Risk-averse Reinforcement Learning for Portfolio Optimization

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express Pub Date : 2024-08-01 DOI:10.1016/j.icte.2024.04.010

引用次数: 0

Abstract

This investigation explores Reinforcement Learning (RL) for dynamic portfolio optimization with risk assessment. The challenges include market complexity, uncertain reactions, and regulatory requirements for risk-averse decisions. Our solution leverages Bayesian Neural Network (BNN) to capture uncertainties. We successfully implemented a risk-averse Reinforcement Learning algorithm, achieving 18 percent lower risk. Reinforcement Learning with risk-aversion shows promise for optimizing portfolios for risk-averse investors.

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投资组合优化的风险规避强化学习

本研究探讨了强化学习（RL）对动态投资组合优化和风险评估的作用。面临的挑战包括市场复杂性、不确定反应以及规避风险决策的监管要求。我们的解决方案利用贝叶斯神经网络（BNN）来捕捉不确定性。我们成功实施了风险规避强化学习算法，将风险降低了 18%。具有风险规避功能的强化学习算法有望为风险规避型投资者优化投资组合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ICT Express Multiple-

CiteScore

10.20

自引率

1.90%

发文量

167

审稿时长

35 weeks

期刊介绍： The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.