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...

In this paper, we provide an estimate of the ex-ante risk premia on earnings announcements based on the option market. We find that the risk premia are time-varying and have predictive power on future stock returns. With our ex-ante risk premia as a measure of uncertainty before each earnings...

We find that investor attention proxies proposed in the literature collectively have a common component that has significant power in predicting stock market risk premium, both in-sample and out-of-sample. This common component is well extracted by using partial least squares, scaled principal...

In this paper, we propose a stop-loss strategy to limit the downside risk of the well-known momentum strategy. At a stop-level of 10%, we find, with data from January 1926 to December 2013, that the maximum monthly losses of the equal- and value-weighted momentum strategies go down from -49.79%...

Using a comprehensive data set and an array of 27 macroeconomic, stock and bond predictors, we find that corporate bond returns are highly predictable based on an iterated combination model. The large set of predictors outperforms traditional predictors substantially, and predictability...

Using a large hand-collected dataset, we provide novel evidence on the additional information embedded in the designs and graphs of financial reports. We find that firms that add graphic financial reports experience a positive 2.7% abnormal returns in the following 3 to 6 months. The finding...

This paper constructs an investor sentiment measure at both individual bond and aggregate levels, uncovering the first evidence that investor sentiment has strong cross- sectional predictive power for corporate bond returns. High bond investor sentiment leads to low future returns. A portfolio...

We use machine learning tools to analyze industry return predictability based on theinformation in lagged industry returns from across the entire economy. Controlling forpost-selection inference and multiple testing, we nd significant in-sample evidence ofindustry return predictability. Lagged...

We show that short interest is arguably the strongest known predictor of aggregate stock returns. It outperforms a host of popular return predictors both in and out of sample, with annual r-squared statistics of 12.89% and 13.24%, respectively. In addition, short interest can generate utility...