Fund2Vec: Mutual Funds Similarity using Graph Learning

TL;DR

Fund2Vec introduces a novel graph learning approach using Node2Vec to identify structural similarities among mutual funds based on their underlying assets, surpassing traditional qualitative and linear methods.

Contribution

This paper presents the first application of weighted bipartite network embedding with Node2Vec for mutual fund similarity, capturing complex portfolio relationships.

Findings

Effective identification of structurally similar funds

Outperforms traditional overlap-based methods

First study applying network embedding to fund-asset data

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.