A stock market is a public market for trading the company’s stock. Prediction provides knowledgeable information regarding the current status of the stock price movement. Hence, it can be utilized in decision making for customers in finalizing whether to buy or sell the particular shares of a given stock. Stock market forecasters focus on developing a successful approach for forecast or predict index values of stock prices. Since in stock market, data are highly time variant and are normally in a nonlinear pattern, pre predicting the future price of a stock is highly challenging. From the evolution of machine learning, researchers from this area are busy to solve this problem effectively. Many different techniques are used to build predicting system. Here we describe the different state of the art techniques used for stock forecasting and compare them with respect to their pros and cons. Many methods like technical analysis, fundamental analysis, time- series analysis etc are used to predict the price but none of these are proved as a consistently acceptable. Neural Network is the best technique till time to predict stock prices especially when some de-noising schemes are applied to a neural network. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. The past data of the selected stock will be used for building and training the models. The results from the model will be used for comparison with the real data to ascertain the accuracy of the model. In this approach, we use back propagation algorithm for training phase and multilayer feed forward network as a network model for predicting the price of a share.
Artificial neural networks, Multi-layer feed forward neural network, back propagation, the stock market.
1. R.K. Dase, D.D. Pawar, Application of artificial neural network for stock market predictions: A review of literature, 2010, International Journal of Machine Intelligence, vol. 2, no. 2, pp. 14-17.
2. H. White, Economic prediction using neural networks: The case of IBM daily stock returns, Department of Economics University of California, San Diego.
3. J.T. Yao, C.L. Tan, Guidelines for financial prediction with artificial neural networks.
4. T.H.K. Yu, K.H. Huarng, A neural network-based fuzzy time series model to improve forecasting, 2010, Elsevier, pp. 3366-3372.
5. T. Akinwale Adio, O.T. Arogundade, F. Adekoya Adebayo, Translated Nigeria stock market price using artificial neural network for effective prediction, 2009, Journal of theoretical and Applied Information technology.
6. D. Enke, S. Thawornwong, The use of data mining and neural networks for forecasting stock market returns, 2005.
7. Y.F. Wang, S.M. Cheng, M.H. Hsu, Incorporating the Markov chain concepts into fuzzy stochastic prediction of stock indexes, 2010, Applied Soft Computing, pp. 613-617.
8. H. Md. Rafiul, N. Baikunth, Stock Market forecasting using Hidden Markov Model: A new approach, 2005, Proceeding of the 2005 5th international conference conference on intelligent Systems Design and Application 07695-2286-06/05, IEEE 2005.
9. M.P. Naeini, H. Taremian, H.B. Hashemi,Stock market value prediction using neural networks, 2010, International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 132-136.
10. M. Majumder, A. Hussian, Forecasting of Indian market index using Artificial Neural Network.
11. C. Bhagwant, B. Umesh, A. Gangathade, S. Kale, Stock market prediction using artificial neural networks, 2014, (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 5, no. 1, pp. 904-907.
12. M. Zekic, Neural network applications in stock market predictions: A methodology analysis.
13. E. Schoeneburg, Stock price prediction using neural networks: A Project Report, 1990, Neurocomputing, vol. 2, pp. 17-27.
14. S. Haykin, Feed forward Neural Networks: An introduction.
15. S. Haykin, Neural Networks and Learning Machines.