Abstract We investigate the pricing of risk-neutral skewness in the stock options market by creating skewness assets comprised of two option positions (one long and one short) and a position in the underlying stock. The assets are created such that exposure to changes in the underlying stock price (delta), and exposure to changes in implied volatility (vega) are removed, isolating the effect of skewness. We find a strong negative relation between risk-neutral skewness and the skewness asset returns, consistent with a positive skewness preference. The returns are not explained by well-known market, size, book-to-market, momentum, short-term reversal, volatility, or option market factors.

Abstract This article demonstrates that rating-based capital requirements, through their impact on insurers’ investment demand, affect corporate bond prices. Consistent with insurers’ low demand for investment-grade bonds with a rating close to noninvestment-grade, these bonds outperform. Consistent with insurers’ high (low) demand for investment-grade bonds with high (low) systematic risk exposure, these bonds underperform (outperform). Insurer demand, measured by insurer holdings, explains most of these pricing effects. We identify rating-based capital requirements as the driver of insurer demand, and thus the pricing effects, by showing that the effects do not exist before these requirements’ implementation in 1993.

We test whether bear market risk, time variation in the probability of future bear market states, is priced. We construct an Arrow–Debreu security that pays off in bear market states (AD Bear) from traded Standard & Poor’s (S&P) 500 index options and use its returns to measure bear market risk. We find that bear beta (exposure to bear market risk) has a strong relation with expected stock returns that is robust, persistent, and remains strong among liquid and large stocks. Historical bear beta also predicts future bear market risk exposure. We conclude that bear market risk is priced in the cross section of stock returns.

We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal, and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out-of-sample.

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.