Finance Stock Price Prediction by Artificial Neural Networks: A Study of Jordanian's Stock Prices (J.S.P)

Abstract

This paper presents a study of artificial neural networks for use in stock price prediction. The data from an emerging market Jordanian's Stock Prices (J.S.P), are applied as a case study. Software was developed by using MATLAB to simulate the performance and efficiency of the algorithm. Simulation was conducted for seven Jordanian companies from service and manufacturing sectors. The companies were sampled from different categories which vary according to the degree of stock stability.
A multilayer perception (M.L.P) neural network model is used to determine and explore the relationship between some variables as independent factors and the level of stock price index as a dependent element in the stock market under study over time. The results show that the neural network models can get better outcomes compared with parametric models like regression and others traditional statistical techniques. Our test also shows that useful predictions can be made without the use of extensive market data or knowledge, and in the data mining process, neural networks can explore some orders which hide in the market structure