The Information Content of Nonearnings Accounting Numbers as

Journal of Accounting Research Vol. 28 No. 1Spring 1990

Printed in U.S.A.

The Information Content of Nonearnings Accounting Numbers

as Earnings Predictors

J A N E A . O U *

1. Introduction

This paper provides empirical evidence on the predictive ability and information content of nonearnings annual report numbers beyond that contained in earnings.' I take an "earnings prediction" approach to address the incremental information content issue.' Under this approach, nonearnings accounting numbers, jointly, play the role of predictors of future earnings changes. Specifically, I investigate whether nonearnings accounting numbers convey information about future earnings that is not reflected in current earnings; and if they do, whether this incremental predictive content is reflected in stock prices.

* Santa Clara University. This paper is based on my dissertation a t the University of California, Berkeley. I gratefully acknowledge the helpful comments and suggestions of William Beaver, George Foster, James Ohlson, Stephen Penman, James Sepe, Neal Ush- man, Peter Wilson, and the workshop participants at U.C. Berkeley and Stanford. I also wish to thank an anonymous referee for providing many insightful suggestions. I am especially indebted to Stephen Penman, my dissertation chairman, for his guidance and assistance throughout the development and revisions of this paper.

' In this paper, "nonearnings accounting numbers" refer to all data items reported in annual financial statements other than earnings, including earnings components.

'Much prior research had identified nonearnings items that are marginally useful in explaining contemporaneous stock returns. Examples include Lipe [I9861 on components of earnings, Kaplan and Patell [1977], Rayburn [1986], and Wilson [1986; 19871 on cash flow and/or accrual components of earnings, Griffin [I9761 on dividends, Gonedes 11975; 19781 and Eskew and Wright [I9761 on extraordinary items, and Gonedes 119741on several financial ratios. Many of these studies found incremental information content in various accounting items. For a review of earlier studies, see Lev and Ohlson [1982].

Copyright 0,Institute of Professional Accounting 1990

NONEARNINGS ACCOUNTING NUMBERS 145

The information perspective of accounting implies that the contemporaneous association between nonearnings accounting numbers and stock returns can be viewed as resulting from a predictive information link between these accounting numbers and some unobservable, value- relevant attributes of the firm.3 Although future dividends and future cash flows have commonly been cited as proxies for these primitive attributes, evidence suggests that future earnings are also va lue- re le~ant .~ Thus, the incremental information content of nonearnings accounting numbers previously found in tests based on their associations with stock returns may have arisen partly from these numbers' ability to predict future earning^.^ This paper complements the existing literature by offering this predictive perspective on, and interpretation of, the incremental information content of nonearnings accounting numbers. The main results of this study provide evidence for both a "predictive information link" between some nonearnings annual report numbers and future earnings changes and a "valuation link" between predicted future earnings changes and stock returns during the annual report dissemination period.

The predictive information link is established by fitting binary one- year-ahead earnings prediction models to selected annual report data and comparing these models' predictions with those of a random walk modele6 Predictive ability of the annual-report-based models over and above that of a random walk model must be attributed to the nonearnings predictors. My results indicate that some nonearnings accounting numbers contain information about future earnings changes not reflected in current and prior earnings.

In this framework, the observed contemporaneous association between accounting data and stock prices is the result of an "information link" between accounting data and future streams of benefits from equity investments and a "valuation link" between future benefits and stock prices. Information disclosure triggers revisions of investors' expectation of the future benefits. These revisions are then reflected in current stock prices. This framework is formally presented in Ohlson [I9791 and Garman and Ohlson [1980], and empirically tested by Easton [1985].

4 F ~ rexample, the findings of Ball and Brown [1968], Beaver and Morse [1978], and Beaver, Lambert, and Morse [I9801 all are consistent with the notion that stock prices reflect information regarding future earnings. Moreover, the efforts expended by security analysts, corporate management, and the general investing public in forecasting earnings seem to indicate that future earnings must be value-relevant.

Nonearnings accounting numbers may convey predictive information about future earnings for at least the following reasons. First, some nonearnings numbers may help to identify the "transitory component" of current earnings which does not persist in the long run. Second, nonearnings data may reflect managerial decisions that have implications for future earnings. The current earnings figure, prepared under GAAP, is commonly believed to reflect some value-relevant information with a lag.

Most studies examining the time series properties of accounting earnings have con- cluded that annual earnings in general follow a martingale type of process (for example, Ball and Watts [1972]).Thus, changes in future earnings (net of drift) cannot be predicted based on current and prior earnings. This has been referred to as annual earnings following a "random walk" or "random walk with a drift."

146 JOURNAL OF ACCOUNTING RESEARCH, SPRING 1990

Given this predictive information link, annual reports can be viewed as providing both current earnings and a prediction of next year's change in earnings. Ball and Brown [I9681 have shown differential returns to portfolios based on current earnings. Using their portfolio approach to analyze the "valuation link" between stock returns and the second signal, I find that during the annual report dissemination period, stock returns reflect both current earnings changes and the annual-report-based predictions of future earnings changes. In other words, stock prices behave as if investors do (at least to some extent) use nonearnings annual report numbers to revise their future earnings expectations and thereby bid up (down) the prices of stocks with favorable (unfavorable) predictions.

My results also provide an additional explanation for a result in Ball and Brown [1968]. They report that the information in reported earnings is anticipated by the market as early as 12 months prior to the preliminary earnings announcement. They speculate that other news events, such as dividend and interim earnings announcements, might explain why "unexpected earnings" are anticipated. The predictive information link documented in this study suggests that the release of the previous year's complete annual report, an event included in Ball and Brown's abnormal return accumulation period, also provides useful information for forming expectations about the current year's earnings.

Section 2 introduces the logit earnings prediction models. Section 3 describes the test samples and the study periods. Section 4 presents the empirical results. Section 5 contains the conclusion.

2. A Probabilistic Model of Earnings Changes

This study investigates the ability of nonearnings annual accounting numbers to predict the sign of the change in one-year-ahead earnings. A correctly specified prediction model with a continuous dependent variable capturing both the sign and the magnitude of the change would be more efficient. However, given that outliers are very common in accounting data series and that available data series are typically short, it is not clear whether such a model would perform better in a new prediction space. A binary model mitigates this problem since an extreme change in earnings has no greater influence on the model parameter estimates than any other observation.

