Fund2Vec: Mutual Funds Similarity using Graph Learning
This addresses the need for reproducible and nuanced fund similarity analysis in financial services, offering a novel method for applications like recommender systems and portfolio analytics.
The paper tackled the problem of identifying similar mutual funds based on underlying portfolios, proposing Fund2Vec, a graph learning approach using Node2Vec on a weighted bipartite network, which captures structural similarities beyond mere overlaps.
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.