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Fund performance evaluation with explainable artificial intelligence

Raghav Kovvuri, Hsuan Fu Orcid Logo, Xiuyi Fan Orcid Logo, Monika Seisenberger Orcid Logo

Finance Research Letters, Volume: 58, Start page: 104419

Swansea University Authors: Raghav Kovvuri, Xiuyi Fan Orcid Logo, Monika Seisenberger Orcid Logo

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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...

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Published in: Finance Research Letters
ISSN: 1544-6123
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64512
first_indexed 2023-09-12T14:33:26Z
last_indexed 2026-05-01T03:57:49Z
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spelling 2026-04-29T16:11:18.0486589 v2 64512 2023-09-12 Fund performance evaluation with explainable artificial intelligence 80d30602cc477edfcfc6b26e24576f6c Raghav Kovvuri Raghav Kovvuri true false a88a07c43b3e80f27cb96897d1bc2534 0000-0003-1223-9986 Xiuyi Fan Xiuyi Fan true false d035399b2b324a63fe472ce0344653e0 0000-0002-2226-386X Monika Seisenberger Monika Seisenberger true false 2023-09-12 MACS 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. Journal Article Finance Research Letters 58 104419 Elsevier BV 1544-6123 Global Open-Ended Funds; Country Portfolios; Herfindahl-Hirschman Index; SHapley Additive exPlanations; Machine Learning; eXtreme Gradient Boosting 1 12 2023 2023-12-01 10.1016/j.frl.2023.104419 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Other 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. 2026-04-29T16:11:18.0486589 2023-09-12T15:20:51.0557148 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Raghav Kovvuri 1 Hsuan Fu 0000-0002-4373-8203 2 Xiuyi Fan 0000-0003-1223-9986 3 Monika Seisenberger 0000-0002-2226-386X 4 64512__28995__99d37324683b401c8bd0401babbc1011.pdf 64512.pdf 2023-11-13T11:04:58.9093398 Output 1120330 application/pdf Version of Record true © 2023 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Fund performance evaluation with explainable artificial intelligence
spellingShingle Fund performance evaluation with explainable artificial intelligence
Raghav Kovvuri
Xiuyi Fan
Monika Seisenberger
title_short Fund performance evaluation with explainable artificial intelligence
title_full Fund performance evaluation with explainable artificial intelligence
title_fullStr Fund performance evaluation with explainable artificial intelligence
title_full_unstemmed Fund performance evaluation with explainable artificial intelligence
title_sort Fund performance evaluation with explainable artificial intelligence
author_id_str_mv 80d30602cc477edfcfc6b26e24576f6c
a88a07c43b3e80f27cb96897d1bc2534
d035399b2b324a63fe472ce0344653e0
author_id_fullname_str_mv 80d30602cc477edfcfc6b26e24576f6c_***_Raghav Kovvuri
a88a07c43b3e80f27cb96897d1bc2534_***_Xiuyi Fan
d035399b2b324a63fe472ce0344653e0_***_Monika Seisenberger
author Raghav Kovvuri
Xiuyi Fan
Monika Seisenberger
author2 Raghav Kovvuri
Hsuan Fu
Xiuyi Fan
Monika Seisenberger
format Journal article
container_title Finance Research Letters
container_volume 58
container_start_page 104419
publishDate 2023
institution Swansea University
issn 1544-6123
doi_str_mv 10.1016/j.frl.2023.104419
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description 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.
published_date 2023-12-01T05:55:56Z
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score 11.103791

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