We provide evidence on the performance and the replication success of a broad sample of 72 synthetic hedge funds from January 2009 to December 2013. Thereby, we assign the term 'synthetic hedge fund' to mutual funds and exchange-traded funds with hedge fund indices as their benchmarks....

Despite its theoretical appeal, Markowitz mean-variance portfolio optimization is plagued by practical issues. It is especially difficult to obtain reliable estimates of a stock's expected return. Recent research has therefore focused on minimum volatility portfolio optimization, which...

Using monthly stock returns from 28 emerging market countries and a total sample period of 21 years, we investigate the predictive power of a broad set of factors. We document that the factor definitions of the Fama and French (2015) five-factor model are less robust compared to alternative...

This paper compares the performance of three enhanced momentum strategies proposed in the literature — idiosyncratic momentum, constant volatility-scaled momentum, and dynamic-scaled momentum. Using data for individual stocks from the U.S. and across 48 international countries, we find that...

Fama and French (2015) propose to augment their classic (1993) 3-factor model with profitability and investment factors, resulting in a 5-factor model, which is likely to become the new benchmark for asset pricing studies. Although the 5-factor model exhibits significantly improved explanatory...

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...

Interested in fundamental analysis and inspired by Bartram and Grinblatt (2018 & 2021), we apply linear regression (LR) and tree-based machine learning (ML) methods to estimate monthly peer-implied fair values of European stocks from 21 accounting variables. Comparing LR and ML models, we...

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...