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.

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. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

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.

We examine whether stock prices fully value firms' intangible assets, specifically research and development (R&D). Under current U.S. accounting standards, financial statements do not report intangible assets and R&D spending is expensed. Nonetheless, the average historical stock returns of firms doing R&D matches the returns of firms without R&D. However, the market is apparently too pessimistic about beaten‐down R&D‐intensive technology stocks' prospects. Companies with high R&D to equity market value (which tend to have poor past returns) earn large excess returns. A similar relation exists between advertising and stock returns. R&D intensity is positively associated with return volatility.