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Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.

Whether the risk factors or firm characteristics cause the value premium of stocks still needs further investigation. This paper shows that the factor‐based models are significant but not sufficient for the stock returns in Taiwan. Size or book‐to‐market ratio alone cannot influence the stock returns under a factor‐based model. However, size along with book‐to‐market is significant under a factor‐based model. Furthermore, the risk characteristics are more influential than the factor load in stock return behavior. We conclude that employing only a factor‐based model or only risk characteristics will not consider some important content in stock returns.

We would like to thank C. Y. Chen, Wenchih Lee, two anonymous referees and the seminar participants at the 2000 FMA annual meeting for their helpful comments and encouragement. All of the remaining errors are our responsibility.

This study examines the five-factor model of Fama and French (2015) on the French stock market by comparing it to the Fama and French (1993)’s base model. The new Fama and French five-factor model directed at capturing two new factors, profitability and investment in addition to the market, size and book to market premiums. The pricing models are tested using a time-series regression and the Fama and Macbeth (1973) methodology. The regularities in the factor’s behavior related to market conditions and to the sovereign debt crisis in Europe are also examined. The findings of Fama and French (2015) for the US market are confirmed on the Paris Bourse. The results show that both models help to explain some of the stock returns. However, the five-factor model is better since it has a marginal improvement over the widely used three-factor model of Fama and French (1993). In addition, the investment risk premium seems to be better priced in the French stock market than the profitability factor. The results are robust to the Fama and Macbeth (1973) methodology. Moreover, profitability and investment premiums are not affected by market conditions and the European sovereign debt crisis.

The purpose of this study is to examine the relevance of an accounting-based fundamental strategy in adding value to value stocks in Indian stock market. The fundamentals-based investment strategy “F-score”, given by Joseph Piotroski, has been used on stocks having high book-to-market ratio to eliminate the firms with poor future prospects from the entire portfolio of value stocks.

The market adjusted performance of all the firms in the sample is examined using one sample t-test. Further, F-score of all the firms in the sample is calculated and the independent sample t-test has been used to examine the significance of mean difference between high-score and low-score firms. Finally, the predictive ability of F-score in explaining the overall stock returns is examined using panel data regression analysis.

Results reveal that the mean market-adjusted return of stocks, meeting all constructs of F-score is significantly larger than the entire portfolio of value stocks by 18.402 per cent annually across the period of study. The results of panel data regression made it evident that one-point improvement in aggregate F-score is associated with an about 4.93 per cent increase in one-year market-adjusted return.

The significant mean return difference found between the high-F-score firms and the low-score firms suggests that an investor could constitute a hedge portfolio that generates positive return by selling expected losers stocks and buying expected winners.

The present study is the first attempt made in emerging economy like India to enrich the literature on value investing strategies by examining the performance of F-score strategy to separate winners from losers in Indian stock market.

The purpose of the study was to provide empirical support for the Miller model. The paper proposes the use of the ratio of individual to institutional holdings as a proxy for heterogeneous expectations of security returns.

Both bivariate t‐tests and regression analysis were used to test whether optimistic valuations existed for stocks with high levels of institutional ownership. Data on open short positions were collected and hypothesized to decrease with the level of institutional holdings. High ratio stocks were compared to glamor stocks and low ratio stocks to value stocks.

For stocks with higher institutional ownership, optimistic valuations dominated resulting in significantly lower future security returns than for stocks with higher individual ownership thereby supporting the Miller model. The results were not sensitive to variations in size, momentum, and book‐to‐market ratios. Further support for the Miller model was provided by the finding that open short positions decreased with the level of institutional holdings. High ratio stocks resembled glamor stocks and low ratio stocks corresponded to value stocks.

This study is limited to the ultra‐short term period of one month after portfolio creation. Future research should extend it to the three‐to‐five year time horizon.

Ultra‐short term investors should hold value stocks, intermediate three‐12 month investors should hold glamor stocks, and long‐term investors should hold value stocks.

The finding of a new proxy for heterogeneous expectations. The paper also establishes a new methodology for testing the Miller model.

The purpose of this paper is to investigate the connection between the accrual quality and the growth/value characteristics (and their return premia) at firm level.

The paper employs a battery of univariate and multivariate cross‐sectional tests. Fama‐MacBeth regressions with main effects and interaction effects are used to identify the relation between accrual quality, book‐to‐market and returns. The analysis is conducted on the overall sample, as well as after conditioning on up and down markets.

Value (growth) stocks are more likely to be associated with high (low) accrual quality. Value stocks earn higher returns mainly in down markets, while poor accrual quality firms have significantly higher returns during up markets, but significantly lower returns during down markets. There is a significant interaction effect between accrual quality and the value premium, which only exhibits in the down markets (i.e. stocks with poor accrual quality earn a higher value premium in down markets than stocks with good accrual quality).

Results in this paper help disentangle between various explanations proposed for the accrual quality premium and the value premium. These findings are consistent with the idea that the same underlying risk factor generating the value premium also generates the cross‐sectional variation in accrual quality responsible for the accrual quality premium. From the corporate managers' perspective, the results imply that value firms can mitigate their higher costs of capital by providing high quality of accounting information. From an analyst's perspective, the study suggests that considering both accrual quality and growth characteristics can help make better portfolio allocation decisions than when these are considered separately.

To test the Miller Price Optimism Model using a new proxy for heterogenous expectations and to examine if high differential stocks behave like glamour stocks and low differential stocks behave like value stocks.

Whisper/analyst forecast differentials were measured for a sample of stocks, combined into portfolios and held for one month. If the Miller model was supported, high differential stocks were expected to have lower portfolio returns than low differential stocks due to the greater divergence between optimistic whisper forecasts and rational analysts consensus forecasts.

High differential quintiles had significantly lower future returns than low differential quintiles supporting the Miller model. High differential stocks resembled glamour stocks while low differential stocks behaved like value stocks.

These results pertain to the ultra‐short time horizon of two months prior to the earnings announcement. Future research should replicate this study for a longer 3‐12 month time horizon.

Ultra short‐term investors should hold glamour stocks and long term investors should hold value stocks. Rising volatility suggests that investors should define the time horizon for holding assets.

It is one of only two studies that directly uses earnings forecasts as a proxy for heterogenous expectations. It adds to the sparse literature on whisper forecasts. It may be used by academicians studying price optimism effects and institutional investors following stock returns during earnings announcements.

This study investigates the economic content of the two firm‐specific characteristics, size and book‐to‐market equity. Size is found to be significantly related to a combination of betas on all of the macro variables proposed in this research. Its significance persists through out the entire sample period. This provides further evidence that size is a proxy for pervasive risk factors in the stock market. The support for book‐to‐market equity’s role as a risk proxy is also evidenced, however to a lesser extent. Securities are then sorted into size and book‐to‐market equity portfolios and their effects on investment decisions are examined in the context of macro variables. Important investment implications are drawn based on the findings.

This study documents that high book‐to‐market (value) and low book‐to‐market (glamour) stock prices react asymmetrically to both common and firm‐specific information. Specifically, we find that value stock prices exhibit a considerably slow adjustment to both common and firm‐specific information relative to glamour stocks. The results show that this pattern of diferential price adjustment between value and glamour stocks is mainly driven by the high arbitrage risk borne by value stocks. The evidence is consistent with the arbitrage risk hypothesis, predicting that idiosyncratic risk, a major impediment to arbitrage activity, amplifies the informational loss of value stocks as a result of arbitrageurs’ (informed investors) reduced participation in value stocks because of their inability to fully hedge idiosyncratic risk.