Personalized finance advisory through case-based recommender systems and diversification strategies

• We introduced a novel framework for recommendation of asset allocation strategies.
• We evaluated the effectiveness of CBRS recommendation strategies in a special and not yet evaluated domain.
• We proposed a greedy diversification algorithm able to diversify the investment strategies over time.
• We evaluated the effectiveness of the framework through an extensive ex-post evaluation.

Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield.