Entropic approximate learning for financial decision-making in the small data regime

Vecchi, Edoardo; Berra, Gabriele; Albrecht, Steffen; Gagliardini, Patrick; Horenko, Illia

doi:10.1016/j.ribaf.2023.101958

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Entropic approximate learning for financial decision-making in the small data regime

Vecchi, Edoardo ORCID Institute of Computing (CI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
Berra, Gabriele Institute of Computing (CI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
Albrecht, Steffen University Medical Center of the Johannes Gutenberg-Universität, Institute of Physiology, 55128 Mainz, Germany
Gagliardini, Patrick Istituto di finanza (IFin), Facoltà di scienze economiche, Università della Svizzera italiana, Svizzera
Horenko, Illia Institute of Computing (CI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera ; Technical University of Kaiserslautern, Faculty of Mathematics, Chair for Mathematics of AI, 67663 Kaiserslautern, Germany

2023

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Research in international business and finance. - 2023, vol. 65, p. 101958

English Financial decision-making problems based on relatively few observations and several explanatory variables can be problematic for the common machine learning (ML) tools, since they cannot efficiently discriminate the relevant information. To investigate the challenges of this ‘‘small data’’ regime, we employ several state-of-the-art ML methods for predicting whether three selected stocks from the Swiss Market Index will outperform the market, by using, as classification features, a set of commonly used technical indicators. We show that the recently introduced entropic Scalable Probabilistic Approximation (eSPA) algorithm significantly surpasses its competitors in both prediction accuracy and computational cost. We then discuss the interpretability of the employed ML methods and suggest some statistically derived heuristics to select the most appropriate and parsimonious financial decision-making candidate model.

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