Sasmita S. Choudhury | Moumita Sen
OPEN ACCESS
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.
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