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2013 | 146 | 59-67

Article title

Porównanie podejścia aproksymującego i klasyfikującego w prognozowaniu kursów wybranych akcji na GPW w Warszawie S.A. z użyciem jednokierunkowych sieci neuronowych



Title variants

Forecasting Stock Prices Using Feed-Forward Neural Network - a Comparison of Approximation and Classification Approaches

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In this paper two approaches to financial time series forecasting using neural networks were compared. First one, the function approximation approach, in which neural networks are trained to forecast the exact one day ahead value of stock price. And the second one, classification approach, in which the output variable is the direction of future stock price movements. The aim of this work was to check if using the classification models can lead to better results in terms of direction of change forecasting and profits generated by their forecasts. This research was conducted on the basis of the time series of daily closing stock prices for three companies listed on the Warsaw Stock Exchange. Simulations show that some of the approximating models achieved satisfactory results in terms of the directional symmetry measure, although the best results for each of the analyzed company have been achieved for classification models.






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Publication order reference



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