Wealth management services have become a priority for most financial services organizations firms. As investors are pressing wealth managers to justify their value proposition, turbulence in financial markets reinforced the need to improve the advisory offering with more customized and sophisticated services. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. However, widespread recommendation approaches, such as content-based (CB) and collaborative filtering (CF), can hardly be put into practice in this domain. In fact, in this domain each user is typically modeled through his risk profile and other simple features, while each financial product is described through a rating provided by credit rating agencies, an average yield and the category it belongs to. In this scenario a pure CB strategy is likely to fail since content information is too poor and not meaningful to feed a CB recommendation algorithm. Furthermore, the over-‐specialization problem, typical of CB recommenders, may collide with the fact that turbulence and fluctuations in financial markets suggest to change and diversify the investments over time. Similarly, CF algorithms can hardly be adopted since they may lead to the well-‐known problem of flocking: given that user-‐based CF provides recommendations by assuming that a user is interested in the asset classes other people similar to her already invested in, this could move many similar users to invest in the same asset classes at the same time, making the recommendation algorithm victim of potential trader attacks1. These dynamics suggest to focus on different recommendation paradigms. Given that financial advisors have to analyze and sift through several investment portfolios2 before providing the user with a solution able to meet his investment goals, the insight behind our recommendation framework is to exploit case-‐based reasoning (CBR) to tailor investment proposals on the ground of a case base of previously proposed investments. Our recommendation process is based on the typical CBR workflow and is structured in three different steps: 1) Retrieve and Reuse: retrieval of similar portfolios is performed by representing each user through a feature vector (as feature risk profile, inferred through the standard MiFiD questionnaire3, investment goals, temporal goals, financial experience, and financial situation were chosen. Each feature is represented on a five-‐point ordinal scale, from very low to very high). Next, cosine similarity is adopted to retrieve the most similar users (along with the portfolios they agreed) from the case base. 2) Revise: candidate solutions retrieved by the first step are typically too many to be consulted by a human advisor. Thus, the Revise step further filters this set to obtain the final solutions. To revise the candidate solutions four techniques were compared: a basic (temporal) ranking, a Greedy diversification which implements a Greedy algorithm to select the solutions with the best compromise between quality and diversity and FCV, a novel scoring methodology which computes how close to the optimal one is the distribution of the asset classes in the portfolio. 3) Review and Retain: in the Review step human advisor and client can further discuss and modify the portfolio, before generating the final solution for the user. If the yield obtained by the newly recommended portfolio is acceptable, the solution is stored in the case base and can be used in the future as input to resolve similar cases. The performance of the framework has been evaluated in an experimental session against 1172 real users. Results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in many experimental settings. Specifically, experiments showed that FCV Ranking significantly outperforms human recommendations (from 0.18 to almost 0.30 of average monthly yield). The experimental results were further confirmed by an ex-‐post evaluation performed on real financial data from January to Aprile 2014. In this setting, our FCV strategy outperforms the recommendations provided by human advisors as well as those based on classical collaborative recommendation algorithm. This confirmed the effectiveness of the approach and paved the way for future research in the area.
Personalized Wealth Management through Case-Based Recommender Systems
SEMERARO, Giovanni;MUSTO, CATALDO
2014-01-01
Abstract
Wealth management services have become a priority for most financial services organizations firms. As investors are pressing wealth managers to justify their value proposition, turbulence in financial markets reinforced the need to improve the advisory offering with more customized and sophisticated services. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. However, widespread recommendation approaches, such as content-based (CB) and collaborative filtering (CF), can hardly be put into practice in this domain. In fact, in this domain each user is typically modeled through his risk profile and other simple features, while each financial product is described through a rating provided by credit rating agencies, an average yield and the category it belongs to. In this scenario a pure CB strategy is likely to fail since content information is too poor and not meaningful to feed a CB recommendation algorithm. Furthermore, the over-‐specialization problem, typical of CB recommenders, may collide with the fact that turbulence and fluctuations in financial markets suggest to change and diversify the investments over time. Similarly, CF algorithms can hardly be adopted since they may lead to the well-‐known problem of flocking: given that user-‐based CF provides recommendations by assuming that a user is interested in the asset classes other people similar to her already invested in, this could move many similar users to invest in the same asset classes at the same time, making the recommendation algorithm victim of potential trader attacks1. These dynamics suggest to focus on different recommendation paradigms. Given that financial advisors have to analyze and sift through several investment portfolios2 before providing the user with a solution able to meet his investment goals, the insight behind our recommendation framework is to exploit case-‐based reasoning (CBR) to tailor investment proposals on the ground of a case base of previously proposed investments. Our recommendation process is based on the typical CBR workflow and is structured in three different steps: 1) Retrieve and Reuse: retrieval of similar portfolios is performed by representing each user through a feature vector (as feature risk profile, inferred through the standard MiFiD questionnaire3, investment goals, temporal goals, financial experience, and financial situation were chosen. Each feature is represented on a five-‐point ordinal scale, from very low to very high). Next, cosine similarity is adopted to retrieve the most similar users (along with the portfolios they agreed) from the case base. 2) Revise: candidate solutions retrieved by the first step are typically too many to be consulted by a human advisor. Thus, the Revise step further filters this set to obtain the final solutions. To revise the candidate solutions four techniques were compared: a basic (temporal) ranking, a Greedy diversification which implements a Greedy algorithm to select the solutions with the best compromise between quality and diversity and FCV, a novel scoring methodology which computes how close to the optimal one is the distribution of the asset classes in the portfolio. 3) Review and Retain: in the Review step human advisor and client can further discuss and modify the portfolio, before generating the final solution for the user. If the yield obtained by the newly recommended portfolio is acceptable, the solution is stored in the case base and can be used in the future as input to resolve similar cases. The performance of the framework has been evaluated in an experimental session against 1172 real users. Results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in many experimental settings. Specifically, experiments showed that FCV Ranking significantly outperforms human recommendations (from 0.18 to almost 0.30 of average monthly yield). The experimental results were further confirmed by an ex-‐post evaluation performed on real financial data from January to Aprile 2014. In this setting, our FCV strategy outperforms the recommendations provided by human advisors as well as those based on classical collaborative recommendation algorithm. This confirmed the effectiveness of the approach and paved the way for future research in the area.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
