A Novel Deep Reinforcement Learning Strategy for Portfolio Management

Yixin Qin, Fengchen Gu, Jionglong Su

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Portfolio management is an investment strategy to redistribute some given fund into different assets, which aims to maximize the return and minimize the risk over the given period. There has been much research on the application of machine learning techniques to portfolio management. In this paper, we propose a novel investment strategy that uses a framework based on the Depthwise convolution, Squeeze and Excitation module, Residual Block, and Gate Recurrent Unit, called DSRG Network. To test the performance of our strategy, we use eight common trading strategies to obtain the corresponding accumulative portfolio values and Sharpe ratios. In addition, the Sharpe ratio is used to measure the adjusted rate of return for each strategy. Results show that our proposed strategy outperforms at least 40% better than other strategies used in otherwise comparison experiments. It still maintained a rate of return of about 70% in an experiment where all other comparison strategies showed losses.

Original languageEnglish
Title of host publication2022 7th International Conference on Big Data Analytics, ICBDA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages366-372
Number of pages7
ISBN (Electronic)9781665479387
DOIs
Publication statusPublished - 2022
Event7th International Conference on Big Data Analytics, ICBDA 2022 - Guangzhou, China
Duration: 4 Mar 20226 Mar 2022

Publication series

Name2022 7th International Conference on Big Data Analytics, ICBDA 2022

Conference

Conference7th International Conference on Big Data Analytics, ICBDA 2022
Country/TerritoryChina
CityGuangzhou
Period4/03/226/03/22

Keywords

  • depthwise convolution
  • portfolio management
  • reinforcement learning
  • residual block
  • the squeeze and excitation module

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