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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:

Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning
Qili Wang, Wei Xu, Xinting Huang, et al.
Neurocomputing (2019) Vol. 347, pp. 46-58
Closed Access | Times Cited: 75

Showing 1-25 of 75 citing articles:

Deep Learning for Time Series Forecasting: A Survey
J. F. Torres, Dalil Hadjout, Abderrazak Sebaa, et al.
Big Data (2020) Vol. 9, Iss. 1, pp. 3-21
Closed Access | Times Cited: 520
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
Ibomoiye Domor Mienye, Yanxia Sun
IEEE Access (2022) Vol. 10, pp. 99129-99149
Open Access | Times Cited: 498
A comprehensive evaluation of ensemble learning for stock-market prediction
Isaac Kofi Nti, Adebayo Felix Adekoya, Benjamin Asubam Weyori
Journal Of Big Data (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 238
A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network
Hongli Niu, Kunliang Xu, Weiqing Wang
Applied Intelligence (2020) Vol. 50, Iss. 12, pp. 4296-4309
Closed Access | Times Cited: 144
Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
Abdulalem Ali, Shukor Abd Razak, Siti Hajar Othman, et al.
Applied Sciences (2022) Vol. 12, Iss. 19, pp. 9637-9637
Open Access | Times Cited: 130
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: 94
Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model
Muhammad Ali, Dost Muhammad Khan, Huda M. Alshanbari, et al.
Applied Sciences (2023) Vol. 13, Iss. 3, pp. 1429-1429
Open Access | Times Cited: 72
A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting
Shuai Zhang, Yong Chen, Wenyu Zhang, et al.
Information Sciences (2020) Vol. 544, pp. 427-445
Closed Access | Times Cited: 111
Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape
Jack Nicholls, Aditya Kuppa, Nhien‐An Le‐Khac
IEEE Access (2021) Vol. 9, pp. 163965-163986
Open Access | Times Cited: 95
An efficient equilibrium optimizer with support vector regression for stock market prediction
Essam H. Houssein, Mahmoud Dirar, Laith Abualigah, et al.
Neural Computing and Applications (2021) Vol. 34, Iss. 4, pp. 3165-3200
Closed Access | Times Cited: 53
A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading
Yuze Li, Shouyang Wang, Yunjie Wei, et al.
IEEE Transactions on Computational Social Systems (2021) Vol. 8, Iss. 6, pp. 1357-1368
Closed Access | Times Cited: 51
Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning
Mahya Seyedan, Fereshteh Mafakheri, Chun Wang
Supply Chain Analytics (2023) Vol. 3, pp. 100024-100024
Open Access | Times Cited: 17
Optimizing Time-Series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction
Ravi Prakash Varshney, Dilip Kumar Sharma
Expert Systems with Applications (2024) Vol. 249, pp. 123487-123487
Closed Access | Times Cited: 7
A parallel multi-module deep reinforcement learning algorithm for stock trading
Cong Ma, Jiangshe Zhang, Junmin Liu, et al.
Neurocomputing (2021) Vol. 449, pp. 290-302
Closed Access | Times Cited: 40
Ensemble of supervised and unsupervised deep neural networks for stock price manipulation detection
Phakhawat Chullamonthon, Poj Tangamchit
Expert Systems with Applications (2023) Vol. 220, pp. 119698-119698
Closed Access | Times Cited: 15
A hybrid stock market prediction model based on GNG and reinforcement learning
Yongming Wu, Zhenghuan Fu, Xiaoxuan Liu, et al.
Expert Systems with Applications (2023) Vol. 228, pp. 120474-120474
Closed Access | Times Cited: 13
Deep unsupervised anomaly detection in high-frequency markets
Cédric Poutré, Didier Chételat, Manuel Morales
The Journal of Finance and Data Science (2024) Vol. 10, pp. 100129-100129
Open Access | Times Cited: 5
Market manipulation detection: A systematic literature review
Samira Khodabandehlou, Alireza Hashemi Golpayegani
Expert Systems with Applications (2022) Vol. 210, pp. 118330-118330
Closed Access | Times Cited: 21
Market Manipulation in Stock and Power Markets: A Study of Indicator-Based Monitoring and Regulatory Challenges
Yuna Hao, Behrang Vand, Benjamín Manrique Delgado, et al.
Energies (2023) Vol. 16, Iss. 4, pp. 1894-1894
Open Access | Times Cited: 13
FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams
Samira Khodabandehlou, Alireza Hashemi Golpayegani
ACM Transactions on Knowledge Discovery from Data (2024) Vol. 18, Iss. 5, pp. 1-29
Closed Access | Times Cited: 4
Major Issues in High-Frequency Financial Data Analysis: A Survey of Solutions
Lu Zhang, Lei Hua
Mathematics (2025) Vol. 13, Iss. 3, pp. 347-347
Open Access
On detecting stock price manipulation attacks: a comprehensive systematic literature review
Amal Alfajeer, Ala Altaweel, Ahmed Bouridane, et al.
Multimedia Tools and Applications (2025)
Closed Access
Interpretable Stock Anomaly Detection Based on Spatio-Temporal Relation Networks With Genetic Algorithm
Mei-See Cheong, Mei-Chen Wu, Szu-Hao Huang
IEEE Access (2021) Vol. 9, pp. 68302-68319
Open Access | Times Cited: 24
Predicting Chinese Commodity Futures Price: An EEMD-Hurst-LSTM Hybrid Approach
Ke Huang, Zuominyang Zhang, Qiumei Li, et al.
IEEE Access (2023) Vol. 11, pp. 14841-14858
Open Access | Times Cited: 10
Forecasting ESG Stock Indices Using a Machine Learning Approach
Eddy Suprihadi, Nevi Danila
Global Business Review (2024)
Closed Access | Times Cited: 3
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