Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323264 
Authors: 
Year of Publication: 
2024
Citation: 
[Journal:] OR Spectrum [ISSN:] 1436-6304 [Volume:] 47 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2024 [Pages:] 177-203
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
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 non-linear impact factors. Therefore it describes a promising alternative to the econometric benchmark approach.
Subjects: 
Finance
Machine learning
Tree ensembles
Interpretable machine learning
Equity premium
JEL: 
C33
C58
G17
G23
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.