Open Access
Issue
E3S Web Conf.
Volume 453, 2023
International Conference on Sustainable Development Goals (ICSDG 2023)
Article Number 01047
Number of page(s) 8
DOI https://doi.org/10.1051/e3sconf/202345301047
Published online 30 November 2023
  1. M. Ali, D. M. Khan, H. M. Alshanbari, and A. A.-A. H. El-Bagoury, “Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model, ” Applied Sciences, vol. 13, no. 3, p. 1429, Jan. 2023, doi:10.3390/app13031429. [CrossRef] [Google Scholar]
  2. D. Sheth and M. Shah, “Predicting stock market using machine learning: best and accurate way to know future stock prices, ” International Journal of System Assurance Engineering and Management. Springer, Feb. 01, 2023. doi:10.1007/s13198-022-01811-1. [Google Scholar]
  3. L. N. Mintarya, J. N. M. Halim, C. Angie, S. Achmad, and A. Kurniawan, “Machine learning approaches in stock market prediction: A systematic literature review, ” Procedia Comput Sci, vol. 216, pp. 96–102, 2023, doi:10.1016/j.procs.2022.12.115. [CrossRef] [Google Scholar]
  4. Haoran Wu, Shuqi Chen, and Yicheng Ding, “Comparison of ARIMA and LSTM for Stock Price Prediction, ” Financial Engineering and Risk Management, vol. 6, no. 1, 2023, doi:10.23977/ferm.2023.060101. [Google Scholar]
  5. W. Wang et al., “An interpretable intuitionistic fuzzy inference model for stock prediction, ” Expert Syst Appl, vol. 213, p. 118908, Mar. 2023, doi:10.1016/J.ESWA.2022.118908. [CrossRef] [Google Scholar]
  6. K. Venkateswararao and B. V. R. Reddy, “LT-SMF: long term stock market price trend prediction using optimal hybrid machine learning technique, ” Artif Intell Rev, 2022, doi:10.1007/s10462-022-10291-5. [Google Scholar]
  7. T. Al-Sulaiman, “Predicting reactions to anomalies in stock movements using a feed-forward deep learning network, ” International Journal of Information Management Data Insights, vol. 2, no. 1, Apr. 2022, doi:10.1016/j.jjimei.2022.100071. [Google Scholar]
  8. A. Gupta and P. Srinath, “A recommender system based on collaborative filtering, graph theory using HMM based similarity measures, ” International Journal of System Assurance Engineering and Management, vol. 13, pp. 533–545, Mar. 2022, doi:10.1007/s13198-021-01537-6. [Google Scholar]
  9. Q. Zhou, C. Zhou, and X. Wang, “Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection, ” PLoS One, vol. 17, no. 2 February, Feb. 2022, doi:10.1371/journal.pone.0262501. [Google Scholar]
  10. A. Lawi, H. Mesra, and S. Amir, “Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately, ” J Big Data, vol. 9, no. 1, Dec. 2022, doi:10.1186/s40537022-00597-0. [Google Scholar]
  11. Z. Hu, Y. Zhao, and M. Khushi, “A survey of forex and stock price prediction using deep learning, ” Applied System Innovation, vol. 4, no. 1. MDPI AG, pp. 1–30, Mar. 01, 2021. doi:10.3390/ASI4010009. [Google Scholar]
  12. P. Yu and X. Yan, “Stock price prediction based on deep neural networks, ” Neural Comput Appl, vol. 32, no. 6, pp. 1609–1628, Mar. 2020, doi:10.1007/s00521-019-04212-x. [CrossRef] [Google Scholar]
  13. A. Moghar and M. Hamiche, “Stock Market Prediction Using LSTM Recurrent Neural Network, ” in Procedia Computer Science, Elsevier B.V., 2020, pp. 1168–1173. doi:10.1016/j.procs.2020.03.049. [CrossRef] [Google Scholar]
  14. G. De Rossi, J. Kolodziej, and G. Brar, “A recommender system for active stock selection, ” Computational Management Science, vol. 17, no. 4, pp. 517–547, Dec. 2020, doi:10.1007/s10287-018-0342-9. [CrossRef] [Google Scholar]
  15. K. Zhang, G. Zhong, J. Dong, S. Wang, and Y. Wang, “Stock Market Prediction Based on Generative Adversarial Network, ” in Procedia Computer Science, Elsevier B.V., 2019, pp. 400–406. doi:10.1016/j.procs.2019.01.256. [CrossRef] [Google Scholar]
  16. A. Sethia and P. Raut, “Application of LSTM, GRU and ICA for stock price prediction, ” in Smart Innovation, Systems and Technologies, Springer Science and Business Media Deutschland GmbH, 2019, pp. 479–487. doi:10.1007/978-981-13-1747-7_46. [Google Scholar]
  17. X. Zhong and D. Enke, “Predicting the daily return direction of the stock market using hybrid machine learning algorithms, ” Financial Innovation, vol. 5, no. 1, Dec. 2019, doi:10.1186/s40854-019-0138-0. [Google Scholar]
  18. M. R. Senapati, S. Das, and S. Mishra, “A Novel Model for Stock Price Prediction Using Hybrid Neural Network, ” Journal of The Institution of Engineers (India): Series B, vol. 99, no. 6, pp. 555–563, Dec. 2018, doi:10.1007/s40031018-0343-7. [CrossRef] [Google Scholar]
  19. M. S. Hegde, G. Krishna, and R. Srinath, “An Ensemble Stock Predictor and Recommender System, ” in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Sep. 2018, pp. 1981–1985. doi:10.1109/ICACCI.2018.8554424. [Google Scholar]
  20. W. Wang and K. K. Mishra, “A novel stock trading prediction and recommendation system, ” Multimed Tools Appl, vol. 77, no. 4, pp. 4203–4215, Feb. 2018, doi:10.1007/s11042-017-4587-z. [CrossRef] [Google Scholar]
  21. H. Yang, X. Y. Liu, and Q. Wu, “A Practical Machine Learning Approach for Dynamic Stock Recommendation, ” in Proceedings 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, Institute of Electrical and Electronics Engineers Inc., Sep. 2018, pp. 1693–1697. doi:10.1109/TrustCom/BigDataSE.2018.00253. [Google Scholar]
  22. R. Singh and S. Srivastava, “Stock prediction using deep learning, ” Multimed Tools Appl, vol. 76, no. 18, pp. 18569–18584, Sep. 2017, doi:10.1007/s11042-016-4159-7. [CrossRef] [Google Scholar]
  23. S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model, ” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Sep. 2017, pp. 1643–1647. doi:10.1109/ICACCI.2017.8126078. [Google Scholar]
  24. C.-H. Chang, “Exploring stock recommenders’ behavior and recommendation receivers’ sophistication, ” Journal of Economics and Finance, vol. 41, no. 1, pp. 1–26, Jan. 2017, doi:10.1007/s12197-015-9330-x. [CrossRef] [Google Scholar]
  25. R. Akita, A. Yoshihara, T. Matsubara, and K. Uehara, “Deep learning for stock prediction using numerical and textual information, ” in 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 Proceedings, Institute of Electrical and Electronics Engineers Inc., Aug. 2016. doi:10.1109/ICIS.2016.7550882. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.