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Quantifying Outlierness of Funds from their Categories using Supervised Similarity

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  • Dhruv Desai
  • Ashmita Dhiman
  • Tushar Sharma
  • Deepika Sharma
  • Dhagash Mehta
  • Stefano Pasquali

Abstract

Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection, as well as by fund managers for peer analysis and competitive positioning. As a result, a (unintended) miscategorization or lack of precision can significantly impact allocation decisions and investment fund managers. Here, we aim to quantify the effect of miscategorization of funds utilizing a machine learning based approach. We formulate the problem of miscategorization of funds as a distance-based outlier detection problem, where the outliers are the data-points that are far from the rest of the data-points in the given feature space. We implement and employ a Random Forest (RF) based method of distance metric learning, and compute the so-called class-wise outlier measures for each data-point to identify outliers in the data. We test our implementation on various publicly available data sets, and then apply it to mutual fund data. We show that there is a strong relationship between the outlier measures of the funds and their future returns and discuss the implications of our findings.

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  • Dhruv Desai & Ashmita Dhiman & Tushar Sharma & Deepika Sharma & Dhagash Mehta & Stefano Pasquali, 2023. "Quantifying Outlierness of Funds from their Categories using Supervised Similarity," Papers 2308.06882, arXiv.org.
    • Handle: RePEc:arx:papers:2308.06882
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    References listed on IDEAS

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    1. 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.
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    3. 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.
    4. Kim, Moon & Shukla, Ravi & Tomas, Michael, 2000. "Mutual fund objective misclassification," Journal of Economics and Business, Elsevier, vol. 52(4), pages 309-323.
    5. Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021. "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers 2106.12987, arXiv.org.
    6. Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022. "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers 2207.04368, arXiv.org, revised Oct 2022.
    7. Dimitrios Vamvourellis & Mate Attila Toth & Dhruv Desai & Dhagash Mehta & Stefano Pasquali, 2022. "Learning Mutual Fund Categorization using Natural Language Processing," Papers 2207.04959, arXiv.org.
    8. Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
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    Cited by:

    1. Gregory Yampolsky & Dhruv Desai & Mingshu Li & Stefano Pasquali & Dhagash Mehta, 2024. "Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals," Papers 2408.06679, arXiv.org.
    2. Nathalia Castellanos & Dhruv Desai & Sebastian Frank & Stefano Pasquali & Dhagash Mehta, 2024. "Can an unsupervised clustering algorithm reproduce a categorization system?," Papers 2408.10340, arXiv.org.

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