[Academic and practitioner research evaluates portfolio performance using size and value/growth attributes or factors. We assess the merits of popular evaluation procedures based on matched-characteristic benchmark portfolios or time-series return regressions by applying them to a sample of active money managers and passive indexes. Estimated abnormal returns display large variation across approaches. The benchmarks typically used in academic research—attribute-matched portfolios from independent sorts, the three-factor time-series model, and cross-sectional regressions of returns on stock characteristics—track returns poorly. Some simple alterations improve the performance of these methods.]

Most mutual funds adopt investment styles that cluster around a broad market benchmark. Few funds take extreme positions away from the index, but those who do are more likely to favor growth stocks and past winners. The bias toward glamour and the tendency of poorly performing value funds to shift styles may reflect agency and behavioral considerations. After adjusting for style, there is evidence that growth managers on average outperform value managers. Though a fund's factor loadings and its portfolio characteristics generally yield similar conclusions about its style, an approach using portfolio characteristics predicts fund returns better.

ABSTRACT Expectations about long‐term earnings growth are crucial to valuation models and cost of capital estimates. We analyze historical long‐term growth rates across a broad cross section of stocks using several indicators of operating performance. We test for persistence and predictability in growth. While some firms have grown at high rates historically, they are relatively rare instances. There is no persistence in long‐term earnings growth beyond chance, and there is low predictability even with a wide variety of predictor variables. Specifically, IBES growth forecasts are overly optimistic and add little predictive power. Valuation ratios also have limited ability to predict future growth.

We examine whether the predictability of future returns from past returns is due to the market's underreaction to information, in particular to past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. Market risk, size, and book-to-market effects do not explain the drifts. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Security analysts' earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.

ABSTRACT We examine whether the predictability of future returns from past returns is due to the market's underreaction to information, in particular to past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. Market risk, size, and book–to–market effects do not explain the drifts. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Security analysts' earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.

This paper relates cross-sectional differences in returns on Japanese stocks to the underlying behavior of four variables: earnings yield, size, book to market ratio, and cash flow yield. Alternative statistical specifications and various estimation methods are applied to a comprehensive, high-quality data set that extends from 1971 to 1988. The sample includes both manufacturing and nonmanufacturing firms, companies from both sections of the Tokyo Stock Exchange, and also delisted securities. Our findings reveal a significant relationship between these variables and expected returns in the Japanese market. Of the four variables considered, the book to market ratio and cash flow yield have the most significant positive impact on expected returns.

This paper relates cross-sectional differences in returns on Japanese stocks to the underlying behavior of four variables: earnings yield, size, book to market ratio, and cash flow yield. Alternative statistical specifications and various estimation methods are applied to a comprehensive, high-quality data set that extends from 1971 to 1988. The sample includes both manufacturing and nonmanufacturing firms, companies from both sections of the Tokyo Stock Exchange, and also delisted securities. The authors' findings reveal a significant relationship between these variables and expected returns in the Japanese market. Of the four variables considered, the book to market ratio and cash flow yield have the most significant positive impact on expected returns.

ABSTRACT This paper relates cross‐sectional differences in returns on Japanese stocks to the underlying behavior of four variables: earnings yield, size, book to market ratio, and cash flow yield. Alternative statistical specifications and various estimation methods are applied to a comprehensive, high‐quality data set that extends from 1971 to 1988. The sample includes both manufacturing and nonmanufacturing firms, companies from both sections of the Tokyo Stock Exchange, and also delisted securities. Our findings reveal a significant relationship between these variables and expected returns in the Japanese market. Of the four variables considered, the book to market ratio and cash flow yield have the most significant positive impact on expected returns.

ABSTRACT A cash‐in‐advance model of a monetary economy is used to derive a money‐based CAPM (M‐CAPM), which allows us to implement tests of asset pricing restrictions without consumption data. A test as in Fama and MacBeth of the model suggests that the money betas have some explanatory power for the cross‐sectional variation of expected returns; however, the model is rejected using conditional information. Consistent with our predictions, estimates of the curvature parameter are lower than those of the consumption CAPM (C‐CAPM) and pricing errors of the M‐CAPM tend to be smaller than those of the C‐CAPM.