大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,The University of Osaka

Market Fragmentation and Trading Aggressiveness

QU Chengcheng (立命館アジア太平洋大学)

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4. Market Fragmentation and Trading Aggressiveness

大阪大学 数理・データ科学セミナー 金融・保険セミナーシリーズ 第155回

Market Fragmentation and Trading Aggressiveness

QU Chengcheng (立命館アジア太平洋大学)

Prior theoretical models predict that when the same asset is traded at multiple exchanges simultaneously, the time priority and the impact of the price weaken across exchanges, motivating traders to trade more aggressively in a fragmented market than in a centralized market. This paper tests this prediction based on public quotes and trades data. Employing a reinforcement learning approach, this paper estimates the optimal level of order splitting to achieve the minimal transaction costs in fragmentation and centralization. The results suggest that a liquidity trader will split the same parent order into child orders of similar sizes under a fragmented market compared with a centralized market, and the execution takes a shorter time under fragmentation. The results imply that the liquidity trader acts more aggressively under fragmentation.