SIG ODIS - Artificial Intelligence and Semantic Technologies for Intelligent Systems
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Paper Type
ERF
Paper Number
1594
Description
Recent empirical evidence indicates that bond excess returns can be predicted using machine learning models. While the predictive power of machine learning models is intriguing, they typically lack transparency. We introduce SHapley Additive exPlanations (SHAP), a state-of-the-art explainable artificial technique, to open the black box of these models. Our analysis identifies the key determinants that drive the predictions of bond excess returns in machine learning models and how these determinants are related to bond excess returns. Thereby, our approach facilitates an in-depth interpretation of the predictions of bond excess returns made by machine learning models.
Recommended Citation
Beckmann, Lars; Debener, Jörn; and Kriebel, Johannes, "Using Explainable AI to Understand Bond Excess Returns" (2022). AMCIS 2022 Proceedings. 5.
https://aisel.aisnet.org/amcis2022/sig_odis/sig_odis/5
Using Explainable AI to Understand Bond Excess Returns
Recent empirical evidence indicates that bond excess returns can be predicted using machine learning models. While the predictive power of machine learning models is intriguing, they typically lack transparency. We introduce SHapley Additive exPlanations (SHAP), a state-of-the-art explainable artificial technique, to open the black box of these models. Our analysis identifies the key determinants that drive the predictions of bond excess returns in machine learning models and how these determinants are related to bond excess returns. Thereby, our approach facilitates an in-depth interpretation of the predictions of bond excess returns made by machine learning models.
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SIG ODIS