  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.
