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Fundamental analysis and stock returns: Japan 1993-2003 Pascal Nguyen This paper investigates the relationship between accounting information and stock returns for TSE firms over the past 10 years. We construct a simple financial score designed to capture short-term changes in firm operating efficiency, profitability and financial policy. Our score exhibits a strong correlation with market-adjusted returns in the current fiscal period. The correlation remains significant with returns in the following period, although its strength decreases when the holding period is lagged. Over the sample period, market-adjusted stock returns exhibit a slight mean reversion. However, stocks with high scores are characterized by momentum. We show that this property derives from a positive autocorrelation in the financial score. Also, the higher return of high score portfolios does not seem to imply a higher level of risk. Corresponding address WBP Financial Integrator Otemachi First Square, 4F East Tower Otemachi 1-5-1 Chiyoda-ku Tokyo 100-0004 Tel: (81) 3 5219 1222 Fax (81) 3 5219 1201 e-mail: pnguyen@wbp.jp

1. Introduction Financial research shows that a small number of firm characteristics (e.g., firm size, value and growth attributes, and past price performance) are useful in predicting future stock returns. These characteristics are shared by a large number of stocks, which provides a large pool of stocks to invest in, and reduces the loss of diversification entailed in trying to exploit the characteristic-based return predictability. The firm characteristics most highly associated with future returns are the book/market ratio and firm size. Banz (1981) and Fama and French (1993) provide evidence that small size (low market capitalization) firms earn positive CAPM-risk-adjusted returns. Fama and French (1992) show that value stocks (high book/market) significantly outperform growth stocks (low book/market). The average return of the highest book/market decile is reported to be one percent per month higher than the average return for the lowest book/market decile. One explanation is that the high expected return on value stocks reflects compensation for some distress-related risk factor. An alternative interpretation is that growth stocks are overpriced glamour stocks that subsequently earn low returns (Lakonishok, Shleifer, and Vishny, 1994). A growing body of financial research investigates the predictability of stock returns based on past price history, i.e. examines whether past price performance predicts future returns. There is mixed evidence to suggest price reversal at short intervals up to a month and over longer horizons of three-to-five years. More compelling evidence suggest price momentum at intermediate intervals of six-to-twelve months. Besides, the momentum effect appears to be robust to adjustment for risk and other technical considerations. For example, Jegadeesh and Titman (1993) document that over a horizon of three to twelve months, past winners on average continue to outperform past losers by about one percent per month. Two explanations have been offered to account for the momentum effect. The first is that stock prices underreact to information. Chan, Jegadeesh and Lakonishok (1996) show that stock prices respond gradually to earnings announcements with a substantial portion of the momentum effect concentrated around subsequent earnings news. The second explanation emphasizes the herding behavior of investors. For example, Grinblatt, Titman, and Wermers (1995) find that the majority of mutual funds purchase

stocks based on their past returns (buy past winners) Lakonishok, Shleifer, and Vishny (1992) also find evidence of pension fund managers either buying or selling in herds. Another large body of financial research indicates long-horizon predictability of returns following a variety of corporate events. The corporate events include stock splits, share repurchases, extreme earnings performance announcements, bond rating changes, dividend initiations and omissions, seasoned equity offerings, initial public offerings. For instance, Michaely, Thaler and Womack (1995) study the price reactions to dividend initiations and omissions. The main conclusion from these studies is that, in many cases, the magnitude of abnormal returns is not only statistically highly significant, but economically large as well. In particular, evidence that suggests that the stock market underreacts to earnings information and recognizes the full impact of the earnings information only gradually over time Ou and Penman (1989), Lev and Thigarajan (1993), and Abarbanell and Bushee (1998), and Piotroski (2000) use financial statement analysis of income statement and balance sheet ratios to forecast future earnings. The primary motivation for this research is identify mispriced securities. However, these authors demonstrate that the information in the earnings prediction signals is helpful in generating abnormal stock returns While practitioners and academics have a long history of interpreting univariate financial ratios as leading indicators of earnings growth (e.g., the P/E ratio) Ou and Penman (1989) initiated rigorous academic research on earnings prediction based on a multivariate analysis of financial ratios. The main idea was to examine whether combining information in individual ratios about future earnings growth can yield more accurate forecasts of future earnings. Ou and Penman use statistical procedures to reduce a large number of financial ratios to a subset that is most effective in forecasting future earnings. The procedure yields a composite measure, which indicates the likelihood of a positive or negative earnings change. Positive abnormal returns can also be achieved with a fundamental strategy based on this measure. Following Ou and Penman’s earnings prediction are Lev and Thiagarajan (1993) and Abarbanell and Bushee (1998), and Piotroski (2000) who use conceptual arguments to study their ratios. They demonstrate that the earnings prediction signals in variables like growth in accounts receivables relative to sales growth and gross margin rate are incrementally associated with contemporaneous stock returns and are significant in

predicting future earnings. This paper is related to the above literature in that we construct a combination of financial ratios designed to capture short-term changes in the firms’ operations, profitability and financial policy. Our financial score is closely related to Abarbanell and Bushee (1998) and Piotroski (2000). However, our emphasis is less on accruals and reporting choice as signals of future earnings growth The choice of variables is more related to changes in dividend yield, capital structure, and operations as signals of private information of the firms’ future prospects Our dataset consists of Japanese non-financial firms listed on the Tokyo Stock Exchange (TSE) and covers 10 years of financial accounts from Fiscal 1992 to Fiscal 2001. Our score exhibits a strong correlation with market-adjusted returns in the same accounting period. The correlation remains significant with returns from the following period, although its strength decreases when the holding period is lagged. We show that this property is related to a positive autocorrelation in the financial score. This feature enables high score stocks to exhibit return momentum, i.e. a continuation of their strong performance in the current period into the next period. Likewise low score stocks are likely to extend their poor performance. The rest of the paper is organized as follows. Section 2 details the construction of the score and presents some of its statistical properties. Section 3 analyzes the relationship between the score and market-adjusted return in different holding periods. Section 4 relates the momentum of market-adjusted returns to the autocorrelation properties of the score. Section 5 evaluates the risk of score based portfolios and their implication in the superior returns to high score portfolios. Section 6 concludes and offers some suggestions for further research.

