Stock Market Prediction on High-Frequency Data Using ANN
Arafat Jahan Nova *
Bangladesh University of Business and Technology, Computer Science and Engineering, Dhaka, Bangladesh.
Zahada Qurashi Mim
Bangladesh University of Business and Technology, Computer Science and Engineering, Dhaka, Bangladesh.
Sanjida Rowshan
Bangladesh University of Business and Technology, Computer Science and Engineering, Dhaka, Bangladesh.
Md. Riad Ul Islam
Bangladesh University of Business and Technology, Computer Science and Engineering, Dhaka, Bangladesh.
Md Nurullah
Bangladesh University of Business and Technology, Computer Science and Engineering, Dhaka, Bangladesh.
Milon Biswas
Bangladesh University of Business and Technology, Computer Science and Engineering, Dhaka, Bangladesh.
*Author to whom correspondence should be addressed.
Abstract
A stock market is a place where company shares are traded to the stockbrokers. Stock price prediction is one of the most challenging problems as a high level of accuracy is the key factor in predicting a stock market. Many methods are used to predict the price in the stock market but none of those methods are proved as a consistently acceptable prediction tool due to its volatile nature. In this paper, we proposed Artificial Neural Network (ANN) technique because ANN can generalize and predict data after learning and analyzing from the initial inputs and their relationships. We used feed forward network and backward propagation algorithm to predict stock prices. In this paper, we introduced a method that can find out the future value of stock prices in a particular day based on some input using ANN back propagation algorithm.
Keywords: Artificial neural networks, stock market, stock price, feed forward artificial neural networks, backward propagation algorithm