Stock Prediction using Neural Networks

Stock price prediction is one of the most challenging tasks. Stock markets are considered to be a promising trading field as it gives easy profits with low risk rate of return. Stock market with its huge and dynamic information sources is considered as a suitable environment for researchers. In this paper we have used Back Propagation Feed-forward neural network, historical data of various companies like TCS, Infosys, SBI listed on Bombay Stock Exchange and situational factors for stock prediction which would help investors in making appropriate decision. According to the results, the Back Propagation Feed Forward algorithm is robust giving results close to actual stock prices with minimum error rate.