2. Construction and properties of the composite score We use eight fundamental signals to measure three areas of the firm’s financial condition: profitability, operating efficiency, and financing decision.

As in Piotroski (2000), each firm’s signal realization is classified as either “good” or “bad” depending on the signal’s implication for future prices and profitability. An indicator variable for the signal is equal to one (zero) if the signal’s realization is good (bad). The aggregate measure, also called F_SCORE, is the sum of the eight individual signals. The aggregate signal is designed to measure the overall strength of the firm’s financial position. Portfolios examined in the following sections are based on the strength of the aggregate signal.

2.1 Profitability Current profitability realizations provide information about the firm’s ability to generate funds internally. Similarly, a positive earnings trend is suggestive of an improvement in the firm’s underlying ability to generate positive future cash flows. Three variables are used to measure these performance-related factors: ROA, ∆ROA, and ∆ROE. ROA is the year operating profit scaled by end of year total assets. If the firm’s ROA is positive, the dummy variable F_ROA equals one, zero otherwise. ∆ROA is equal to the current year’s ROA less the prior year’s ROA. If ∆ROA > 0, the dummy variable F_VROA equals one, zero otherwise. ROE is the year net profit scaled by end of year total equity. Similarly, ∆ROE is equal to the current year’s ROE less the prior year’s ROE. If ∆ROE > 0, the dummy variable F_VROE equals one, zero otherwise. Although potentially useful for predicting future profitability, measures of cash flows and change in cash flows are not included due to data availability in the Nikkei NEEDS database.

2.2 Operating Efficiency

Three signals are designed to capture changes in the efficiency of the firm’s operations: Change in turnover, change in inventory, and change in inventory turnover. Asset turnover TURN is the year sales divided by end of year total assets. ∆TURN is equal to the current year’s TURN less the prior year’s TURN. If ∆TURN > 0, the dummy variable F_TURN equals one, zero otherwise. INVT is the end of year inventory scaled by the end of year total assets. ∆INVT is given by the current year’s INVT less the prior year’s INVT. If ∆INVT < 0, the dummy variable F_INVT equals one, zero otherwise. Finally, inventory turnover SLINV is the year sales scaled by end of year inventory. ∆SLINV is the current year’s SLINV less the prior year’s SLINV. If ∆SLINV > 0, the dummy variable F_SLINV equals one, zero otherwise. Controlled increase in asset and inventory turnover is usually associated with subsequent if not current profit increase. A reduction in inventory also signals improved efficiency in the firm’s operations, implies lower financing needs and costs, and thus higher profits. 2.3 Leverage and dividends The remaining two signals are designed to measure changes in the firm’s capital structure and dividend policy: ∆LEVG and ∆DIVEQ. ∆LEVG captures changes in the firm’s long-term debt levels. It is measured as the change in the ratio of total long-term debt to total book value of equity. The dummy variable F_VLEVG is one (zero) if the firm’s leverage ratio fell (rose) in the year. This interpretation may be controversial as an increase in leverage can be either a positive (e.g., Harris and Raviv, 1990) or negative (e.g. Myers and Majluf, 1984; Miller and Rock, 1985) signal. However, Japanese firms tend to exhibit high leverage ratios so that a decrease in leverage may have more positive consequences through a reduction of agency costs or improved monitoring. In addition, an increase in long-term debt is likely to place additional constraints on the firm’s financial flexibility.

The dividend yield DIVEQ is the dividend paid in the year scaled by end of year book value of equity. ∆DIVEQ is equal to the current year’s DIVEQ less the previous year’s

DIVEQ. If ∆DIVEQ > 0, the dummy variable F_ DIVEQ equals one, zero otherwise. Consistent with Michaely, Thaler and Womack (1995), dividend increases are assumed to convey a positive signal about the firm’s future financial condition.

2.4 Composite Score F_SCORE is simply the sum of the individual binary signals:

F_SCORE = F_ROA + F_VROA + F_VROE + F_VTURN + F_VINVT + F_VSLINV + F_VLEVG + F_VDIVEQ

A low F_SCORE represents a firm with very few good signals while a high F_SCORE indicates the firm has mostly good fundamental signals. Given the eight underlying signals, F_SCORE can range from a low of 0 to a high of 8. As an aggregate measure of historical performance, F_SCORE should be positively associated with changes in the firm’s contemporaneous stock return but also be indicative of the firm’s future stock performance. The investment strategy considered in this paper is involves selecting firms with high F_SCORE signals, instead of purchasing firms based on the relative realization of any particular signal.

It should be noted that the individual signals included in the composite score do not represent the optimal set of performance measures for distinguishing good investments from bad investments. Also statistical techniques such as factor analysis may more aptly extract an optimal combination of signals, at the potential cost of over-fitting the observations and prove less reliable in out of sample data. 2.5 Data sample and composite score properties Firms listed on the First and Second sections of the Tokyo stock exchange (TSE) are selected from the Nikkei NEEDS/corporate information and security prices database. The sample period is from fiscal year 1992 (end-March 1993) to fiscal year 2001 (end-March 2002). This gives a total of 2096 firms over a 10-year period. Firm characteristics such as market value of equity, and P/B ratio are calculated at fiscal year end. Transaction volumes are also computed over the fiscal year ending in March.

In reality, the number of observations is smaller than 2096 for each year. First, financial firms (banks, insurance companies, securities firms) are only listed in name; their data is reported in a separate section of the Nikkei NEEDS database. Second, some firms have been listed after FY1992 while other have been delisted before FY2001 for a variety of reasons (bankruptcies, mergers and acquisitions, etc.). Table 1 provides summary statistics for firms depending on their score. The data is pooled across the entire sample period. The distribution of scores reported in Panel A shows that most of the observations are clustered around F_SCORES between 3 and 5, indicating that a vast majority of the firms have conflicting individual performance signals. Hence, the benefit of aggregating the signals. There are however, a sufficiently large number of firms with high scores (1784 with scores higher or equal to 7) or with low scores (1409 with scores lower or equal to 1) from whom to construct F_SCORE based investment portfolios. Panel B details the contribution of each individual signal to the composite score. Obviously F_SCORE equal to zero (eight) requires all individual signals to be null (equal to one). Low F_SCORE beside zero indicates essentially a positive ROA, and a decrease in the inventory ratio to a lesser extent. High F_SCORE beside 8 indicates a positive ROA with a very high probability, but only half the probability of an increasing dividend yield compared to a F_SCORE equal to 8. Table 2 provides summary statistics for firms depending on their F_SCORE. In terms of Price/Earnings, high score firms have generally lower P/E ratios than low score firms as their usually tend to have higher earnings. High score firms are also characterized by higher Price/Book ratios indicating their relatively strong perception by investors. This also strengthens the view that their low P/E ratio is due to high earnings. The two profitability measures (ROA and ROE) show that an increasing F_SCORE is indicative of superior profitability (both ROA and ROE). Moreover, the evolution of ROA and ROE with F_SCORE is monotonous increasing. Sales/assets and Sales/inventory are also monotonous increasing with F_SCORE. The dividend yield (measured by dividend//book value of equity) is also increasing with the firm’s

