Fund performance evaluation with explainable artificial intelligence
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DOI: 10.1016/j.frl.2023.104419
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References listed on IDEAS
- S. P. Kothari & Jerold B. Warner, 2001. "Evaluating Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 56(5), pages 1985-2010, October.
- Javier Gil‐Bazo & Pablo Ruiz‐Verdú, 2009. "The Relation between Price and Performance in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 64(5), pages 2153-2183, October.
- Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
- Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Lin, Boqiang & Bai, Rui, 2022. "Machine learning approaches for explaining determinants of the debt financing in heavy-polluting enterprises," Finance Research Letters, Elsevier, vol. 44(C).
- Nelson Camanho & Harald Hau & Hélène Rey, 2022.
"Global Portfolio Rebalancing and Exchange Rates,"
The Review of Financial Studies, Society for Financial Studies, vol. 35(11), pages 5228-5274.
- Nelson Camanho & Harald Hau & Hélène Rey, 2018. "Global Portfolio Rebalancing and Exchange Rates," NBER Working Papers 24320, National Bureau of Economic Research, Inc.
- Rey, Hélène & Camanho, Nelson & Hau, Harald, 2020. "Global Portfolio Rebalancing and Exchange Rates," CEPR Discussion Papers 15617, C.E.P.R. Discussion Papers.
- Nelson Camanho & Harald Hau & Hélène Rey, 2018. "Global Portfolio Rebalancing and Exchange Rates," Swiss Finance Institute Research Paper Series 18-03, Swiss Finance Institute, revised Jun 2018.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Oh, Natalie Y. & Parwada, Jerry T., 2007. "Relations between mutual fund flows and stock market returns in Korea," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(2), pages 140-151, April.
- Berger, Theo, 2023. "Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains," Finance Research Letters, Elsevier, vol. 54(C).
- Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
- Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
- Li, Ang & Liu, Mark & Sheather, Simon, 2023. "Predicting stock splits using ensemble machine learning and SMOTE oversampling," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
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Cited by:
- Garitta, Claudio & Grassi, Laura, 2025. "Predicting break-even in FinTech startups as a signal for success," Finance Research Letters, Elsevier, vol. 74(C).
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More about this item
Keywords
Global Open-Ended Funds; Country portfolios; Herfindahl–Hirschman Index; SHapley Additive exPlanations; Machine learning; eXtreme Gradient Boosting;All these keywords.
JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
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