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Classification-based financial markets prediction using deep neural networks

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. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised May 2025.
  3. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
  4. Zhi Su & Heliang Xie & Lu Han, 2021. "Multi-Factor RFG-LSTM Algorithm for Stock Sequence Predicting," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1041-1058, April.
  5. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
  6. Krzysztof Piasecki & Michał Dominik Stasiak, 2020. "Optimization Parameters of Trading System with Constant Modulus of Unit Return," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
  7. Peter B. Lerner, 2022. "Fourier Integral Operator Model of Market Liquidity: The Chinese Experience 2009–2010," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
  8. Yang Qiao & Yiping Xia & Xiang Li & Zheng Li & Yan Ge, 2023. "Higher-order Graph Attention Network for Stock Selection with Joint Analysis," Papers 2306.15526, arXiv.org.
  9. Zineb Lanbouri & Saaid Achchab, 2019. "A new approach for Trading based on Long-Short Term memory technique [Une nouvelle approche pour le Trading basée sur la technique Long-Short Term Memory]," Post-Print hal-02396905, HAL.
  10. Şirin Özlem & Omer Faruk Tan, 2022. "Predicting cash holdings using supervised machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
  11. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
  12. Shen, Jiancheng & Wang, Jia & Ma, Xuejun & Chen, Xin & Cang, Han, 2026. "Volatility and skewness predictability with deep learning and big data: Chinese futures market case," Pacific-Basin Finance Journal, Elsevier, vol. 96(C).
  13. Ben Moews & Gbenga Ibikunle, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Papers 2002.10385, arXiv.org.
  14. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  15. Ying Liu & Zengyu Wei & Long Chen & Cai Xu & Ziyu Guan, 2025. "Multi-Modal Temporal Dynamic Graph Construction for Stock Rank Prediction," Mathematics, MDPI, vol. 13(5), pages 1-20, March.
  16. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
  17. Sheng Xiang & Dawei Cheng & Chencheng Shang & Ying Zhang & Yuqi Liang, 2023. "Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction," Papers 2305.08740, arXiv.org.
  18. S M Raju & Ali Mohammad Tarif, 2020. "Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis," Papers 2006.14473, arXiv.org.
  19. Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.
  20. Mohammad El Hajj & Jamil Hammoud, 2023. "Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets: A Comprehensive Analysis of AI Applications in Trading, Risk Management, and Financial Operations," JRFM, MDPI, vol. 16(10), pages 1-16, October.
  21. Artur Sokolovsky & Luca Arnaboldi & Jaume Bacardit & Thomas Gross, 2021. "Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications," Papers 2103.12419, arXiv.org, revised May 2022.
  22. Keonvin Park, 2026. "Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction," Papers 2603.19288, arXiv.org.
  23. Andreas Koukorinis & Gareth W. Peters & Guido Germano, 2025. "Generative-Discriminative Machine Learning Models for High-Frequency Financial Regime Classification," Methodology and Computing in Applied Probability, Springer, vol. 27(2), pages 1-32, June.
  24. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
  25. Takuya Shintate & Lukáš Pichl, 2019. "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," JRFM, MDPI, vol. 12(1), pages 1-15, January.
  26. Lu Zhang & Lei Hua, 2025. "Major Issues in High-Frequency Financial Data Analysis: A Survey of Solutions," Mathematics, MDPI, vol. 13(3), pages 1-40, January.
  27. Jireh Yi-Le Chan & Seuk Wai Phoong & Wai Khuen Cheng & Yen-Lin Chen, 2022. "Support Resistance Levels towards Profitability in Intelligent Algorithmic Trading Models," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
  28. Lei Ruan & Heng Liu, 2021. "Financial Distress Prediction Using GA-BP Neural Network Model," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(3), pages 1-1, March.
  29. Yoojeong Song & Jae Won Lee & Jongwoo Lee, 2022. "Development of Intelligent Stock Trading System Using Pattern Independent Predictor and Turning Point Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 27-38, January.
  30. Yoshiyuki Suimon & Hiroki Sakaji & Kiyoshi Izumi & Hiroyasu Matsushima, 2020. "Autoencoder-Based Three-Factor Model for the Yield Curve of Japanese Government Bonds and a Trading Strategy," JRFM, MDPI, vol. 13(4), pages 1-21, April.
  31. Fabian Waldow & Matthias Schnaubelt & Christopher Krauss & Thomas Günter Fischer, 2021. "Machine Learning in Futures Markets," JRFM, MDPI, vol. 14(3), pages 1-14, March.
  32. Hyeong Kyu Choi, 2018. "Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model," Papers 1808.01560, arXiv.org, revised Oct 2018.
  33. Lianxu Wang & Xu Chen, 2025. "Sustainable Factor Augmented Machine Learning Models for Crude Oil Return Forecasting," JRFM, MDPI, vol. 18(7), pages 1-27, June.
  34. Reza Yarbakhsh & Mahdieh Soleymani Baghshah & Hamidreza Karimaghaie, 2023. "Predicting risk/reward ratio in financial markets for asset management using machine learning," Papers 2311.09148, arXiv.org.
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