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Journal of Artificial Intelligence
Year: 2008  |  Volume: 1  |  Issue: 2  |  Page No.: 70 - 77

Stock Price Direction Prediction Using Artificial Neural Network Approach: The Case of Turkey

D. Senol and M. Ozturan    

Abstract: In this study, it is aimed to illustrate that Artificial Neural Network (ANN) can be used for predicting the stock price behaviour in terms of its direction. Financial daily statistical data, derived from raw price data obtained from Istanbul Stock Exchange (ISE), which is the only stock market in Turkey, have been defined in terms of five independent variables that are grouped in seven different Prediction System (PS) models to which eight different ANN and Logistic Regression (LR) models have been applied. For this purpose, a software library package is developed using C#.NET to run the ANN models whereas a commercial statistical analysis software package is used to run the LR model. At the end of the study; the best PS and ANN models are determined for ANN methodology by comparing the average mean squared errors of training sets and the best PS model is determined for LR methodology by eliminating the insignificant independent variables; the outputs of the developed software library package and a commercial ANN software are compared on the basis of prediction success rate and the accuracies of prediction by ANN and LR methodologies are compared on the basis of coefficient of determination. The results show that; the best results are obtained for the PS model that has used stochastic indicator for 14 days (K14%), stochastic moving average (D3%) and relative strength index of 14 days (RSI14) simultaneously for both ANN and LR methodologies whereas the best ANN model has consisted of three inputs, 11 hidden neurons in single hidden layer and one output; developed software library package performs statistically same as the commercial software; statistically ANN methodology outperforms LR methodology; and there is relevant empirical evidence that ISE-30 is not weak form efficient.

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