Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 14, 2023
Article Number 8
Number of page(s) 6
DOI https://doi.org/10.1051/smdo/2023009
Published online 23 August 2023
  1. Q. Liu, Q. Yao, G. Zhao, Model averaging estimation for conditional volatility models with an application to stock market volatility forecast, J. Forecasting 39, 841–863 (2020) [CrossRef] [Google Scholar]
  2. J. Wang, Q. Cui, X. Sun, M. He, Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model, Eng. Appl. Artif. Intel. 113, 1–21 (2022) [Google Scholar]
  3. S.D. Chen, Y.L. Sun, Y. Liu, Forecast of stock price fluctuation based on the perspective of volume information in stock and exchange market, China Finance Rev. Int. 8, 297–314 (2018) [CrossRef] [Google Scholar]
  4. A. Kanwal, M.F. Lau, S.P.H. Ng, K.Y. Sim, S. Chandrasekaran, BiCuDNNLSTM-1dCNN − A hybrid deep learning-based predictive model for stock price prediction, Expert Syst. Appl. 202(Sep.), 1–15 (2022) [Google Scholar]
  5. W. Zhang, S. Zhang, S. Zhang, D. Yu, N. Huang, A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search, Neurocomputing, 240(May31), 13–24 (2017) [CrossRef] [Google Scholar]
  6. S. Yang, H. Guo, J. Li, CNN-GRUA-FC stock price forecast model based on multi-factor analysis, J. Adv. Comput. Intell. Intell. Inform. 26(4 TN.157), 600–608 (2022) [CrossRef] [Google Scholar]
  7. C. Xiao, W. Xia, J. Jiang, Stock price forecast based on combined model of ARI-MA-LS-SVM, Neural Comput. Appl. 32, 5379–5388 (2020) [CrossRef] [Google Scholar]
  8. Z.K. He, Prediction of amazon's stock price based on ARIMA, XGBoost, and LSTM models, Busin. Econ. Res. 5, 127–136 (2022) [Google Scholar]
  9. C.R. Ko, H.T. Chang, LSTM-based sentiment analysis for stock price forecast, PeerJ Comput. Sci. 7, 1–23 (2021) [Google Scholar]
  10. Q. Wang, K. Kang, Z. Zhang, D. Cao, Application of LSTM and CONV1D LSTM network in stock forecasting model, Adv. Artif. Intell. 3, 36–43 (2021) [CrossRef] [Google Scholar]
  11. Y. Yan, D. Yang, A stock trend forecast algorithm based on deep neural networks, Sci. Programming 2021, 1–7 (2021) [Google Scholar]
  12. M. Liu, J. Ye, L. Yu, Volatility prediction via hybrid LSTM models with GARCH type parameters, Busin. Econ. Res. 5, 37–46 (2022) [Google Scholar]
  13. J. Li, T. Zhou, X. Hu, Prediction algorithm of stock holdings of hong kong-funded institutions based on optimized PCA-LSTM model, Int. J. Innov. Comput. I. 18, 999–1008 (2022) [Google Scholar]
  14. L. Sun, W. Xu, J. Liu, Two-channel attention mechanism fusion model of stock price prediction based on CNN-LSTM. ACM T, Asian Low-Reso. 20, 1–12 (2021) [Google Scholar]
  15. C. Anand, Comparison of stock price prediction models using pre-trained neural networks, J. Ubiq. Comput. Commun. Technol. 3, 122–134 (2021) [Google Scholar]
  16. D. Saravagi, S. Agrawal, M. Saravagi, Indian stock market analysis and prediction using LSTM model during COVID-19, Int. J. Eng. Syst. Model. 12, 139–147 (2021) [Google Scholar]
  17. H.K. Konan, F.A. Kouassi, M. Coulibaly, O. Asseu, Prediction of stock prices via recurrent neural networks with LSTM (long shortterm memory) architecture, Far East J. Math. Sci. 128, 53–65 (2021) [Google Scholar]
  18. X. Zhang, W. Qi, Z. Zhan, A study on machine-learning-based prediction for bitcoin's price via using LSTM and SVR, J. Phys. Conf. Ser. 1732, 1–5 (2021) [Google Scholar]
  19. L.Z. Tao, Predicting Google's stock price with LSTM model, Busin. Econ. Res. 5,82–87 (2022) [Google Scholar]
  20. N. Jing, Z. Wu, H. Wang, A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction, Expert Syst. Appl. 178, 115019 (2021) [CrossRef] [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.