This paper explores the ability of conditional versions of the CAPM and the consumption CAPM—jointly the (C)CAPM—to explain the cross section of average stock returns. Central to our approach is the use of the log consumption–wealth ratio as a conditioning variable. We demonstrate that such conditional models perform far better than unconditional specifications and about as well as the Fama-French three-factor model on portfolios sorted by size and book-to-market characteristics. The conditional consumption CAPM can account for the difference in returns between low-book-to-market and high-bookto-market portfolios and exhibits little evidence of residual size or book-to-market effects. We are grateful to Eugene Fama and Kenneth French for graciously providing the

This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work and on the trade-off between risk and return. Modern research seeks to understand the behavior of the stochastic discount factor ~SDF! that prices all assets in the economy. The behavior of the term structure of real interest rates restricts the conditional mean of the SDF, whereas patterns of risk premia restrict its conditional volatility and factor structure. Stylized facts about interest rates, aggregate stock prices, and cross-sectional patterns in stock returns have stimulated new research on optimal portfolio choice, intertemporal equilibrium models, and behavioral finance. This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work. Theorists develop models with testable predictions; empirical researchers document “puzzles”—stylized facts that fail to fit established theories—and this stimulates the development of new theories. Such a process is part of the normal development of any science. Asset pricing, like the rest of economics, faces the special challenge that data are generated naturally rather than experimentally, and so researchers cannot control the quantity of data or the random shocks that affect the data. A particularly interesting characteristic of the asset pricing field is that these random shocks are also the subject matter of the theory. As Campbell, Lo, and MacKinlay ~1997, Chap. 1, p. 3! put it: What distinguishes financial economics is the central role that uncertainty plays in both financial theory and its empirical implementation. The starting point for every financial model is the uncertainty facing investors, and the substance of every financial model involves the impact of uncertainty on the behavior of investors and, ultimately, on mar-* Department of Economics, Harvard University, Cambridge, Massachusetts

this paper are covered by U.S. Patent 5,893,069

I review empirical research on the relation between capital markets and financial statements.The principal sources of demand for capital markets research in accounting are fundamental analysis and valuation, tests of market efficiency, and the role of accounting numbers in contracts and the political process.The capital markets research topics of current interest to researchers include tests of market efficiency with respect to accounting information, fundamental analysis, and value relevance of financial reporting.Evidence from research on these topics is likely to be helpful in capital market investment decisions, accounting standard setting, and corporate financial

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. Several of these studies conclude that technical analysis does have merit, however, it is noted that the effects of data-snooping are not fully accounted for. In this paper we utilize White's Reality Check bootstrap methodology (White (1997)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a means of calculating a comprehensive test of performance across all trading rules. In particular, we consider the study of Brock, Lakonishok, and LeBaron (1992), expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data-snooping. During the sample period inspected by Brock, Lakonishok and LeBaron, we find that the best technical trading rule is capable of generating superior performance even after accounting for data- snooping. However, we also find that the best technical trading rule does not provide superior performance when used to trade in the subsequent 10-year post-sample period.

This paper investigates nonlinear pricing kernels in which the risk factor is endogenously determined and preferences restrict the definition of the pricing kernel. These kernels potentially generate the empirical performance of nonlinear and multifactor models, while maintaining empirical power and avoiding ad hoc specifications of factors or functional form. Our test results indicate that preferencerestricted nonlinear pricing kernels are both admissible for the cross section of returns and are able to significantly improve upon linear single- and multifactor kernels. Further, the nonlinearities in the pricing kernel drive out the importance of the factors in the linear multi-factor model. A PRINCIPAL IMPLICATION OF THE Capital Asset Pricing Model ~CAPM! is that the pricing kernel is linear in a single factor, the portfolio of aggregate wealth. Numerous studies over the past two decades have documented violations of this restriction. 1 In response, researchers have examined the performance of alternative models of asset prices. These models have generally fallen into two classes: ~1! multifactor models such as Ross ’ APT or Merton’s ICAPM, in which factors in addition to the market return determine asset prices; or ~2! nonparametric models, such as Bansal et al. ~1993!, Bansal and Viswanathan ~1993!, and Chapman ~1997!, in which the pricing kernel is not

In this paper we provide an analytical review of previous estimates of the rate of return on schooling investments and measure how these estimates vary by country, over time, and by estimation method. We find evidence of reporting (or "file drawer") bias in the estimates and, after due account is taken of this bias, we find that differences due to estimation method are much smaller than is sometimes reported, although some are statistically significant. We also find that estimated returns are higher in the U.S. and they have increased in the last two decades.

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Economics is primarily a non-experimental science. Typically, we cannot generate new data sets on which to test hypotheses independently of the data that may have led to a particular theory. The common practice of using the same data set to formulate and test hypotheses introduces data-snooping biases that, if not accounted for, invalidate the assumptions underlying classical statistical inference. A striking example of a datadriven discovery is the presence of calendar effects in stock returns. There appears to be very substantial evidence of systematic abnormal stock returns related to the day of the week, the week of the month, the month of the year, the turn of the month, holidays, and so forth. However, this evidence has largely been considered without accounting for the intensive search preceding it. In this paper we use 100 years of daily data and a new bootstrap procedure that allows us to explicitly measure the distortions in statistical inference induced by data-snooping. We find that although nominal P-values of individual calendar rules are extremely significant, once evaluated in the context of the full universe from which such rules were drawn, calendar effects no longer remain significant.

The optimal portfolio strategy is developed for an investor who has detected an asset pricing anomaly but is not certain that the anomaly is genuine rather than merely apparent. The analysis takes account of the fact that the parameters of both the underlying asset pricing model and the anomalous returns are estimated rather than known. The value that an investor would place on the ability to invest to exploit the apparent anomaly is also derived and illustrative calculations are presented for the Fama-French SMB and HML portfolios, whose returns are anomalous relative to the CAPM. An asset pricing anomaly is a statistically significant difference between the realized average returns associated with certain characteristics of securities, or on portfolios of securities formed on the basis of those characteristics, and the returns that are predicted by a particular asset pricing model. What is anomalous with respect to one model may be consistent with the predictions of other asset pricing models. For example, an excess return associated with a security’s dividend yield is anomalous with respect to the basic Capital Asset Pricing Model but is consistent with extensions that incorporate investor taxes. Some anomalies are inconsistent with any known rational asset pricing model; they appear to represent “money left on the table”; such examples include the