This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although...

Machine learning (ML) models for predicting stock returns are typically trained on one-month forward returns. While these models show impressive full-sample gross alphas, their performance net of transaction costs post 2004 is close to zero. By training on longer prediction horizons and using...

Short-term alpha signals are generally dismissed in traditional asset pricing models, primarily due to market friction concerns. However, this paper demonstrates that investors can obtain a significant net alpha by combining signals applied on a liquid global universe with simple buy/sell...

We extract the news component of short-selling activity by accounting for important cross-sectional, distributional differences in short interest data. The resulting measure of surprise in short interest negatively predicts the cross section of both U.S. and international equity returns. Our...

While some of the modern performance measures used in managerial accounting rely on cash flow based figures others try to take advantage of the information content of accounting figures. However, whether the additional information content in the accrual components of earnings improves the...