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<p>This paper investigates the potential of AI-driven anomaly detection models to identify exploitable inefficiencies in stock markets. Using Isolation Forest for anomaly detection and CatBoost regression for predictive modeling, we analyze a subset of S&amp;P 500 stocks to assess whether machine learning techniques can uncover arbitrage opportunities. Our findings suggest that AI-based strategies can generate excess returns testing the market efficiency hypothesis.</p>

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AI-Driven Anomaly Detection in Stock Markets: Testing Market Efficiency with Machine Learning

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Vittorio Carlei,
  • Donatella Furia,
  • Piera Cascioli,
  • Alessandro Ceccarelli

摘要

This paper investigates the potential of AI-driven anomaly detection models to identify exploitable inefficiencies in stock markets. Using Isolation Forest for anomaly detection and CatBoost regression for predictive modeling, we analyze a subset of S&P 500 stocks to assess whether machine learning techniques can uncover arbitrage opportunities. Our findings suggest that AI-based strategies can generate excess returns testing the market efficiency hypothesis.