We propose a simple model of equilibrium asset pricing in which there are differences in the amounts of information available for developing inferences about the returns parameters of alternative securities. In contrast with earlier work, we show that parameter uncertainty, or estimation risk, can have an effect upon market equilibrium. Under reasonable conditions, securities for which there is relatively little information are shown to have relatively higher systematic risk when that risk is properly measured, ceteris paribus. The initially very limited model is shown to be robust with respect to relaxation of a number of its principal assumptions. We provide theoretical support for the empirical examination of at least three proxies for relative information: period of listing, number of security returns observations available, and divergence of analyst opinion.

This paper investigates hedge funds’ ability to time industry-specific returns and shows that funds’ timing ability in the manufacturing industry improves their future performance, probability of survival, and ability to attract more capital. The results indicate that the best industry-timing hedge funds in the manufacturing sector have the highest return exposure to earnings surprises. This, together with persistently sticky earnings surprises, transparent information environment in regards to earnings releases, and large post-earnings-announcement drift in the manufacturing industry, explain to a great extent why best-timing hedge funds can generate significantly larger future returns compared to worst-timing hedge funds.

The low (high) abnormal returns of stocks with high (low) beta, which we refer to as the beta anomaly, is one of the most persistent anomalies in empirical asset pricing research. This article demonstrates that investors’ demand for lottery-like stocks is an important driver of the beta anomaly. The beta anomaly is no longer detected when beta-sorted portfolios are neutralized to lottery demand, regression specifications control for lottery demand, or factor models include a lottery demand factor. The beta anomaly is concentrated in stocks with low levels of institutional ownership and it exists only when the price impact of lottery demand is concentrated in high-beta stocks.