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Fund2Vec: Mutual Funds Similarity using Graph Learning

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  • Vipul Satone
  • Dhruv Desai
  • Dhagash Mehta

Abstract

Identifying similar mutual funds with respect to the underlying portfolios has found many applications in financial services ranging from fund recommender systems, competitors analysis, portfolio analytics, marketing and sales, etc. The traditional methods are either qualitative, and hence prone to biases and often not reproducible, or, are known not to capture all the nuances (non-linearities) among the portfolios from the raw data. We propose a radically new approach to identify similar funds based on the weighted bipartite network representation of funds and their underlying assets data using a sophisticated machine learning method called Node2Vec which learns an embedded low-dimensional representation of the network. We call the embedding \emph{Fund2Vec}. Ours is the first ever study of the weighted bipartite network representation of the funds-assets network in its original form that identifies structural similarity among portfolios as opposed to merely portfolio overlaps.

Suggested Citation

  • Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021. "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers 2106.12987, arXiv.org.
    • Handle: RePEc:arx:papers:2106.12987
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    References listed on IDEAS

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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.
    2. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets," Papers 2211.06378, arXiv.org.
    3. Bhaskarjit Sarmah & Nayana Nair & Dhagash Mehta & Stefano Pasquali, 2022. "Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning," Papers 2207.07183, arXiv.org.
    4. Alejandro Rodriguez Dominguez, 2022. "Portfolio Optimization based on Neural Networks Sensitivities from Assets Dynamics respect Common Drivers," Papers 2202.08921, arXiv.org, revised Dec 2022.
    5. Vadim Zlotnikov & Jiayu Liu & Igor Halperin & Fei He & Lisa Huang, 2023. "Model-Free Market Risk Hedging Using Crowding Networks," Papers 2306.08105, 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.

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