Portfolio dynamic trading strategies using deep reinforcement learning

doi:10.1007/s00500-023-08973-5

機構典藏 > College of Business and Management > Department of Management Sciences > Journal Article > Item 987654321/125463

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125463

Title:	Portfolio dynamic trading strategies using deep reinforcement learning
Authors:	Day, M. Y., Yang, C. Y., & Ni, Y.
Keywords:	Artificial intelligence;Deep reinforcement learning;Financial technology;Portfolio management;Trading strategy
Date:	2023-07-30
Issue Date:	2024-04-01 12:05:20 (UTC+8)
Publisher:	Springer
Abstract:	Using the constituent stocks of the iShares MSCI US ESG Select Index ETF, a matrix of technical indicators, returns, and covariance is incorporated to represent the inherent information characteristics of the stock market. In this study, based on the proposed Deep Reinforcement Learning for Portfolio Management on Environmental, Social, and Governance (DRLPMESG) architecture model, investors who use active portfolio management reap the greatest rewards, as the portfolio with 5 stocks performing the best, with an annualized return of 46.58%, a Sharpe ratio of 1.37, and a cumulative return of 115.18%, indicating that the results have the potential to win the market and generate excess profits. In contrast to the efficient market hypothesis, this new understanding of proven effectiveness in obtaining satisfactory rewards would help improve investment strategies for portfolio management. Furthermore, this study proposed that holding 5 stocks in a portfolio can lead to higher returns, laying the foundation for future research on the number of holdings. Moreover, when compared to previous static strategies, this model offering a dynamic strategy may generate a more stable return in the face of market fluctuations.
Relation:	Soft Computing
DOI:	10.1007/s00500-023-08973-5
Appears in Collections:	[Department of Management Sciences] Journal Article

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