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Forecasting stock prices with long-short term memory neural network based on attention mechanism

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Cited by:

  1. Johanna Karina Solano Meza & David Orjuela Yepes & Javier Rodrigo-Ilarri & María-Elena Rodrigo-Clavero, 2023. "Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
  2. Qihang Zhou & Changjun Zhou & Xiao Wang, 2022. "Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-20, February.
  3. Conall Butler & Martin Crane, 2023. "Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods," Mathematics, MDPI, vol. 11(9), pages 1-26, May.
  4. Renigier-Biłozor Małgorzata & Janowski Artur, 2024. "Human-Machine Synergy in Real Estate Similarity Concept," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 13-30.
  5. Dhruhi Sheth & Manan Shah, 2023. "Predicting stock market using machine learning: best and accurate way to know future stock prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 1-18, February.
  6. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
  7. Gaurang Sonkavde & Deepak Sudhakar Dharrao & Anupkumar M. Bongale & Sarika T. Deokate & Deepak Doreswamy & Subraya Krishna Bhat, 2023. "Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications," IJFS, MDPI, vol. 11(3), pages 1-22, July.
  8. Subba Rao Polamuri & K. Srinnivas & A. Krishna Mohan, 2023. "Prediction of stock price growth for novel greedy heuristic optimized multi-instances quantitative (NGHOMQ)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 353-366, February.
  9. Janusz Gajda & Rafał Walasek, 2020. "Fractional differentiation and its use in machine learning," Working Papers 2020-32, Faculty of Economic Sciences, University of Warsaw.
  10. Ziyuan Xia & Jeffery Chen & Anchen Sun, 2021. "Mining the Relationship Between COVID-19 Sentiment and Market Performance," Papers 2101.02587, arXiv.org, revised Mar 2023.
  11. Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Stock price forecast with deep learning," Papers 2103.14081, arXiv.org.
  12. Jaydip Sen & Saikat Mondal & Gourab Nath, 2022. "Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model," Papers 2204.01850, arXiv.org.
  13. Akash Doshi & Alexander Issa & Puneet Sachdeva & Sina Rafati & Somnath Rakshit, 2020. "Deep Stock Predictions," Papers 2006.04992, arXiv.org.
  14. Wu, Ling & Hock Ow, Siew, 2021. "The Impact of News Sentiment on the Stock Market Fluctuation: The Case of Selected Energy Sector," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 55(3), pages 1-21.
  15. Ghaemi Asl, Mahdi & Ben Jabeur, Sami, 2024. "Could the Russia-Ukraine war stir up the persistent memory of interconnectivity among Islamic equity markets, energy commodities, and environmental factors?," Research in International Business and Finance, Elsevier, vol. 69(C).
  16. Yinan Wang & Xiangbing Kong & Kai Guo & Chunjing Zhao & Jintao Zhao, 2023. "Intelligent Extraction of Terracing Using the ASPP ArrU-Net Deep Learning Model for Soil and Water Conservation on the Loess Plateau," Agriculture, MDPI, vol. 13(7), pages 1-23, June.
  17. Bivas Dinda, 2024. "Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy," Papers 2406.02604, arXiv.org.
  18. Yiqi Deng & Siu Ming Yiu, 2022. "Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News," Papers 2206.14452, arXiv.org.
  19. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
  20. Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
  21. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
  22. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
  23. Luca Di Persio & Matteo Garbelli & Kai Wallbaum, 2021. "Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis," Risks, MDPI, vol. 9(2), pages 1-16, February.
  24. Shruthi Komarla Rammurthy & Sagar B. Patil, 2021. "An LSTM-Based Approach to Predict Stock Price Movement for IT Sector Companies," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(4), pages 1-12, October.
  25. Jaydip Sen & Abhishek Dutta, 2022. "A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks," Papers 2210.00984, arXiv.org.
  26. 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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