Prediction of stock price direction by artificial neural network approach

Abstract

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. It carries a higher risk than any other investment area, due to its high rate of uncertainty and volatility, thus making the stock price behavior difficult to forecast. For years, conventional methods have been developed but they have succeeded partially or have completely failed to deal with the nonlinear and complex behavior of stock prices. Artificial neural networks approach is a relatively new, active and promising field on the prediction of stock price behavior. Artificial neural networks (ANNs) are mathematical models simulating the learning and decision making processes of the human brain. Because of their nature of easy adaptation to noisy data, and solving complex and nonlinear problems, they fit into the area of stock price behavior prediction. The Istanbul Stock Exchange (ISE) is the only stock market in Turkey, which has an emerging economy. The market situations and economic fluctuations in Turkey create more uncertainty and volatility in the stock market when compared to emerged markets. This study tries to reduce the effect of this uncertainty and volatility by modeling the change in stock price direction of stocks, identifying the theory and steps involved in applying ANN in financial markets and developing a software package to be used for predicting directional daily stock price behavior. It also discusses the appropriate ways to use this process in developing trading systems, further discussing the superiority of ANN over traditional methodologies.