Coalition Feature Interpretation and Attribution in Algorithmic Trading Models

James V. Hansen ()
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James V. Hansen: Brigham Young University

Computational Economics, 2021, vol. 58, issue 3, No 12, 849-866

Abstract: Abstract The ability to correctly interpret a prediction model’s output is critically important in many problem spheres. Accurate interpretation generates user trust in the model, provides insight into how a model may be improved, and supports understanding of the process being modeled. Absence of this capability has constrained algorithmic trading from making use of more powerful predictive models, such as XGBoost and Random Forests. Recently, the adaptation of coalitional game theory has led to the development of consistent methods of determining feature importance for these models (SHAP).This study designs and tests a novel method of integrating the capabilities of SHAP into predictive models for algorithmic trading.

Keywords: SHAP; Feature importance; Algorithmic trading; Back-testing; Portfolio optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10614-020-10053-x

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