Let Ait denote a vector of firm i7s accounting descriptors based on its annual reports of year t and prior years, and O be a vector of estimated parameters. Now denote the probability of observing an earnings increase in year t + 1,the subsequent year, for a given Ait and O by Pr(Ait, O). For simplicity in computation and interpretation, the following logistic distribution function is assumed for Pr(Ait, O): Pr(Ait, O) = (1 + exp (-Ait'@))-'. Given any Ait, the output of the logit model is an estimated probability of an earnings increase in year t + 1for firm i. This probability measure, denoted Prit, or P r for short, summarizes the information contained in Ait about firm i's earnings of year t + 1.

mailto:(-Ait'@))-'


Because of the limited number of time-series observations for individual firms, the maximum likelihood estimate of the parameter vector O is derived from pooling data across all sample firms over the model estimation period. Therefore, a general prediction model is applied to all firms. To the extent that firm-specific characteristics (such as industry membership) imply differing earnings processes, using a general model rather than industry-specific or even firm-specific models may weaken the model's predictive ability.

To translate each probablistic prediction, Pr , into a binary prediction of an earnings increase or decrease, I use two preset probability cutoff schemes: (.5, .5) and (.6, .4). Under the (.5, .5) scheme, the prediction is an earnings increase for P r >0.5 and a decrease otherwise. Alternatively, under the (.6, .4) scheme, the prediction is an earnings increase when P r r 0.6 and a decrease when P r 5 0.4; observations with P r between 0.6 and 0.4 are dropped.

The change in firm i's earnings in year t + 1, denoted AXltcl, is specified as: AX,,,, = EPS,,+l - EPS,, - drift,,, where EPS,,+l and EPS,, are firm i7s "as reported7' earnings per share before extraordinary items for years t + 1and t, respectively. The drift term for year t is estimated as firm i's mean earnings-per-share change over the four years prior to year t + 1.This specification of earnings changes approximates "unexpected earnings" under a submartingale p r o ~ e s s . ~

Although it may be desirable to include a large set of accounting predictors in the model in order to explore fully the information content of nonearnings accounting numbers, such a model would inevitably place too heavy demands on the data. Firm observations will have to be dropped if just one accounting item is not available. For this reason, and partly for simplicity, I construct simple logit models based on a parsimonious set of predictors.

To identify these predictors, I screened 61 predictor candidates (mostly financial ratios) over the period 1965-77.8 The screening took two steps. First, a univariate logit model was fitted for each candidate and only those 13 descriptors with an estimated coefficient significant at the 10% level were retained. Second, multivariate logit models were fitted for these 13 descriptors on two independent data sets. Eight descriptors with a coefficient significant at the 10% level in at least one of these two data sets were kept as predictors to fit the final logit models:

(1) GWINVN: percentage growth in the "inventory to total assets" ratio;

'Since earnings increases significantly outnumber earnings decreases in the test period, this specification of earnings changes also removes firm-specific trends and balances the number of observations on the two sides of earnings changes. The main results of this study are unchanged when earnings changes are not adjusted for the drift term, and when earnings are deflated by owners' equity rather than by the number of outstanding shares. These results are reported in Ou [1984].

These 61 accounting descriptors are reported in Ou [1984, appendix A].

148 JANE A. OU

(2) GWSALE: percentage growth in the "net sales to total assets" ratio;

(3) CHGDPS: change in "dividends per share," relative to that of the previous year;

(4) GWDEP: percentage growth in "depreciation expense"; (5) GWCPXI: percentage growth in the "capital expenditure to total

assets" ratio; (6) GWCPX2: GWCPXI, with a one-year lag; (7) ROR: the accounting rate of return, i.e., "income before extraor-

dinary items7' divided by "total owners7 equity as of the beginning of the year7';

(8) AROR: change in ROR, relative to the previous year's ROR.

Most of these eight descriptors have been directly or indirectly tested in prior incremental information content studies under different ap- pro ache^.^

3. Data and Study Periods

The logit prediction models were estimated based on a pooled data set of 391 firms over the period 1965-77. The sample firms met the following data requirements in each year: (1)all data items needed for computing the eight earnings predictors and the subsequent year's earnings change (net of drift) were available on the 1982 Cornpustat Industrial Annual File, and (2) fiscal year ended in December.

The predictive ability of the logit models was then assessed over the period 1978-83. Use of a test period independent of the model estimation period avoids potential bias from statistical overfitting. The test sample for predictive performance includes 637 firms with a December fiscal year-end and complete data on the 1986 Cornpustat file for calculating the eight accounting predictors and earnings change in each year of the test period. Of the 637 firms, 303 are among the 391 model estimation firms.''

About 1,700 Cornpustat firms were eliminated from the test sample. Taken together, the fiscal year-end requirement and the requirement

For example, Wilson 119861 and Rayburn 119861 report that the current accruals component of earnings has incremental information content beyond cash (and/or working capital) from operations. A significant portion of current accruals can be attributed to changes in inventory levels. In examining the incremental information content of earnings components over the aggregate earnings, Lipe 119861 includes depreciation expense and gross profit as earnings components; sales is a major element of gross profit. Other tests include Wilson [I9861 on capital expenditures, Freeman, Ohlson, and Penman [I9821 on accounting rate of return (ROR), and Griffin 119761 and Gonedes 119781 on dividends.

loThe predictive performance of the logit models was very similar for these two groups of firms examined separately. Therefore, this paper reports only the results based on the aggregate sample.


that data be continuously available for the years 1978-83 eliminated about 48% of all Compustat firms. Of the eight accounting predictors, inventory and capital expenditure are the top two contributors to missing data. Of all firms with at least one data item available on Compustat in each of the six years, approximately 29% have missing data on inventory and 18% have missing data on capital expenditure.

The relation between stock returns and the earnings predictions was examined over the same six-year period: 1978-83. For each year, a test sample for security return analysis was selected from the original 637 firms. To be included in the test sample for a particular year, a firm must have daily returns data available on the Daily Returns Tape of the Center for Research in Security Prices (CRSP) during a period starting 72 months prior to, and ending 3 months after, the end of the fiscal year covered by the annual report. A sample of 3,692 firm-year observations was obtained: 598, 605, 612, 622, 626, 629 firms for the six years 1978- 83, respectively.

4. Results

4.1 THE MODELS

The Logist procedure of Statistical Analysis System (SAS) was used to fit multivariate logit models to the pooled data. Three estimated models are reported in table 1. Model 1,based on all eight accounting descriptors, is the primary prediction model of this study. To demonstrate that the predictive power of model 1does not depend on ROR or AROR (which include the current earnings figure in the conditioning information set), model 2 (which eliminates ROR and AROR) is also presented. In model 3, ROR is the only predictor, so it includes only one nonearnings accounting number, common equity, in the conditioning information set. Test results based on model 3 will be compared with those based on model 1to examine the effects of expanding the conditioning information set to include more nonearnings numbers (see table 1).

