Detection of Factors Influencing Market Liquidity Using an Agent-Based Simulation

Yagi, Isao; Masuda, Yuji; Mizuta, Takanobu

doi:10.1007/978-981-13-8319-9_6

Cited by 9 publications

(21 citation statements)

References 13 publications

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“…We have built a new artificial market model with an HFT agent on the basis of the artificial market model put forward by Yagi et al [46], because their model can reproduce the statistical characteristics of the kinds of long-term price fluctuations observed in empirical analyses. We seek to investigate the relationship among the four market liquidity indicators by changing the parameters.…”

Section: Artificial Market Modelmentioning

confidence: 99%

“…One of the objectives of artificial markets is to reproduce the statistical features of the price process with minimal hypotheses about the intelligence of agents [60]. Early artificial markets appeared unable to account for the ubiquitous scaling laws of returns; however, recent artificial markets have been able to reproduce fat tails and volatility clustering [46], [52], [61]- [64]. Therefore, we set the artificial market parameters as listed in Table I as well as Yagi et al [46] so as to replicate these features.…”

Section: B Validation Of Proposed Artificial Marketmentioning

confidence: 99%

“…Early artificial markets appeared unable to account for the ubiquitous scaling laws of returns; however, recent artificial markets have been able to reproduce fat tails and volatility clustering [46], [52], [61]- [64]. Therefore, we set the artificial market parameters as listed in Table I as well as Yagi et al [46] so as to replicate these features. The values of these parameters were determined by trial-and-error experiments in Mizuta et al [36], which described the base model used in Yagi et al [46].…”

Section: B Validation Of Proposed Artificial Marketmentioning

confidence: 99%

“…Some artificial market simulations have been fruitful in terms of producing useful findings [44], [45]. For example, Yagi et al [46] set out to investigate the relationship among liquidity indicators by changing artificial market factors. Specifically, they explored the relationship among four typical indicators-volume, tightness, resiliency, and depthby changing artificial market factors such as the tick size of market price.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity

Yagi

Masuda

Mizuta

2020

IEEE Trans. Comput. Soc. Syst.

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Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern, but no studies have systematically discussed their relationship with major market liquidity indicators, including volume, tightness, resiliency, and depth. In this study, we used agent-based simulations to compare the major liquidity indicators in an artificial market where an HFT participated was compared to one where no HFT participated. The results showed that all liquidity indicators in the market where an HFT participated improved more than those in the market where no HFT participated. Furthermore, as a result of investigating the correlations between the major liquidity indicators in our simulations and the extant empirical literature, we found that market liquidity can be measured not only by the major liquidity indicators but also by execution rate. Therefore, it is suggested that it could be appropriate to employ execution rate as a novel liquidity indicator in future studies.

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Section: Artificial Market Modelmentioning

confidence: 99%

Section: B Validation Of Proposed Artificial Marketmentioning

confidence: 99%

Section: B Validation Of Proposed Artificial Marketmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity

Yagi

Masuda

Mizuta

2020

IEEE Trans. Comput. Soc. Syst.

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“…In the proposed model, only one risk asset (i.e., the underlying asset of a leveraged ETF) is available for trading. e mechanism for determining the price in this model is a continuous double auction (continuous trading session) [14]. It means that if there are some sell (buy) order prices in the order book that are lower (higher) than the agent's buy (sell) order price, then the agent's order is immediately matched to the lowest sell order (highest buy order) in the order book.…”

Section: Artificial Market Modelmentioning

confidence: 99%

Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation

2020

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A leveraged ETF is a fund aimed at achieving a rate of return several times greater than that of the underlying asset such as Nikkei 225 futures. Recently, it has been suggested that rebalancing trades of a leveraged ETF may destabilize the financial markets. An empirical study using an agent-based simulation indicated that a rebalancing trade strategy could affect the price formation of an underlying asset market. However, no leveraged ETF trading method for suppressing the increase in volatility as much as possible has yet been proposed. In this paper, we compare different strategies of trading for a proposed trading model and report the results of our investigation regarding how best to suppress an increase in market volatility. As a result, it was found that as the minimum number of orders in a rebalancing trade increases, the impact on the market price formation decreases.

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