| Project/Area Number |
17K18959
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Social systems engineering, Safety engineering, Disaster prevention engineering, and related fields
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| Research Institution | Osaka City University |
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Principal Investigator |
Takada Teruko 大阪市立大学, 大学院経営学研究科, 准教授 (30347504)
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| Project Period (FY) |
2017-06-30 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2018: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 非対称度 / 相転移予測 / 投資家行動 / 株式バブル / データ駆動型 / ノンパラメトリック / システム不安程度計測 / 高頻度株式統計 / ノンパラメトリック確率密度推定 |
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| Outline of Final Research Achievements |
We aimed at predicting phase transition with higher precision by using the extracted system instability feature focusing on the “symmetry breaking” investor behavior. First, several precursory price change patterns useful for predicting phase transitions are newly found. Moreover, the small cap stocks change patterns and investor risk appetite levels are added as the most sensitive factors reflecting the system instability level. Then, we proposed fully data-driven long-term trend direction prediction method which outperforms conventional methods in the crash avoidance performance by taking several measures for challenging but inevitable problems of nonstationarity and class imbalance for predicting phase transitions. It is also verified that the asymmetric price change pattern is induced by expanding investor expectation, leading to a subsequent large price decline.
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| Academic Significance and Societal Importance of the Research Achievements |
株式バブル崩壊のような相転移現象は社会に大きな損失をもたらすため、その予測や制御は、社会的喫緊の課題である。本研究の成果は、金融市場のみならず、様々なシステムにおける大きな変化をもたらす相転移現象への活用への発展が可能であり、社会的意義が高い。相転移現象の予測や制御は、被害をもたらす現象の情報が少ない一方で、それを生み出すシステムは多くの要素が絡む非常に複雑なシステムであるため、現代科学にとっての難題である。本研究が実現した、データの大規模化による情報増大と、非定常性問題や価格下落方向の情報の少なさといった本質的な問題への対処による暴落回避性能の向上は、学術的にも意義が高いものである。
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