Towards Enhancing Stock Market Watching Based on Neural Network Predictions
نویسندگان
چکیده
Under the growth of the stock market sector and the widespread of stock market applications, the stock market prediction has become one of the most important and challenging tasks in the stock market. Many data mining techniques are exploited to predict the stock prices in order to help investors in making investing decisions. One of the most common and widely used techniques is Artificial Neural Networks (ANN). In this paper, we aim to present a model for stock market prediction based on artificial neural networks. This model uses the variables of technical analysis of stock market indicators for predicting stock market prices. The proposed model is tested and evaluated using Palestine Exchange Trading (PEX) data, the experimental validations show satisfactory results that help investors and traders to make qualitative decisions. The proposed model employs an adaptive process for optimizing neural network weights based on back-propagation learning strategy. The proposed model improves the effectiveness of forecasting the stock prices of Palestine Exchange Trading (PEX).
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