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All issues Volume 56 (2023) ITM Web Conf., 56 (2023) 05009 References
Open Access
Issue
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
Article Number 05009
Number of page(s) 8
Section Machine Learning & Neural Networks
DOI https://doi.org/10.1051/itmconf/20235605009
Published online 09 August 2023
  1. L. Di Persio, O. Honchar, Artificial neural networks architectures for stock price prediction: Comparisons and Applications, International Journal of Circuits, Systems, and Signal Processing, 10, 403-413, (2016). [Google Scholar]
  2. E. F. Fama, Random walks in stock market prices, Financial Analysts Journal, 51 (1), 75-80, (1995). [CrossRef] [Google Scholar]
  3. B. G. Malkiel, Efficient Market Hypothesis, in Eatwell, J., Milgate, M., Newman, P. (eds) Finance. The New Palgrave. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-20213-3_13 [Google Scholar]
  4. E. K. Ampomah, Z. Qin, G. Nyame, Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement, Information, 11, no. 6, 332, (2020). [Google Scholar]
  5. B. M. Henrique, V. A. Sobreiro, H. Kimura, Stock priceprediction using support vector regression on daily and up to theminute prices, The Journal of Finance and Data Science, 4, Issue 3, pp. 183-201, (2018). [CrossRef] [Google Scholar]
  6. M. Ouahilal, M. El Mohajir, M. Chahhou, B. E. El Mohajir, Optimizing stock market price prediction using a hybrid approach based on HP filter and support vector regression, 4th IEEE International Colloquium on Information Science and Technology (CiSt), Tangier, Morocco, 290-294, (2016) DOI: 10.1109/CIST.2016.7805059. [Google Scholar]
  7. Q. Qin, Q. G. Wang, J. Li, S. S. Ge, Linear and Nonlinear Trading Models with Gradient Boosted Random Forests and Application to Singapore Stock Market, Journal of Intelligent Learning Systems and Applications, 5, 1-10, (2013). [CrossRef] [Google Scholar]
  8. D. Avramov, S. Cheng, L. Metzker, Machine Learning versus Economic Restrictions: Evidence from Stock Return Predictability, Management Science, (2021). [Google Scholar]
  9. Y. Chen, Y. Hao, A feature-weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction, Expert Syst. Appl. 80, C., 340-355, (2017). https://doi.org/10.1016/j.eswa.2017.02.044 [CrossRef] [Google Scholar]
  10. J. A. Nelder, R. W. Wedderburn, Generalized Linear Models, Journal of the Royal Statistical Society. Series A (General), 135 (3), 370-384, (1972). [CrossRef] [Google Scholar]
  11. J. R. Quinlan, Induction of decision trees, Machine Learning, 1 (1), 81-106, (1986). [Google Scholar]
  12. L. Breiman, Random forests, Machine learning, 45(1), 5-32, (2001). [Google Scholar]
  13. J. H. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, Annals of Statistics, 29 (5), 1189-1232, (1999). [Google Scholar]
  14. V. N. Vapnik, A.Y.A. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities, Theory of Probability & Its Applications, 8 (2), 264-280, (1963). [Google Scholar]
  15. D. Srivastava, B. Lekha, Data Classification Using Support Vector Machine, Journal of Theoretical and Applied Information Technology, 12 (1), 1-7, (2010). [Google Scholar]

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