Factoring in the Low-Volatility Factor
Amar Soebhag (Erasmus University Rotterdam), et al.
June 2025
Low-volatility stocks have historically delivered higher risk-adjusted returns than their high-volatility peers. Despite extensive evidence and widespread adoption in the investment industry, the so-called low-volatility factor is absent from standard asset pricing models. This paradox is attributable to asymmetry in factor legs and real-life investment frictions. A low-volatility factor substantially improves performance of factor models once accounting for these dimensions in various in-sample and out-of-sample exercises, across different low-risk measures and across methodological choices. We advocate integrating the low-volatility factor into asset pricing models, accounting for the asymmetry and frictions.

Revisiting factor momentum: A one-month lag perspective
Mikael Rönkkö (U. of Eastern Finland) and Joonas Holmi (independent researcher)
July 2025
Recent studies have questioned the relevance of factor momentum by showing that its profitability is driven by a static tilt toward factors with positive historical means and that only a minority of individual factors exhibit significant momentum. This paper shows that replacing the traditional one-year formation window with a one-month window yields significant alpha after controlling for tilt toward positive-mean factors and doubles the number of factors with significant momentum from roughly 20% to 40%. Furthermore, we show that the positive autocorrelation between the one-month formation window and the subsequent month’s return is twice as high as in the traditional one-year formation window. In the modern era of electronic trading, this autocorrelation is nearly 14 times higher. Our findings highlight that the robustness and profitability of factor momentum strategies depend critically on the formation window length.

Breaks and Trends in Factor Premia
Liyuan Cui (City University of Hong Kong), et al.
June 2025
This paper investigates structural breaks and regime-dependent premia in risk factors. We analyze the slope factor model (e.g., Fama and French, 2020; Smith and Timmermann, 2022), where characteristic loadings align with factor risk premia, and propose a predictive regression with time-varying coefficients to accommodate heterogeneous breaks and piecewise constant loadings. Our method captures time-varying trends in slope factor returns through detected breaks and regimedependent premia estimates. We establish the consistency of our time-varying parameter estimates and derive large-sample properties for the estimator. Using U.S. stock returns and numerous firm characteristics, we identify dynamic shifts in factor premia, especially during financial crises. Our results show that this method enhances investment performance for selected regime-dependent slope factor models. For instance, SUE (unexpected earnings) consistently predicts with high factor premia, while SP (sales-to-price) and ABR (abnormal returns) display regime-dependent patterns. Moreover, a regime-based out-of-sample factor timing strategy outperforms traditional buy-and-hold approaches for most slope factors, evidencing time-varying factor premia.

A FOMO-based Capital Asset Pricing Model
Mohammed Kaddouhah (Swansea University), et al.
May 2025
Investors experience a Fear of Missing Out (FOMO) when observing peers’ investment choices outperforming their own. Agents require compensation for holding stocks that deliver lower returns than the equities held by their peers. This paper provides a theoretical and empirical introduction to the FOMO Capital Asset Pricing Model (FOMO-CAPM). Analysis of US equities between 1980 and 2024 shows FOMO is a significant determinant of asset prices. An annualized downside FOMO risk premium of 17.4% is demonstrated. The explanatory power of FOMO for stock returns is robust to a battery of alternative peer group portfolios, risk exposure controls and sub-period analyses.

Conditional Betas: a Non-Standard Approach
Paulo Rodrigues (Maastricht University), et al.
June 2025
The exposure of stock returns to risk factors is known to vary over time. Traditional methods, such as rolling windows, have been widely used to capture this variation. However, these methods are highly dependent on the choice of window length and may fail to capture nonlinearities in the data adequately. We propose a novel approach that employs a neural network to estimate stock exposure to market risk on a time-varying basis. Our findings demonstrate that this neural network-based estimator outperforms regression-based estimators in out-of-sample predictive performance and exhibits no systematic bias across model-implied expected beta quintile portfolios. Additionally, the proposed estimator effectively classifies stocks into quintiles based on forecasted betas.

Assessing Asymmetry Risk Premium in U.S. Stock Markets
Yakun Liu (Hunan University)
February 2025
In this research, we find that asymmetry, determined through a newly suggested density function-based metric, is negatively correlated with average future returns across U.S. stock returns. Our empirical findings are consistent with the results of the theoretical demonstration of Han et al. (2022). Conversely, when employing the traditional skewness metric, the link between idiosyncratic asymmetry and future stock returns becomes less distinct. Grouping individual stocks into portfolios according to their asymmetry levels yields economically substantial and statistically significant disparities in ensuing portfolio returns. These disparities continue to be significant even after adjusting for other company characteristics and explanatory variables previously associated with anticipated stock returns. Moreover, the negative predictive link between the asymmetry metric and next month stock returns remain robust when accounting for various factors, such as volatility, investor sentiment, market liquidity, and arbitrage trading.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno