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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:

High-Frequency Trading Strategy Based on Deep Neural Networks
Andrés Arévalo, Jaime Niño, Germán Hernández, et al.
Lecture notes in computer science (2016), pp. 424-436
Closed Access | Times Cited: 83

Showing 1-25 of 83 citing articles:

A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning
Shaveta Dargan, Munish Kumar, Maruthi Rohit Ayyagari, et al.
Archives of Computational Methods in Engineering (2019) Vol. 27, Iss. 4, pp. 1071-1092
Closed Access | Times Cited: 885
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies
Eunsuk Chong, Chulwoo Han, Frank C. Park
Expert Systems with Applications (2017) Vol. 83, pp. 187-205
Open Access | Times Cited: 692
CNNpred: CNN-based stock market prediction using a diverse set of variables
Ehsan Hoseinzade, Saman Haratizadeh
Expert Systems with Applications (2019) Vol. 129, pp. 273-285
Closed Access | Times Cited: 450
Predicting the direction of stock markets using optimized neural networks with Google Trends
Hongping Hu, Li Tang, Shuhua Zhang, et al.
Neurocomputing (2018) Vol. 285, pp. 188-195
Closed Access | Times Cited: 170
Portfolio formation with preselection using deep learning from long-term financial data
Wuyu Wang, Weizi Li, Ning Zhang, et al.
Expert Systems with Applications (2019) Vol. 143, pp. 113042-113042
Open Access | Times Cited: 154
An application of deep reinforcement learning to algorithmic trading
Thibaut Théate, Damien Ernst
Expert Systems with Applications (2021) Vol. 173, pp. 114632-114632
Open Access | Times Cited: 148
Diffusion models in bioinformatics and computational biology
Zhiye Guo, Jian Liu, Yanli Wang, et al.
Nature Reviews Bioengineering (2023) Vol. 2, Iss. 2, pp. 136-154
Open Access | Times Cited: 43
Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications
Luigi Troiano, E. Mejuto-Villa, Vincenzo Loia
IEEE Transactions on Industrial Informatics (2018) Vol. 14, Iss. 7, pp. 3226-3234
Closed Access | Times Cited: 86
A New CNN-Based Model for Financial Time Series: TAIEX and FTSE Stocks Forecasting
Melih Kirisci, Özge Cağcağ Yolcu
Neural Processing Letters (2022) Vol. 54, Iss. 4, pp. 3357-3374
Closed Access | Times Cited: 38
A novel CNN-DDPG based AI-trader: Performance and roles in business operations
Suyuan Luo, Xudong Lin, Zunxin Zheng
Transportation Research Part E Logistics and Transportation Review (2019) Vol. 131, pp. 68-79
Closed Access | Times Cited: 68
Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations
Cătălin Stoean, Wiesław Paja, Ruxandra Stoean, et al.
PLoS ONE (2019) Vol. 14, Iss. 10, pp. e0223593-e0223593
Open Access | Times Cited: 64
Enhanced Prediction of Intra-day Stock Market Using Metaheuristic Optimization on RNN–LSTM Network
Krishna Kumar, Md. Tanwir Uddin Haider
New Generation Computing (2020) Vol. 39, Iss. 1, pp. 231-272
Closed Access | Times Cited: 62
A Double-Layer Neural Network Framework for High-Frequency Forecasting
Hao Chen, Keli Xiao, Jinwen Sun, et al.
ACM Transactions on Management Information Systems (2017) Vol. 7, Iss. 4, pp. 1-17
Closed Access | Times Cited: 61
Machine Learning Based Stock Market Analysis: A Short Survey
Hrishikesh Vachhani, Mohammad S. Obiadat, Arkesh Thakkar, et al.
Lecture notes on data engineering and communications technologies (2020), pp. 12-26
Closed Access | Times Cited: 35
Novel modelling strategies for high-frequency stock trading data
Xuekui Zhang, Yuying Huang, Ke Xu, et al.
Financial Innovation (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 11
An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction
Dushmanta Kumar Padhi, Neelamadhab Padhy, Akash Kumar Bhoi, et al.
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-18
Open Access | Times Cited: 16
A hierarchical Deep neural network design for stock returns prediction
Oussama Lachiheb, Mohamed Salah Gouider
Procedia Computer Science (2018) Vol. 126, pp. 264-272
Open Access | Times Cited: 28
Deep Learning Based Forecasting in Stock Market with Big Data Analytics
Gözde ŞİŞMANOĞLU, Mehmet Ali ÖNDE, Furkan Kocer, et al.
(2019), pp. 1-4
Closed Access | Times Cited: 24
LightTrader: A Standalone High-Frequency Trading System with Deep Learning Inference Accelerators and Proactive Scheduler
Sungyeob Yoo, Hyunsung Kim, Jinseok Kim, et al.
(2023), pp. 1017-1030
Closed Access | Times Cited: 7
Market index price prediction using Deep Neural Networks with a Self-Similarity approach
Carlos Mendoza, Werner Kristjanpoller, Marcel C. Minutolo
Applied Soft Computing (2023) Vol. 146, pp. 110700-110700
Closed Access | Times Cited: 7
Neural Ordinary Differential Equation Networks for Fintech Applications Using Internet of Things
Jiacheng Li, Wei Ping Chen, Yican Liu, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 12, pp. 21763-21772
Closed Access | Times Cited: 2
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
Adversarial attacks on machine learning systems for high-frequency trading
Micah Goldblum, Avi Schwarzschild, Ankit Patel, et al.
(2021), pp. 1-9
Open Access | Times Cited: 17
User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks
pegah eslamieh, Mehdi Shajari, Ahmad Nickabadi
Mathematics (2023) Vol. 11, Iss. 13, pp. 2950-2950
Open Access | Times Cited: 6
Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction
Li Xie, Sheng Yu
Concurrency and Computation Practice and Experience (2021) Vol. 33, Iss. 16
Closed Access | Times Cited: 14
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