The signs of the estimated coefficients in these models show that growth in inventory (GWINVN), change in dividends per share (CHGDPS), growth in depreciation (GWDEP), growth in capital expenditures and its lagged term (GWCPXI and GWCPX2), and accounting rate of return (ROR) are negatively correlated with the chance of observing an earnings increase in the following year.11 On the other hand, growth in sales (GWSALE) and change in accounting rate of return (AROR) correlate positively with the chance of observing an earnings

"To investigate the negative correlation between current dividend changes and future earnings changes, changes in dividends per share were standardized (by standard deviation) for each firm, and observations with zero dividend change were dropped from the sample. The sign of the dividend coefficient remained negative after these modifications.

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T A B L E 1 Multivariate Logit Earnings Prediction Models (1965-77)

Pr(AXi,+,>OIA,,@) = (l+exp(-Ai,'@))-'"

Model 1 Model 2 Model 3

Number of Observations 5,083 5,083 5,083 G t + l > O 2,852 2,852 2,852 AXt+,5 0 2,231 2,231 2,231

Model x2(d.f.)b 323.20 (8Ib 175.42 (6)b 226.80 (l)b D-statisticc .221 .I68 .I86 % of Concordant Pairsd .664 .648 .628 Rank Correlationd .329 .298 .257

Accounting Descriptor O xZe O xZe @ xZ8

Intercept G WIN VN G WSALE GHGDPS G WDEP G WCPXl GWCPX2 ROR AROR +1.69 9.66

'Pr(AX,,+,>OIA,,,@) is the estimated probability that firm i will have an increase in earnings per share (net of the estimated drift) in year t+l, given A,, and 8 . A,, is a vector of accounting descriptors obtained from firm i's annual reports of year t and prior years. 8 is a vector of maximum likelihood estimates of the coefficients of the accounting descriptors. This vector is estimated based on pooled cross-sectional and time-series data.

This statistic tests the null hypothesis that all coefficients in the model are equal to zero. These x2 values are significant a t the ,0001 level.

'This value is a measure of the goodness-of-fit of the model. It is the value such that D ( n - p ) ( l -D ) = model x2,where p is the number of variables in the model including the intercept, and n is the number of observations.

For matched pairs of estimated probability of an earnings increase (P r ) and directional realized earnings changes. Under the null hypothesis, % concordant pairs is 50% and rank correlation is zero.

T h e p-values of these X 2 values are all significant a t the . O 1 level or better except those with *.

increase.12 The negative sign of the ROR coefficient is consistent with results in Freeman, Ohlson, and Penman r19821.

Descriptive statitics for the estimated logit models are also reported in table 1.For each model, the "model x2," which tests the null hypothesis that all coefficients in the model are zero, is significant at the .0001 level. For model 1, the D-statistic, which measures the goodness-of-fit of the logit model, is .221.13 The rank correlation between the predicted proba-

"To examine the consistency of coefficient signs across time and across samples, I reestimated model 1 for two subperiods and for two random (and exhaustive) subsets of the 391 firms. No sign of any coefficient changed.

l3The D-statistic is an analogue of the R2 value in the ordinary least squares (OLS) setting. I t is the value such that D(n - p ) (1 - D ) = model x2, where p is the number of variables in the model including the intercept, and n is the number of observations. The adjusted R2 from the OLS regression on the same data with the magnitude of earnings changes as dependent variable was 13.2%.

bility of an earnings increase and the realized directional earnings change is 0.329. The percentage of concordant pairs between these two measures is 66.4%. Under the null hypothesis of no association, the rank correlation is zero and the percentage of concordant pairs is 50%. The tests statistics for model 2 and model 3 show similar but slightly weaker results.

4.2 PREDICTIVE PERFORMANCE

Table 2 presents each model's predictive performance during the period 1978-83 under the two probability cutoff schemes: (.5, .5) and (.6, .4). The accuracy of the earnings predictions under each model is assessed relative to random walk predictions in a 2 x 2 contingency table setting. Given that I define an earnings change as the difference between two consecutive years' earnings per share net of the estimated drift, the "random walk with drift" model implies predictive performance equiva- lent to the outcome of a random-guess strategy in this setting.

Panel A of the table 2 reports model 1's performance. Under the (.5, .5) scheme, the resulting X 2 value under a "fixed row" x2 test is 174.10, significant at the 1% level. Since this X 2 value does not distinguish between a better-than-random-guess prediction and a worse-than-random-guess prediction, the percentage of correct predictions (61%) is also reported; both the "increase" predictions and the "decrease" predictions have the same predictive accuracy. Moreover, when one moves into the (.6, .4) scheme where relatively vague predictions have been deleted, both the x2 statistic and the percentage values show substantial improve- ment. Now 69% of all "increase" predictions and 67% of all "decrease" predictions are correct. Compared with the expected outcome under the random-guess strategy, these results are consistent with the notion that annual earnings do not follow a "random walk" if nonearnings accounting numbers are also included in the conditioning information set.'*

The predictive performance of model 2 (with ROR and AROR omitted) is presented in panel B of table 2. The x2 value is still significant a t the 1%level, and 58% of all predictions are correct under the (.5, .5) scheme. Under the (.6, .4) scheme, 64% of the predictions are correct. Further- more, both the "increase" and the "decrease" predictions still have a better than 50% chance of being correct. These results indicate that nonearnings accounting numbers alone do convey information about future earnings.

Panel C reports the predictive ability of model 3, where ROR is the only predictor. The predictive performance of this most parsimonious

l4 The predictive performance of model 1was also compared to that of a "trend model," where the subsequent earnings change is in the same direction as the current earnings change, and a "reversal model," where the subsequent earnings change is in the opposite direction to the current earnings change. In a binomial test, model 1 outperforms both earnings-based models a t the 1%significance level.

152 JANE A. OU

T A B L E 2 Predictive Performance of Earnings Prediction Models (1978-83)

Earnings Changes Are Predicted One Year Ahead on the Basis of P r a

Panel A: Model 1, Based on All Eight Accounting Descriptors Probabilitv Cutoff Schemeb (.5. .5) (.6. .4)

Number of Observations 3,822 1,968 X2 of 2x2 Tablec 174.10 230.15 % of Predictions Correctly Made 61% 68% Further Breakdown According to Predicted Predicted Predicted Predicted

Model's Prediction As As As As Increase Decrease Increase Decrease

Number of Observations 2,348 1,474 1,314 654 Correct 1,422 905 904 440 Incorrect 926 569 410 214

% Correct 61% 61% 69% 67%

Panel B: Model 2, Based on Six Accounting Descriptors Probability Cutoff Schemeb (.5, .5) (.6, .4)

Number of Observations 3,822 1,365 X2 of 2x2 Tablec 109.94 75.63 % of Predictions Correctly Made 58% 64% Further Breakdown According to Predicted Predicted Predicted Predicted


Number of Observations 2,954 868 1,113 252 Correct 1,675 552 703 169 Incorrect 1,279 316 410 83

% Correct 57% 64% 63% 67%

Panel C: Model 3, Based Only on ROR (Accounting Rate of Return) Probability Cutoff Schemeb (.5, .5) ( 3 % .4)

Number of Observations 3,822 1,751 x2of 2x2 Tablec 88.28 120.40 % of Predictions Correctly Made 58% 66% Further Breakdown According to Predicted Predicted Predicted Predicted


Number of Observations 2,426 1,396 1,250 501 Correct 1,404 809 840 308 Incorrect 1,022 587 410 193

% Correct 58% 58% 67% 61%

" Pr is the estimated probability of an earnings increase indicated by the prediction models summa- rized in table 1.

Under the (.5, .5) scheme, the prediction is an earnings increase for Pr > 0.5, and a decrease otherwise. Under the (.6, .4) scheme, the prediction is an earnings increase when Pr 2 0.6, and a decrease when Pr 5 0.4; observations with Pr between 0.6 and 0.4 are dropped.

'Based on a "fixed row" X 2 test. A x2value of 6.63 is significant a t the . O 1 level.

model is similar to that of model 2. Since the current earnings figure is used in calculating ROR, it is desirable to examine whether the predictive power of models 3 and 1 is indeed "incremental" to that of current earnings. For this purpose, I estimated a logit model with "earnings before extraordinary items" (the numerator of ROR) as the sole predictor


and applied this model to the same test sample. The resulting X 2 is not significant at the 10% level. The model predicts 99% of all observations as "increase7' and has a correct prediction rate of 52%. It thus seems fair to infer that nonearnings accounting numbers do contain information about future earnings that is not available in current earnings.

As an exception to the "random walk" properties of earnings, Brooks and Buckmaster [1976; 19801 have found that an extreme earnings change tends to be followed by one in the opposite direction. To ensure that the results presented in table 2 did not arise from observations with extreme current earnings changes, the predictive performance test was replicated on a sample in which observations with absolute current earnings change exceeding one standard deviation were deleted. For this sample, model 1's percentage of correct predictions dropped from 61% to 58% under the (.5, .5) scheme, and from 68% to 64% under the (.6, .4) scheme; but the

values of the 2 x 2 tables were still significant at the 1% level. When I further narrowed down the allowed absolute magnitude of current earnings changes to below one standard deviation (and thus trimmed away more observations), model 1's predictive accuracy did not deterio- rate any further. I t seems that a t least a significant portion of model 1's predictive ability is independent of Brooks and Buckmaster's finding and thus truly represents incremental predictive content over current earnings.

The models' predictive performance on a yearly basis is reported in table 3. The results indicate that the superiority of the three logit models over the random-guess strategy is consistent across individual years, except for 1981 (in the prediction of 1982 earnings). Under model 1,the x2 value in each of the six years is significant at the 1% level. The percentages of correct predictions range from 57% to 70% under the (.5, .5) scheme, except for 1981; and each model's prediction accuracy con- sistently improves when the (.6, .4) scheme is used.

Comparing the predictive performance of model 1, based on all eight accounting descriptors, with that of model 3, based only on one nonearnings number, isolates the effects of expanding the conditioning information set to include more nonearnings numbers. Results in tables 2 and 3 indicate that model 1outperforms model 3 in the 2 x 2 setting, although the significance of the difference is not clear. An additional test (not presented in the tables) shows that model 1 and model 3 predict differ- ently in 534 of 3,822 cases under the (.5, .5) cutoff scheme. In these 534 cases, 61% of the time model 1 produced the correct prediction. A binomial test yields a z-value of 4.93, significant at the 1% level.15 Thus, the additional predictors used in model 1 seem to have conveyed information about future earnings that is not available in ROR.

l5 Under the (.6, .4) cutoff, these two models produce different predictions in 85 cases. Model 1 has the correct prediction in 59 of these cases (61%) (binomial test z-value of 2.061).

154 J A N E A. OU

T A B L E 3 Yearly Performance of Earnings Prediction Models

Earnings Changes Are Predicted One-Year Ahead o n the Basis of Pr"

Probability Cutoff i.5, .5) i.6, .4)

Schemeb

Number of x2of 2 Correct Number of CorrectPredictionsPre;kt)ions '2% (%)

Year Observations Observations

Panel A: Model 1, Based on All Eight Accounting Descriptors 1978 637 15.88 59% 274 21.25 65% 1979 637 7.14 57% 283 16.59 64% 1980 637 32.68 61% 310 42.32 69% 1981 637 15.31 50% 326 22.45 56% 1982 637 51.63 69% 370 35.91 76% 1983 637 21.08 70% 405 30.42 76%

Panel B: Model 2, Based on Six Accounting Descriptors 1978 637 13.79 65% 164 2.80 66% 1979 637 5.85 51% 158 5.81 58% 1980 637 31.53 59% 207 22.45 66% 1981 637 18.86 42% 228 5.30 42% 1982 637 19.45 65% 280 6.87 71% 1983 637 2.45 68% 328 0.0 73%

Panel C: Model 3, Based Only on ROR (Accounting Rate of Return) 1978 637 1.92 55% 229 12.36 64% 1979 637 1.25 53% 242 6.35 59% 1980 637 12.65 57% 270 23.64 66% 1981 63 7 7.46 48% 286 8.94 50% 1982 637 28.96 66% 358 15.44 76% 1983 637 20.71 68% 366 25.45 73%

"See n. a to table 2. See n. b to table 2.

'See n. c to table 2.

