Risk-Aware Multi-Armed Bandit Problem with Application to Portfolio Selection

TL;DR

This paper introduces a risk-aware multi-armed bandit algorithm for portfolio selection, balancing risk and return by integrating market structure filtering and coherent risk measures within a reinforcement learning framework.

Contribution

It extends the classic multi-armed bandit model by incorporating risk-awareness and market topology, providing a novel approach to sequential portfolio optimization.

Findings

Effective risk-return balance achieved

Incorporates market topology into portfolio selection

Demonstrates improved decision-making under uncertainty

Abstract

Sequential portfolio selection has attracted increasing interests in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision making under uncertainty, namely the exploration versus exploitation dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk-awareness into the classic multi-armed bandit setting and introduce an algorithm to construct portfolio. Through filtering assets based on the topological structure of financial market and combining the optimal multi-armed bandit policy with the minimization of a coherent risk measure, we achieve a balance between risk and return.