This post maintains a curated list of papers about asset pricing.
The link on the title of the paper is a direct download link when available. Otherwise, it takes you to the journal page for the paper.
Brou, A., & Luger, R. (2026). A new decomposition approach to modeling financial returns: Conditioning sign on magnitude. Journal of Banking & Finance, 189, 107716.
Brou and Luger (2026) propose a nonlinear return-forecasting framework that decomposes market excess returns into two objects: the sign of the return and its magnitude. Rather than forecasting returns directly with a linear predictive regression, the paper models magnitude as a volatility-like object and then conditions the probability of a positive return on that contemporaneous magnitude and lagged predictors. The idea is that volatility clustering and investor behavior can make direction partly predictable even when mean returns are hard to forecast. In monthly U.S. equity-premium data, the conditioning-sign-on-magnitude model improves out-of-sample forecasting and market-timing performance relative to the historical average, linear regressions, complete subset regression, and several nonlinear benchmarks, with especially strong gains at moderate predictor-set sizes.
Xu, X., & Liu, W.-H. (2024). Forecasting the equity premium: Can machine learning beat the historical average? Quantitative Finance, 24(10), 1445-1461.
This paper asks whether modern machine learning methods can beat the historical average benchmark in forecasting the U.S. equity premium. They evaluate 17 models, including OLS, 15 machine learning methods, and a forecast combination, using monthly S&P 500 excess returns from 1926 to 2020 and an out-of-sample period from 1957 to 2020, with both macroeconomic predictors and technical indicators. The striking result is that machine learning often looks powerful in sample, especially tree-based models such as XGBoost, but that performance largely disappears out of sample: in the main expanding-window test, only PCR produces a positive out-of-sample R-squared, and the historical average still has the better success ratio and the best market-timing Sharpe ratio. The paper’s practical message is that aggregate equity-premium prediction is a small-sample, low-signal-to-noise problem where model complexity can easily become overfitting; the right benchmark is not whether a model explains the past, but whether it reliably improves on the historical average in real time.
Nicolas, M. L. D. (2026). Tail risk exposure and the cross section of expected stock returns. Journal of Banking & Finance, 184, 107626.
Nicolas studies whether stocks earn a premium for exposure to market tail events, measured through tail dependence between individual stocks and the market. The paper’s key point is that many tail risk exposure measures are contaminated by ordinary market correlation: high-correlation stocks can appear tail-exposed even when the estimator is partly capturing average comovement, while low-correlation stocks may hide crash sensitivity that only appears in extreme states. Using U.S. stocks from 1965 to 2024, simulations, portfolio sorts, and Fama-MacBeth regressions, the paper finds that tail risk is priced mainly among low-correlation stocks and proposes a double-sort strategy that first controls for correlation and then sorts on tail risk exposure. That strategy produces stronger risk-adjusted performance than standard single-sort approaches, but the result also comes with practical caveats because the relevant stocks tend to be smaller and less liquid.
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