So far, the logit models have been evaluated based on the frequency of correct predictions without regard to magnitudes. Since the magnitude of unexpected earnings is known to be positively correlated with residual stock returns (Beaver, Clarke, and Wright [1979]), an ability to predict large earnings changes is more desirable than an ability to predict small changes. Table 4 shows that, on average, model 1predicts large earnings changes more successfully than small earnings changes. For firms whose earnings increase (panel A) and decrease (panel B) in year t + 1,table 4 reports the magnitudes of these earnings changes. The average magnitude of the earnings increase or decrease is substantially higher for firms whose earnings changes have been correctly predicted than for those incorrectly predicted. This relation is consistent across years in the test period with two exceptions (in panel B, 1978 and 1983, under the (.6, .4) scheme) where the differences seem to be immaterial. Thus, the results in table 4 suggest that the overall predictive accuracy rate of 61% reported


T A B L E 4 Magnitude of Realized Subsequent Earnings Changes for the Correct and the Incorrect

Predictionsa

Panel A: Cases Where the Subsequent Earnings Changes Are an Increase (i.e., AX,,,,> 0) Probability Cutoff Schemeb ( . 5 , .5) (.6, .4)

Prediction of Earnings Change As Increase As Decrease As Increase As Decrease (Correct) (Incorrect) (Correct) (Incorrect)

Number of Observations 1,422 569 Mean Standardized Subsequent Earnings Change:"

1978 1.05 0.74 1979 0.91 0.65 1980 1.03 0.65 1981 1.09 0.90 1982 1.56 1.16 1983 1.82 1.29

Mean Difference Across Six Yearsd +0.34

Panel B: Cases Where the Subsequent Earnings Changes Are a Decrease (i.e., AX,,,,50) Probabilitv Cutoff Schemeb ( . 5 . .5) (.6. .4)

Prediction of Earnings Change As Decrease As Increase As Decrease As Increase (Correct) (Incorrect) (Correct) (Incorrect)

Number of Observations 905 926 440 410 Mean Standardized Subsequent Earnings Change:"

1978 -0.83 -0.79 -0.88 -0.90 1979 -1.23 -1.02 -1.27 -0.93 1980 -1.31 -0.98 -1.51 -0.95 1981 -2.01 -1.67 -1.96 -1.50 1982 -1.74 -1.28 -2.06 -1.20 1983 -2.38 -2.37 -1.64 -1.77

Mean Difference Across Six Yearsd -0.31 -0.34

"Predictions of these earnings changes are based on prior years' annual reports and model 1 in table 1.

See n. b to table 2. 'Standardized by each firm's standard deviation of yearly earnings changes. dYearly difference between the mean standardized subsequent earnings change of the correctly

predicted firms and that of the incorrectly predicted firms, averaged across six years.

in table 2 might have captured most of the large, and presumably more important, earnings changes.

4.3 ASSOCIATION WITH STOCK RETURNS

In this study, a firm's annual report of year t is viewed as containing two binary signals. First, signal E (Earnings) indicates the direction of the change in current (year t ) earnings. This signal is usually released in a preliminary earnings announcement16 prior to the publication of the

"Using quarterly Cornpustat data, I examined reporting lags between the end of December and preliminary earnings announcements for my sample firms during the test period 1978-83. In each year, about 80% of the sample firms made preliminary earnings announcements by the end of February. The yearly median reporting lags range from 40 to 43 days. This is similar to Chambers and Penman's [I9841 finding of a mean and median reporting lag of 44 days during 1970-76.

156 JANE A. OU

complete annual report. Second, signal F (Forecast) is an ex ante forecast of the direction of the firm's earnings change in the following year (year t + 1). This forecast is based on the accounting numbers contained in the year t annual report. This subsection examines stock returns7 incremental association with signal F over signal E during the year t annual report dissemination period.17

Ball and Brown [I9681 partitioned sample firms into portfolios according to the direction of current earnings changes (signal E) and demon- strated differential returns between good news firms (E+, those with an increase in current earnings) and bad news firms (E-, those with a decrease in current earnings) during the 12 months prior to preliminary earnings announcements. I use annual report data and a logit prediction model to produce forecasts of the direction of next year's earnings changes (signal F)and further partition each of the E+ and E- portfolios into two subsets: (1)F+,those with an "increase" forecast, and (2) F-, those with a "decrease" forecast. This procedure yields four portfolios in each year: E+F+, E+F-, E-F+, and E-F-. This study assesses the incremental stock return response to signal Fover signal E by examining the differential return behavior between portfolios E+F+ and E+F-, and that between portfolios E-F+ and E-F- over the period 1978-83. Similar results were obtained for all three prediction models; reported results are based on model 1, unless specified otherwise.

I assume that sample firms' complete annual reports are available within three months after the end of December (month 0); Is some of the predictors are usually known earlier. Therefore, the three-month period following the year-end, January through March (months +I, +2, and +3), is designated the dissemination period of the predictors.

Each portfolio's performance was measured by its cumulative abormal return (CAR). The CAR of a portfolio held from month m to month n (both relative to month 0) was computed as follows:

where eiStis the market model residual of month t for firm i in year s,19 and N is the total number of firm-year observations in the p o r t f o l i ~ . ~ ~

'?A correlation between signal E and signal F is expected since my logit prediction models incorporate current earnings changes and some components of current earnings in the calculation of the predictors. This makes controlling for the effects of signal E necessary.

''This assumption is based on the SEC requirement that companies file annual 10-K reports within 90 days of the fiscal year-end. "Monthly stock returns of individual firms were calculated from the CRSP daily returns

tape. For calculating e,,, a, and pi were estimated from an OLS regression of firm i's monthly returns on the equally weighted monthly market returns of all NYSE firms over a 60-month period prior to month t. Note that a, and p, were reestimated each month.

20 Using monthly returns and an assumed announcement period runs the risk of including various confounding events. Tests based on exact information dates, such as those in Wilson [1987], will probably produce stronger results.

- -

Panel A of table 5 reports the CARs of the four portfolios E+F+, E+F-, E-F+, and E-F- over the 12-month period [-8, +3], assuming that the portfolios were formed a t the beginning of month -8 (April) based on knowledge of year t's annual reports. The CARs of the E+ portfolio and the E- portfolio, based only on the change in current

T A B L E 5 Cumulative Abnormal Returns of Portfolios Based o n Current Earnings Changes (Signal E )

and Predictionsa of Nex t Year's Earnings Changes (Signal F) (1978-83)

Portfoliob CARS Month Relative to the End

of Year t (1) (2) (3) (4) A11Firms E+ E- E+F+ E+F- E-F+ E-F-

Panel A: CARS of Portfolios Formed at the Beginning of Month -8, Based on the (.5, .5) Scheme -8 (Apr) -.0002 .0191 -.0209 ,0153 .0241 -.0234 -.0155 -7 (May) ,0023 .0311 -.0286 .0344 ,0270 -.0285 -.0288 -6 (Jun) -.0043 ,0365 -.0479 .0378 .0348 -.0475 -.0488 -5 (Jul) -.0055 .0480 -.0626 .0505 ,0448 -.0626 -.0624 -4 (Aug) -.0082 ,0507 -.0710 ,0513 .0499 -.0695 -.0742 -3 ( S ~ P ) -.0130 .0537 -.0843 ,0543 .0530 -.0849 -.0829 -2 (Oct) -.0213 .0597 -.lo78 .0564 .0641 -.lo86 -.lo59 -1 (Nov) -.0235 .0688 -.I221 .0619 .0777 -.I253 -.I154 0 (Dec) -.0218 .0729 -.I229 ,0661 .0816 -.I214 -.I261 +1(Jan) -0153 .0829 -.I202 ,0917 .0715 -.I104 -.I406 +2 (Feb) -.0130 .0880 -.I209 .0997 .0729 -.lo67 -.I506 +3 (Mar) -.0157 ,0809 -.I189 .lo35 ,0519 -.lo23 -.I539 Number of Observations 3,692 1,907 1,785 1,073 834 1,210 575

