Artificial Financial Market to analyze algorithm trade and institution
| Project/Area Number |
15K01195
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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 |
Social systems engineering/Safety system
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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), 准教授 (90295717)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 人工市場 / 高頻度アルゴリズム取引 / 人工知能 / 機械学習 / 深層学習 / ABM / HFT / ディープラーニング / アルゴリズム取引 / 高頻度トレード / 東証Arrows |
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| Outline of Final Research Achievements |
We develop analytical tools of order flow and compare between data of 2007 and 2012. Because TSE Arrowhead started 2010, we can find effects of HFT. At the first step, we count the number of events (including market orders, limit orders and cancel). The number of event increase 5 times from 2007 to 2012, but half of brands decrease the number of events. On the other hand, 22% of the brands increase more than ten times. It suggests that HFT trades mainly in a part of market. Next, We try to use Machine Learning especially Deep learning to apply market analysis. We developed four kinds of models, and observe their profit. Then we find there is no strongest model in these four models.
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Report
(4 results)
Research Products
(18 results)
