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Financial forecasting using random subspace classifier

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1 Author(s)
Zhora, D.V. ; Inst. of Software Syst., Kiev, Ukraine

Random subspace classifier is used for prediction of a stock price return. While obtaining interesting results with a basic model it's possible to construct more competitive network by using several approaches for improving the prediction accuracy and performance characteristics. The following methods are considered in this work: normalizing input data, generating a sensitive classifier structure and variance structure selection. The best average success rate achieved in the prediction of the stock price change direction is 58.1%.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004