Abstract: We provide a comprehensive analysis of the timing success for equity risk factors. Our analysis covers over 300 risk factors (factor zoo) and a high dimensional set of predictors. The performance of almost all groups of factors can be improved through timing, with improvements being highest for profitability and value factors. Past factor returns and volatility stand out as the most successful individual predictors of factor returns. However, both are dominated by aggregating many predictors using partial least squares. The median improvement of a timed vs. untimed factor is about 2% p.a. A timed multifactor portfolio leads to a 20% increase in return relative to its untimed counterpart.

Presentations: AFA (2024, scheduled), DGF(2023, scheduled), NFA (2023), CICF (2023), SGF (2023), AWG (2022)

Media coverage: institutionalinvestor.com (Apr. 2023)

Abstract: While the academic literature primarily investigates factor exposures based on covariances (i.e. beta exposure), most practitioners apply characteristics-based scorings to obtain factor portfolios. It hereby remains largely unexplored how firm-level characteristics can be combined to obtain optimal factor portfolios. This paper derives multi-factor portfolios that are formed via a combination of stock characteristic scores. Portfolios that are formed on multiple characteristics are less volatile, and exhibit higher after cost returns compared to the market and single factor portfolios. In addition, return, risk and turnover preferences are very sensitive to buy- and sell-thresholds. We further identify optimal weights for individual factor characteristics, but have to recognize the 1/N factor portfolio as a tough benchmark. 

Presentations:  APEF (2022), University of Liechtenstein (2022), Asian Finance Association (2021), ATINER (2021)

Media coverage: alphaarchitect.com (Jun. 2022)