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Entropy 2010, 12(12), 2397-2417; doi:10.3390/e12122397
Article

Forecasting the Stock Market with Linguistic Rules Generated from the Minimize Entropy Principle and the Cumulative Probability Distribution Approaches

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Received: 28 October 2010 / Revised: 18 November 2010 / Accepted: 22 November 2010 / Published: 3 December 2010
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Abstract

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2) the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach) and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR) in forecast accuracy.
Keywords: minimize entropy principle approach; cumulative probability distribution approach; rough set theory; stock market forecasting minimize entropy principle approach; cumulative probability distribution approach; rough set theory; stock market forecasting
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Su, C.-H.; Chen, T.-L.; Cheng, C.-H.; Chen, Y.-C. Forecasting the Stock Market with Linguistic Rules Generated from the Minimize Entropy Principle and the Cumulative Probability Distribution Approaches. Entropy 2010, 12, 2397-2417.

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