composite score. High score firms have lower inventory level to assets compared with low score firms. Leverage measured by long term debt to book value of equity is less obvious to interpret 3. Score values and market returns The purpose of this section is investigates the relationship of our score and market-adjusted returns. A strong correlation between the firm’s score and its returns in the current period would be indicative of the relevance of the composite score as a measure of the firm’s improving financial condition. Further, a significant correlation between the score and subsequent returns would provide evidence of return predictability based on past corporate improvement. 3.1 Calculation of returns Returns are computed for holding periods of one year. The contemporaneous period starts April 1 and ends March 31, and coincides with the fiscal year for which the composite score is calculated. We use end of month stock prices retrieved from the Nikkei NEEDS market data to compute raw annual returns for each stock. A few monthly prices are missing for thinly traded stocks, which were not traded on the last day of the month under consideration. In this case, the next end of month price was used to calculate the return (instead of next trading day for computational reasons). The annual return on the Nikkei 225 index serves for adjusting the returns for market movements. The choice of the Nikkei index as a market proxy has clear drawbacks. Because the Nikkei 225 comprises large firms and because small firms have been documented to enjoy higher returns (e.g. Banz, 1981), it follows that market adjusted returns have a significantly positive mean. An equally weighted portfolio of all available stocks could be an alternative for the market proxy. 3.2 Correlation between scores and returns Table 3 presents the correlation between the score and market-adjusted returns in different periods. Clearly, the correlation coefficient decreases the longer the holding period is lagged.

With returns computed from July 1 to June 31 (MAR_3M), corresponding to a 3-month lapse after the accounting period, correlation is still significantly positive, indicating that returns are predictable based on a combination of financial information publicly available at the time of portfolio formation. After 12 months (MAR_12M) the sign of the correlation between score and return changes while the strength becomes much less significant. At this point much of the information contained in the score has been completely integrated into stock prices. Panel B show that each point of the score has yielded a hefty 4.29% in the contemporaneous period (only accessible with the benefit of hindsight) and a still sizable 0.55% for stocks purchased 3 months after the end of fiscal year (hence, accessible in real time). The strength of the score-return relationship seems to have increased over time.

Panel C present the same results for the past 5 years. For stocks picked with a three-month lag, the annual is increased by 1.47% for each point of the score.

Table 4 presents the correlation between the score and market-adjusted returns. It also details the correlation between the score components (individual signals) and market-adjusted returns in the current period and the subsequent period. Panel A shows that all the individual signals except the inventory change ratio are positively and significantly correlated with market-adjusted returns in the current period. Change in ROA, change in ROE, and change in turnover are the most significant ratios when considering current market-adjusted returns. As a composite indicator, F_SCORE present the highest correlation with contemporaneous market-adjusted returns. Panel B shows that the correlation structure has markedly strengthened over the past 5 years. However, the inventory change ratio still remains insignificant as a contemporaneous indicator of market-adjusted returns Over the subsequent holding period and for the whole sample period, the correlation structure weakens noticeably. In particular some individual signals that where highly informative of contemporaneous market-adjusted returns become less significant while

retaining a positive correlation with market adjusted returns. On the other hand, the inventory change ratio that was insignificant to contemporaneous market-adjusted returns is shown to play a significant role in signaling subsequent stock returns. For the past 5 years, all individual signals exhibit a significant correlation with subsequent market-adjusted returns, which contributes to enhance the power of the composite score to predict returns.

3.3 Score based portfolio and returns

Table 5 shows the benefit of holding stocks with high scores in the current and the next accounting periods. In both cases, the returns are obtained with hindsight and not available in real time. One striking observation is that, taken on average, market-adjusted returns are monotonously increasing with the score in the contemporaneous accounting period. This is consistent with the view that markets are rapidly integrating information into stock prices. Firms whose financial condition is improving as measured by their financial scores are characterized by average returns substantially above the market return. In contrast, firms whose financial condition shows little improvement, or deterioration, experience significant stock under performance Hence, the market validates the application of the score as a screening tool to identify and characterize firms with improving financial conditions. Over the sample period, firms with the highest F_SCORE outperform firms with the lowest F_SCORE by almost 45% annually. This remains true when the score is applied with the subsequent holding period. Market-adjusted returns are also increasing monotonously with the firms’ score. However, the difference between high score and low score returns is less pronounced. Still, firms with the highest score still achieve returns that are more than 20% above the returns achieved by firms with the lowest score.

4. Score values, market returns, and momentum This section examines the autocorrelation properties of market adjusted returns and relate them to the autocorrelation of the composite score 4.1 continuation and reversal in stock returns To analyze the predictability of stock returns based on previous returns, we rank returns in the previous accounting/fiscal period in 5 quintiles from the lowest to the highest returns. The returns in the next period are then averaged within each quintile. Finally, the returns in each quintile are averaged across the sample period. The process is performed again twice. First, only the 2 top scores are retained in each quintile. Then, only the 2 lowest scores are retained in each quintile. We prefer to consider the two highest and lowest scores, instead of just one score, in order to avoid small portfolio samples with potentially unstable returns. Table 6 shows that for the entire sample period, stocks that performed either very well or rather poorly in the previous period are likely to under perform in the next period. In other words, a return reversal occurs for good performers while a return continuation is displayed for bad performers. Picking high score stocks increases the return in the next period across previous past performance quintiles. Similarly, low score stocks are likely to produce lower returns in their respective quintiles. However, close inspection of returns for each year in the sample period reveals that much of the observed returns in Panel A are impaired by the exceptional pattern of returns in FY2000 (April 2000 to March 2001). As a matter of fact, returns in this period are characterized by heavy mean reversal. Stocks that outperformed in FY1999 in particular telecommunication, media and technology stocks under-performed dramatically in FY2000. Returns computed outside this specific period are shown in Panel B. The continuation in returns (momentum) appears quite clearly when the score is not taken into consideration. There is also a momentum pattern for portfolios of high score stocks taken in each of the quintiles. Stocks characterized by a high score manage,

