Abstract
As it is known to all, many factors may have influence on the movement of stock index. In stock index forecasting, how many quantitative indicators should be introduced in order to obtain the best forecasting result? And is it true that more indicators translate into higher forecasting accuracy? These issues have long been puzzling to researchers of stock index forecasting. In this paper, we carried out data mining on some quantitative indicators with influence on the movement of stock index, then we had short-term forecasting of Shanghai Component Stock Index with BP+GA model. Results of our research are as follows: forecasting with combination of indicators has better result than forecasting with single indicators; combinations of indicators through selection and optimization have the best result; more indicators introduced into forecasting model do not translate into higher accuracy. The results of our research in this paper demonstrate the necessity and significance of data mining in stock index forecasting.
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Acknowledgments
This work is supported by Beijing Natural Science Foundation (9132011).
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© 2014 Springer-Verlag Berlin Heidelberg
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Shen, W., Wu, X., Zhang, T. (2014). Shanghai Component Stock Index Forecasting Model Based on Data Mining. In: Wen, Z., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54930-4_30
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DOI: https://doi.org/10.1007/978-3-642-54930-4_30
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