Abstract
This study presents a stock selective system by using hybrid models to look for sound financial companies that are really worth making investment in stock markets. The following are three main steps in this study: First, we utilize rough sets theory to sift out the core of the financial indicators affecting the ups and downs of a stock price. Second, based on the core of financial indicators coupled with the technology of decision tree, we establish hybrid classificatory models and predictable rules that would affect the ups and downs of a stock price. Third, by sifting the sound investing targets out, we use the established rules to set out to invest and calculate the rates of investment. These evidences reveal that the average rates of reward are far larger than the mass investment rates.
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Cheng, SH. (2013). An Intelligent Stock-Selecting System Based on Decision Tree Combining Rough Sets Theory. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_51
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DOI: https://doi.org/10.1007/978-3-642-38577-3_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38576-6
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