著者

西岡 伸 鳥居 拓馬 楠本 拓矢 松本 渉 和泉 潔

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌

巻号頁・発行日

vol.32, no.5, pp.AG16-C_1-10, 2017

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<p>In recent financial market, high frequency traders (HFTs) and dark pools have been increasing their share. Financial analysts have speculated that they might decrease market transparency and malfunction price discovery, and their interaction would make the situation worse.To validate speculations, artificial market simulation is a tool of study by constructing virtual markets on computers. In this research, by constructing an artificial market simulation, we analyzed how the interaction between HFTs and a dark pool impacts on the market efficiency (in the sense of price discovery) of a (lit) stock market. In simulations, two types of trader agents enter the market. A market maker agent, a representative strategy of HFTs, submit orders to the lit market. We analyzed the market maker's interest rate spread, or simply the spread, as a key parameter for their strategy. Stylized trader agents submit orders to either the lit market or the dark pool with some probability given as a parameter.The simulation results suggest that on the condition that market makers have little impact to market pricing (having a large spread), moderate use of dark pools can promote market pricing. On the other hand, on the condition that market makers have big impact to market pricing, excessive use of dark pools can inhibit market pricing, while using dark pools do not have bad influence when the rate of use is not high. On the influence of market makers, our results suggest that the bigger the impact to market pricing (a small spread), the more it can promote market pricing.</p>