Elucidation of qualitative changes in securities markets caused by accelerating transactions and AI
| Project/Area Number |
18K01692
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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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| Review Section |
Basic Section 07060:Money and finance-related
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| Research Institution | Osaka City University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
森 直樹 大阪府立大学, 工学(系)研究科(研究院), 教授 (90295717)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 人工市場 / HFT / U-Mart / 人工知能 / 組成 / 逆問題 / AI / 株価 |
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| Outline of Final Research Achievements |
The following three studies were conducted to ready the impact of the acceleration of the securities market, thee increase in algorithm trading, and the development of AI technology on the securities market. (1) Through simulation using artificial markets, we research the complexity arisen from trading by various agents and the complexity brought about by their interactions. We found even if each trading algorithms are simple traditional methods, combination of trading algorithm arise the complexity. (2) We developed a trading agents using deep learning and the agents showed sufficient performance in the artificial electric power market. (3) In order to carry out the above research, the artificial market simulator U-Mart system was improved and used.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では、証券市場の高速化とアルゴリズム取引の急増及びAI技術の進展が証券市場にもたらす影響を調べるための方法として、エージェント・ベース・シミュレーションの１形態である人工市場研究が有効である事を示すことができた。実際、単純な取引アルゴリズムが複数組み合わされることで生じる複雑さは、複雑系研究で分類された範疇を超えるものであり、暴落や暴騰の予測可能性などを議論する際には、こうした実験的方法が必要となる。また、また、ディープラーニングを始めとした複雑な内部規則を持つアルゴリズム取引の有効性を評価するためには、様々な環境における行動と結果を吟味する必要がある。
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Report
(5 results)
Research Products
(22 results)
