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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:

Quantitative Trading on Stock Market Based on Deep Reinforcement Learning
Jia Wu, Chen Wang, Lidong XIONG, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2019), pp. 1-8
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges
Santosh Sahu, Anil Mokhade, Neeraj Dhanraj Bokde
Applied Sciences (2023) Vol. 13, Iss. 3, pp. 1956-1956
Open Access | Times Cited: 92
Artificial intelligence techniques in financial trading: A systematic literature review
Fatima Dakalbab, Manar Abu Talib, Qassim Nasir, et al.
Journal of King Saud University - Computer and Information Sciences (2024) Vol. 36, Iss. 3, pp. 102015-102015
Open Access | Times Cited: 18
A Survey of Forex and Stock Price Prediction Using Deep Learning
Zexin Hu, Yiqi Zhao, Matloob Khushi
Applied System Innovation (2021) Vol. 4, Iss. 1, pp. 9-9
Open Access | Times Cited: 65
Deep learning in the stock market—a systematic survey of practice, backtesting, and applications
Kenniy Olorunnimbe, Herna L. Viktor
Artificial Intelligence Review (2022) Vol. 56, Iss. 3, pp. 2057-2109
Open Access | Times Cited: 65
Multi-scale local cues and hierarchical attention-based LSTM for stock price trend prediction
Xiao Teng, Xiang Zhang, Zhigang Luo
Neurocomputing (2022) Vol. 505, pp. 92-100
Closed Access | Times Cited: 45
Stock Trading Strategies Based on Deep Reinforcement Learning
Yawei Li, Peipei Liu, Ze Wang
Scientific Programming (2022) Vol. 2022, pp. 1-15
Open Access | Times Cited: 23
A Deep Reinforcement Learning-Based Decision Support System for Automated Stock Market Trading
Yasmeen Ansari, Sadaf Yasmin, Sheneela Naz, et al.
IEEE Access (2022) Vol. 10, pp. 127469-127501
Open Access | Times Cited: 19
ML computational inference techniques and indicator metrics for analyzing uncertainties in stock market data
Santosh Kumar Henge, Sanjeev Kumar Mandal, Amit Sharma, et al.
Multimedia Tools and Applications (2025)
Closed Access
Event-Driven LSTM For Forex Price Prediction
Ling Qi, Matloob Khushi, Josiah Poon
2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (2020) Vol. 14, pp. 1-6
Open Access | Times Cited: 32
Deep Reinforcement Learning with the Random Neural Network
Will Serrano
Engineering Applications of Artificial Intelligence (2022) Vol. 110, pp. 104751-104751
Open Access | Times Cited: 18
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Chuheng Zhang, Yuanqi Li, Xi Chen, et al.
2021 IEEE International Conference on Data Mining (ICDM) (2020), pp. 781-790
Open Access | Times Cited: 19
FinRL-podracer
Zechu Li, Xiao-Yang Liu, Jiahao Zheng, et al.
(2021), pp. 1-9
Open Access | Times Cited: 11
Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks
David Alaminos, M. Belén Salas, Ángela Callejón Gil
Quantitative Finance and Economics (2024) Vol. 8, Iss. 1, pp. 153-209
Open Access | Times Cited: 1
Automated Trading System for Stock Index Using LSTM Neural Networks and Risk Management
Thalita R. Silva, Audeliano W. Li, Edson de Oliveira Pamplona
2022 International Joint Conference on Neural Networks (IJCNN) (2020), pp. 1-8
Closed Access | Times Cited: 9
Deep Reinforcement Learning For Trading - A Critical Survey
Adrian Millea
(2021)
Open Access | Times Cited: 9
Market Making Strategy Optimization via Deep Reinforcement Learning
Tianyuan Sun, Dechun Huang, Jie Yu
IEEE Access (2022) Vol. 10, pp. 9085-9093
Open Access | Times Cited: 6
FinBrain 2.0: when finance meets trustworthy AI
Jun Zhou, Chaochao Chen, Longfei Li, et al.
Frontiers of Information Technology & Electronic Engineering (2022) Vol. 23, Iss. 12, pp. 1747-1764
Closed Access | Times Cited: 6
A novel crude oil futures trading strategy based on volume-price time-frequency decomposition with ensemble deep reinforcement learning
Xiao-xu Du, Zhenpeng Tang, Kaijie Chen
Energy (2023) Vol. 285, pp. 129394-129394
Closed Access | Times Cited: 3
A tabular sarsa-based stock market agent
Renato Arantes de Oliveira, Heitor S. Ramos, Daniel H. Dalip, et al.
(2020), pp. 1-8
Open Access | Times Cited: 8
Improving quantitative stock trading prediction based on MAD using Q-learning technology
Zhao Pengcheng, Siyuan Yin, Burra Venkata Durga Kumar, et al.
(2023), pp. 57-60
Closed Access | Times Cited: 2
Forecasting Next-Time-Step Forex Market Stock Prices Using Neural Networks
Mostafa Pahlevanzadeh, Mahdi Navaei
Advances in Machine Learning & Artificial Intelligence (2024) Vol. 5, Iss. 2, pp. 01-10
Open Access
Deep reinforcement learning for stock prediction
Mingkai Wang
Applied and Computational Engineering (2024) Vol. 69, Iss. 1, pp. 85-90
Open Access
Research on Deep Reinforcement Learning Futures Investment Model: Based on Long Short Term Memory Networks
Rongyun Zhong
(2024), pp. 372-376
Closed Access
Explorando os limites da aprendizagem por reforço profundo em ambientes simulados: um estudo sobre negociação de ativos financeiros e dimensionamento de lotes.
Leonardo Kanashiro Felizardo
(2024)
Open Access
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Chuheng Zhang, Yuanqi Li, Xi Chen, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 1
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