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Learning Mutual Fund Categorization using Natural Language Processing

Author

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  • Dimitrios Vamvourellis
  • Mate Attila Toth
  • Dhruv Desai
  • Dhagash Mehta
  • Stefano Pasquali

Abstract

Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long served the financial analysts to perform peer analysis for various purposes starting from competitor analysis, to quantifying portfolio diversification. The categorization methodology usually relies on fund composition data in the structured format extracted from the Form N-1A. Here, we initiate a study to learn the categorization system directly from the unstructured data as depicted in the forms using natural language processing (NLP). Positing as a multi-class classification problem with the input data being only the investment strategy description as reported in the form and the target variable being the Lipper Global categories, and using various NLP models, we show that the categorization system can indeed be learned with high accuracy. We discuss implications and applications of our findings as well as limitations of existing pre-trained architectures in applying them to learn fund categorization.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2207.04959
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    References listed on IDEAS

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    1. Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021. "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers 2106.12987, arXiv.org.
    2. 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.
    3. Kim, Moon & Shukla, Ravi & Tomas, Michael, 2000. "Mutual fund objective misclassification," Journal of Economics and Business, Elsevier, vol. 52(4), pages 309-323.
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    Cited by:

    1. 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.

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