Research

Working papers:

1. Low-Frequency Risk Factors and Their Fundamental Drivers

Abstract:

There is a “zoo” of factors that capture systematic risk premia and a large number of economic variables that explain their time variation, which poses a doubly high-dimensional challenge to understanding how economic fundamentals relate to the time-varying dynamics of risk premia. I propose a method to regularize this problem by identifying low-frequency risk factors, whose risk premia are driven by latent low-frequency state variables. Empirically, one below-business-cycle-frequency factor and one business-cycle-frequency factor, whose variation concentrates on cycles longer than eight years and between 1.5 and eight years, explain the expected returns of individual stocks and characteristic-managed portfolios. The below-business-cycle-frequency factor has a high Sharpe ratio, and stocks whose current size is small compared to their long-term average load on it. Moreover, selected macroeconomic and financial variables have statistically and economically significant out-of-sample predictive power for the returns of the two low-frequency factors.

2. Asset-Pricing Factors with Economic Targets, with Svetlana Bryzgalova, Victor DeMiguel, and Markus Pelger

1. • Winner of the Bates-White Best Paper Award, 2023 SoFiE Conference

   • Winner of the 2023 INFORMS Finance Student Paper competition

Abstract:

We propose a novel method to estimate latent asset-pricing factors that incorporate economic structure. Our estimator generalizes principal component analysis by including economically motivated cross-sectional and time-series moment targets that help to detect weak factors. Cross-sectional targets may capture monotonicity constraints on the loadings of factors or their correlation with fundamental macroeconomic innovations. Time-series targets may reward explaining expected returns or reducing mispricing relative to a benchmark reduced-form model. In an extensive empirical study, we show that these targets nudge risk factors to better span the pricing kernel, leading to substantially higher Sharpe ratios and lower pricing errors than conventional approaches.

Presentation at: AFA, WFA, EFA, CICF, NFA, SoFiE*, INFORMS, CFE-CMStatistic, TOM European Seminar Series, Lancaster Financial Econometrics Conference, London Business School, Universite Catholique de Louvain, Hunan University

Publication:

1. Comparing Factor Models with Price-Impact Costs, with Victor DeMiguel and Alberto Martin-Utrera

1. • Journal of Financial Economics, 162, 1--26, (2024)

   • Finalist for the 2020 INFORMS Finance Student Paper competition

   • Semi-finalist for the 2021 FMA Best Paper Award in Investment

Abstract:

We propose a formal statistical test to compare asset-pricing models in the presence of price impact. In contrast to the case without trading costs, we show that in the presence of price-impact costs different models may be best at spanning the investment opportunities of different investors depending on their absolute risk aversion. Empirically, we find that the five-factor model of Hou, Mo, Xue, and Zhang (2021), the six-factor model of Fama and French (2018) with cash-based operating profitability, and a high-dimensional model are best at spanning the investment opportunities of investors with high, medium, and low absolute risk aversion, respectively.

Presentation at: AFA PhD Student Poster Session, FMA, INFORMS, LBS Trans-Atlantic Doctoral Conference, 7th International Young Finance Scholars’ Conference, London Business School*, Maastricht University*, Tilburg University*, City University of Hong Kong*