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Effective machine learning estimates of stock market returns using Taylor-rule inputs

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  • Roumani, Y.F.
  • AlSalman, Z.
  • Murphy, A.

Abstract

We apply a machine learning approach to estimate 1-year-ahead U.S. excess stock market returns over the 1964-2024 period using lagged inflation and output gap variables as inputs in a Taylor-rule framework. Utilizing a 60-month rolling training window, our Support Vector Regression (SVR) estimates account for 40% of the ex-post variation in realized excess returns. Subsample analysis confirms significant explanatory power across different monetary-policy environments. We show that the SVR framework captures nonlinear, time-varying relationships between Taylor-rule inputs and equity risk premia, suggesting that SVR effectively detects the dynamically complex interrelationships between the Federal Reserve’s policy targets and future equity returns.

Suggested Citation

  • Roumani, Y.F. & AlSalman, Z. & Murphy, A., 2026. "Effective machine learning estimates of stock market returns using Taylor-rule inputs," Economics Letters, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:ecolet:v:259:y:2026:i:c:s0165176525006421
    DOI: 10.1016/j.econlet.2025.112805
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    References listed on IDEAS

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