A milestone discovery in the subject of computer science in field of “Artificial Intelligence” to predict the future using Neural Network of Small events or limited variable events having few floating variables plays’s an essential role in our project. These variables can be economical as well as political or power shift in variable for predicting stock indices to have accurate prediction. This variable is used in hidden layer at multistage to iterate the value for best outcomes. To overcome such a huge calculation and trained our machine to operate individually to take such decision, we need to develop a neuron-like structure to look every possibility of outcome using “Neural Network”. Current demands of rocket trend in stock market for the assessment of health of country market and consumer power along with trust-building on company. In this paper we had implemented our research on TCS-SET’s (in Indian Stock Market) using Neural Network. This Research paper support Neural Network as it has fast computational advantage along with handling many variables at a time. The stock market closing is very important as it contributes to national growth, so a cat eye is needed on stock closing price. It also promotes the investor to invest or withdraw their share value from stock market before fall of its value. This unique quest of time and money in trade with computer knowledge help in forecasting of stock market along with Neural Network.

Artificial Neural Network, TCS, Stock Market

[1] S. Lee, D. Enke, and Y. Kim, “A relative value trading system based on a correlation and rough set analysis for the foreign exchange futures market,” Eng. Appl. Artif. Intell., vol. 61, no. February, pp. 47–56, 2017.
[2] S. Pyo, J. Lee, M. Cha, and H. Jang, “Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets,” PLoS One, vol. 12, no. 11, pp. 1–17, 2017.
[3] F. Wang, Y. Zhang, Q. Rao, K. Li, and H. Zhang, “Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction,” Soft Comput., vol. 21, no. 12, pp. 3193–3205, 2017.
[4] C. M. Anish and B. Majhi, “Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis,” J. Korean Stat. Soc., vol. 45, no. 1, pp. 64–76, 2016.
[5] F. Ye, L. Zhang, D. Zhang, H. Fujita, and Z. Gong, “A novel forecasting method based on multi-order fuzzy time series and technical analysis,” Inf. Sci. (Ny)., vol. 367–368, pp. 41–57, 2016.
[6] C. Bousono-Calzon, J. Bustarviejo-Munoz, P. Aceituno-Aceituno, and J. J. Escudero-Garzas, “On the Economic Significance of Stock Market Prediction and the No Free Lunch Theorem,” IEEE Access, vol. 7, pp. 75177–75188, 2019.
[7] A. M. Rather, A. Agarwal, and V. N. Sastry, “Recurrent neural network and a hybrid model for prediction of stock returns,” Expert Syst. Appl., vol. 42, no. 6, pp. 3234–3241, 2015.
[8] L. Y. Wei, “A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting,” Appl. Soft Comput. J., vol. 42, pp. 368–376, 2016.
[9] K. Chourmouziadis and P. D. Chatzoglou, “An intelligent short term stock trading fuzzy system for assisting investors in portfolio management,” Expert Syst. Appl., vol. 43, pp. 298–311, 2016.
[10] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques,” Expert Syst. Appl., vol. 42, no. 1, pp. 259–268, 2015.
[11] R. Geng, I. Bose, and X. Chen, Prediction of financial distress: An empirical study of listed Chinese companies using data mining, vol. 241, no. 1. Elsevier B.V., 2015.
[12] M. Ballings, D. Van Den Poel, N. Hespeels, and R. Gryp, “Evaluating multiple classifiers for stock price direction prediction,” Expert Syst. Appl., vol. 42, no. 20, pp. 7046–7056, 2015.
[13] P. C. Chang, J. L. Wu, and J. J. Lin, “A Takagi-Sugeno fuzzy model combined with a support vector regression for stock trading forecasting,” Appl. Soft Comput. J., vol. 38, pp. 831–842, 2016.
[14] S. Deng, K. Yoshiyama, T. Mitsubuchi, and A. Sakurai, “Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates,” Comput. Econ., vol. 45, no. 1, pp. 49–89, 2013.
[15] E. Chong, C. Han, and F. C. Park, “Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies,” Expert Syst. Appl., vol. 83, pp. 187–205, 2017.
[16] M. Thenmozhi and G. Sarath Chand, “Forecasting stock returns based on information transmission across global markets using support vector machines,” Neural Comput. Appl., vol. 27, no. 4, pp. 805–824, 2016.
[17] R. Dash and P. K. Dash, “Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique,” Expert Syst. Appl., vol. 52, pp. 75–90, 2016.