Abstract
This study presents a 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 neural network theory to find out the core of the financial indicators affecting the ups and downs of a stock price. Second, based on the core of the financial indicators coupled with the technology of decision tree, we establish the hybrid classificatory models and the predictable rules that 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. (2014). Predicting Stock Returns by Decision Tree Combining Neural Network. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_37
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DOI: https://doi.org/10.1007/978-3-319-05458-2_37
Publisher Name: Springer, Cham
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