however, to break away from the momentum pattern to produce a strong reversal (positive return while a negative return continuation would be expected). Regression run over the entire sample period, and over a period excluding FY2000, confirm the remarks made above. Returns exhibit momentum most of the time. Although the consideration of past returns slightly reduces the benefit of F_SCORE, the momentum effect does not subsume the impact of considering F_SCORE in the screening process. 4.2 Continuation in the score An interesting question is whether the predictability of stock returns evidenced through the momentum effect is related to persistence in the improvement of the firms’ financial condition. Given that current financial improvements are strongly correlated with current market adjusted returns (cf. table 4, panels A & B), predictability of a firm’s score based on its current F_SCORE would imply the predictability of the firm’s future stock returns. Table 8 presents the transition matrix from the score in the current period to the score in the next period for the whole sample. High score firms (characterized by improving financial figures) are likely to extend their performance onto the next period (continue to improve their financial numbers). For example, firms with F_SCORE equal to 8 have a score in the next period, which is on average 1 point higher than firms whose F_SCORE equal 0. They have also a probability of retaining the top score 3.7 times higher than firms with F_SCORE equal to 0 (even 3.4 times higher than firms with F_SCORE below 4). On the other hand, firms with top score are less likely to achieve a low F_SCORE in the next period. Spearman correlation coefficient between F_SCORE and the lagged F_SCORE is also significant at 1% level (and equal to 11%, see table 9). Hence, the predictability of an improvement in the firms’ financial condition may help to predict the firms’ stock return in the next period.

Table 9 shows that the current score is not significantly, nor positively, correlated with all the individual signals of the subsequent score. However, it positively and significantly correlated with the composite score made form these individual signals. Moreover, the strength of the autocorrelation in the score has increased over time. 5. Adjustment for risk As for the small firm effect (Banz, 1981) and other stock market anomalies, it may be argued that high score firms generate a higher return simply because they are more exposed to various risk factors (Fama and French, 1992). 5.1 volatility and distribution of returns We first assess the riskiness of high score firms by computing the standard deviation of equally weighted portfolios composed with stocks of identical score. The figures on table 10 indicate that portfolios of high score firms have a significantly higher variance of returns compared to portfolios of low score firms. This tends to support the view that high score stocks are more risky, and thus should yield a higher return. In this case, the predictability of their higher returns only reflects the higher risk premium that these stocks should pay.

However, high score stocks have also a much higher average return compared to other stocks which may more than compensate for the added risk carried by these stocks. Table 11 presents the proportion of returns below the market return (measured by the Nikkei 225) for each score-based portfolio and each year of the sample period. Clearly high score portfolios contain less loosers (stocks with return less than the market return) than low score portfolios which may in some years contain only loosers (e.g. in 1997-98 for F_SCORE equal 0). The different percentile levels of the distribution of returns for each score-based portfolio reported in table 12 show that the application of the score not only modifies the average return, but also shifts the whole distribution of returns. For example, the return distribution for the highest score portfolio is shifter upwards by 1-2 deciles while the return distribution for the lowest score portfolio is shifter downwards by about 2 deciles. Hence, it doesn’t appear that a higher level of risk accompanies the application of the score, even though high score portfolios give up some of the benefits of diversification.

5.2 size and book/market risk factors Finally, we investigate the possibility that some of the excess return achieved by high score portfolios be due to the size and the book/market factors suggested by Fama and French (1996). In case of TSE listed stocks and for the sample period covered here, neither the book/market ratio nor the firm size appear to affect the contribution of the F_SCORE. Indeed, it would be surprising that these characteristics alone capture the information contained in the score and convey the improvement in the firm’s financial condition. 6. Conclusion This paper has investigated the relationship between accounting information and stock returns. We have shown that fundamental analysis is helpful in predicting future stock returns and for explaining the momentum phenomenon in stock prices. Although our score is based on a more limited set of accounting information compared to Abarbanell and Bushee (1998), Lev and Thiagarajan (1993) or Piotroski (2000), high score stocks achieve a remarkable 11% return above the market with portfolio revision occurring 3 months after fiscal year end. This performance derives from the persistence of improvement in the firms’ financial condition, which extends from the current period to the next period. The superior performance of score-based portfolios does not appear to be accompanied by increasing risk though some of the benefit of diversification may be forgone. The effect of screening with the score function is not subsumed by other risk factors considered in Fama and French (1992). High book/market stocks provide their own risk and reward while size appears to benefit large firms instead of small firms contrary to the evidence reported in Banz (1981) for the US market. Further improvement and research are necessary. First the score should incorporate more information about cash flows and external financing decisions. For example, indication of recent open market share repurchases (increasingly popular in Japan at this moment) should be included. It would have a significant power to predict future stock

return especially if the underreaction reported in Ikenberry, Lakonishok and Vermaelen (1995) applies in Japan. Second, returns may be computed cum dividends. This would emphasize the benefit of high score stocks which are characterized by a higher dividend yield. Third, the use of fiscal year-end figures means that the high contemporaneous returns for high score stocks are forgone. Analyst forecast (in some cases) and management mid-year forecast (in most cases) may be used to revise portfolios before the final numbers. The difference between actual and forecasted figures can also be used to generate individual signals and be included to produce a more discriminant score. Finally, a kind of out of sample test with JASDAQ stocks whose characteristics are distinct from TSE stocks would provide a robustness check on the ability of the score to predict future stock returns. References [1] Abarbanell, J., Bushee, B., 1998, Abnormal returns to a fundamental analysis

strategy, The Accounting Review 73, 19-45 [2] Banz, R., 1981, The relationship between return and market value of common

stocks, Journal of Financial Economics 9, 3-18 [3] Chan, L., Jegadeesh, N. and J. Lakonishok, 1996, Momentum Strategies, Journal

of Finance 51, 1681-1713 [4] Fama, E., French, K., 1992, The cross-section of expected returns, Journal of

