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Fund performance evaluation with explainable artificial intelligence
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Xiuyi Fan ,
Monika Seisenberger
Finance Research Letters, Volume: 58, Start page: 104419
Swansea University Authors:
Raghav Kovvuri, Xiuyi Fan , Monika Seisenberger
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DOI (Published version): 10.1016/j.frl.2023.104419
Abstract
We apply explainable artificial intelligence (xAI) to a large dataset of global equity funds. Our approach combines the XGBoost model with Shapley values; the former is a machine learning framework that enhances model fitness while the latter is an xAI method that provides informed explanations rega...
| Published in: | Finance Research Letters |
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| ISSN: | 1544-6123 |
| Published: |
Elsevier BV
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64512 |
| Abstract: |
We apply explainable artificial intelligence (xAI) to a large dataset of global equity funds. Our approach combines the XGBoost model with Shapley values; the former is a machine learning framework that enhances model fitness while the latter is an xAI method that provides informed explanations regarding the direction and significance of predictors. Based on macro-finance and fund-level factors, our fund performance evaluation of G10 countries uncovers novel insights into the diversification of country portfolios: both over- and under-diversification are associated with poor performance. Our analysis establishes consistency through a benchmark linear regression model and robustness at country level. |
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| Keywords: |
Global Open-Ended Funds; Country Portfolios; Herfindahl-Hirschman Index; SHapley Additive exPlanations; Machine Learning; eXtreme Gradient Boosting |
| College: |
Faculty of Science and Engineering |
| Funders: |
H. Fu gratefully acknowledges financial support from the Social Sciences and Humanities Research Council of the Government of Canada and the AMF–GIRIF Fund at Laval University. H. Fu also thanks the grant from the Program of financial support to faculties for knowledge mobilization activities and the dissemination and promotion of research results, from Presses de l’Université Laval Development Fund. We all gratefully acknowledge financial support from the cooperation programme between the governments of Quebec and Wales, and honor the financial support we received for knowledge exchange in the Horizon 2020 project CID. |
| Start Page: |
104419 |