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arXiv Machine Learning By Cameron Mouangue, Tristan Cazenave

$L_0$ Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression

Read the original on arXiv Machine Learning →

The paper introduces a feature selection framework for high‑dimensional financial regression that uses Monte Carlo Tree Search with Generalized Nested Rollout Policy Adaptation. It treats feature selection as a sequential decision process and evaluates candidate subsets by out‑of‑sample validation performance, incorporating subset size and multicollinearity into a lexicographic objective. Applied to a NASDAQ‑100 weekly return regression with 1,072 engineered features, the method outperforms an optimized Lasso baseline, achieving an out‑of‑sample R² of 0.6961 versus 0.6313.

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