[1] D. P. Gandhmal and K. Kumar, “Systematic analysis and review of stock market prediction techniques,”

Computer Science Review, vol. 34, p. 100190, 2019.

[2] M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, and E. Salwana, “Deep learning for stock market

prediction,” Entropy, vol. 22, no. 8, p. 840, 2020.

[3] B. Qian and K. Rasheed, “Stock market prediction with multiple classifiers,” Applied Intelligence, vol.

26, no. 1, pp. 25–33, 2007.

[4] C.-S. Lin, S.-H. Chiu, and T.-Y. Lin, “Empirical mode decomposition–based least squares support vector

regression for foreign exchange rate forecasting,” Economic Modelling, vol. 29, no. 6, pp. 2583–2590,

2012.

[5] F. E. H. Tay and L. Cao, “Application of support vector machines in financial time series forecasting,”

omega , vol. 29, no. 4, pp. 309–317, 2001.

[6] M. Lam, “Neural network techniques for financial performance prediction: integrating fundamental and

technical analysis,” Decision support systems, vol. 37, no. 4, pp. 567–581, 2004.

[7] J. J. Murphy, Technical analysis of the financial markets: A comprehensive guide to trading methods

and applications. Penguin, 1999.

[8] K. Miao, F. Chen, and Z. G. Zhao, “Stock price forecast based on bacterial colony RBF neural network,”

Journal of Qingdao University (Natural Science Edition), vol. 2, no. 11, 2007.

[9] K. N. Arman, Y. W. Teh, and N. C. L. David, “A novel FOREX prediction methodology based on

fundamental data,” African Journal of Business Management , vol. 5, no. 20, pp. 8322–8330, 2011.

[10] H. Haleh, B. A. Moghaddam, and S. Ebrahimijam, “A new approach to forecasting stock price with EKF

data fusion,” International Journal of Trade, Economics and Finance, vol. 2, no. 2, p. 109, 2011.

[11] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and

control. John Wiley & Sons, 2015.

[12] D. A. Kumar and S. Murugan, “Performance analysis of Indian stock market index using neural network

time series model,” in 2013 International Conference on Pattern Recognition, Informatics and Mobile

Engineering, 2013, pp. 72–78.

[13] J.-Z. Wang, J.-J. Wang, Z.-G. Zhang, and S.-P. Guo, “Forecasting stock indices with back propagation

neural network,” Expert Systems with Applications, vol. 38, no. 11, pp. 14346–14355, 2011.

[14] M. Taddy, Business data science: Combining machine learning and economics to optimize, automate,

and accelerate business decisions. McGraw Hill Professional, 2019.

[15] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data

mining, inference, and prediction, vol. 2. Springer, 2009.

[16] C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning, vol. 4, no. 4. Springer,

2006.

[17] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018.

[18] X. Zhu and A. B. Goldberg, “Introduction to semi-supervised learning,” Synthesis lectures on artificial

intelligence and machine learning, vol. 3, no. 1, pp. 1–130, 2009.

[19] J.-H. Kim, “Estimating classification error rate: Repeated cross-validation, repeated hold-out and

bootstrap,” Computational statistics & data analysis, vol. 53, no. 11, pp. 3735–3745, 2009.

[20] S. R. Sain, “The nature of statistical learning theory.” Taylor & Francis, 1996.