We evaluate the performance of the Conditional Autoencoder (CAE) model by Gu et al. (2021) in an international context and under economic constraints, such as the exclusion of microcap and illiquid firms, and accounting for transaction costs. The CAE model leverages latent factors and factor...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading...

We propose a parsimonious measure based solely on daily stock returns to characterize the severity of microstructure frictions at the individual stock level and assess the impact of frictions on the cross section of stock returns. Stocks with the largest frictions command a value-weighted return...

Der deutsche Aktienmarkt sah sich in den letzten 15 Jahren substantiellen Veränderungen gegenüber, welche unter anderem in eine zunehmende Internationalisierung und deutlich erhöhten Streubesitz mündeten. In der vorliegenden Arbeit untersuchen wir, inwieweit dies die aus klassischen...

Der deutsche Aktienmarkt sah sich in den letzten 15 Jahren substantiellen Veränderungen gegenüber, welche unter anderem in eine zunehmende Internationalisierung und deutlich erhöhten Streubesitz mündeten. In der vorliegenden Arbeit untersuchen wir, inwieweit dies die aus klassischen...

This paper studies portfolio optimization through improvements of ex-ante conditional covariance estimates. We use the cross-section of stock returns over a 52-year sample to analyze trading performance by implementing the machine learning algorithm of hierarchical clustering. We find that...

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we...