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An ensemble of LSTM neural networks for high‐frequency stock market classification

Citations

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Cited by:

  1. Adam Korniejczuk & Robert Ślepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers 2024-09, Faculty of Economic Sciences, University of Warsaw.
  2. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
  3. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
  4. Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
  5. Vecchi, Edoardo & Berra, Gabriele & Albrecht, Steffen & Gagliardini, Patrick & Horenko, Illia, 2023. "Entropic approximate learning for financial decision-making in the small data regime," Research in International Business and Finance, Elsevier, vol. 65(C).
  6. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
  7. Ibanga Kpereobong Friday & Sarada Prasanna Pati & Debahuti Mishra & Pradeep Kumar Mallick & Sachin Kumar, 2025. "CAGTRADE: Predicting Stock Market Price Movement with a CNN-Attention-GRU Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(2), pages 583-608, June.
  8. Gil Cohen, 2022. "Artificial Intelligence in Trading the Financial Markets," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 101-110.
  9. Hussain, Syed Mujahid & Ahmad, Nisar & Ahmed, Sheraz, 2023. "Applications of high-frequency data in finance: A bibliometric literature review," International Review of Financial Analysis, Elsevier, vol. 89(C).
  10. Giovanni Ballarin & Jacopo Capra & Petros Dellaportas, 2025. "Multi-Horizon Echo State Network Prediction of Intraday Stock Returns," Papers 2504.19623, arXiv.org.
  11. Yonghong Li & Zhixian Li & Yuting Chen & Yayun Wang & Sidong Xian & Zhiqiang Zhao & Linyan Zhou & Ji Li, 2025. "MLBGK: A Novel Feature Fusion Model for Forecasting Stocks Prices," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 2565-2592, September.
  12. Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
  13. Jianyuan Zhong & Zhijian Xu & Saizhuo Wang & Xiangyu Wen & Jian Guo & Qiang Xu, 2024. "DSPO: An End-to-End Framework for Direct Sorted Portfolio Construction," Papers 2405.15833, arXiv.org.
  14. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
  15. Cohen, Gil & Aiche, Avishay, 2023. "Forecasting gold price using machine learning methodologies," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
  16. Gil, Cohen, 2022. "Intraday Trading of Precious Metals Futures Using Algorithmic Systems," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
  17. Zeng, Qing & Lu, Xinjie & Xu, Jin & Lin, Yu, 2024. "Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach," International Review of Financial Analysis, Elsevier, vol. 96(PB).
  18. Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
  19. Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Stock price forecast with deep learning," Papers 2103.14081, arXiv.org.
  20. Hong Cao & Hao Chen & Yulong Lian & Hong-Kun Zhang, 2025. "Volatility-informed SPY forecasting: From CGR-SPY analysis to DLSTM prediction," PLOS Complex Systems, Public Library of Science, vol. 2(8), pages 1-16, August.
  21. Mohammad Zoynul Abedin & Mahmudul Hasan Moon & M. Kabir Hassan & Petr Hajek, 2025. "Deep learning-based exchange rate prediction during the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 1335-1386, February.
  22. Firuz Kamalov, 2019. "Forecasting significant stock price changes using neural networks," Papers 1912.08791, arXiv.org.
  23. Chang Liu, 2023. "Exploiting Supply Chain Interdependencies for Stock Return Prediction: A Full-State Graph Convolutional LSTM," Papers 2303.09406, arXiv.org, revised Dec 2025.
  24. Nestoras Chalkidis & Rahul Savani, 2021. "Trading via Selective Classification," Papers 2110.14914, arXiv.org, revised Oct 2021.
  25. Zi‐yu Chen & Fei Xiao & Xiao‐kang Wang & Min‐hui Deng & Jian‐qiang Wang & Jun‐Bo Li, 2022. "Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1458-1482, November.
  26. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
  27. Chu Myaet Thwal & Ye Lin Tun & Kitae Kim & Seong-Bae Park & Choong Seon Hong, 2024. "Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting," Papers 2402.06638, arXiv.org.
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