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March 15, 2026Open Access

OLYMPIA: Market-Regime-Conditioned Machine Learning for Equity Signal Generation

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Authors

MRMichael Rupert

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Overview

Machine learning improves out-of-sample precision in equity signal generation, indicating significant market regime effects.

Key Points

  • This research aims to enhance equity signal generation using machine learning by addressing regimenon-stationarity.
  • Developed Olympia framework for conditioning probability scores on market regime features.
  • Utilized a five-feature broad market regime layer derived from the S&P 500.
  • Implemented gradient-boosted decision trees with isotonic probability calibration.
  • Achieved a five-fold improvement in out-of-sample precision from approximately 12% to 62%.
  • Reached approximately 90% top-10 out-of-sample precision under favorable market conditions.
  • The automated pipeline can scan the entire US equity universe in about 22 minutes.

Cite This Study

Michael Rupert (2026) studied this question.

synapsesocial.com/papers/69b5ff4f83145bc643d1b9dahttps://doi.org/10.5281/zenodo.19003116
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