Stock Market Prediction is the demonstration of attempting to decide the future estimation of an organization stock or other money related instrument exchanged on a trade. Prediction on stock market is a great challenge as it is complex, dynamic and non-linear in nature. The main focus is on closing price of next day. High, Low, Volume is of importance but the closing price is of more value. There are numerous instances of Machine Learning algorithms possessed the capacity to achieve attractive outcomes while doing that kind of forecast. In this paper, the LSTM networks are used to predict future closing price of stock market based on the price history, alongside with technical analysis indicators. For that objective, a forecast model was built, and a series of experiments were performed and their outcomes were examined against various measurements to survey if this kind of calculation presents and enhancements when contrasted with other Machine Learning techniques.

Stock Market, Prediction, LSTM

[1] Kumar, D. Ashok, and S. Murugan. "Performance analysis of Indian stock market index using neural network time series model." In Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on, pp. 72-78. IEEE, 2013.
[2] Abhishek, Kumar, Anshul Khairwa, Tej Pratap, and Surya Prakash. "A stock market prediction model using ANN." In Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on, pp. 1-5. IEEE, 2012.
[3] Hagenau, Michael, Michael Liebmann, Markus Hedwig, and Dirk Neumann. "Automated news reading: Stock price prediction based on financial news using context-specific features." In System Science (HICSS), 2012 45th Hawaii International Conference on, pp. 1040-1049.IEEE, 2012.
[4] US. Sutton and A.G. Barto, Reinforcement Learning, The MIT Press, 1998.
[5] R.S. Sutton, ``Learning to foresee by the technique for worldly contrasts," Machine Learning, 3:9-44, 1988.
[6] P.J. Werbos, “Building and understanding adaptive systems: A statistical/numericaI approach to factory automation and brain research,” IEEE Transactions on Systems, Man, and Cybernetics, 17:7-20, 1987.
[7] S.M. Kendall and K. Ord, Time Series, Oxford University Press, New York, 1997.
[8] N. BaBa and M. Kozaki, “An intelligent forecasting system of stock price using neural networks,” Proc. IJCNN, Baltimore, Maryland, 652-657, 1992.
[9] R.J. Kuo, “A decision support system for the stock market through integration of fuzzy neural networks and fuzzy Delphi,” Applied Arlijkial Intelligence, 6501 -520, 1998.
[10] Sheta, A. F., Ahmed, S. E. M., & Faris, H. (2015). A comparison between regression, artificial neural networks and support vector machines for predicting stock market index. Soft Computing, 7, 8.
[11] oone, L., Giorno, C., & Richardson, P. (1998), Stock market fluctuations and consumption behaviour: some recent evidence (No. 208). OECD Publishing.
[12] Gharehchopogh, F.S., Mohammadi, P., &Hakimi, P. (2012). Application of Decision Tree Algorithm for Data Mining in Healthcare Operations: A Case Study. International Journal of Computer Applications, 52(6), 21-26.