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We present evidence supporting the hypothesis that due to investor specialization and market segmentation, value-relevant information diffuses gradually in financial markets. Using the stock market as our setting, we find that (i) stocks that are in economically related supplier and customer industries cross-predict each other's returns, (ii) the magnitude of return cross-predictability declines with the number of informed investors in the market as proxied by the level of analyst coverage and institutional ownership, and (iii) changes in the stock holdings of institutional investors mirror the model trading behavior of informed investors.

This paper provides evidence that analysts who have predicted earnings more accurately than the median analyst in the previous four quarters tend to be simultaneously less accurate and further from the consensus forecast in their subsequent earnings prediction. This phenomenon is economically and statistically meaningful. The results are robust to different estimation techniques and different control variables. Our findings are consistent with an attribution bias that leads analysts who have experienced a short-lived success to become overconfident in their ability to forecast future earnings.

We propose a general equilibrium model with multiple securities in which investors’ risk preferences and expectations of dividend growth are time‐varying. While time‐varying risk preferences induce the standard positive relation between the dividend yield and expected returns, time‐varying expected dividend growth induces anegativerelation between them. These offsetting effects reduce the ability of the dividend yield to forecast returns and eliminate its ability to forecast dividend growth, as observed in the data. The model links the predictability of returns to that of dividend growth, suggesting specific changes to standard linear predictive regressions for both. The model’s predictions are confirmed empirically.

A large body of empirical evidence now shows that certain macroeconomic series explain cross-sectional differences in stocks' average returns. Using a jackknife analysis, I find that, even after making standard small-sample adjustments, the significance of any of these factors is likely to be due to the same small number of quarters in the returns data, which I term “influential quarters.” These quarters occur around recession periods, suggesting a link between the cross-section of average returns and the business cycle and, in turn, providing support for conditional CCAPM models. However, the finding that the same short list of influential quarters, pre-specified from the returns data, determines the statistical significance of any given factor poses two challenges. The first is an inference problem caused by the continual search for new factors using the same sample of returns. The second is that discrimination between factors based on higher t-statistics will merely pick up a factor that is more susceptible to large innovations during these quarters. The results ultimately suggest that the current sample of returns is too small to identify the true economic channel governing stock returns.

Working Paper No. 9217 In this paper we propose a general equilibrium model that successfully reproduces the historical experience of the cross section of US stock prices as well as the realized history of the market portfolio. The model achieves this while addressing traditional concerns in the asset pricing literature: A high equity premium and volatility of returns, the long horizon predictability, and a low volatility of the risk free rate. The model combines a rich payoff structure with a habit persistence discount factor, which allows us to identify the effect on prices of idiosyncratic cash flow shocks versus business cycle components.