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...

This paper demonstrates how deep learning can be used to price and calibrate models of credit risk. Deep neural networks can learn structural and reduced-form models with high degrees of accuracy. For complex credit risk models, whose closed-form solutions are not available, deep learning offers...

We revisit a series of popular anomalies: seasonal, announcement and momentum. We comment on statistical significance and persistence of these effects and propose useful investment strategies to incorporate this information. We investigate the creation of a seasonal anomaly and trend model...

This paper examines the behavior of futures prices and trader positions around the occurrence of price limits in commodity futures markets. We ask whether limit events are the result of shocks to fundamental volatility or the result of temporary volatility induced by the trading of...

The standard way to measure economic growth in an economy is through increases in Gross Domestic Product (GDP). However, because prices for digital products and services have been rapidly falling for years, the rapid growth of the digital economy is not well-captured by GDP. In this paper, we...

Downside volatility and volatility typically comove but are not highly correlated during the most volatile times. We show that portfolios scaled by downside volatility expand the ex post mean-variance frontiers constructed using the original portfolios and volatility-managed portfolios of...

We examine the private information associated with insider trades using a Chinese data set. Insider buys positively forecast individual stock returns and insider sales negatively forecast individual stock returns. Classifying insiders as corporate managers and institutional investors, we find...