Abstract In both corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms and across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.

We propose and test a novel economic mechanism that generates stock return predictability on both the time series and the cross section. In our model, investors’ income has two sources, wages and dividends, that grow stochastically over time. As a consequence, the fraction of total income produced by wages changes over time depending on economic conditions. We show that as this fraction fluctuates, the risk premium that investors require to hold stocks varies as well. We test the main implications of the model and find substantial support for it. A regression of stock returns on lagged values of the labor income to consumption ratio produces statistically significant coefficients and adjusted R 2 ’s that are larger than those generated when using the dividend price ratio. Tests of the cross sectional implication find considerable improvements on the performance of both the conditional CAPM and CCAPM when compared to their unconditional counterparts.

In this paper we investigate the properties of the standard two-pass methodology of testing beta pricing models with misspecified factors. In a setting where a factor is useless, defined as being independent of all the asse t returns, we provide theoretical results and simulation evidence that the second-pass cross-sectional regression tends to find the beta risk of the useless factor priced more often than it should. More surprisingly, this misspecification bias exacerbates when the number of time series observations increases. Possible ways of detecting useless factors are also examined. When testing asset pricing models relating risk premiums on assets to their betas, the primary question of interest is whether the beta risk of a particular factor is priced (i.e., whether the estimated risk premium associated with a given factor is significantly di#erent from zero). Black, Jensen, and Scholes (1972) and Fama and MacBeth (1973) develop a two-pass methodology in which the beta of each asset with respect to a factor is estimated in a first-pass time series regression, and estimated betas are then used in second-pass cross-sectional regressions (CSRs) to estimate the risk premium of the factor. This two-pass methodology is very intuitive and has been widely used in the literature. The properties of the test statistics and goodness-of-fit measures under the two-pass methodology are usually developed under the assumptions that the asset pricing model is correctly specified and that the factors are correctly identified. Shanken (1992) provides an excellent discussion of this two-pass methodology, especially the large sample properties of the two-pass CSR for the correctly specified model under the assumption that returns are conditionally homoskedastic. Jagannathan and Wa...

This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in the paper are those of the authors and are not necessarily reflective of views at the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

We develop an external habit persistence model where the time series of the aggregate portfolio and the cross section of stock returns are simultaneously studied and tested. By applying a slightly modified version of the model of Campbell and Cochrane (1999), we obtain closed form solutions for individual securities prices and returns and a full characterizations of the dynamics of the risk-return characteristics of individual securities. We find that each stock return “beta” with respect to the total wealth portfolios is jointly determined by an aggregate variable that depends on the habit level, and an idiosyncratic asset characteristics that depends on the contribution of the security to total consumption relative to its long-run average contribution. This functional form imposes tight predictions on the cross sectional test, including sign and magnitude of the coefficients, and insures that the explanatory power of the beta comes from the predictable part of the realization of returns. An estimate of the model for a set of 20 industry portfolios is able to explain cross-sectional variation in the conditional expected returns. Moreover, the model generates price consumption ratios for individual industries that track well the empirical ones.

“Analytical cross-sectional tests of asset pricing models ” and “On cross-sectional stock returns: maximum

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This article develops a framework that applies to single securities to test whether asset pricing models can explain the size, value, and momentum anomalies. Stock level beta is allowed to vary with firm-level size and book-to-market as well as with macroeconomic variables. With constant beta, none of the models examined capture any of the market anomalies. When beta is allowed to vary, the size and value effects are often explained, but the explanatory power of past return remains robust. The past return effect is captured by model mispricing that varies with macroeconomic variables. The capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965) has long been a basic tenet of finance. However, subsequent work

Theoretical asset pricing models routinely assume that investors have heterogeneous informa-tion. We provide direct evidence of the importance of information asymmetry for asset prices and investor demands using plausibly exogenous variation in the supply of information caused by the closure of 43 brokerage firms ’ research operations in the U.S. Consistent with predictions derived from a Grossman and Stiglitz-type model, share prices and uninformed investors ’ demands fall as information asymmetry increases. Cross-sectional tests support the comparative statics: Prices and uninformed demand experience larger declines, the more investors are uninformed, the larger and more variable is stock turnover, the more uncertain is the asset’s payoff, and the noisier is the better-informed investors ’ signal. We show that at least part of the fall in prices is due to expected returns becoming more sensitive to liquidity risk. Our results imply that information asymmetry has a substantial effect on asset prices and that a primary channel linking asymmetry to prices is liquidity.

A growing literature contends that, since returns are not normal, higher-order co-moments matter to risk-averse investors. Fama and French (1993, 1995) find that non-market risk factors based on size and book-to-market ratio are priced by investors. We test the hypothesis that the Fama-French factors simply proxy for the pricing of higher-order co-moments. Using portfolio returns over various time horizons, we show that adding a set of systematic comoments (but not standard moments) of order 3 through 10 reduces the explanatory power of the Fama-French factors to insignificance in almost every case. * We are grateful for helpful comments and suggestions from Warren Bailey, Javier Estrada,