In this paper, we propose a cross-sectional option momentum strategy that is based on the risk component of delta-hedged option returns. We find strong evidence of risk continuation in option returns. Specifically, options with a high risk component significantly outperform those with a low risk...

We examine how many factors out of a wide range of 207 that have incremental information in explaining cross-sectional stock returns. First, we find that the significance of each factor changes drastically over time. After accounting for false discovery rate (FDR), only 157 out of 207 factors...

In this paper, we provide a novel performance analysis of three widely used survey forecasts, by investigating how well they predict the stock market. Judging by their out-of-sample R2s, a standard measure in the market predictability literature, we find that none of the survey forecasts...

We find that anomaly returns are generally unchanged during FOMC days, though a small group of anomalies may have substantial changes. But if they do, their changes exacerbate pricing errors. Hence, our evidence challenges existing studies that find that the CAPM performs better over the FOMC...

We examine how many factors out of a wide range of 207 that have incremental information in explaining cross-sectional stock returns. First, we find that the significance of each factor changes drastically over time. After accounting for false-discovery (FDR), only 157 out of 207 factors are...

While there are hundreds of cross-sectional predictors in the equity market, whether corporate bonds are predictable in the cross-section is an open question. This paper proposes to use trend signals in returns, which exploit short-, intermediate- and long-term trends simultaneously, to predict...

Automated machine learning extends the search space to include hyperparameters and algorithm selection. We apply automated machine learning (AutoML) to cross sectional stock return prediction with factors. We formulate factor dimension reduction and hyperparameter tuning in conventional ML...

We provide a short and selected review of the vast literature on cross-section predictability. We focus on the state of art methods used to forecast the cross-section of stock returns with major predictors and are primarily interested in the ideas, methods, and their applications. To understand...

The market risk premium is central in finance, and has been analyzed by numerous studies in the time-series predictability literature and by growing studies in the options literature. In this paper, we provide a novel link between the two literatures. Theoretically, we derive a lower bound on...

We study how equity option trading affects the market risk premium. We find that a measure of aggregate call order imbalance (ACIB), defined as the cross-sectional average of the difference between open-buy and open-sell volume, negatively forecasts future stock market returns significantly from...