[01] Amir Hosein Ghaderi, Neuroscience Lab., University of Tabriz, Tabriz, Iran. [02] Bahar Barani, Medical School, University of Kansas, Kansas City, USA. [03] Hoda Jalalkamali, Neuroscience Lab., University of Tabriz, Tabriz, Iran.

Recently artificial neural networks (ANNs) have become crucial for the analysis of several phenomena in the world. Stock market prices is a nonlinear phenomenon that several studies are accomplished in this context using ANN. Determine the input dimension of network is a basic problem in application of ANN to prediction of stock prices. On the other hand, stock market behaves as a chaotic system with nonlinear and deterministic manner. Therefore chaos theory can be helpful to determine the input dimension of network. In this study a multilayer perceptron with Backpropagation learning algorithm is used for forecasting of stock prices in Tehran Stock Exchange (TSE). Prediction accuracy of ANN with various input dimensions is compared and the best result is achieved when the input dimension is equal to Takens embedding dimension. It is concluded that selection of Takens embedding as ANN input dimension can be led to very accurate predictions and best results in TSE.

Neural Network, Chaos Theory, Multilayer Perceptron, Embedding Dimension, Stock Market, Forecasting

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