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International Conference on Software Technology and Engineering (ICSTE 2012)

Jianhong Zhou
Jianhong Zhou
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ASME Press
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Financial time series forecasting has long been an application area for soft computing techniques. The essential problem is to find a soft computing model that is compatible with technical analysis models used in stock markets. Backpropagation neural networks have often been used to emulate a technical analyst’s prediction. In this paper, two primary technical indicators, William’s %R and Moving Average Convergence Divergence (MACD), have been used for feature extraction from price data. We have focused on optimizing the separabilty of the resulting feature space. Levenberg-Marquardt variation of backpropagation algorithm has been found to be an effective decision engine to predict...

1. Introduction
2. Proposed System
3. Measures of Performance
4. Results
5. Conclusion
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