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Cited by 89Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesFinancial Analysis and Corporate Governance

A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networks

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Zou, Jie, Lou, Jiashu, Wang, Baohua, Liu, Sixue·2024·Expert Systems with Applications
DOI 10.1016/j.eswa.2023.122801
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Cited by

Sorted by citations

Authors
Zou, J., Lou, J., Wang, B., & Liu, S.
Affiliations
Shenzhen University (CN)
Corresponding author
Wang, Baohua
ORCID
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2024-05
Volume
242
Pages
122801
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2023.122801
OpenAlex
W4389219081
Cited by
89
References
10
Citations by year
2026: 27 · 2025: 43 · 2024: 18 · 2023: 1
FWCI
14.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Expert Systems with Applications (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
Keywords
automated stock trading, deep reinforcement learning, proximal policy optimization, daily stock data, time series feature extraction, stock market indices, trading strategy comparison, cumulative returns

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