Open Access
MATEC Web Conf.
Volume 381, 2023
1st International Conference on Modern Technologies in Mechanical & Materials Engineering (MTME-2023)
Article Number 01017
Number of page(s) 11
Section Mechanical Engineering
Published online 13 June 2023
  1. T. Puschmann, “Fintech,” Business & Information Systems Engineering, vol. 59, no. 1, pp. 69–76, 2017 [CrossRef] [Google Scholar]
  2. M.-C. Tsai, C.-H. Cheng, M.-I. Tsai, and H.-Y. Shiu, “Forecasting leading industry stock prices based on a hybrid time-series forecast model,” PloS one, vol. 13, no. 12, p. e0209922, 2018 [CrossRef] [Google Scholar]
  3. D. Bhuriya, G. Kaushal, A. Sharma, and U. Singh, “Stock market predication using a linear regression,” in 2017 international conference of electronics, communication and aerospace technology (ICECA), 2017, vol. 2: IEEE, pp. 510–513. [CrossRef] [Google Scholar]
  4. M. Khashei and M. Bijari, “A novel hybridization of artificial neural networks and ARIMA models for time series forecasting,” Applied soft computing, vol. 11, no. 2, pp. 2664–2675, 2011 [CrossRef] [Google Scholar]
  5. K. H. Sadia, A. Sharma, A. Paul, S. Padhi, and S. Sanyal, “Stock market prediction using machine learning algorithms,” Int. J. Eng. Adv. Technol, vol. 8, no. 4, pp. 25–31, 2019 [Google Scholar]
  6. X. Ding, Y. Zhang, T. Liu, and J. Duan, “Deep learning for event-driven stock prediction,” in Twenty-fourth international joint conference on artificial intelligence, 2015. [Google Scholar]
  7. S. Sohangir, D. Wang, A. Pomeranets, and T. M. Khoshgoftaar, “Big Data: Deep Learning for financial sentiment analysis,” Journal of Big Data, vol. 5, no. 1, pp. 1–25, 2018 [CrossRef] [Google Scholar]
  8. A. A. Ariyo, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the ARIMA model,” in 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 2014: IEEE, pp. 106–112. [CrossRef] [Google Scholar]
  9. Y. Kara, M. A. Boyacioglu, and Ö.K.J.E.S.W.A. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange,” vol. 38, no. 5, pp. 5311–5319, 2011 [Google Scholar]
  10. J. Patel, S. Shah, P. Thakkar, and K.J.E.S.W.A. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,” vol. 42, no. 1, pp. 259–268, 2015 [Google Scholar]
  11. J. Patel, S. Shah, P. Thakkar, and K.J.E.S.W.A. Kotecha, “Predicting stock market index using fusion of machine learning techniques,” vol. 42, no. 4, pp. 2162–2172, 2015 [Google Scholar]
  12. S. Asadi, E. Hadavandi, F. Mehmanpazir, and M.M.J.K.-B.S. Nakhostin, “Hybridization of evolutionary Levenberg-Marquardt neural networks and data preprocessing for stock market prediction,” vol. 35, pp. 245–258, 2012 [Google Scholar]
  13. M. Qiu, Y. Song, F. J. C. Akagi, Solitons, and Fractals, “Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market,” vol. 85, pp. 1–7, 2016 [Google Scholar]
  14. F. A. de Oliveira, C. N. Nobre, and L.E.J.E.S.W.A. Zarate, “Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index-Case study of PETR4, Petrobras, Brazil,” vol. 40, no. 18, pp. 7596–7606, 2013 [Google Scholar]
  15. W. Khan et al., “Stock market prediction using machine learning classifiers and social media, news,” pp. 1–24, 2020 [Google Scholar]
  16. A. Zaffar and S. M. A. Hussain, “Modeling and prediction of KSE - 100 index closing based on news sentiments: an applications of machine learning model and ARMA (p, q) model,” Multimedia Tools and Applications, vol. 81, no. 23, pp. 33311–33333, 2022/09/01 2022 [CrossRef] [Google Scholar]
  17. M. Hameed, K. Iqbal, R. Ghazali, F. H. Jaskani, and Z. Saman, “Karachi Stock Exchange Price Prediction using Machine Learning Regression Techniques,” EAI Endorsed Transactions on Creative Technologies, vol. 8, no. 28, p. e5, 08/24 2021 [Google Scholar]
  18. H. Maqsood et al., “A local and global event sentiment based efficient stock exchange forecasting using deep learning,” vol. 50, pp. 432–451, 2020 [Google Scholar]

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