A Novel Deep Reinforcement Learning Strategy in Financial Portfolio Management

Bowen Cui, Ruoyu Sun, Jionglong Su

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

1 Citation (Scopus)

Abstract

Reinforcement learning algorithms are used in various fields widely, such as cryptocurrency market forecasting, image recognition, and natural language processing. In this research, we use the Reinforcement learning algorithm to solve the portfolio management problem. In the Reinforcement learning algorithm, we adopt the squeeze-and-excitation in the neural network neural network to realize the Ensemble of Identical Independent Evaluators proposed by Jiang et al. The Squeeze-and-Excitation block works by adaptively recalibrating channel-wise feature responses, which improves the ability of the network in extracting information from the financial environment. To further improve the performance of the network, we adopt the soft thresholding function as nonlinear transformation layers to effectively eliminate the noise-related features. The cryptocurrency market is used to test the efficacy of our strategy along with eight traditional portfolio management strategies as well as Jiang et al.'s strategies. In our experiments, we use the Accumulated Portfolio Value, Sharpe Ratio, and Maximum Downward to assess the efficacies of the strategies. In conclusion, our strategy outperforms Jiang et al.'s strategies and other traditional strategies. Although our strategy has a nearly 30% Maximum Downward as metrics in back-tests, Accumulated Portfolio Value can reach nearly 170% in two different two-month back-tests, which is about 160% greater than the traditional strategy and Jiang et al.'s strategy. Moreover, our result Sharp ratios are 393.88% and 96.18% higher than the traditional strategy and Jiang et al.'s strategy, respectively.

Original languageEnglish
Title of host publication2022 7th International Conference on Big Data Analytics, ICBDA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages341-348
Number of pages8
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

  • ResNeXT
  • algorithmic trading
  • cryptocurrency
  • deep reinforcement learning
  • ensemble identical independent evaluators
  • portfolio management
  • soft thresholding function
  • squeeze-and-excitation network

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