Presentation 2013/3/4
Detection of Anomaly order for Phase Transition in the stock Market
Toshimitsu UMEOKA, Fujio TORIUMI, Takatsugu HIRAYAMA, Yu ENOKIBORI, Kenji MASE,
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Abstract(in English) Since Lehman Crisis, studies to deal with financial market stabilization have come under the spotlight. As one of the financial theories, there is the concept of phase transition which is the analogy of a thermodynamic system from one phase or state of matter to another. This concept is useful for understanding of state transition in the stock market. Therefore, we propose a method to detect state transitions in the stock market, using a Gaussian Mixture Model.
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Keyword(in English) Data Mining / Finance / Big Data / Gaussian Mixture Model / Anomaly Detection / Phase Transition
Paper # AI2012-52
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Committee AI
Conference Date 2013/3/4(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Detection of Anomaly order for Phase Transition in the stock Market
Sub Title (in English)
Keyword(1) Data Mining
Keyword(2) Finance
Keyword(3) Big Data
Keyword(4) Gaussian Mixture Model
Keyword(5) Anomaly Detection
Keyword(6) Phase Transition
1st Author's Name Toshimitsu UMEOKA
1st Author's Affiliation Graduate School of Information Science, Nagoya University()
2nd Author's Name Fujio TORIUMI
2nd Author's Affiliation School of Engineering, The University of Tokyo
3rd Author's Name Takatsugu HIRAYAMA
3rd Author's Affiliation Graduate School of Information Science, Nagoya University
4th Author's Name Yu ENOKIBORI
4th Author's Affiliation Graduate School of Information Science, Nagoya University
5th Author's Name Kenji MASE
5th Author's Affiliation Graduate School of Information Science, Nagoya University
Date 2013/3/4
Paper # AI2012-52
Volume (vol) vol.112
Number (no) 477
Page pp.pp.-
#Pages 6
Date of Issue