2015
DOI: 10.1002/isaf.1374
|View full text |Cite
|
Sign up to set email alerts
|

Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations

Abstract: SUMMARYWe built an artificial market model and investigated the impact of large erroneous orders on financial market price formations. Comparing the case of consented large erroneous orders in the short term with that of continuous small erroneous orders in the long term, if amounts of orders are the same, we found that the orders induced almost the same price fall range. We also analysed effects of price variation limits for erroneous orders and found that price variation limits that employ a limitation term … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
11
0

Year Published

2017
2017
2022
2022

Publication Types

Select...
5
2

Relationship

1
6

Authors

Journals

citations
Cited by 32 publications
(11 citation statements)
references
References 22 publications
(41 reference statements)
0
11
0
Order By: Relevance
“…This latter point is especially crucial nowadays that most transactions taking place on markets are, in fact, achieved by computerized trading agents whose rationality is, indeed, inferred from algorithms. In addition, as the previous studies evaluated the impact of regulation policies on financial markets by the artificial market model (see [33,41,44]), one interesting application of this work consists in testing and evaluating the impact of regulation policies on real markets.…”
Section: Discussionmentioning
confidence: 99%
“…This latter point is especially crucial nowadays that most transactions taking place on markets are, in fact, achieved by computerized trading agents whose rationality is, indeed, inferred from algorithms. In addition, as the previous studies evaluated the impact of regulation policies on financial markets by the artificial market model (see [33,41,44]), one interesting application of this work consists in testing and evaluating the impact of regulation policies on real markets.…”
Section: Discussionmentioning
confidence: 99%
“…Their study was based on (Chiarella and Iori 2002), which presented stylized trader models including only fundamental, chartist, and noise factors. Mizuta et al (2016) tested the effect of tick size, i.e., the price unit for orders, which led to a discussion of tick-size devaluation in the Tokyo stock exchange. As a platform for artificial market simulation, Torii et al (2017) proposed the platform "Plham".…”
Section: Related Workmentioning
confidence: 99%
“…In this simulation, agents are constructed by imitating traders in the real financial market. In some previous work, such as Mizuta et al (2016); Torii et al (2015), this type of approach was used. In addition, Mizuta (2019) argued that artificial market simulation could contribute to the improvements of the structures or rules and regulations of financial markets.…”
Section: Introductionmentioning
confidence: 99%
“…This limitation is easily overcome using the empirical framework of Artificial Markets with virtual investors. Multi‐agent modeling is widely applied to financial market studies (Bajo et al 2017; Biondi & Righi, 2017; Jacobs et al 2010; Mizuta et al 2015; Veryzhenko et al 2017; Yang et al 2020), since these models are able to reflect the complexity and automation of financial markets. The idea of agent‐based simulations is to study complex systems by representing each of the microscopic elements or agents individually and by simulating the behavior of the entire system, keeping track of all the individual elements and their interactions over time.…”
Section: Introductionmentioning
confidence: 99%