Finance, 47, 427-465 [5] Fama, E., French, K., 1993, Common risk factors in the returns on stocks and

bonds, Journal of Financial Economics 33, 3-56 [6] Grinblatt, M., S. Titman, and R. Wermers, 1995, Momentum strategies, portfolio

performance, and herding: A study of mutual fund behavior, American Economic Review, 85, 1088-1105

[7] Harris, M. and A. Raviv, 1990, Capital Structure and the Informational Role of Debt, Journal of Finance 45, 321-349

[8] Ikenberry, D., Lakonishok, J. and T. Vermaelen, 1995, Market Underreaction to Open Market Share Repurchases, Journal of Financial Economics 39, 181-208

[9] Jegadeesh, N., Titman, S., 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, 65-91

[10] Lakonishok, J., A. Shleifer and R. Vishny, 1994, Contrarian Investment, Extrapolation and Risk, Journal of Finance 44, 1541-1578

[11] Lev, B., Thiagarajan, R., 1993, Fundamental information analysis, Journal of

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and omissions: overreaction or drift, Journal of Finance 50, 997-997 [13] Miller. M. and K. Rock, 1985, Dividend Policy under Asymmetric Information,

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when Firms have Information that Investors do not have, Journal of Financial Economics 13, 187-221.

[15] Ou, J., Penman, S., 1989, Financial statement analysis and the prediction of stock returns, Journal of Accounting & Economics 11, 295-329

[16] Piotroski, J., 2000, Value investing: The use of historical financial statement information to separate winners from losers, Journal of Accounting Research, forthcoming.

Table 1: Distribution and components of F_SCORE

Panel A – count and percentage

F_SCORE 0 1 2 3 4 5 6 7 8

COUNT 217 1192 2562 3478 3964 3577 2512 1374 410

PROPORTION 0.011252 0.061806 0.132842 0.180338 0.205538 0.185471 0.13025 0.071243 0.021259

Panel B – F_SCORE and individual signals

F_SCORE 0 1 2 3 4 5 6 7 8

F_ROA 0 0.64597 0.82006 0.89563 0.9339 0.96365 0.98009 0.99927 1

F_VLEVG 0 0.06879 0.26502 0.3801 0.49218 0.584 0.66759 0.81149 1

F_VROA 0 0.0109 0.0683 0.22714 0.43718 0.64383 0.84195 0.96433 1

D_VROE 0 0.01677 0.08977 0.2165 0.38319 0.58345 0.75358 0.90393 1

F_VTURN 0 0.06291 0.14598 0.25704 0.39909 0.57981 0.71377 0.87117 1

F_VINVT 0 0.15352 0.32162 0.48303 0.56104 0.62035 0.73049 0.85298 1

F_VSLINV 0 0.0109 0.09523 0.31512 0.50025 0.64243 0.82961 0.97088 1

F_VDIVEQ 0 0.0302 0.19398 0.22541 0.29313 0.38244 0.48288 0.62591 1

Table 2: Firm characteristics depending on F_SCORE

F_SCORE 0 1 2 3 4 5 6 7 8

PBR 2.388 1.292 1.201 1.937 1.964 1.971 2.195 2.024 2.351

PER 225.06 78.21 71.29 90.79 74.50 70.23 87.47 60.05 52.68

LIQTY 1.6637 2.0706 2.2819 1.8177 1.7088 1.7040 1.6608 1.6360 1.4983

LEVG 0.6392 0.3991 0.2698 0.4889 0.4725 0.5146 0.4514 0.3747 0.4053

ROA -0.0323 0.0009 0.0201 0.0366 0.0426 0.0484 0.0542 0.0598 0.0692

ROE -0.5262 -0.1249 -0.0738 -0.0814 0.0048 0.0424 0.0625 0.0805 0.0856

TURN 0.8905 0.9207 0.9637 1.0174 1.0659 1.0593 1.0711 1.0856 1.1349

INVT 0.1477 0.1230 0.1162 0.1107 0.1104 0.1088 0.1076 0.1009 0.1048

SLS/INVT 9.34 46.11 67.19 109.20 132.43 112.77 149.30 100.22 168.57

DIV/EQTY 0.0026 0.0060 0.0088 0.0102 0.0107 0.0111 0.0120 0.0129 0.0171

Table 3: Relation between F_SCORE and market-adjusted returns in different holding periods

Panel A – Spearman correlation coefficients

MAR_CRNT MAR_0M MAR_3M MAR_12M

MAR_CRNT 1 0.008 -.022(**) .110(**)

MAR_0M - 1 .754(**) 0.008

RET_3M - - 1 .199(**)

MAR_12M - - - 1

F_SCORE .198(**) .058(**) .027(**) -.020(*)

(**) Correlation is significant at the .01 level (2-tailed).

(*) Correlation is significant at the .05 level (2-tailed).

Panel B – Pooled regression for whole sample period

MAR_CRNT MAR_0M MAR_3M MAR_12M

Constant -0.141 -0.005 0.015 0.076

(-15.716) (-0.617) (-1.841) (-8.211)

F_SCORE 4.29.E-02 1.50.E-02 5.52.E-03 -6.32.E-03

(-21.339) (-7.743) (-2.97) (-3.016)

R Square 0.028 0.003 0.000 0.001

Panel C – Pooled regression sub sample period 1998-2002 (5 years)

MAR_CRNT MAR_0M MAR_3M MAR_12M

Constant -0.175 6.31E-03 7.20E-02 0.215

(-12.711) (-0.451) (-5.388) (-13.466)

F_SCORE 6.26E-02 3.58E-02 1.47E-02 -1.13E-02

(-19.655) (-11.115) (-4.768) (-3.102)

R Square 0.041 0.013 0.002 0.001

Table 4:

Correlation between F_SCORE, individual signals and current market-adjusted returns

Spearman coefficient

Panel A - Entire sample period

F_ROA F_VLEVG F_VROA F_VROE F_VTURN F_VINVT F_VSLINV F_VDVEQ

T

F_SCORE

F_ROA 1 .109(**) .141(**) .111(**) .054(**) -0.008 .057(**) .121(**) .306(**)

