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On the information content of explainable artificial intelligence for quantitative approaches in finance

  • We simulate economic data to apply state-of-the-art machine learning algorithms and analyze the economic precision of competing concepts for model agnostic explainable artificial intelligence (XAI) techniques. Also, we assess empirical data and provide a discussion of the competing approaches in comparison with econometric benchmarks, when the data-generating process is unknown. The simulation assessment provides evidence that the applied XAI techniques provide similar economic information on relevant determinants when the data generating process is linear. We find that the adequate choice of XAI technique is crucial when the data generating process is unknown. In comparison to econometric benchmark models, the application of boosted regression trees in combination with Shapley values combines both a superior fit to the data and innovative interpretable insights into nonlinear impact factors. Therefore it describes a promising alternative to the econometric benchmark approach.

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Metadaten
Author:Theo BergerORCiDGND
URN:urn:nbn:de:bsz:960-opus4-31876
DOI:https://doi.org/10.25968/opus-3187
DOI original:https://doi.org/10.1007/s00291-024-00769-9
ISSN:0171-6468
Parent Title (English):OR Spektrum
Document Type:Article
Language:English
Year of Completion:2024
Publishing Institution:Hochschule Hannover
Release Date:2024/06/17
Tag:Equity premium; Finance; Interpretable machine learning; Machine learning; Tree ensembles
GND Keyword:Maschinelles Lernen; Explainable Artificial Intelligence
Page Number:27
Institutes:Fakult├Ąt IV - Wirtschaft und Informatik
DDC classes:330 Wirtschaft
004 Informatik
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International