Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)

Suvonkulov; Eugene Q.

doi:https://doi.org/10.54216/FinTech-I.040101

Review Article

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)

Suvonkulov Abdulaziz ^1*

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,

Eugene Q. Castro ¹

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¹Department of Computer Science, Central Asian University, Tashkent, Uzbekistan

* Corresponding Author.

DOI https://doi.org/10.54216/FinTech-I.040101

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Received: January 12, 2024 Revised: March 01, 2024 Accepted: June 28, 2024

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Abstract

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.

Keywords

Systematic literature review Machine learning Stock market prediction Regime classification XGBoost Random Forest LSTM Deep learning Feature engineering

References

[1] M. J. Gierl, H. Lai, and S. R. Turner, “Using automatic item generation to create multiple-choice test items,” Medical Education, vol. 46, no. 8, pp. 757–765, 2012, doi: 10.1111/j.1365-2923.2012.04289.x.

[2] H. Lu, J. Wang, Q. Wang, and H. Li, “A review of automatic question generation for education,” Artificial Intelligence Review, vol. 56, no. 9, pp. 8979–9025, 2023.

[3] M. Heilman and N. A. Smith, “Good question! Statistical ranking for question generation,” in Proc. Human Language Technologies: The 2010 Annual Conf. North American Chapter of the Association for Computational Linguistics, Los Angeles, CA, USA, 2010, pp. 609–617.

[4] B. K. Britton, A. Glynn, M. Meyer, and T. Penland, “Effects of text structure on use of cognitive capacity during reading,” Journal of Educational Psychology, vol. 74, no. 1, pp. 51–61, 1982.

[5] A. Tack and C. Piech, “The AI teacher test: Measuring the pedagogical ability of Blender and GPT-3 in educational dialogues,” in Proc. Neural Information Processing Systems Datasets and Benchmarks Track, New Orleans, LA, USA, 2022.

[6] E. Kasneci, K. Sessler, S. Kuechemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. Guennemann, E. Huellermeier, S. Krusche, G. Kutyniok, T. Michaeli, C. Nerdel, J. Pfeffer, O. Poquet, M. Sailer, A. Schmidt, T. Seidel, M. Stadler, J. Weller, J. Kuhn, and G. Kasneci, “ChatGPT for good? On opportunities and challenges of large language models for education,” Learning and Individual Differences, vol. 103, Art. no. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.

[7] O. Zawacki-Richter, V. I. Marin, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education: Where are the educators?” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, Art. no. 39, 2019, doi: 10.1186/s41239-019-0171-0.

[8] S. Narciss, “Feedback strategies for interactive learning tasks,” in Handbook of Research on Educational Communications and Technology, 3rd ed., J. M. Spector, M. D. Merrill, J. van Merrienboer, and M. P. Driscoll, Eds. New York, NY, USA: Routledge, 2008, pp. 125–144.

[9] M. D. Shermis and J. Burstein, Eds., Handbook of Automated Essay Evaluation: Current Applications and New Directions. New York, NY, USA: Routledge, 2013.

[10] M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J. M. Grimshaw, A. Hrobjartsson, M. M. Lalu, T. Li, E.W. Loder, E. Mayo-Wilson, S. McDonald, L. A. McGuinness, L. A. Stewart, J. Thomas, A. C. Tricco, V. A. Welch, P. Whiting, and D. Moher, “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, Art. no. n71, 2021, doi: 10.1136/bmj.n71.

[11] V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative Research in Psychology, vol. 3, no. 2, pp. 77–101, 2006, doi: 10.1191/1478088706qp063oa.

[12] B. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” Keele University and Durham University, Keele, U.K., EBSE Tech. Rep. EBSE-2007-01, 2007.

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Abdulaziz, Suvonkulov, Castro, Eugene Q.. "Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)." Financial Technology and Innovation, vol. Volume 4, no. Issue 1, 2024, pp. 01–11. DOI: https://doi.org/10.54216/FinTech-I.040101

Abdulaziz, S., Castro, E. (2024). Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024). Financial Technology and Innovation, Volume 4(Issue 1), 01–11. DOI: https://doi.org/10.54216/FinTech-I.040101

Abdulaziz, Suvonkulov, Castro, Eugene Q.. "Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)." Financial Technology and Innovation Volume 4, no. Issue 1 (2024): 01–11. DOI: https://doi.org/10.54216/FinTech-I.040101

Abdulaziz, S., Castro, E. (2024) 'Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)', Financial Technology and Innovation, Volume 4(Issue 1), pp. 01–11. DOI: https://doi.org/10.54216/FinTech-I.040101

Abdulaziz S, Castro E. Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024). Financial Technology and Innovation. 2024; Volume 4(Issue 1):01–11. DOI: https://doi.org/10.54216/FinTech-I.040101

S. Abdulaziz, E. Castro, "Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)," Financial Technology and Innovation, vol. Volume 4, no. Issue 1, pp. 01–11, 2024. DOI: https://doi.org/10.54216/FinTech-I.040101

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