F_VLEVG - 1 .097(**) .104(**) .087(**) -.022(**) .045(**) .021(**) .386(**)

F_VROA - - 1 .441(**) .320(**) -0.013 .137(**) .091(**) .613(**)

F_VROE - - - 1 .189(**) -0.008 .095(**) .091(**) .557(**)

F_VTURN - - - - 1 -.122(**) .193(**) .061(**) .494(**)

F_VINVT - - - - - 1 .489(**) -0.014 .357(**)

F_VSLINV - - - - - - 1 .045(**) .577(**)

F_VDVEQT - - - - - - - 1 .342(**)

MAR-CRN

T

.138(**) .036(**) .197(**) .180(**) .087(**) 0.001 .057(**) .067(**) .198(**)

Panel B - Sub-period 1998-2003

F_ROA F_VLEVG F_VROA F_VROE F_VTURN F_VINVT F_VSLINV F_VDVEQ

T

F_SCORE

F_ROA 1 .095(**) .174(**) .121(**) .074(**) -0.018 .072(**) .151(**) .339(**)

F_VLEVG - 1 .110(**) .114(**) .077(**) 0 .070(**) .030(**) .405(**)

F_VROA - - 1 .416(**) .301(**) 0.018 .118(**) .100(**) .619(**)

F_VROE - - - 1 .165(**) 0.005 .080(**) .100(**) .552(**)

F_VTURN - - - - 1 -.116(**) .138(**) .065(**) .471(**)

F_VINVT - - - - - 1 .367(**) -.024(*) .341(**)

F_VSLINV - - - - - - 1 .069(**) .534(**)

F_VDVEQT - - - - - - - 1 .374(**)

MAR-CRN

T

.180(**) .046(**) .203(**) .189(**) .121(**) -0.02 .064(**) .102(**) .228(**)

(**) Correlation is significant at the .01 level (2-tailed).

Table 4 (continued)

Correlation between F_SCORE, individual signals and subsequent market-adjusted return

Spearman coefficient

Panel A - Entire sample period

F_ROA F_VLEVG F_VROA F_VROE F_VTURN F_VINVT F_VSLINV F_VDVEQ

T

F_SCORE

F_ROA 1 .109(**) .141(**) .111(**) .054(**) -0.008 .057(**) .121(**) .306(**)

F_VLEVG - 1 .097(**) .104(**) .087(**) -.022(**) .045(**) .021(**) .386(**)

F_VROA - - 1 .441(**) .320(**) -0.013 .137(**) .091(**) .613(**)

F_VROE - - - 1 .189(**) -0.008 .095(**) .091(**) .557(**)

F_VTURN - - - - 1 -.122(**) .193(**) .061(**) .494(**)

F_VINVT - - - - - 1 .489(**) -0.014 .357(**)

F_VSLINV - - - - - - 1 .045(**) .577(**)

F_VDVEQ

T

- - - - - - - 1 .342(**)

MAR_0M .037(**) 0.004 .061(**) .052(**) 0.008 .029(**) .020(**) 0.013 .058(**)

Panel B - Sub-period 1998-2003

F_ROA F_VLEVG F_VROA F_VROE F_VTURN F_VINVT F_VSLINV F_VDVEQ

T

F_SCORE

F_ROA 1 .095(**) .174(**) .121(**) .074(**) -0.018 .072(**) .151(**) .339(**)

F_VLEVG - 1 .110(**) .114(**) .077(**) 0 .070(**) .030(**) .405(**)

F_VROA - - 1 .416(**) .301(**) 0.018 .118(**) .100(**) .619(**)

F_VROE - - - 1 .165(**) 0.005 .080(**) .100(**) .552(**)

F_VTURN - - - - 1 -.116(**) .138(**) .065(**) .471(**)

F_VINVT - - - - - 1 .367(**) -.024(*) .341(**)

F_VSLINV - - - - - - 1 .069(**) .534(**)

F_VDVEQ

T

- - - - - - - 1 .374(**)

MAR_0M .074(**) .056(**) .109(**) .105(**) .070(**) .029(**) .044(**) .035(**) .139(**)

(**) Correlation is significant at the .01 level (2-tailed).

Table 5: F_SCORE based portfolios and market-adjusted return

Panel A - Current accounting period (MAR_CRNT)

F_SCORE TOP BOTTOM

PERIOD 8 7 6 5 4 3 2 1 0 ALL 7 & 8 0 & 1

1994 0.2575 0.2170 0.1966 0.1606 0.0893 0.0693 0.0614 0.0098 -0.0003 0.1025 0.2270 0.0081

1995 0.0584 0.0531 0.0350 0.0059 -0.0334 -0.0671 -0.0955 -0.1071 -0.0796 -0.0197 0.0545 -0.1044

1996 0.0506 0.0003 -0.0056 -0.0797 -0.0627 -0.1129 -0.1586 -0.0941 -0.1621 -0.0721 0.0111 -0.1002

1997 0.0570 -0.0179 -0.0454 -0.0688 -0.1039 -0.1321 -0.1255 -0.1899 -0.2705 -0.0796 -0.0011 -0.1974

1998 0.0863 -0.0296 -0.0877 -0.1295 -0.1637 -0.1957 -0.2209 -0.2644 -0.3752 -0.1637 -0.0062 -0.2807

1999 0.8486 0.3369 0.2658 0.1797 0.0973 -0.0162 -0.0800 -0.1575 -0.2729 0.0583 0.4285 -0.1819

2000 0.3082 0.0396 0.0108 -0.1144 -0.1578 -0.1437 -0.2385 -0.3220 -0.3377 -0.1040 0.0928 -0.3246

2001 0.4945 0.5839 0.6041 0.5607 0.4728 0.3866 0.4145 0.5559 0.1594 0.4952 0.5658 0.4785

2002 0.6197 0.1753 0.1675 0.1028 0.0514 0.0080 -0.0146 -0.0690 0.0982 0.0616 0.2721 -0.0388

AVERAGE 0.3090 0.1510 0.1268 0.0686 0.0210 -0.0226 -0.0509 -0.0709 -0.1379 0.0309 0.1827 -0.0824

Panel B - Subsequent accounting period (MAR_0M)

