Global Analysis of Factors by Considering Trends to Investment Support
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.
KeywordsFactor analysis State space model Trend detection
This work was partly supported by JSPS KAKENHI (16K12532).
- 2.Mahajan, A., Dey, L., Haque, S.M.: Mining financial news for major events and their impacts on the market. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01, pp. 423–426. IEEE (2008)Google Scholar
- 3.Awano, Y., Ma, Q., Yoshikawa, M.: Causal analysis for supporting users’ understanding of investment trusts. In: Proceedings of the 16th International Conference on Information Integration and Web-based Applications and Services, pp. 524–528. ACM (2014)Google Scholar
- 5.Ando, T.: Bayesian state space modeling approach for measuring the effectiveness of marketing activities and baseline sales from POS data. In: Sixth International Conference on Data Mining (ICDM 2006), pp. 21–32. IEEE (2006)Google Scholar
- 6.Onishi, N., Ma, Q.: Factor analysis of investment trust products by using monthly reports and news articles. In: 2017 Twelfth International Conference on Digital Information Management (ICDIM), pp. 32–37. IEEE (2017)Google Scholar
- 7.Suzuki, T., Ota, M., et al.: Nonlinear prediction for top and bottom values of time series. Trans. Math. Model. Appl. 2(1), 123–132 (2009). In JapaneseGoogle Scholar
- 9.Wu, J.L., Chang, P.C.: A trend-based segmentation method and the support vector regression for financial time series forecasting. Math. Probl. Eng. 2012, 20 p. (2012)Google Scholar