Data mining application to financial market to discover the behavior of entry point – a case study of Taiwan index futures market

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Abstract

The value of the investment method is that investors who are anxious to pursue, there are many value investing methods have been proposed, but only a minority of the value investing method were proved to be effective. The study is based on messages generated defined value of the investment by Steidlmayer in 1984 proposed market profile theory. In order to extract trading behavior of dealer and product value by the huge financial trading information, the model used the trading data to capture feature patterns, and find the double distribution trend day generated by market profile. The experimental results show the single print as an entry point, and the reference to historical support and pressure line as an exit point. The results show the returns are 24.09 points and the accuracy achieved 57.45%.The results had shown the analysis model can find the investment goods real value from the huge trading information, and help investors obtain excess returns.

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Wu, M. C., Yang, B. W., Lin, C. H., Huang, Y. H., & Chen, A. P. (2016). Data mining application to financial market to discover the behavior of entry point – a case study of Taiwan index futures market. In Advances in Intelligent Systems and Computing (Vol. 382, pp. 295–303). Springer Verlag. https://doi.org/10.1007/978-3-662-47926-1_29

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