The Effects of Neural Networks Training Factors on Stock Price Prediction Errors

Ahmed F. Aleroud, Izzat M. Alsmadi, Ahmad I. Alaiad, and Qasem A. Al-Radaideh


Neural networks, Feed forward network, Training time


Machine learning approaches have been widely used for many financial applications. Neural network is an evolutionary computational approach which has proved its effectiveness in stock price prediction. This study evaluates the effects of changing two training factors on the performance of neural networks in short and long term stock price prediction error rates. The training time and the length of training period are boosted in a regular manner. Their effect on prediction error rate is evaluated. The results have shown that the length of training time has a significant effect on minimizing error rate in short term prediction, compared with its effect on long term prediction error rate. The results of training period factor indicated that increasing the training period has more effects on minimizing error rate of long term stock price prediction than its effect on the short term price prediction.

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