Abstract

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.

Autore Pugliese

• Musto Cataldo , Semeraro Giovanni , De Gemmis Marco , Lops Pasquale

Tutti gli autori

• MUSTO C.;SEMERARO G.;de GEMMIS M.;LOPS P.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2015

ISSN

0167-9236

ISBN

Non Disponibile

Numero di citazioni Wos

10

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

13

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile