Abstract
This study presents a hybrid model to predict stock returns. The following are four main steps in this study: First, logistic stepwise regression theory is used to find out the core financial indicators by computing the importance of 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 classification and regression tree, a hybrid classificatory model is established. Third, the predictable rules that affect the ups and downs of a stock price are obtained by employing the proposed hybrid classificatory model. Fourth, we use the established rules to sift out the sound investing targets to invest and calculate the rates of investment. These results of simulated investment reveal that the average rates of reward are far larger than the mass investment rates.
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© 2015 Springer International Publishing Switzerland
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Cheng, SH. (2015). A Hybrid Predicting Stock Return Model Based on Logistic Stepwise Regression and CART Algorithm. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_60
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DOI: https://doi.org/10.1007/978-3-319-15702-3_60
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