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Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

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Cited by:

  1. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
  2. Ghada A. Altarawneh & Ahmad B. Hassanat & Ahmad S. Tarawneh & Ahmad Abadleh & Malek Alrashidi & Mansoor Alghamdi, 2022. "Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods," Economies, MDPI, vol. 10(2), pages 1-18, February.
  3. 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.
  4. Deniz Can Yıldırım & Ismail Hakkı Toroslu & Ugo Fiore, 2021. "Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-36, December.
  5. I. Marta Miranda García & María‐Jesús Segovia‐Vargas & Usue Mori & José A. Lozano, 2023. "Early prediction of Ibex 35 movements," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1150-1166, August.
  6. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
  7. Ahmad M Awajan & Mohd Tahir Ismail & S AL Wadi, 2018. "Improving forecasting accuracy for stock market data using EMD-HW bagging," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
  8. Sadefo Kamdem, Jules & Bandolo Essomba, Rose & Njong Berinyuy, James, 2020. "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
  9. Ao Yang & Qing Ye & Jia Zhai, 2024. "Volatility forecasting with Hybrid‐long short‐term memory models: Evidence from the COVID‐19 period," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2766-2786, July.
  10. Ehsan Hoseinzade & Saman Haratizadeh, 2018. "CNNPred: CNN-based stock market prediction using several data sources," Papers 1810.08923, arXiv.org.
  11. Hakan Gunduz, 2021. "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
  12. Catullo, Ermanno & Gallegati, Mauro & Russo, Alberto, 2022. "Forecasting in a complex environment: Machine learning sales expectations in a stock flow consistent agent-based simulation model," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
  13. Rohitash Chandra & Yixuan He, 2021. "Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
  14. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
  15. Koki, Constandina & Leonardos, Stefanos & Piliouras, Georgios, 2022. "Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models," Research in International Business and Finance, Elsevier, vol. 59(C).
  16. Bivas Dinda, 2024. "Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy," Papers 2406.02604, arXiv.org.
  17. Sugai Han & Ansheng Li & Hongchao Wang & Xiaoyun Gong & Liangwen Wang & Yixiang Huang & Yanming Li & Wenliao Du, 2020. "A health management system for large vertical mill," International Journal of Distributed Sensor Networks, , vol. 16(3), pages 15501477209, March.
  18. Pegah Eslamieh & Mehdi Shajari & Ahmad Nickabadi, 2023. "User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks," Mathematics, MDPI, vol. 11(13), pages 1-26, July.
  19. Hyungjin Ko & Jaewook Lee & Junyoung Byun & Bumho Son & Saerom Park, 2019. "Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis," Sustainability, MDPI, vol. 11(12), pages 1-24, June.
  20. Po Yun & Chen Zhang & Yaqi Wu & Xianzi Yang & Zulfiqar Ali Wagan, 2020. "A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
  21. Haibin Xie & Yuying Sun & Pengying Fan, 2023. "Return direction forecasting: a conditional autoregressive shape model with beta density," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
  22. Farzan Soleymani & Eric Paquet, 2021. "Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket," Papers 2105.08664, arXiv.org.
  23. Javier Oliver Muncharaz, 2020. "Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks [Comparativa de los models clásicos de series temporales con la red neuronal recurrente ," Post-Print hal-03149342, HAL.
  24. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  25. Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.
  26. Edson Kambeu, 2019. "Trading volume as a predictor of market movement: An application of Logistic regression in the R environment," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 8(2), pages 57-69, April.
  27. Munish Khanna & Mohak Kulshrestha & Law K. Singh & Shankar Thawkar & Kapil Shrivastava, 2022. "Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-30, January.
  28. Hongping Hu & Yangyang Li & Yanping Bai & Juping Zhang & Maoxing Liu, 2019. "The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction," Complexity, Hindawi, vol. 2019, pages 1-12, August.
  29. Jakub Frankowski & Maciej Zaborowicz & Jacek Dach & Wojciech Czekała & Jacek Przybył, 2020. "Biological Waste Management in the Case of a Pandemic Emergency and Other Natural Disasters. Determination of Bioenergy Production from Floricultural Waste and Modeling of Methane Production Using Dee," Energies, MDPI, vol. 13(11), pages 1-15, June.
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