A Robo-Advisor System: expected utility modeling via pairwise comparisons

TL;DR

This paper presents a robo-advisor system that uses pairwise comparison questionnaires to model user preferences via expected utility, enabling personalized investment portfolio recommendations through optimization.

Contribution

It introduces a novel preference elicitation method using pairwise comparisons and three optimization strategies to estimate utility functions for personalized portfolio recommendations.

Findings

Effective preference elicitation from pairwise comparisons

Successful portfolio recommendations based on estimated utilities

Validated approach with simulated and human user tests

Abstract

We introduce a robo-advisor system that recommends customized investment portfolios to users using an expected utility model elicited from pairwise comparison questionnaires. The robo-advisor system comprises three fundamental components. First, we employ a static preference questionnaire approach to generate questionnaires consisting of pairwise item comparisons. Next, we design three optimization-based preference elicitation approaches to estimate the nominal utility function pessimistically, optimistically, and neutrally. Finally, we compute portfolios based on the nominal utility using an expected utility maximization optimization model. We conduct a series of numerical tests on a simulated user and a number of human users to evaluate the efficiency of the proposed model.