Panel B: CARS of Portfolios Formed at the Beginning of Month +1, Based on the (.5, .5) Scheme

+1(Jan) ,0083 .0104 ,0060 .0265 -.0102 .0167 -.0165 +2 (Feb) ,0098 .0158 .0034 .0366 -.0109 ,0191 -.0295 +3 (Mar) .0076 .0109 .0041 .0405 -.0272 ,0227 -.0350 Median C A R over -.0038 .0003 -.0080 .0221 -.0289 ,0050 -.0358

[+I, +31

Panel C: Portfolio CARS over the Three-Month Period [+I , +3],Based on the (.6, .4)Schemec

E+F+ E+F- E-F+ E-F-

C A R over [+I, +3] Number of Observations

"All predictions are based on model 1of table 1. These portfolios are formed based on the sample firms' current earnings changes (AX,,) and model

1's predicted probabilities (Prs) of an earnings increase in the subsequent year (year t+l ) : E+ consists of firms with AX,, > 0; E- consists of firms with AX,, 5 0; E+F+ consists of firms with AX,, > 0 and P r > 0.5; E+F- consists of firms with AX,, > 0 and P r 5 0.5; E-F+ consists of firms with AX,, 5 0 and P r > 0.5; E-F- consists of firms with AX,, 5 0 and P r 5 0.5. Wnder the ( 3 , .4) scheme, portfolios are formed as follows: E+F+ consists of firms with AX,, > 0 and P r 2 0.6; E+F- consists of firms with AX,, > 0 and P r 5 0.4; E-F+ consists of firms with AX,, 5 0 and P r 2 0.6; E-F- consists of firms with AX,, 5 0 and P r 2 0.4.

158 JANE A. OU

earnings, are also reported. These reproduce Ball and Brown's results: portfolio E+ shows positive and continuously increasing CAR; while portfolio E- has negative and continuously decreasing CAR during this period. However, these trends stop before month +3, possibly because preliminary earnings are usually announced prior to month +3.

Columns (3) and (4) of table 5 report the CAR behavior of the four portfolios conditional on the signals E and F (based on a (.5, .5) cutoff scheme). Differential CAR behavior between portfolios E+F+ and E+F-first appears in month +1 (January), when the CAR of E+F- begins to drop, while the CAR of E+F+ continues to rise. Similarly, the differential behavior between portfolios E-F+ and E-F- begins in month 0 (Decem-ber), when the CAR of E-F+ begins to rise, while the CAR of E-F-continues to drop. This incremental response of stock returns to signal F over signal E is illustrated in figure 1.

Since the stock returns' response to signal F did not begin until toward

-0.20 , I I I I I I I I I I I I -9 -8 -7 -6 -5 -4 -3 -2 1 0 1 2 3

MONTH RELATIVE TO YEAR-END

FIG.1.-Cumulative abnormal returns of portfolios based on current earnings changes (signal E ) and predictions of next year's earnings changes (signal F ) , 1978-83. In this figure, W denotes all firms; denotes the E + portfolio (firms with an increase in current earnings); denotes the E- portfolio (firms with a decrease in current earnings); 0denotes the E+F+ portfolio (firms with an increase in current earnings and a predicted increase in next year's earnings); A denotes the E+F- portfolio (firms with an increase in current earnings and a predicted decrease in next year's earnings); x denotes the E-F+ portfolio (firms with a decrease in current earnings and a predicted increase in next year's earnings); V denotes the E-F- portfolio (firms with a decrease in current earnings and a predicted decrease in next year's earnings). All earnings predictions are based on the ( . 5 ,.5) probability cutoff scheme.


the end of December (month O), the CAR behavior was reexamined in the three-month period January through March. This period contains preliminary earnings announcements and annual reports release dates." Panel B of table 5 shows that although the CARs of both the E+ and the E- portfolios are relatively low in magnitude during this period, stock returns react substantially to signal F. The CARs of the F+ portfolios (i.e., E+F+ and E-F+, firms with good news regarding future earnings) are positive and continuously increasing throughout this period. The CARs of the F- portfolios (i.e., E+F- and E-F-, firms with bad news regarding future earnings) are negative and continuously decreasing. The mean three-month CARs are +4.05% and -2.72% for E+F+ and E+F-, and +2.27% and -3.50% for E-F+ and E-F-, respectively. A simulation test which randomly assigned the sample firms to these portfolios showed that the probablity of obtaining CARs with magnitudes similar to those reported in table 5 was less than one out of 10,000.

Qualitatively similar results were obtained when signal F was calculated based on model 2 or model 3. For the four portfolios E+F+,E+F-, E-F+, and E-F-, the mean three-month CARs during [+I, +3] are +2.10%, -2.34%, +1.54%, and -3.47% under model 2, and +4.07%, -2.14%, +1.60%, and -3.55% under model 3, respectively. This is consistent with the conjecture that ROR and other nonearnings predictors contain similar information about future earnings.

Panel C of table 5 shows that under the (.6, .4) scheme, the differences in CARs between the portfolios with a favorable forecast and those with an unfavorable forecast widen substantially. The CARs of the four portfolios E+F+, E+F-, E-F+, and E-F- are now +7.51%, -5.09%, +3.81%, and -4.89%, respectively.

The CAR behavior in each year over the three-month period [+I, +3] is reported in table 6. For each of the six years, portfolio E+F+ consist-ently outperforms E+F-, and portfolio E-F+ outperforms E-F-. A Mann-Whitney rank-sum U-statistic was calculated for each of the portfolio pairs [E+F+ versus E+F-] and [E-F+ versus E-F-] for each year. In most cases, the resulting asymptotic z-values of the U-statistics are significant a t the 1% level. When portfolios are pooled across the six years, the resulting z-values are significant a t the 0.1% level.

Results presented so far suggest that the logit predictions of future earnings changes have incremental explanatory power for cross-sectional differences in return distributions over that of the direction of current earnings changes. One might question whether the observed incremental return response to signal F is a proxy for the stock market's response to the magnitude of signal E . To examine this possibility, I calculated each portfolio's mean change in current (year t ) earnings per share (scaled by

This period does not include the announcement of the following year's first-quarter earnings, thus the test results are not affected by the market's response to this announcement.

