Entropic approximate learning for financial decision-making in the small data regime
Informazioni aggiuntive
Autori
Vecchi E.,
Berra G.,
Albrecht S.,
Gagliardini P.,
Horenko I.
Tipo
Articolo pubblicato in rivista scientifica
Anno
2023
Lingua
Inglese
Sommario
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.
Parole chiave
Approximate learning, Entropic classification, Decision-making
Periodico
Research in international business and finance
Volume
65
Pagine (o numero dell’articolo)
101958
Diffusione
Licenza
CC BY-NC-ND
Visibilità
Pubblico
Status open access
Hybrid
