A hybrid time lagged network for predicting stock prices

Traditionally, technical analysis approach, that predicts stock prices based on historical prices and volume, basic concepts of trends, price patterns and oscillators, is commonly used by stock investors to aid investment decisions. Advanced intelligent techniques, ranging from pure mathematical models and expert systems to neural networks, have also been used in many financial trading systems for predicting stock prices. In this paper, we propose the Hybrid Time Lagged Network (HTLN) which integrates the supervised Multilayer Perceptron using temporal back-propagation algorithm with the unsupervised Kohonen network for predicting the chaotic stock series. This attempts to combine the strengths of both supervised and unsupervised networks to perform more precise prediction. The proposed network has been tested with stock data obtained from the main board of Kuala Lumpur Stock Exchange (KLSE). In this paper, the design, implementation and performance of the proposed neural network are described.

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