OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Ensemble Investment Strategies Based on Reinforcement Learning
Fangyi Li, Zhixing Wang, Peng Zhou
Scientific Programming (2022) Vol. 2022, pp. 1-9
Open Access | Times Cited: 6

Showing 6 citing articles:

A multi-agent reinforcement learning framework for optimizing financial trading strategies based on TimesNet
Yuling Huang, Chujin Zhou, Kai Cui, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121502-121502
Closed Access | Times Cited: 21

A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading
Yuling Huang, Xiaoxiao Wan, Lin Zhang, et al.
Expert Systems with Applications (2023) Vol. 240, pp. 122581-122581
Closed Access | Times Cited: 18

Improving algorithmic trading consistency via human alignment and imitation learning
Yuling Huang, Chujin Zhou, Kai Cui, et al.
Expert Systems with Applications (2024) Vol. 253, pp. 124350-124350
Closed Access | Times Cited: 2

A new hybrid method of recurrent reinforcement learning and BiLSTM for algorithmic trading
Yuling Huang, Yunlin Song
Journal of Intelligent & Fuzzy Systems (2023) Vol. 45, Iss. 2, pp. 1939-1951
Closed Access | Times Cited: 4

A novel trading system for the stock market using Deep Q-Network action and instance selection
Myeongseok Park, Jaeyun Kim, David Enke
Expert Systems with Applications (2024) Vol. 257, pp. 125043-125043
Closed Access | Times Cited: 1

Comprehensive Study on Reinforcement Learning and Deep Reinforcement Learning Schemes
Muhammad Azhar, Mansoor Ahmed Khuhro, Muhammad Waqas, et al.
Sir Syed University Research Journal of Engineering & Technology (2024) Vol. 14, Iss. 2, pp. 1-6
Closed Access

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