Large-scale Recommendation for Portfolio Optimization
This addresses the need for low-cost, risk-managed portfolio optimization for individual investors using online brokers, though it appears incremental as it combines existing methods.
The paper tackled the problem of providing automated, personalized investment recommendations for large-scale online brokers by developing a hybrid approach based on Modern Portfolio Theory and Collaborative Filtering, which was validated as effective in a domain expert-based study.
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.