{"version":"1","article":{"id":"monte-carlo-tree-search-sparse-financial-regression","slug":"monte-carlo-tree-search-sparse-financial-regression","language":"en","url":"https://haberpaper.com/en/news/monte-carlo-tree-search-sparse-financial-regression","api_url":"https://haberpaper.com/api/v1/articles/monte-carlo-tree-search-sparse-financial-regression?lang=en","title":"Monte Carlo tree search compares sparse feature selection in financial regression","summary":"A preprint by authors affiliated with Paris Dauphine-PSL and CNRS evaluates the out-of-sample fit of search-based feature selection for explanatory NASDAQ-100 regression.","category":"finance","region":"world","topics":["ai"],"published_at":"2026-10-13T20:11:52+03:00","modified_at":"2026-10-13T20:11:52+03:00","publication_basis":"publication-record","event_period":"2026-10-09","event_period_basis":"event-or-reference-period","sources":[{"name":"arXiv","url":"https://arxiv.org/html/2610.13574v1","publishedAt":"2026-10-09","checkedAt":"2026-10-13"}],"translations":[{"language":"tr","url":"https://haberpaper.com/tr/news/monte-carlo-tree-search-sparse-financial-regression","api_url":"https://haberpaper.com/api/v1/articles/monte-carlo-tree-search-sparse-financial-regression?lang=tr"},{"language":"en","url":"https://haberpaper.com/en/news/monte-carlo-tree-search-sparse-financial-regression","api_url":"https://haberpaper.com/api/v1/articles/monte-carlo-tree-search-sparse-financial-regression?lang=en"},{"language":"ar","url":"https://haberpaper.com/ar/news/monte-carlo-tree-search-sparse-financial-regression","api_url":"https://haberpaper.com/api/v1/articles/monte-carlo-tree-search-sparse-financial-regression?lang=ar"},{"language":"fr","url":"https://haberpaper.com/fr/news/monte-carlo-tree-search-sparse-financial-regression","api_url":"https://haberpaper.com/api/v1/articles/monte-carlo-tree-search-sparse-financial-regression?lang=fr"},{"language":"es","url":"https://haberpaper.com/es/news/monte-carlo-tree-search-sparse-financial-regression","api_url":"https://haberpaper.com/api/v1/articles/monte-carlo-tree-search-sparse-financial-regression?lang=es"}],"data_types":[],"data":null,"data_compilations":[],"data_downloads":[],"content":{"paragraphs":["Submitted to arXiv on 9 October, the study combines Monte Carlo Tree Search with Generalized Nested Rollout Policy Adaptation to select a smaller subset from 1,072 engineered features. Candidate subsets are scored on out-of-sample validation performance rather than training error; the method can also use an objective that accounts for subset size and multicollinearity. The first page lists both authors at LAMSADE, Université Paris Dauphine-PSL and CNRS in Paris, France.","For an explanatory regression of weekly NASDAQ-100 returns covering January 2000 to March 2026, the authors report an out-of-sample R² of 0.6961 for their method and 0.6313 for an optimized Lasso comparison. The paper specifically describes the target as contemporaneously observed explanatory regression, so the result does not represent prospective return-forecasting performance. This is a preprint finding within a particular financial dataset and experimental protocol. The work proposes a search-based alternative for selecting interpretable sparse models when many correlated features are available."],"key_points":[],"sections":[]},"text":"A preprint by authors affiliated with Paris Dauphine-PSL and CNRS evaluates the out-of-sample fit of search-based feature selection for explanatory NASDAQ-100 regression.\n\nSubmitted to arXiv on 9 October, the study combines Monte Carlo Tree Search with Generalized Nested Rollout Policy Adaptation to select a smaller subset from 1,072 engineered features. Candidate subsets are scored on out-of-sample validation performance rather than training error; the method can also use an objective that accounts for subset size and multicollinearity. The first page lists both authors at LAMSADE, Université Paris Dauphine-PSL and CNRS in Paris, France.\n\nFor an explanatory regression of weekly NASDAQ-100 returns covering January 2000 to March 2026, the authors report an out-of-sample R² of 0.6961 for their method and 0.6313 for an optimized Lasso comparison. The paper specifically describes the target as contemporaneously observed explanatory regression, so the result does not represent prospective return-forecasting performance. This is a preprint finding within a particular financial dataset and experimental protocol. The work proposes a search-based alternative for selecting interpretable sparse models when many correlated features are available.","corrections":[],"editorial_illustration":{"kind":"symbolic-editorial-illustration","documentary":false,"scope":"section","asset_id":"finance","url":"https://haberpaper.com/assets/illustrations/finance-1536.webp","width":1536,"height":1024,"alt":"Symbolic financial system with a vault door, columns and unmarked coins.","language":"en"},"section_illustration":{"kind":"symbolic-editorial-illustration","documentary":false,"scope":"section","asset_id":"finance","url":"https://haberpaper.com/assets/illustrations/finance-1536.webp","width":1536,"height":1024,"alt":"Symbolic financial system with a vault door, columns and unmarked coins.","language":"en"}}}