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We investigate how information choices affect equity returns and risk. Building on an existing theoretical model of information and investment choice, we estimate a learning index that reflects the expected benefits of learning about an asset. High learning index stocks have lower future returns and risk compared to low learning index stocks. Analysis of a conditional asset pricing model, long-run patterns in returns and volatilities, other measures of information flow, and the information environment surrounding earnings announcements reinforce our interpretation of the learning index. Our findings support the model’s predictions and illustrate a novel empirical measure of investor learning.

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We use various samples of portfolios (Fama-French portfolios formed on size and book-to-market, Fama-French industry portfolios, and exchange traded funds) as test assets to investigate whether the negative relation between lagged idiosyncratic volatility (IVOL) and future average returns initially documented by Ang, Hodrick, Xing, and Zhang (2006) is due to a missing risk factor. Analytically, we show that if IVOL proxies for a missing risk factor, then the negative relation between IVOL and returns persists at a portfolio level since systematic risk is not eliminated through diversification. However, when we take it to the data, we do not find economically and statistically significant evidence of a relation between lagged IVOL and subsequent average returns. Taken together, our results suggest that the IVOL puzzle is not due to a missing risk factor.

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