FactorEngine mines alpha factors as Turing-complete code via LLM-guided directional search, parameter separation, and a multi-agent pipeline that converts financial reports into executable programs, delivering higher IC/ICIR and Sharpe ratios than baselines in backtests.
The Review of Financial Stud- ies33(5), 2019–2133 (2020)
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Adaptive specification search in financial machine learning produces statistically significant backtests even when no predictability exists, and a new audit using synthetic null environments plus an absolute magnitude gap can detect and quantify such spurious results.
citing papers explorer
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FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment
FactorEngine mines alpha factors as Turing-complete code via LLM-guided directional search, parameter separation, and a multi-agent pipeline that converts financial reports into executable programs, delivering higher IC/ICIR and Sharpe ratios than baselines in backtests.
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Spurious Predictability in Financial Machine Learning
Adaptive specification search in financial machine learning produces statistically significant backtests even when no predictability exists, and a new audit using synthetic null environments plus an absolute magnitude gap can detect and quantify such spurious results.