Active Preference Learning for Personalized Portfolio Construction

TL;DR

This paper introduces a method for learning individual preferences over portfolios to improve personalized portfolio construction, using active preference learning and Bayesian optimization to identify diverse, high-performing portfolios aligned with user beliefs.

Contribution

It presents a novel active preference learning mechanism that captures personal portfolio preferences and integrates them into Bayesian optimization for tailored asset management.

Findings

Personal beliefs significantly influence portfolio diversity and performance.

The proposed method effectively learns user preferences for portfolio selection.

Numerical experiments demonstrate improved portfolio personalization and performance.

Abstract

In financial asset management, choosing a portfolio requires balancing returns, risk, exposure, liquidity, volatility and other factors. These concerns are difficult to compare explicitly, with many asset managers using an intuitive or implicit sense of their interaction. We propose a mechanism for learning someone's sense of distinctness between portfolios with the goal of being able to identify portfolios which are predicted to perform well but are distinct from the perspective of the user. This identification occurs, e.g., in the context of Bayesian optimization of a backtested performance metric. Numerical experiments are presented which show the impact of personal beliefs in informing the development of a diverse and high-performing portfolio.