .427 .238 .361 .351 (Log) Assets-total assets -.010 .027 .0511 -.010 .027 .0661

-.460 1.064 2.395 -.380 1.161 2.580 -.050 .115 .251 -.041 .125 .269

(Log) Captos-capital expend- .013 .019 -.029 -.095 .003 -.017 itures to sales .178 .243 -.430 -1.324 .040 -.222

.019 .026 -.047 -.142 .004 -.024 CFTODT-income plus - .0111 -.001 -.002 -.0141 - .0152 -.001

depreciation to debt -2.344 -.881 -1.311 -2.574 -2.941 -.844 -.246 -.095 -.141 -.269 -.304 -.091

(Industry dummy variable results not reported.) Two tail test of t ratio significance, 1 significant at < .05 level, 2 at < .01 level. Companies were eliminated from the sample with a negative result for one of the variables for which the

log was to be taken (the log of a negative result is undefined). This resulted in n = 97 for each of the above regressions.

APPENDIX

Data Used to Compute Regression Variables

Compustat Variable Definition item number

TA total assets 6 DEBT long term debt + preferred stock 9 + 10 ANW book value common equity 11 S sales 12 ANOI net operating income 13 ANI net income (available for common) 20 D dividends 21 EQ market value of equity (average of high and 2 (22 + 23) *25

low for year times adjusted number of shares outstanding)

CE capital expenditures 30 INCDEPR net income + depreciation and amortization 14 + 20




Symbols WT8 Exponentially declining weights for t years used to compute weighted averages

(smoothing coefficient .8):

cross section year- (t -1) years (first year used for average): ( 8)i D- (.8)i (.8)1

cross section year: z:i (.8).

t Number of years included in average. Five years were in this study (t = 5).

Dependent Variable

R2T (Earnings Yield) = (D:(Tr-+1) (WT8)ANIi)/EQT.

T is cross section year.

Accounting Model Independent Variables

Salests (stability of sales over time)

= (s=(-tl)S/t/vv*=(-t1) St- fA4 + B4(i) }]2/t - 1

where B4 is the slope and A4 the intercept of the regression S(i) = A4 + B4(i), i = (it- t + 1), ... T.

OCFGRO (growth in operating cash flow over time)

= B6T/(E :T-t+l) INCDEPRilt)

where B6 is the regression coefficient, INCDEPR (i) = A6 + B6(i), i = (T - t + 1) ... V. Payout (payout ratio) = [=(T-t+l) (WT8) (DM/ANI)1-100.

OPMARG (operating margin) = -(T-t+l) WT8 (ANOIdSi).

BKROR (book rate of return) a (TT-t+1) WT8 (ANIiIANWi).

Assets (total assets) = Ei-(T-t+l) WT8 (TAt).

Captos (capital expenditures to sales) = &-(T-t+1) WT8 (CEM/Si).

CFTODT (cash flow to debt)- E (T-t+1) WT8 (INCDEPRi/DEBTi).

COMPANIES USED IN COMPUSTAT SAMPLE

1311 OIL-CRUDE PRODUCERS 16400 Amerada Petroleum Corp. 32620 American Petrofina, Inc. 58550 Aztec Oil Gas 91300 British American Oil Co., Ltd.

118450 Canadian Export Gas & Oil, Ltd. 119320 Canadian Homestead Oils, Ltd. 195300 Creole Petroleum Corp. 221150 Dome Petroleum Ltd. 395540 Jefferson Lake Petrochemicals of Canada 432950 Livingston Oil Co. 478300 Midwest Oil Corp.



28 ALVIN MARTIN

644286 Scope Industries, Inc. 646330 Scurry-Rainbow Oil Ltd. 702300 Superior Oil Co.

2800 CHEMICALS 6700 Air Products/Chemicals Inc. 6900 Air Reduction Co.

13000 Allied Chemical Corp. 21900 American Cyanamid Co. 53841 Atlas Chemical Industries Inc.

131500 Celanese Corp. 144244 Chemetron Corp. 171700 Commercial Solvents Corp. 215701 Diamond Shamrock Corp. 225000 Dow Chemical 229300 Dupont, (E.I.) De Nemours & Co. 236400 Eastman Kodak Co. 251330 Essex Chemical Corp. 281740 FMC Corp. 293526 GAF Corp. 319900 Grace (W.R.) & Co. 334701 Gulf Resources & Chemical Corp. 351700 Hercules Inc. 381200 Interchemical 413600 Koppers Co. 443000 MacAndrews and Forbes 482800 Minnesota Mining & Manufacturing 'Co. 491010 Monsanto Company 512900 National Lead Co. 551600 Olin Mathieson Chemical Corp. 556796 PPG Industries 571850 Pennsalt Chemicals Corp. 613950 Reichhold Chemicals Inc. 617800 Rexall Drug/Chemical Co. 627900 ROHM/HASS Co. 691900 Stauffer Chemical Co. 693154 Stephan Chemical Co. 734100 Union Carbide Corp. 746460 U.S. Borax Chemical Corp. 794550 Witco Chemical Co. 798150 Wyandotte Chemicals

3310 STEEL 9200 Alan Wood Steel Co.

11600 Allegheny Luclum Steel Corp. 46800 Armco Steel Corp. 76700 Bethlehem Steel Corp.

105976 C.F. and I. Steel 127400 Carpenter Steel Co. 187900 Continental Steel Corp. 189500 Copperweld Steel 202455 Cyclops Corp. 214000 Detroit Steel Corp. 235400 Eastern Stainless Steel Corp. 322500 Granite City Steel Co.




378800 Inland Steel Co. 381710 Interlake Steel Co. 398600 Jones & Laughlin Steel Corp. 401300 Kaiser Steel Corp. 408335 Keystone Consolidated Inc. 421700 Latrobe Steel Co. 441200 Lukens Steel Co. 464750 Mclouth Steel Corp. 516900 National Steel Corp. 542950 Northwestern Steel & Wire Co. 589000 Pittsburgh Steel Co. 616800 Republic Steel Corp. 65540O Sharon Steel Corp. 692700 Steel Co. of Canada 743000 United Engineering & Foundry Co. 752200 U.S. Steel Corp. 786100 Wheeling Steel Corp. 801700 Youngstown Sheet & Tube Co.

3714 AUTO PARTS AND ACCESSORIES 48300 Arvin Industries Inc. 85100 Borg-Warner Corporation 99000 Budd Company

142530 Champion Spark Plug 159700 Clevite Corp. 203800 Dana Corporation 237236 Eaton Yale and Towne Inc. 251025 ESB, Inc. 259600 Federal Hogul Inc. 351480 Hercules Galion Products, Inc. 362910 Houdaille Industries, Inc. 404000 Kelsey Hayes Co. 462800 McCord Corp. 503250 Napco Industries Inc. 605400 Purolator Products Inc. 609400 Raybestos-Manhattan, Inc. 665100 Smith (A.O.) Corp. 718900 Timken Roller Bearing Co.

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