Study of time correlation in stock markets using self-excited polymer model

2023 Fiscal Year Final Research Report

• Project/Area Number: 18K04612
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 25010:Social systems engineering-related
• Research Institution: Okayama University
• Principal Investigator: Murai Joshin 岡山大学, 社会文化科学学域, 教授 (00294447)
• Project Period (FY): 2018-04-01 – 2024-03-31
• Keywords: 長期記憶 / クラスター展開 / ハースト指数 / 株式市場

Outline of Final Research Achievements

As analysis of high-frequency data in the stock market has progressed, many phenomena that could not be captured by daily data have become apparent. In this study, we focused on the causes of long memory phenomenon of trade signs, and conducted a theoretical study using a statistical mechanics. In addition to this, we focused on the interactions of market participants with different trading frequencies in order to unveil how complex phenomena such as intermittency and multifractals observed in the market arise. We then theoretically replicate these complex phenomena by assuming a cascading structure of investment behaviour from a group of infrequently traded market participants to a group of frequently traded market participants.

Free Research Field

確率モテル論，経済物理学

Academic Significance and Societal Importance of the Research Achievements

累積取引符号を表す離散型確率過程のスケール極限の連続時間確率過程がブラウン運動と異なるハースト指数を持つ複数の非整数ブラウン運動の重ね合わせになることを示したが，これらのハースト指数は1/2以上，すなわち得られた確率過程の増分は長期記憶を持つことが示された。さらに、異なる取引頻度をもつ市場参加者間のカスケード構造を導入することで、市場で観測される間欠性やマルチフラクタル性を再現する理論モデルを構築した。