Volume 4 • Issue 1 • PP: 01–11 • 2024
Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)
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© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The application of supervised machine learning (ML) algorithms for equity market regime classification has gained significant attention in recent years. This systematic literature review (SLR) synthesizes findings from 16 peerreviewed studies published between 2015 and 2024 to address three research questions: (1) How do supervised ML algorithms (XGBoost, Random Forest, SVM, Neural Networks, Ensemble methods) compare in accuracy, robustness, and computational efficiency for market regime classification? (2) What feature engineering approaches are most effective? (3) How generalizable are these models across different equity markets and time periods? Following PRISMA 2020 guidelines, we searched IEEE Xplore, ScienceDirect, and Springer, identifying 2953 records and including 16 studies after screening. Our findings indicate that ensemble methods (particularly Random Forest and XGBoost) and deep learning approaches (LSTM, DNN) consistently outperform traditional classifiers. Technical indicators remain the most common features, though novel approaches including event embeddings, network centrality measures, and signal decomposition show promise. Generalizability remains a challenge, with most studies focusing on developed markets. We identify gaps in cross-market validation and interpretability, providing directions for future research.
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