160 JANE A. OU

T A B L E 6 Portfolio" Cumulative Abnormal Returns over the Three-Month Period [+I, +3]P

Yearly Results

(1) (2) (3) (4) AllFirms E+ E- E+F+ E+F- E-F+ E-F-

1978 .0011 1979 .0250 1980 -.0004 1981 -.0153 1982 .0114 1983 ,0142 Pooled Across Years .0076

Mann-Whitney Rank-Sum U-Test: E+F+ V S . E+F- E-F+ V S . E-F-

z-Value z-Value

1982 1.64 6.63* 1983 5.43* 2.68* Pooled Across Years 8.50* 6.85*

"All predictions are based on model 1and the ( . 5 , .5) probability cutoff scheme. See n. b to table 5 for the assignment of sample firms to portfolios.

This period covers January through March, the three months immediately following the sample firms' fiscal year-end.

"Significant at the . O l level.

standard deviation). These mean changes are +.810 and +1.058 for E+F+ and E+F-, and -.989 and -.318 for E-F+ and E-F-. In addition, the Spearman rank correlation between the magnitude of the scaled current earnings change and the predicted probability (Pr) of an earnings increase in the following year is negative: -.0401, significant at the 5% level. Thus, on average, firms with a favorable future earnings prediction (F+)are experiencing a relatively unfavorable current earnings change (signal E), while firms with an unfavorable future earnings prediction (F-) are experiencing a relatively favorable current earnings change. This result is inconsistent with the notion that the incremental stock return response to signal F simply proxies for the return response to the magnitude of current earnings changes.

Results in section 4.2 indicate that model 1, based on more nonearnings accounting descriptors, outperforms model 3 in predicting future earnings changes. To see whether this superior predictive power is also reflected in stock returns, I examined whether a portfolio strategy based only on model 3 is outperformed by one based on both model 1 and model 3. Table 7 reports the results. Under the first strategy, two portfolios, F3+ and F3-, were formed according to each firm's signal F (earnings forecast) based on model 3 and a (.5, .5) probability cutoff scheme. Under the second stategy, F3+ was further partitioned into two subportfolios,

T A B L E 7 Comparison of CARs of Portfolios Based o n Model 3 Alone with CARs of Portfolios Based

on Model 1 and Model 3 Together (1978-83)

Portfolios" Based Only on Portfolios" Based on Both Model Model 3's Prediction 1's and Model 3's Predictions

(1) (2) (3) F3+ F3- F3+F1+ F3+F1- F3-F1+ F3-F1-

Three-Month C A R over .0264 -.0258 .0335 -.0223 ,0084 -.0326 [+I, +3Ib

Number o f Observations 2,365 1,327 2,064 301 219 1,108 Mann-Whi tney Rank-

S u m U-Test: F3+Fl+ vs. F3+F1- F3-F1+ vs. F3-F1-

"All predictions are based on the ( 5 , .5) cutoff scheme. F3+ consists of firms with Pr > 0.5 based on model 3; F3- consists of firms with Pr 5 0.5 based on model 3; F3+F1+ consists of firms with Pr > 0.5 based on both model 3 and model 1; F3iF1- consists of firms with Pr > 0.5 based on model 3, but Pr 5 0.5 based on model 1; F3-F1+ consists of firms with Pr 5 0.5 based on model 3, but Pr > 0.5 based on model 1; F3-F1- consists of firms with Pr 5 0.5 based on both model 3 and model 1.

This period covers January through March, the three months immediately following the sample firms' fiscal year-end.

* Significant at the .O1 level.

F3+F1+ and F3+F1-, according to each firm's signal F based on model 1and a (.5, .5) cutoff. Similarly, F3- was partitioned into F3-F1+ and F3-F1-. The three-month CARs presented in table 7 indicate that model 1 significantly screens the predictions based on model 3, and the stock returns reflect this incremental predictive power.

In sum, results presented in this subsection indicate that stock returns not only reflect changes in current earnings, but also respond to the logit models' predictions of future earnings changes during a period surround- ing the release of annual report data. In other words, stock prices behave as if investors revise their expectations of future earnings based on nonearnings annual report numbers.

5. Conclusion

This paper takes an earnings prediction approach to investigate the incremental information content of nonearnings annual report numbers over earnings. It provides empirical evidence for a predictive information link between these nonearnings numbers and future earnings changes. The findings suggest that a firm's nonearnings annual report numbers contain information concerning the direction of its next year's earnings change that is not reflected in its current earnings. A valuation link between stock returns and this annual-report-based prediction is also documented. During the annual report dissemination period, the stock return response to the prediction of future earnings changes is over and beyond its response to current earnings. This result is consistent with

the view that the disclosure o f nonearnings annual report numbers triggers revisions o f investors' f u t u r e earnings expectations.

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The Information Content of Nonearnings Accounting Numbers As Earnings PredictorsJane A. OuJournal of Accounting Research, Vol. 28, No. 1. (Spring, 1990), pp. 144-163.Stable URL:

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2 The Information Contained in the Components of EarningsRobert C. LipeJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 37-64.Stable URL:

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2 The Association of Operating Cash Flow and Accruals with Security ReturnsJudy RayburnJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 112-133.Stable URL:

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2 The Relative Information Content of Accruals and Cash Flows: Combined Evidence at theEarnings Announcement and Annual Report Release DateG. Peter WilsonJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 165-200.Stable URL:

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2 Competitive Information in the Stock Market: An Empirical Study of Earnings, Dividendsand Analysts' ForecastsPaul A. GriffinThe Journal of Finance, Vol. 31, No. 2, Papers and Proceedings of the Thirty-Fourth AnnualMeeting of the American Finance Association Dallas, Texas December 28-30, 1975. (May, 1976),pp. 631-650.Stable URL:

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2 Risk, Information, and the Effects of Special Accounting Items on Capital MarketEquilibriumNicholas J. GonedesJournal of Accounting Research, Vol. 13, No. 2. (Autumn, 1975), pp. 220-256.Stable URL:

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2 Corporate Signaling, External Accounting, and Capital Market Equilibrium: Evidence onDividends, Income, and Extraordinary ItemsNicholas J. GonedesJournal of Accounting Research, Vol. 16, No. 1. (Spring, 1978), pp. 26-79.Stable URL:

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2 An Empirical Analysis of Differential Capital Market Reactions to Extraordinary AccountingItemsRobert K. Eskew; William F. WrightThe Journal of Finance, Vol. 31, No. 2, Papers and Proceedings of the Thirty-Fourth AnnualMeeting of the American Finance Association Dallas, Texas December 28-30, 1975. (May, 1976),pp. 651-674.Stable URL:

