Abstract
Understanding the factors affecting financial products is important for making investment decisions. Conventional factor analysis methods focus on revealing the impact of factors over a certain period locally, and it is not easy to predict net asset values. As a reasonable solution for the prediction of net asset values, in this paper, we propose a trend shift model for the global analysis of factors by introducing trend change points as shift interference variables into state space models. In addition, to realize the trend shift model efficiently, we propose an effective trend detection method, TP-TBSM (two-phase TBSM), by extending TBSM (trend-based segmentation method). The experimental results validate the proposed model and method.
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Acknowledgments
This work was partly supported by JSPS KAKENHI (16K12532).
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Kirihata, M., Ma, Q. (2018). Global Analysis of Factors by Considering Trends to Investment Support. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_8
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DOI: https://doi.org/10.1007/978-3-319-98809-2_8
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