2019/11/04 by Agostino Capponi, Capponi, Agostino, S. Ólafsson +3 · 1 citation
Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Economic theories and models #FOS: Economics and business #FOS: Mathematics #Financial Markets and Investment Strategies #Optimization and Control (math.OC) #Portfolio Management (q-fin.PM)
paper · pdf · doi:10.48550/arxiv.1911.01391
openalex publication_date 2019/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automated investment managers, or robo-advisors, have emerged as an\nalternative to traditional financial advisors. The viability of robo-advisors\ncrucially depends on their ability to offer personalized financial advice. We\nintroduce a novel framework, in which a robo-advisor interacts with a client to\nsolve an adaptive mean-variance portfolio optimization problem. The risk-return\ntradeoff adapts to the client's risk profile, which depends on idiosyncratic\ncharacteristics, market returns, and economic conditions. We show that the\noptimal investment strategy includes both myopic and intertemporal hedging\nterms which are impacted by the dynamics of the client's risk profile. We\ncharacterize the optimal portfolio personalization via a tradeoff faced by the\nrobo-advisor between receiving client information in a timely manner and\nmitigating behavioral biases in the risk profile communicated by the client. We\nargue that the optimal portfolio's Sharpe ratio and return distribution improve\nif the robo-advisor counters the client's tendency to reduce market exposure\nduring economic contractions when the market risk-return tradeoff is more\nfavorable.\n