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Global Analysis of Factors by Considering Trends to Investment Support

  • Makoto Kirihata
  • Qiang Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11029)

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.

Keywords

Factor analysis State space model Trend detection 

Notes

Acknowledgments

This work was partly supported by JSPS KAKENHI (16K12532).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Kyoto UniversityKyotoJapan

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