Large-scale Recommendation for Portfolio Optimization

TL;DR

This paper presents a hybrid recommendation system combining Modern Portfolio Theory and Collaborative Filtering to automate personalized portfolio optimization for online investors, aiming to replicate traditional advisory services efficiently.

Contribution

It introduces a novel hybrid approach for large-scale, personalized portfolio recommendations that integrates risk management with collaborative filtering techniques.

Findings

Effective in recommending financial assets across various domains

Outperforms baseline methods in domain expert evaluations

Applicable to stocks and other financial assets

Abstract

Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by full-service brokers. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner for a very large number of users. Because of the care required in recommending financial products, we focus on a risk-management approach tailored to each user's portfolio and risk profile. We show that our hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering, provides a sound and effective solution. The method is applicable to stocks as well as other financial assets, and can be easily combined with various financial forecasting models. We validate our proposal by comparing it with several baselines in a domain expert-based study.