Existing empirical research investigating the size of the equity premium has largely consisted of a series of innovations around a common theme: producing a better estimate of the equity premium by using better data or a better estimation technique. The equity premium estimate that emerges from most of this work matches one moment of the data alone: the mean difference between an estimate of the return to holding equity and a risk-free rate. We instead match multiple moments of U.S. market data, exploiting the joint distribution of the dividend yield, return volatility, and realized excess returns, and find that the equity premium lies within 50 basis points of 3.5%, a range much narrower than was achieved in previous studies. Additionally, statistical tests based on the joint distribution of these moments reveal that only those models of the conditional equity premium that embed time variation, breaks, and/or trends are supported by the data. In order to develop the joint distribution of the dividend yield, return volatility, and excess returns, we need a model of price and return fundamentals. We document that even recently developed analytically tractable models that permit autocorrelated dividend growth rates and discount rates impose restrictions that are rejected by the data. We therefore turn to a wider range of models, requiring numerical solution methods and parameter estimation by the simulated method of moments.

Markets should be inefficient enough to allow returns to security analysis to adequately compensate the marginal analyst for his efforts. Cross-sectional differences in the costs of analysis therefore imply cross-sectional differences in market efficiency and in before-cost returns to smart investors. Small growth stocks are difficult to analyze and costly to trade. I find that the abnormal returns of mutual fund investments in small growth stocks from 1980 to 2006 averaged 0.76% per month. Large value stocks are easier to analyze and cheaper to trade. Mutual funds earned average monthly abnormal returns of only 0.05% in large value stocks during the same period.

This paper tests whether the persistence of the momentum and reversal effects is the result of idiosyncratic risk limiting arbitrage. Idiosyncratic risk deters arbitrage, regardless of the arbitrageur’s diversification. Reversal is prevalent only in high idiosyncratic risk stocks, suggesting that idiosyncratic risk limits arbitrage in reversal mispricing. This finding is robust to controls for transaction costs, informed trading, and systematic relations between idiosyncratic risk and subsequent returns. Momentum is not related to idiosyncratic risk. Momentum generates a smaller aggregate return than reversal, so the findings along with those in related studies suggest that transaction costs are sufficient to prevent arbitrageurs from eliminating momentum mispricing.

In this paper we examine the relation between equity mispricing and arbitrage risk and find that stocks with high arbitrage risk have higher estimated mispricing than stocks with low arbitrage risk. These results are not limited to high book-to-market or small capitalization stocks, and they are not sensitive to transaction and short-selling costs. In addition, they remain robust to alternative multifactor return generating specification models and mispricing measures. Overall, our empirical results are consistent with the conjecture that mispricing is a manifestation of the inability of arbitrageurs to hedge idiosyncratic risk, a major deterrent to arbitrage activity.

We examine the extent to which investment opportunities and/or mispricing motivate equity issuance and contribute to post-issue stock underperformance. We decompose market-to-book ratios into misvaluation and growth option components and find that issuing firms are both overvalued and have greater growth opportunities relative to nonissuers. Firms with greater growth opportunities invest more in capital expenditures and research and development (R&D) after issuance but do not experience lower post-issue stock returns. In contrast, issuing firms with greater mispricing tend to decrease long-term debt and/or increase cash holdings and do earn lower returns. Our findings are consistent with behavioral explanations for post-issue stock price underperformance.

This paper proposes a way to allow Bayesian priors to reflect the objectives of an economic problem. That is, we impose priors on the solution to the problem rather than on the primitive parameters whose implied priors can be backed out from the Euler equation. Using monthly returns on the Fama-French 25 size and book-to-market portfolios and their 3 factors from January 1965 to December 2004, we find that investment performances under the objective-based priors can be significantly different from those under alternative priors, with differences in terms of annual certainty-equivalent returns greater than 10% in many cases. In terms of an out-of-sample loss function measure, portfolio strategies based on the objective-based priors can substantially outperform both strategies under alternative priors and some of the best strategies developed in the classical framework.

We find that the market’s recent cross-sectional dispersion in stock returns is positively related to the subsequent value book-to-market premium and negatively related to the subsequent momentum premium. The partial relation between return dispersion (RD) and the subsequent value and momentum premiums remains strong when controlling for macroeconomic state variables suggested by the literature. Our findings are consistent with recent theoretical insights and empirical evidence that suggest that the market’s RD may serve as a leading countercyclical state variable, that the value premium is countercyclical, and that the momentum premium is procyclical.

This paper numerically solves the decision problem of a multiperiod constant relative risk aversion individual who faces transaction costs and has access to two risky assets, both with predictable returns. With proportional transaction costs and independent and identically distributed returns, we numerically find the rebalancing rule to be a no-trade region for the portfolio weights with rebalancing to the boundary. The shape of the no-trade region depends on the correlation between the two risky assets. With predictable returns, there is instead a no-trade region for each state. We also examine several important economic questions, including the utility cost of not being able to buy on margin or short stock.

Option pricing models and longer-term value-at-risk (VaR) models generally require volatility forecasts over horizons considerably longer than the data frequency. The typical recursive procedure for generating longer-term forecasts keeps the relative weights of recent and older observations the same for all forecast horizons. In contrast, we find that older observations are relatively more important in forecasting at longer horizons. We find that the Ederington and Guan (2005) model and a modified EGARCH (exponential generalized autoregressive conditional heteroskedastic) model in which parameter values vary with the forecast horizon forecast better out-of-sample than the GARCH (generalized autoregressive conditional heteroskedastic), EGARCH, and Glosten, Jagannathan, and Runkle (GJR) models across a wide variety of markets and forecast horizons.

This paper offers a rational explanation for the puzzling empirical fact that stock returns decrease with an increase in the volatility of liquidity. We model liquidity as a stochastic price impact process and define the liquidity premium as the additional return necessary to compensate a multiperiod investor for the adverse price impact of trading. The model demonstrates that a fully rational, utility maximizing, risk-averse investor can take advantage of time-varying liquidity by adapting his trades to the state of liquidity. We provide new empirical evidence supportive of the model.