ASSET ALLOCATION OPTIMIZATION IN ROBO-ADVISING:A PERSPECTIVE BASED ON REINFORCEMENT LEARNING

Abstract

The rapid expansion of robo-advising platforms has underscored the need for automated and intelligent asset allocation strategies that can adapt to dynamic financial markets. Traditional methods often rely on static models, which may fail to capture the complexities of market behavior and investor preferences. This study aims to address these limitations by proposing a reinforcement learning (RL) framework for optimizing asset allocation in robo-advising systems. The approach employs a deep Q-network (DQN) to model sequential decision-making, incorporating real-time market data and investor risk profiles to dynamically adjust portfolio weights. Experimental results, based on historical financial data, demonstrate that the RL-based strategy significantly outperforms traditional mean-variance optimization in terms of risk-adjusted returns and adaptability to market volatility. The findings highlight the potential of reinforcement learning to enhance the efficiency and personalization of robo-advising services, paving the way for more resilient and investor-centric financial solutions.