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 …

Different aspects of liquidity impact the performance of short-run reversals in different ways,
consistent with the predictions of microstructure models. Higher volatility is associated with …

The emerging literature suggests that machine learning (ML) is beneficial in many asset
pricing applications because of its ability to detect and exploit nonlinearities and interaction …

Abstract Machine learning (ML) models for predicting stock returns are typically trained on
one-month forward returns. Although these models show impressive full-sample gross …

The number of factors allegedly driving the cross-section of stock returns has grown steadily
over time. We explore how much this 'factor zoo'can be compressed, focusing on explaining …

This study explores the speed of information absorption in global stock markets using the
Damodaran model. Analysing daily stock returns from 2005 to 2015 across 25 countries and …

Researchers and practitioners hope that machine learning strategies will deliver better
performance than traditional methods. But do they? This study documents that stock return …

Firm characteristics like value, momentum, quality, or low volatility help explain the cross
section of stock returns and have become core pillars in the practice of factor investing …

The classic short-term reversal effect has steadily weakened over time to the point of now
having vanished entirely in most regions. The strategy, however, can be revived by …

Momentum profits collapse and reversal occurs when preceding market volatility is relatively
high. Based on these intertemporal patterns, we implement an investment strategy that …