We propose a one-month market-timing model constructed from 15 diverse variables. We use weighted least squares with stepwise variable selection to build a predictive model for the one-month-ahead market excess returns. From our statistical model, we transform our forecasts into investable...

Commonality in idiosyncratic volatility cannot be completely explained by time-varying volatility. We decompose the common factor in idiosyncratic volatility (CIV) of Herskovic et al. (2016) into two components: idiosyncratic volatility innovations (VIN) and time-varyingidiosyncratic volatility...

This paper takes a cross-country and cross-sector perspective to investigate the drivers of commodity momentum strategies. Commodity momentum strategies deployed in the U.S. and Chinese markets generate positive average returns with non-negligible correlations, but their premia are primarily...

Size, value, and momentum are well known common factors to stock returns. I document size, value, and momentum premiums in industries are strongly correlated with themselves across industries. The correlation structures indicate strong common factor structure at the industry level. The strong...

We introduce a novel application of support vector machines (SVM), an important machine learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, forecasting a condition in the present time because the full information will not be available until later, is...