Effects of dark pools on financial markets’ efficiency and price discovery function: an investigation by multi-agent simulations

Mizuta, Takanobu; Kosugi, Shintaro; Kusumoto, Takuya; Matsumoto, Wataru; Izumi, Kiyoshi

doi:10.1007/s40844-015-0020-3

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Price deviation, also called 'relative absolute deviation' (Stöckl, Huber, & Kirchler, 2010) or 'market inefficiency' (Mizuta, Kosugi, Kusumoto, Matsumoto, & Izumi, 2015), measures the difference between stock price and fundamental value, which captures the degree of price distortion from the fundamental value. It is defined as…”

Section: Price Deviationmentioning

confidence: 99%

Tick size and market quality: Simulations based on agent‐based artificial stock markets

Yang

Zhang

2020

Intell Sys Acc Fin Mgmt

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Summary This paper investigates the way that minimum tick size affects market quality based on an agent‐based artificial stock market. Our results indicate that stepwise and combination systems can promote market quality in certain aspects, compared with a uniform system. A minimal combination system performed the best to improve market quality. This is the first study to analyse tick size systems that remain at the theory stage and compare four types of system under the same experimental environment. The results suggests that a minimal combination system could be considered a new direction for market policy reform to improve market quality.

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Section: Price Deviationmentioning

confidence: 99%

Tick size and market quality: Simulations based on agent‐based artificial stock markets

Yang

Zhang

2020

Intell Sys Acc Fin Mgmt

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“…In this research, we constructed an artificial market model on the basis of the artificial market model of Chiarella et al [4] and Yagi et al [22], because they built simple agents and a pricing mechanism which could reproduce the statistical characteristics of the kinds of long-term price fluctuations observed in empirical analyses. Moreover, algorithm agents [14] and a position-based market maker [9] were added to our model. The normal agents used in the basis models [4,22] correspond to both takers and makers in real financial markets, where the algorithm agents correspond to takers and the position-based market makers correspond to makers.…”

Section: Artificial Market Modelmentioning

confidence: 99%

Analysis of the impact of maker-taker fees on the stock market using agent-based simulation

Yagi

Hoshino

Mizuta

2020

Proceedings of the First ACM International Conference on AI in Finance

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Recently, most stock exchanges in the U.S. employ maker-taker fees, in which an exchange pays rebates to traders placing orders in the order book and charges fees to traders taking orders from the order book. Maker-taker fees encourage traders to place many orders that provide market liquidity to the exchange. However, it is not clear how maker-taker fees affect the total cost of a taking order, including all the charged fees and the market impact. In this study, we investigated the effect of maker-taker fees on the total cost of a taking order with our artificial market model, which is an agent-based model for financial markets. We found that makertaker fees encourage market efficiency but increase the total costs of taking orders. CCS CONCEPTS• Applied computing → Economics; • Computing methodologies → Multi-agent systems.

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“…Many studies have investigated the effects of kinds of investors on price formation and the effects of several changing regulations and rules using artificial market simulations, for example, leveraged ETFs (Yagi and Mizuta 2016), high-frequency traders (HFTs) (Gsell 2009;Wang et al 2013;Xiong et al 2015;Leal and Napoletano 2016;Hanson and Zaima 2016), arbitrage traders between markets that have different latencies (Wah and Wellman 2013), market impacts (Cui and Brabazon 2012;Oesch 2014), financial market crashes (Yagi et al 2012;Paddrik et al 2012;Torii et al 2015), price variation limits (Yeh and Yang 2010;Mizuta et al 2013Mizuta et al , 2014a, frequent batch auctions , dark pools (Mo and Yang 2013;Mizuta et al 2014bMizuta et al , 2015, investor networks and herding (Wang and Toriumi 2017), the increasing speed of order matching systems on financial exchanges , market efficiency (Pruna et al 2016), the rules for investment diversification (Yagi et al 2017), market liquidities on the network of banks (Sakiyama and Yamada 2016), interaction between option markets and underlying markets (Kawakubo et al 2014a, b), extension of trading hours (Miwa 2018) and (Kita et al 2016), reviewed the U-Mart project which is one of the Japanese top artificial market research projects in the 2000s.…”

Section: Introductionmentioning

confidence: 99%

Mechanism by which active funds make market efficient investigated with agent-based model

Mizuta

Horie

2018

Evolut Inst Econ Rev

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Since active managed funds, in which a manager chooses stocks expected to rise in price, invest on the basis of the intrinsic fundamental value of companies, they discover the fundamental price and make market prices converge with the fundamental price (make a market efficient); therefore, they play an important role in allocating capital, which is an important function in capitalism. A previous empirical study showed active funds that trade infrequently, "patient" active funds, earn more. At first glance, what patient active funds trade infrequently seems inconsistent with making a market efficient. In this study, we modeled agents who reflect the characteristics of patient active funds that trade infrequently and "impatient" active funds that trade frequently. We succeeded in figuring out the mechanism of how patient and impatient funds impacted market prices and in proving that what patient active funds trade infrequently is not inconsistent with making a market efficient. Concretely, the simulation results indicated that patient active funds trade frequently only in the rare situation that a market became unstable and inefficient. These trades, occurring only at a necessary time, impact market prices and lead them to converge with the fundamental price. The results also indicated that patient active funds earn less not so much because of a more efficient market, but because the market is too inefficient, so changes in price formation due to trades of impatient active funds reduce the chance that patient active funds will realize profit. KeywordsAgent-based model • Multi-agent simulation • Artificial market simulation • Active managed fund • Passive managed fund • Index fund • Market efficiency • Price discovery function The opinions contained herein are solely those of the authors and do not necessarily reflect those of SPARX Asset Management Co., Ltd.

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Effects of dark pools on financial markets’ efficiency and price discovery function: an investigation by multi-agent simulations

Cited by 9 publications

References 14 publications

Tick size and market quality: Simulations based on agent‐based artificial stock markets

Tick size and market quality: Simulations based on agent‐based artificial stock markets

Analysis of the impact of maker-taker fees on the stock market using agent-based simulation

Mechanism by which active funds make market efficient investigated with agent-based model

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