F_SCORE TOP BOTTOM

PERIOD 8 7 6 5 4 3 2 1 0 ALL 7 & 8 0 & 1

1993 0.3272 0.1323 0.0927 0.0916 0.1093 0.0980 0.1076 0.1022 0.0982 0.1024 0.1760 0.1016

1994 -0.0771 -0.0649 0.0044 -0.0071 -0.0356 -0.0206 -0.0248 -0.0134 0.0279 -0.0197 -0.0682 -0.0064

1995 0.0699 -0.0531 -0.0391 -0.0755 -0.0956 -0.0774 -0.0716 -0.0591 -0.1590 -0.0721 -0.0222 -0.0685

1996 -0.0300 -0.0248 -0.0639 -0.0799 -0.0938 -0.1048 -0.1062 -0.0983 -0.1001 -0.0796 -0.0259 -0.0985

1997 -0.0787 -0.1654 -0.1549 -0.1678 -0.1568 -0.1784 -0.2301 -0.2415 -0.3531 -0.1636 -0.1461 -0.2519

1998 0.5064 0.2888 0.0881 0.1191 0.0317 0.0473 -0.0078 -0.0089 -0.1467 0.0583 0.3422 -0.0292

1999 0.0152 -0.0547 0.0490 -0.0872 -0.1405 -0.1195 -0.1525 -0.1400 -0.1803 -0.1040 -0.0418 -0.1484

2000 0.6457 0.4559 0.4789 0.5490 0.4871 0.4520 0.5142 0.4824 0.4899 0.4951 0.4932 0.4836

2001 0.1187 0.0540 0.0778 0.0268 0.1142 0.0708 0.0200 -0.0026 -0.1635 0.0615 0.0669 -0.0334

2002 0.2948 0.3050 0.2731 0.2251 0.2373 0.2429 0.1489 0.1128 0.1140 0.2197 0.3027 0.1130

AVERAGE 0.1792 0.0873 0.0806 0.0594 0.0457 0.0410 0.0198 0.0134 -0.0373 0.0498 0.1077 0.0062

Panel B - Subsequent accounting period (MAR_3M)

F_SCORE TOP BOTTOM

PERIOD 8 7 6 5 4 3 2 1 0 ALL 7 & 8 0 & 1

1993 0.1820 0.0349 0.0001 0.0011 0.0187 0.0256 0.0601 0.0302 0.0661 0.0238 0.0679 0.0358

1994 -0.1557 -0.1055 -0.0516 -0.0680 -0.0924 -0.0544 -0.1176 -0.1298 -0.1798 -0.0764 -0.1187 -0.1382

1995 0.0614 -0.0200 0.0119 -0.0124 -0.0246 0.0002 0.0182 0.0765 0.1927 -0.0070 0.0007 0.0874

1996 -0.1056 -0.0660 -0.1083 -0.1603 -0.1674 -0.1850 -0.1940 -0.1802 -0.1345 -0.1507 -0.0745 -0.1761

1997 -0.0307 -0.1143 -0.1051 -0.1258 -0.1087 -0.1304 -0.1944 -0.1811 -0.2165 -0.1144 -0.0957 -0.1845

1998 0.6636 0.4611 0.2156 0.2663 0.1157 0.1684 0.0818 0.1564 0.1267 0.1717 0.5108 0.1520

1999 -0.0931 -0.0157 0.0521 -0.0716 -0.0457 -0.0304 0.0168 -0.0241 0.0312 -0.0158 -0.0299 -0.0126

2000 0.4014 0.2492 0.2841 0.3389 0.2686 0.2855 0.3613 0.2372 0.3180 0.3007 0.2796 0.2505

2001 0.0001 -0.0195 0.0348 -0.0086 0.0588 0.0391 0.0061 0.0027 -0.2021 0.0195 -0.0156 -0.0364

2002 0.1996 0.2202 0.1729 0.1752 0.1902 0.1990 0.1246 0.0939 0.0660 0.1723 0.2154 0.0888

