Financial decision support with hybrid genetic and neural based modeling tools |
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| Affiliation: | 1. Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt;2. Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), 12582 Al Hadaba Al Wosta, Cairo, Egypt;1. Department of Mining Engineering, University of Tehran, Iran;2. Faculty of Health, Engineering and Science, Edith Cowan University, Perth, Australia;3. Department of Mining Engineering, University of Chile, Santiago, Chile;4. Advanced Mining Technology Center, University of Chile, Santiago, Chile |
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| Abstract: | This paper presents a comparative investigation of hybrid genetic classifiers vis-a-vis neural classifiers and statistical models in the financial domain. It is hypothesized that the proposed hybrid genetic classifier will perform better than the statistical counterpart. We provide a brief overview of the hybrid genetic classifier and discuss the design issues when applied to developing classification models for financial decision support. Further, the models are tested on a liquidation-merger problem. Results are consistent with the hypothesized premise. The proposed genetic classifiers outperform the statistical model. Implications of the comparison and issues for future research are addressed. |
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