The value premium in U.S. stock returns is robust. The positive relation between average return and book-to-market equity is as strong for 1929 to 1963 as for the subsequent period studied in previous papers. A three-factor risk model explains the value premium better than the hypothesis that the book-tomarket characteristic is compensated irrespective of risk loadings. Firms with high ratios of book value to the market value of common equity have higher average returns than firms with low book-to-market ratios (Rosenberg, Reid, and Lanstein (1985)). Because the capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965) does not explain this pattern in average returns, it is typically called an anomaly. There are four common explanations for the book-to-market (BE/ME) anomaly. One says that the positive relation between BE/ME and average return (the so-called value premium) is a chance result unlikely to be observed out of sample (Black (1993), MacKinlay (1995)). Out-of-s...

Value stocks have higher returns than growth stocks in markets around the world. For the period 1975 through 1995, the difference between the average returns on global portfolios of high and low book-to-market stocks is 7.68 percent per year, and value stocks outperform growth stocks in twelve of thirteen major markets. An international capital asset pricing model cannot explain the value premium, but a two-factor model that includes a risk factor for relative distress captures the value premium in international returns. INVESTMENT MANAGERS CLASSIFY FIRMS that have high ratios of book-to-market equity ~B0M!, earnings to price ~E0P!, or cash flow to price ~C0P! as value stocks. Fama and French ~1992, 1996! and Lakonishok, Shleifer, and Vishny ~1994! show that for U.S. stocks there is a strong value premium in average returns. High B0M, E0P, or C0P stocks have higher average returns than low B0M, E0P, or C0P stocks. Fama and French ~1995! and Lakonishok et al. ~1994! also show that the value premium is associated with relative distress.

This paper links the firm’s book-to-market ratio and its conditional market beta. If real investment is largely irreversible, the book value of assets of a distressed firm remains fairly constant and its book-to-market ratio is high. Returns on such a firm are sensitive to aggregate conditions. The firm’s extra installed capital capacity allows it to expand production easily in response to a positive aggregate shock without undertaking any costly investment, yielding a large payoff to the equity holders. In contrast, a low bookto-market firm must undertake investment in order to fully benefit from the shock. Thus, high book-to-market firms have a higher systematic risk. The paper provides empirical evidence that supports the time series predictions of the model. I am grateful to my committee members John Heaton, Owen Lamont, Toby Moskowitz and especially Steve Davis and George Constantinides for their guidance and encouragement. This paper has also

Four decades later, the CAPM is still widely used in applications, such as estimating the cost of capital for firms and evaluating the performance of managed portfolios. It is the centerpiece of MBA investment courses. Indeed, it is often the only asset pricing model taught in these courses. 1 The attraction of the CAPM is that it offers powerful and intuitively pleasing predictions about how to measure risk and the relation between expected return and risk. Unfortunately, the empirical record of the model is poor – poor enough to invalidate the way it is used in applications. The CAPM’s empirical problems may reflect theoretical failings, the result of many simplifying assumptions. But they may also be caused by difficulties in implementing valid tests of the model. For example, the CAPM says that the risk of a stock should be measured relative to a comprehensive “market portfolio ” that in principle can include not just traded financial assets, but also consumer durables, real estate, and human capital. Even if we

After 1979, the rate at which new firms are listed on the major U.S. stock exchanges increases sharply, asset growth rates of new lists are high, but their profitability declines and remains low for at least five years after listing. New lists also become less likely to survive, primarily because of delisting for poor performance. Overall, market prices reflect the volatile dynamics of new list fundamentals. Thus, for the full 1926 to 2000 period and the 1973 to 2000 Nasdaq period, value-weight and equalweight new list returns are close to benchmark returns. For the high action 1980 to 2000 period, equalweight new list returns are low, but value-weight returns are again close to benchmark returns. * Graduate School of Business, University of Chicago (Fama), and Tuck School of Business, Dartmouth College (French). We gratefully acknowledge the helpful comments of Frank Easterbrook, Kenneth Lehn, Jonathan Macey, Richard Roll, Hans Stoll, and seminar participants at UCLA. The market...

In this paper we analyze the use and implications of (return based) style analysis. First, style analysis may be used to estimate the relevant factor exposures of a fund. We use a simple simulation experiment to show that imposing portfolio and positivity constraints in style analysis leads to significant efficiency gains if the factor loadings are indeed positively weighted portfolios, in particular when the factors have low cross-correlations. If this is not the case though, imposing the constraints can lead to biased exposure estimates. Second, style analysis may be used in performance measurement. If the actual factor exposures are a positively weighted portfolio and if the risk free rate is one of the benchmarks, then the intercept coincides with the Jensen measure. In general, the intercept in the style regression can only be interpreted as a special case of the familiar Jensen measure. Third, style estimates may be compared with actual portfolio holdings. We show that the actual portfolio holdings will in general not reveal the actual investment style of a fund because of cross exposures between the asset classes and because fund managers may hold securities that on average do not have a beta of one relative to their own asset class. Although return based style analysis is less suitable to predict future portfolio holdings, our empirical analysis suggests that it performs better than holding based style analysis in predicting future fund returns.

Better proxies for the information about future returns contained in firm characteristics such as size, book-to-market equity, cash flow-to-price, percent change in employees, and various past return measures are obtained by breaking these explanatory variables into two industry-related components. The components represent (1) the difference between firms ’ own characteristics and the average characteristics of their industries (within-industry variables), and (2) the average characteristics of firms ’ industries (across-industry variables). Each variable is reliably priced within-industry and measuring the variables within-industry produces more precise estimates than measuring the variables in their more common form. Contrary to Moskowitz and Grinblatt [1999], we find that within-industry momentum (i.e., the firm’s past return less the industry average return) has predictive power for the firm’s stock return beyond that captured by across-industry momentum. We also document a significant short-term (one-month) industry momentum effect which remains strongly significant when we restrict the sample to only the most liquid firms. 1 Introduction. The theory of asset pricing attributed to Sharpe [1964] and Linter [1965] is an empirical failure. Beta does not suffice, nor even help, to explain the cross-section of realized stock returns (Fama

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A great deal of the literature in financial economics contains the assumption that returns are a linear function of a set of observable or unobservable factors. The specification of the variables in the linear process (known as the return-generating process) is one of the key issues in finance today. The return-generating process is an important building block in asset pricing models, portfolio optimization, risk management models, mutual fund evaluation, and event studies. For many purposes (such as in developing asset pricing models and evaluating mutual fund performance), it is important to separate systematic from nonsystematic factors. There have been numerous attempts to examine the number and type of systematic factors in equity returns. Approaches to identifying the return-generating process include purely statistical

Abstract: In this paper, I study the behavior of an investor with unit risk aversion who maximizes a utility function defined over the mean and the variance of a portfolio’s return. Conditioning information is accessible without cost and an unconditionally riskless asset is available in the market. The proposed approach makes it possible to compare the performance of a benchmark tangency portfolio (formed from the set of unrestricted estimates of portfolio weights) to the performance of a restricted tangency portfolio which uses single-index and multi-index asset pricing models to constrain the first moments of asset returns. The main findings of the paper are summarized as follows: i) The estimates of the constant and timevarying tangency portfolio weights are extremely volatile and imprecise. Using an asset pricing model to constrain mean asset returns eliminates extreme short positions in the underlying securities and improves the precision of the estimates of the weights. ii) Partially restricting mean asset returns according to single-index and multi-index asset pricing models improves the out-of-sample performance of the tangency portfolio. iii) Active investment strategies (i.e., strategies that incorporate the role played by conditioning information in investment decisions) strongly dominate passive investment strategies in-sample but do not provide any