Nonlinear time-series analysis focusing on the background dynamics and its application to financial engineering
| Project/Area Number |
25330280
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Soft computing
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| Research Institution | Ibaraki University |
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Principal Investigator |
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2013: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 非線形時系列予測 / 集団型機械学習 / ポートフォリオ理論 / 金融工学 / テクニカル分析 / 複雑系 / 金融情報学 / 集団学習 / カオス理論 |
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| Outline of Final Research Achievements |
This project had aimed to improve nonlinear time-series analysis and prediction by learning the background dynamics hidden in the observed time-series data as precisely as possible. For this purpose, the time-series data was carefully observed so as not to destroy the background dynamics. However, because it is impossible to completely eliminate prediction errors, we considered them as prediction risks, and proposed how to estimate them beforehand and reduce them effectively. Since this approach based on both the prediction and its risk management can be applied to financial engineering such as the portfolio theory, this project proposed a new framework named "nonlinear financial engineering" as an application of nonlinear dynamical theory.
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Report
(4 results)
Research Products
(40 results)
