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: Alpine Finance Summit (2024, scheduled), Citigroup (2024, scheduled), Arrowstreet Capital(2024), AFA (2024), Kepos Capital, DGF(2023), NFA (2023), CICF (2023), SGF (2023), AWG (2022)

Media coverage: institutionalinvestor.com (04/23), institutional-money.com (03/23) 

Abstract: This paper demonstrates that portfolio performance can be substantially enhanced by simultaneously utilizing the volatilities of historical factor returns and option-derived market volatilities to determine factor exposures. The extent of improvement is particularly pronounced in risk regimes exhibiting option-implied right-skewed and high volatility market returns. Further gains in risk-adjusted portfolio returns are achieved when model parameters are separately estimated for different regimes. Importantly, these results are not contingent on a specific set of factors; similar enhancements are observed when employing principal components derived from a diverse set of factors. Furthermore, the findings are robust to transaction costs and out-of-sample estimation.

Performance in different implied volatility and skewness regimes unconditional sorts 

Abstract: While the academic literature focuses on beta exposure, most practitioners apply characteristics-based scorings to obtain factor portfolios. This paper explores how firm-level characteristics can be combined for optimal factor portfolios. Portfolios encompassing multiple factors are less volatile and have higher after-cost returns than the market or single factor portfolios. We also demonstrate that buy- and sell-thresholds play a critical role in shaping portfolio return, risk, and turnover preferences. Our empirical findings reveal optimal weights for the combination of individual factors, though we acknowledge the 1/N portfolio as a challenging benchmark. 

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

Media coverage: alphaarchitect.com (06/22)