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3 Accounting Earnings and Security Valuation: Empirical Evidence of the Fundamental LinksPeter D. EastonJournal of Accounting Research, Vol. 23, Studies on Accounting Earnings and Security Valuation:Current Research Issues. (1985), pp. 54-77.Stable URL:

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4 An Empirical Evaluation of Accounting Income NumbersRay Ball; Philip BrownJournal of Accounting Research, Vol. 6, No. 2. (Autumn, 1968), pp. 159-178.Stable URL:

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6 Some Time Series Properties of Accounting IncomeRay Ball; Ross WattsThe Journal of Finance, Vol. 27, No. 3. (Jun., 1972), pp. 663-681.Stable URL:

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9 The Association of Operating Cash Flow and Accruals with Security ReturnsJudy RayburnJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 112-133.Stable URL:


9 The Information Contained in the Components of EarningsRobert C. LipeJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 37-64.Stable URL:




9 Book Rate-of-Return and Prediction of Earnings Changes: An Empirical InvestigationRobert N. Freeman; James A. Ohlson; Stephen H. PenmanJournal of Accounting Research, Vol. 20, No. 2, Part II. (Autumn, 1982), pp. 639-653.Stable URL:

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9 Competitive Information in the Stock Market: An Empirical Study of Earnings, Dividendsand Analysts' ForecastsPaul A. GriffinThe Journal of Finance, Vol. 31, No. 2, Papers and Proceedings of the Thirty-Fourth AnnualMeeting of the American Finance Association Dallas, Texas December 28-30, 1975. (May, 1976),pp. 631-650.Stable URL:


9 Corporate Signaling, External Accounting, and Capital Market Equilibrium: Evidence onDividends, Income, and Extraordinary ItemsNicholas J. GonedesJournal of Accounting Research, Vol. 16, No. 1. (Spring, 1978), pp. 26-79.Stable URL:


6 Timeliness of Reporting and the Stock Price Reaction to Earnings AnnouncementsAnne E. Chambers; Stephen H. PenmanJournal of Accounting Research, Vol. 22, No. 1. (Spring, 1984), pp. 21-47.Stable URL:

http://links.jstor.org/sici?sici=0021-8456%28198421%2922%3A1%3C21%3ATORATS%3E2.0.CO%3B2-B

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Some Time Series Properties of Accounting IncomeRay Ball; Ross WattsThe Journal of Finance, Vol. 27, No. 3. (Jun., 1972), pp. 663-681.Stable URL:

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The Association between Unsystematic Security Returns and the Magnitude of EarningsForecast ErrorsWilliam H. Beaver; Roger Clarke; William F. WrightJournal of Accounting Research, Vol. 17, No. 2. (Autumn, 1979), pp. 316-340.Stable URL:

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Further Evidence of the Time Series Properties of Accounting IncomeLeRoy D. Brooks; Dale A. BuckmasterThe Journal of Finance, Vol. 31, No. 5. (Dec., 1976), pp. 1359-1373.Stable URL:

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Timeliness of Reporting and the Stock Price Reaction to Earnings AnnouncementsAnne E. Chambers; Stephen H. PenmanJournal of Accounting Research, Vol. 22, No. 1. (Spring, 1984), pp. 21-47.Stable URL:

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Accounting Earnings and Security Valuation: Empirical Evidence of the Fundamental LinksPeter D. EastonJournal of Accounting Research, Vol. 23, Studies on Accounting Earnings and Security Valuation:Current Research Issues. (1985), pp. 54-77.Stable URL:

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An Empirical Analysis of Differential Capital Market Reactions to Extraordinary AccountingItemsRobert K. Eskew; William F. WrightThe Journal of Finance, Vol. 31, No. 2, Papers and Proceedings of the Thirty-Fourth AnnualMeeting of the American Finance Association Dallas, Texas December 28-30, 1975. (May, 1976),pp. 651-674.Stable URL:

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Book Rate-of-Return and Prediction of Earnings Changes: An Empirical InvestigationRobert N. Freeman; James A. Ohlson; Stephen H. PenmanJournal of Accounting Research, Vol. 20, No. 2, Part II. (Autumn, 1982), pp. 639-653.Stable URL:

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Information and the Sequential Valuation of Assets in Arbitrage-Free EconomiesMark B. Garman; James A. OhlsonJournal of Accounting Research, Vol. 18, No. 2. (Autumn, 1980), pp. 420-440.Stable URL:

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Capital Market Equilibrium and Annual Accounting Numbers: Empirical EvidenceNicholas J. GonedesJournal of Accounting Research, Vol. 12, No. 1. (Spring, 1974), pp. 26-62.Stable URL:

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Risk, Information, and the Effects of Special Accounting Items on Capital MarketEquilibriumNicholas J. GonedesJournal of Accounting Research, Vol. 13, No. 2. (Autumn, 1975), pp. 220-256.Stable URL:

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Corporate Signaling, External Accounting, and Capital Market Equilibrium: Evidence onDividends, Income, and Extraordinary ItemsNicholas J. GonedesJournal of Accounting Research, Vol. 16, No. 1. (Spring, 1978), pp. 26-79.Stable URL:





http://links.jstor.org/sici?sici=0021-8456%28198223%2920%3A2%3C639%3ABRAPOE%3E2.0.CO%3B2-H&origin=JSTOR-pdf

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Competitive Information in the Stock Market: An Empirical Study of Earnings, Dividendsand Analysts' ForecastsPaul A. GriffinThe Journal of Finance, Vol. 31, No. 2, Papers and Proceedings of the Thirty-Fourth AnnualMeeting of the American Finance Association Dallas, Texas December 28-30, 1975. (May, 1976),pp. 631-650.Stable URL:


Market-Based Empirical Research in Accounting: A Review, Interpretation, and ExtensionBaruch Lev; James A. OhlsonJournal of Accounting Research, Vol. 20, Supplement: Studies on Current Research Methodologiesin Accounting: A Critical Evaluation. (1982), pp. 249-322.Stable URL:

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The Information Contained in the Components of EarningsRobert C. LipeJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 37-64.Stable URL:


Risk, Return, Security-Valuation and the Stochastic Behavior of Accounting NumbersJames A. OhlsonThe Journal of Financial and Quantitative Analysis, Vol. 14, No. 2. (Jun., 1979), pp. 317-336.Stable URL:

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The Association of Operating Cash Flow and Accruals with Security ReturnsJudy RayburnJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 112-133.Stable URL:






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The Relative Information Content of Accruals and Cash Flows: Combined Evidence at theEarnings Announcement and Annual Report Release DateG. Peter WilsonJournal of Accounting Research, Vol. 24, Studies on Alternative Measures of Accounting Income.(1986), pp. 165-200.Stable URL:




