Holder Recommendations using Graph Representation Learning & Link Prediction

TL;DR

This paper introduces a graph-based machine learning framework for recommending financial fund holders by analyzing transactional data and investor behavior, outperforming traditional content-based methods.

Contribution

The paper presents a novel graph representation learning approach using GraphSage for holder recommendation in financial investments, incorporating behavioral data.

Findings

Outperforms baseline content-based filtering by 42%, 22%, and 14% in hit rate.

Achieves 18%, 19%, and 18% improvement on unseen holders.

Demonstrates effectiveness of graph ML in capturing investor profiles.

Abstract

Lead recommendations for financial products such as funds or ETF is potentially challenging in investment space due to changing market scenarios, and difficulty in capturing financial holder's mindset and their philosophy. Current methods surface leads based on certain product categorization and attributes like returns, fees, category etc. to suggest similar product to investors which may not capture the holder's investment behavior holistically. Other reported works does subjective analysis of institutional holder's ideology. This paper proposes a comprehensive data driven framework for developing a lead recommendations system in holder's space for financial products like funds by using transactional history, asset flows and product specific attributes. The system assumes holder's interest implicitly by considering all investment transactions made and collects possible meta information to detect holder's investment profile/persona like investment anticipation and investment behavior. This paper focusses on holder recommendation component of framework which employs a bi-partite graph representation of financial holders and funds using variety of attributes and further employs GraphSage model for learning representations followed by link prediction model for ranking recommendation for future period. The performance of the proposed approach is compared with baseline model i.e., content-based filtering approach on metric hits at Top-k (50, 100, 200) recommendations. We found that the proposed graph ML solution outperform baseline by absolute 42%, 22% and 14% with a look ahead bias and by absolute 18%, 19% and 18% on completely unseen holders in terms of hit rate for top-k recommendations: 50, 100 and 200 respectively.