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Machine Learning Fund Categorizations

Author

Listed:
  • Dhagash Mehta
  • Dhruv Desai
  • Jithin Pradeep

Abstract

Given the surge in popularity of mutual funds (including exchange-traded funds (ETFs)) as a diversified financial investment, a vast variety of mutual funds from various investment management firms and diversification strategies have become available in the market. Identifying similar mutual funds among such a wide landscape of mutual funds has become more important than ever because of many applications ranging from sales and marketing to portfolio replication, portfolio diversification and tax loss harvesting. The current best method is data-vendor provided categorization which usually relies on curation by human experts with the help of available data. In this work, we establish that an industry wide well-regarded categorization system is learnable using machine learning and largely reproducible, and in turn constructing a truly data-driven categorization. We discuss the intellectual challenges in learning this man-made system, our results and their implications.

Suggested Citation

  • Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.
    • Handle: RePEc:arx:papers:2006.00123
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    References listed on IDEAS

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    1. Luis Ferruz Agudo & Cristina Ortiz Lázaro, 2005. "Does Mutual Fund Management in India Correspond to its Investment Objective Classification?," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 659-685.
    2. McDonald, John G., 1974. "Objectives and Performance of Mutual Funds, 1960–1969," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(3), pages 311-333, June.
    3. Miceli, M.A. & Susinno, G., 2004. "Ultrametricity in fund of funds diversification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 95-99.
    4. Athanasios Orphanides, "undated". "Compensation Incentives and Risk Taking Behavior: Evidence from Mutual Funds," Finance and Economics Discussion Series 1996-21, Board of Governors of the Federal Reserve System (U.S.), revised 10 Dec 2019.
    5. Fan Cai & Nhien-An Le-Khac & Tahar Kechadi, 2016. "Clustering Approaches for Financial Data Analysis: a Survey," Papers 1609.08520, arXiv.org.
    6. Pattarin, Francesco & Paterlini, Sandra & Minerva, Tommaso, 2004. "Clustering financial time series: an application to mutual funds style analysis," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 353-372, September.
    7. Rajna Gibson & Sébastien Gyger, 2007. "The Style Consistency of Hedge Funds," European Financial Management, European Financial Management Association, vol. 13(2), pages 287-308, March.
    8. Moreno, David & Marco, Paulina & Olmeda, Ignacio, 2006. "Self-organizing maps could improve the classification of Spanish mutual funds," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1039-1054, October.
    9. Edwin J. Elton & Martin J. Gruber & Christopher R. Blake, 2003. "Incentive Fees and Mutual Funds," Journal of Finance, American Finance Association, vol. 58(2), pages 779-804, April.
    10. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    11. Nandita Das, 2003. "Hedge Fund Classification using K-means Clustering Method," Computing in Economics and Finance 2003 284, Society for Computational Economics.
    12. repec:onb:oenbwp:y:2005:i:9:b:1 is not listed on IDEAS
    13. Brown, Stephen J. & Goetzmann, William N., 1997. "Mutual fund styles," Journal of Financial Economics, Elsevier, vol. 43(3), pages 373-399, March.
    14. Kim, Moon & Shukla, Ravi & Tomas, Michael, 2000. "Mutual fund objective misclassification," Journal of Economics and Business, Elsevier, vol. 52(4), pages 309-323.
    15. Ramin Baghai-Wadj & Rami El-Berry & Stefan Klocker & Markus Schwaiger, 2005. "The Consistency of Self-Declared Hedge Fund Styles — A Return-Based Analysis with Self-Organizing Maps," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 9, pages 64-76.
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    Cited by:

    1. Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021. "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers 2106.12987, arXiv.org.
    2. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.

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