We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

The WRDS Corporate Bond Database, introduced in April 2017, offers a clean and publicly accessible dataset for corporate bond research. In this article, we construct and replicate the Bai, Bali, and Wen (2019, BBW) factors using the WRDS bond returns with the SAS codes in the appendix. Using the...

For the last three decades, one of the most extensively investigated topics in financial economics is the crosssectional variation in stock returns. There are certain patterns in equity portfolios that are considered as anomalies because they cannot be explained by well-known asset pricing...

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and fund...

This paper investigates the risk versus mispricing explanation of superior returns to contrarian strategies using the interactions between value-to-market indicators and corporate financing transactions that increase or decrease a firm's outstanding equity. Portfolio-level analyses and...

This paper examines the intertemporal relation between downside risk and expected stock returns. Value at Risk (VaR), expected shortfall, and tail risk are used as measures of downside risk to determine the existence and significance of a risk-return tradeoff. We find a positive and significant...

It has become increasingly popular to advise investors to relocate their funds from a primarily stock portfolio to a primarily bond portfolio as they get older. However, the well-known decision rules such as mean-variance or stochastic dominance rules are unable to explain this common practice....