Personalized Robo-Advising: Enhancing Investment through Client Interaction

TL;DR

This paper presents a personalized robo-advising framework that dynamically adapts investment strategies based on client interactions, market conditions, and behavioral biases to optimize portfolio performance.

Contribution

It introduces a novel adaptive mean-variance optimization model that personalizes investment advice through client interaction and accounts for behavioral biases.

Findings

Optimal strategies include myopic and hedging components.

Personalization improves Sharpe ratio and return distribution.

Counteracting behavioral biases enhances portfolio performance.

Abstract

Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework, in which a robo-advisor interacts with a client to solve an adaptive mean-variance portfolio optimization problem. The risk-return tradeoff adapts to the client's risk profile, which depends on idiosyncratic characteristics, market returns, and economic conditions. We show that the optimal investment strategy includes both myopic and intertemporal hedging terms which are impacted by the dynamics of the client's risk profile. We characterize the optimal portfolio personalization via a tradeoff faced by the robo-advisor between receiving client information in a timely manner and mitigating behavioral biases in the risk profile communicated by the client. We argue that the optimal portfolio's Sharpe ratio and return distribution improve if the robo-advisor counters the client's tendency to reduce market exposure during economic contractions when the market risk-return tradeoff is more favorable.