Beyond random walks: exploring the learnability threshold of AI agents in algorithmic markets

Küçükoğlu S.

Expert Systems with Applications, 2026 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.eswa.2026.131776
  • Dergi Adı: Expert Systems with Applications
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Public Affairs Index
  • Anahtar Kelimeler: Adaptive decision systems, Agent-based computational economics, Algorithmic trading, C45, C63, D83, G14, G17, Learnability threshold, Market complexity, Market efficiency
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

Financial markets contain statistically detectable patterns whose economic exploitability remains uncertain. This study introduces the Learnability Threshold the boundary beyond which detectable patterns cannot yield positive net-of-cost returns for AI agents. This study compares a rule-based heuristic with Proximal Policy Optimization (PPO) agents (trained tabula rasa and via imitation) in simulated markets with transaction costs. To ensure robustness, aligned-path evaluations are conducted and richer observation spaces are tested. Results show DRL agents consistently fail to exploit long-memory dynamics, converging to inactivity or loss-making behavior, whereas the heuristic delivers stable risk-adjusted returns. The findings formally distinguish statistical detectability from economic exploitability and reposition DRL as a diagnostic decision-support tool for identifying unexploitable market regimes rather than a standalone profit engine.