Model Update Strategies for Machine Learning in Dynamic Index Constituents Investing

Model Update Strategies for Machine Learning in Dynamic Index Constituents Investing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 July 2025 V1 Latest version Share on Model Update Strategies for Machine Learning in Dynamic Index Constituents Investing Authors : Ligang Zhou 0000-0003-3714-8562 [email protected] and Kwo Ping Tam 0000-0002-0121-8082 Authors Info & Affiliations https://doi.org/10.22541/au.175369921.14142288/v1 201 views 181 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Abstract Previous research has rarely explored the updating strategies of machine learning models in the context of stock investment. Our study investigates how update strategies influence the predictive performance and backtesting outcomes in stock investment. Using the dynamic constituents of the CSI300 index from 2012 to 2023, we evaluate seven machine learning models under five distinct update strategies, including periodic updates, Build-Once-Use-Forever (BOUF), and a novel Data Drift-Detection-Based (DDDB) approach. Our comprehensive analysis incorporates both predictive accuracy metrics and rigorous backtesting of investment performance. While predictive accuracies remain relatively stable across models and strategies, investment performance exhibits significant variation. Our results underscore the importance of incorporating update strategies in the development and evaluation of machine learning models for stock investment, emphasizing the intricate interplay among model updates, predictive accuracy, and investment outcomes. Our research establishes a foundation for future studies on adaptive modeling techniques in quantitative finance. Supplementary Material File (updatestrategiescsi300components.pdf) Download 931.32 KB Information & Authors Information Version history V1 Version 1 28 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords backtesting dynamic csi300 constituents machine learning stock investment update strategies Authors Affiliations Ligang Zhou 0000-0003-3714-8562 [email protected] Macau University of Science and Technology View all articles by this author Kwo Ping Tam 0000-0002-0121-8082 Macau University of Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 201 views 181 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ligang Zhou, Kwo Ping Tam. Model Update Strategies for Machine Learning in Dynamic Index Constituents Investing. Authorea . 28 July 2025. 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