We find that price and earnings momentum are pervasive features of international equitymarkets when controlling for data snooping biases. For European countries, we find that pricemomentum is subsumed by earnings momentum on an aggregate level. However, this rationaledoes not apply to each and...

Using the G10 currencies, we show that parametric portfolio policies can help guide an optimal currency strategy when tilting towards cross-sectional factor characteristics. While currency carry serves as the main return generator in this tilting strategy, momentum and value are implicit...

While gold is a volatile asset, it is often considered a safe haven that offers protection during bear markets. We study this safe haven hypothesis by analyzing a strategic allocation to gold for a loss averse investor with a 1-year evaluation horizon. A modest allocation to gold indeed helps to...

Portfolio construction seeks an optimal trade-off between a portfolio's mean return and its associated risk. Since risk may not be properly described by return volatility we optimize portfolios with respect to various measures of downside risk in an empirical out-of-sample setting. These...

We find that price and earnings momentum are pervasive features of international equity markets even when controlling for data snooping biases. For European countries, we find that price momentum is subsumed by earnings momentum on an aggregate level. However, this rationale does not apply to...

We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, Copula-GARCH and dynamic GAS models. Utilizing a loss function that overcomes the lack of elicitability for Expected Shortfall, we propose a novel Expected...

We investigate the benefits of forecast combination for timing equity factors based on predictive regressions using macro predictors. Relative to standard predictive regression models, forecast combination reduces the noise of forecasts and hence improves their out-of-sample predictive accuracy....

Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. We confirm this finding when predicting one-month forward-looking returns based on a set of common stock characteristics, including predictors such as short-term...

Maximizing for diversification in the multi-asset multi-factor universe, the literature advances diversified risk parity strategies across economic clusters. For handling overly complex correlation matrices, hierarchical clustering techniques have recently been put forward to guide risk parity...

We investigate portfolio diversification strategies based on hierarchical clustering. These hierarchical risk parity strategies use graph theory and unsupervised machine learning to build diversified portfolios by acknowledging the hierarchical structure of the investment universe. In this...