Fusion of multifactor modeling and supervised learning algorithms in quantitative finance: a comparative analysis of predictive and explanatory power

[1] Verma, A., Buonocore, R. J., & Di Matteo, T. (2019). A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering. Quantitative finance(6), 19. Search in Google Scholar

[2] Sun, Y., Liu, L., Fang, J., Zeng, X., & Wan, Z. (2023). Musu: a medium-term investment strategy by integrating multifactor model with industrial supply chain. International Journal of Financial Engineering. Search in Google Scholar

[3] Ji, Xiaoyu, Ke, & Hua. (2017). No-arbitrage theorem for multi-factor uncertain stock model with floating interest rate. Fuzzy Optimization and Decision Making: A Journal of Modeling and Computation Under Uncertainty. Search in Google Scholar

[4] Zhang, W., Zhang, S., Zhang, S., Yu, D., & Huang, N. N. (2017). A multi-factor and high-order stock forecast model based on type-2 fts using cuckoo search and self-adaptive harmony search. Neurocomputing, 240(May31), 13-24. Search in Google Scholar

[5] Olkhov, V. (2017). Econophysics of macro-finance: local multi-fluid models and surface-like waves of financial variables. Papers. Search in Google Scholar

[6] Davis, J., Devos, L., Reyners, S., & Schoutens, W. (2021). Gradient boosting for quantitative finance. The journal of computational finance(4), 24. Search in Google Scholar

[7] Monge, J. F. (2017). Cardinality constrained portfolio selection via factor models. Optimization Letters. Search in Google Scholar

[8] Lu, M., Chen, C. Y. H., & Hardle, W. H. (2017). Copula-based factor model for credit risk analysis. Review of Quantitative Finance & Accounting, 49(4), 949-971. Search in Google Scholar

[9] Menden, C. P. C. R. (2017). Dissecting the financial cycle with dynamic factor models. Quantitative finance, 17(12). Search in Google Scholar

[10] Krishnan, K. G., & Arekar, K. (2017). Active portfolio management using factor analysis model. Asian Journal of Research in Banking and Finance, 7(5), 152. Search in Google Scholar

[11] Li, Z., Liu, X. Y., Zheng, J., Wang, Z., Walid, A., & Guo, J. (2021). Finrl-podracer: high performance and scalable deep reinforcement learning for quantitative finance. Papers. Search in Google Scholar

[12] Consiglio, A., Carollo, A., & Zenios, S. A. (2017). Quantitative finance a parsimonious model for generating arbitrage- free scenario trees a parsimonious model for generating arbitrage-free scenario trees. Social Science Electronic Publishing. Search in Google Scholar