Iranian Journal of Finance

Causal Alpha: Identifying Structural Drivers of Excess Returns in the US Equity Market Using Causal Machine Learning

Document Type : Original Article

Authors

Moslem Nilchi 1
Mahdi Zerang 2

1 Assistant Prof, Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran

2 Ph.D. Candidate, Department of Economics, Faculty of Economics, Allameh Tabataba'i University, Tehran, Iran

10.66224/ijf.2026.593966.1593
Abstract
This study investigates the causal relationship between uncertainty shocks and factor premia in the US equity market. While traditional asset pricing models rely on correlation-based factor structures, this research argues that identifying true alpha necessitates a causal framework. Employing a novel combination of Double Machine Learning (DML) and Causal Forest algorithms on Kenneth French's five-factor data and VIX-based uncertainty shocks from 2001 to 2025, robust evidence is provided demonstrating that uncertainty shocks exert significant causal effects on factor returns. The findings reveal that the market factor (MKT-RF) and the size factor (SMB) exhibit strong negative causal responses to uncertainty shocks. In contrast, the profitability factor (RMW) demonstrates a positive and significant effect. The value (HML) and investment (CMA) factors, however, show no statistically detectable causal effect. Substantial heterogeneity in these effects is documented across high- and low-volatility regimes, with the market's negative response being substantially larger in high-uncertainty periods. From an economic significance perspective, a long-only trading strategy based on causal signals delivers a competitive Sharpe ratio of 0.437, with a substantially lower maximum drawdown (-32.5%) compared to the market portfolio (-51.4%). The results survive rigorous robustness checks and statistical tests, providing evidence consistent with a causal effect of uncertainty shocks on factor premia. This study contributes to the growing literature on causal machine learning in finance and offers practical implications for risk management and portfolio construction.

Keywords

Causal Alpha
Uncertainty Shocks
Double Machine Learning
Causal Forest
Fama-French Factors
Allen, F., & Gale, D. (2004). Financial intermediaries and markets. Econometrica, 72(4), 1023–1061.
Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), 1148–1178.
Bachmann, R., Elstner, S., & Sims, E. R. (2013). Uncertainty and economic activity: Evidence from business survey data. American Economic Journal: Macroeconomics, 5(2), 217-249.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636.
Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623-685.
Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82.
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47(2), 427–465.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.
Fama, E. F., & French, K. R. (2006). Profitability, investment, and average returns. Journal of Financial Economics, 82(3), 491–518.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22.
Fama, E. F., & French, K. R. (2017). International tests of a five-factor asset pricing model. Journal of Financial Economics, 123(3), 441–463.
Imbens, G. W., & Rubin, D. B. (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.
Kumar, S., Sharma, R., & Rao, P. (2024). Causal inference in finance: A comprehensive review of methods, applications, and future directions. Journal of Financial Economics, 158, 103-128.
Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13–37.
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768-783.
Novy-Marx, R. (2013). The other side of value: The gross profitability premium. Journal of Financial Economics, 108(1), 1–28.
Pearl, J. (1986). A constraint-propagation approach to probabilistic reasoning. In Uncertainty in Artificial Intelligence (pp. 357–370). Elsevier.
Pearl, J. (2009). Causality. Cambridge University Press.
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.
Swedroe, L. E., & Berkin, A. L. (2017). Your complete guide to factor-based investing. Buckingham.
Titman, S., Wei, K. C., & Xie, F. (2004). Capital investments and stock returns. Journal of Financial and Quantitative Analysis, 39(4), 677–700.
Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242.
French, K. R. (n.d.). Kenneth R. French Data Library. Available at: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html (Accessed: 2025).
Iranian Journal of Finance
Volume 10, Issue 2
2026
Pages 158-185

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