AVERAGE 0.1123 0.0624 0.0506 0.0335 0.0213 0.0318 0.0163 0.0082 0.0068 0.0324 0.0740 0.0067

Table 6: Average returns in subsequent period depending on past return

Panel A – Entire sample period

Return in previous period

lowest highest ALL

1 2 3 4 5

all stocks 0.0421 0.0448 0.0524 0.0432 0.0371 0.0439

high score 0.1864 0.0559 0.0967 0.1158 0.0929 0.1096

low score -0.0202 0.0028 0.0245 0.0051 -0.0154 -0.0006

Panel B – Sample period excluding FY2000

Return in previous period

lowest highest ALL

1 2 3 4 5

all stocks -0.0403 -0.0339 -0.0117 -0.0083 0.0251 -0.0138

high score 0.1179 -0.0157 0.0284 0.0558 0.0731 0.0519

low score -0.0798 -0.0813 -0.0349 -0.0113 -0.0146 -0.0444

Table 7: Average returns in subsequent period depending on past return

Panel A – Entire sample period

Constant MAR_CRNT F_SCORE R squared

coeff 0.0531 -0.0362 0.001

t-stat 14.99 -4.55

coeff -0.0053 0.0150 0.003

t-stat -0.62 7.74

coeff -0.0226 -0.0469 0.0185 0.006

t-stat -2.42 -5.67 8.79

Panel B – Sample period excluding FY2000

Constant MAR_CRNT F_SCORE R squared

coeff -0.0133 0.0997 .010

t-stat -3.99 12.33

coeff -0.0340 0.0091 0.002

t-stat -4.26 5.00

coeff -0.0416 0.0923 0.0069 0.011

t-stat -4.83 10.96 3.48

Table 8: Transition matrix for F_SCORE

Whole sample period 1993-2003

NEXT

CURRENT

0 1 2 3 4 5 6 7 8 AVERAGE

0 0.0385 0.1154 0.1374 0.1429 0.1703 0.1758 0.1264 0.0769 0.0165 3.8077

1 0.0183 0.0944 0.1378 0.185 0.2071 0.1753 0.1108 0.0588 0.0125 3.8064

2 0.0136 0.0748 0.147 0.1893 0.2007 0.1752 0.1215 0.0629 0.015 3.9045

3 0.0139 0.0664 0.1302 0.1827 0.2126 0.1856 0.1273 0.0635 0.0177 4.0026

4 0.0095 0.0507 0.1208 0.1867 0.2066 0.199 0.1343 0.0735 0.0185 4.1421

5 0.0091 0.0556 0.1145 0.1745 0.2197 0.2035 0.1267 0.0727 0.0234 4.1610

6 0.0057 0.0401 0.1132 0.1715 0.2098 0.1894 0.155 0.0871 0.0283 4.3330

7 0.0079 0.0401 0.0858 0.1511 0.1888 0.203 0.1707 0.1109 0.0417 4.5696

8 0.0026 0.0211 0.0974 0.1474 0.1605 0.1868 0.1868 0.1368 0.0605 4.7974

Table 9: Correlation between current F_SCORE and subsequent individual signals

CURRENT F_ SCORE F_ SCORE

SUBSEQUENT Since 1993 Since 1998

F_ROA .189(**) .180(**)

F_VLEVG .134(**) .139(**)

F_VROA -.027(**) -.057(**)

F_VROE 0.005 -.045(**)

F_VTURN -0.002 -0.012

F_VINVT -.084(**) -.109(**)

F_VSLINV .107(**) .218(**)

F_VDVEQT .147(**) .209(**)

F_SCORE .110(**) .126(**)

Table 10: Standard deviation of returns depending on F_SCORE

F_SCORE

PERIOD 8 7 6 5 4 3 2 1 0 ALL

1994 0.38066 0.28450 0.32245 0.28306 0.22750 0.22252 0.23466 0.19175 0.22175 0.25282

1995 0.22897 0.19781 0.23512 0.24589 0.21382 0.19902 0.20272 0.22817 0.14060 0.22202

1996 0.20916 0.21447 0.34974 0.18103 0.26534 0.17343 0.16995 0.18643 0.15618 0.23644

1997 0.24558 0.24328 0.24273 0.23359 0.23327 0.20253 0.22139 0.24006 0.09840 0.23506

1998 0.32251 0.32099 0.28339 0.28181 0.25896 0.27357 0.21232 0.20517 0.12309 0.26628

1999 1.58300 0.66651 0.57743 0.60176 0.56652 0.34709 0.31063 0.21997 0.17400 0.51677

2000 0.74581 0.74020 0.85288 0.49976 0.46599 0.68160 0.55108 0.26952 0.17374 0.62881

2001 0.51733 0.61315 0.55118 0.56289 0.49908 0.41028 0.57991 1.16392 0.36133 0.55935

2002 1.00887 0.36932 0.34751 0.31163 0.30757 0.26876 0.30324 0.29312 1.09685 0.36106

AVERAGE 0.58243 0.40558 0.41805 0.35571 0.33756 0.30876 0.30954 0.33312 0.28288 0.36429

Table 11: Percentage of stocks with returns below market return

F_SCORE

PERIOD 8 7 6 5 4 3 2 1 0 ALL

1994 0.3636 0.1940 0.2727 0.2767 0.3761 0.3832 0.4772 0.5357 0.6087 0.3762

1995 0.4255 0.4326 0.4509 0.5397 0.6310 0.6322 0.7516 0.7234 0.7000 0.5710

1996 0.3488 0.5705 0.6235 0.7219 0.7477 0.8051 0.8938 0.7375 0.8750 0.7289

1997 0.4727 0.6263 0.6189 0.6984 0.7200 0.7375 0.7584 0.7941 1.0000 0.6812

1998 0.5238 0.6386 0.7110 0.6985 0.7929 0.8509 0.8512 0.8869 1.0000 0.7807

1999 0.2273 0.3861 0.3432 0.4688 0.5389 0.6275 0.7437 0.7917 0.8667 0.5827

2000 0.4571 0.6408 0.7059 0.7604 0.7886 0.8042 0.8763 0.9000 1.0000 0.7674

2001 0.1351 0.1507 0.0899 0.1244 0.1134 0.1643 0.1649 0.1714 0.2941 0.1366

2002 0.2593 0.2577 0.3095 0.3526 0.4186 0.4906 0.5560 0.6364 0.6765 0.4339

AVERAGE 0.3570 0.4330 0.4584 0.5157 0.5697 0.6106 0.6748 0.6863 0.7801 0.5621

Note: Market-adjusted returns computed for current accounting period

Table 12: Distribution of returns and F_SCORE

F_SCORE

PERCENTILE 8 7 6 5 4 3 2 1 0 ALL

0.9 0.8399 0.6246 0.6285 0.5722 0.4787 0.4153 0.3426 0.2458 0.1863 0.4962

0.8 0.5149 0.3697 0.3561 0.3174 0.2395 0.1933 0.1411 0.0849 0.0442 0.2458

0.7 0.3363 0.2159 0.205 0.1597 0.1109 0.0727 0.0342 -0.0015 -0.0702 0.1155

0.6 0.1717 0.1127 0.0957 0.0644 0.0278 0.0006 -0.0515 -0.0651 -0.1484 0.0306

0.5 0.096 0.0362 0.0172 -0.0147 -0.0411 -0.0676 -0.1147 -0.1417 -0.2198 -0.0408

0.4 0.0254 -0.0388 -0.0508 -0.0835 -0.1061 -0.138 -0.178 -0.2056 -0.2864 -0.1069

0.3 -0.0462 -0.1016 -0.1143 -0.1512 -0.1744 -0.2029 -0.243 -0.2663 -0.3437 -0.1766

0.2 -0.1022 -0.1813 -0.1945 -0.2364 -0.257 -0.2807 -0.3081 -0.3394 -0.3865 -0.2577

0.1 -0.2218 -0.2726 -0.3067 -0.3413 -0.3601 -0.3848 -0.3981 -0.4245 -0.4439 -0.3582

Table 13: Regression of next period returns on potential risk factors

Constant F_SCORE BTOM SIZE

coeff -0.00526 0.01498

t-stat -0.617 7.743

R squared 0.003

coeff -0.131 0.0192 0.119

t-stat -13.327 10.033 24.44

R squared 0.036

coeff -0.148 0.01902 0.125 4.71E-05

t-stat -13.802 9.935 24.529 3.969

R squared 0.037