Impact of information cost and switching of trading strategies in an artificial stock market

Liu, Yifang; Zhang, Wei; Xu, Chao; Andersen, Jørgen Vitting; Xu, Hai-Chuan

doi:10.1016/j.physa.2014.04.004

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Martinez-Jaramillo [22] point out that fat tails, autocorrelation and volatility can be main factors to test the validity of the agent-based models. Liu et al, [27] show that investors' switching between chartist and fundamentalist strategies is the main reason that lead to volatility clustering and the emergence of fat-tailed returns. Similar results are present in most financial data.…”

Section: Fact 3: Volatility Clusteringmentioning

confidence: 99%

Reproduce stylized facts of artificial financial market and comparison with real data

Rekik¹,

Souissi²

2015

AIR

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Agent-based computational models represent a big challenge in many disciplines. A vital approach receiving much interest is agent-based models, which gives a new area providing some ways to tackle some of the restrictions of the analytical models in finance. The aim of our research is to contribute to the behavioral finance and agent-based artificial markets by studying their market-wise implications using computational simulations. We investigate and analyze the behavioral foundations of the stylized facts of empirical data such as that characterize real data in financial markets. Our results confirm the existence of most the stylized facts such as leptokurtosis, non-independently distributed, and volatility clustering. From this attention, the artificial financial market will for all time be evaluated in order to have explication about market dynamics in Tunisian financial market.

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Section: Fact 3: Volatility Clusteringmentioning

confidence: 99%

Reproduce stylized facts of artificial financial market and comparison with real data

Rekik¹,

Souissi²

2015

AIR

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“…It is hard to thoroughly and effectively explain the dynamic features of financial markets through traditional methods based on the analysis of rational investors. Under this background, some research tries to discuss stock market risk through artificial stock markets built by agent technology, such as Krichene and El-Aroui [21], Hafezi et al [22], Manahov and Hudson [23], Liu et al [24], Zhang et al [25], Wu and Duan [26] and Xu et al [27].…”

Section: Introductionmentioning

confidence: 99%

Investor Behavior and Risk Contagion in an Information-Based Artificial Stock Market

2020

IEEE Access

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This paper designs an artificial stock market model to discuss investor behavior and risk contagion caused by market information. Considering the investors' trading decisions are attributed to the change of market information, new market information no matter from the macro field or the micro field can influence investor behavior and risk contagion. In our artificial stock market model, we assume new information is the sole factor affecting the fluctuation of stock prices. And investor sentiment is influenced by investor sensitivity to new information, investment decisions of neighboring investors, and investor understanding preference for new information. Through simulation experiments, some conclusions can be drawn as follows. Firstly, stock price volatility becomes stronger with increasing investor sensitivity to new information, under the condition of a smaller fundamental contagion coefficient or larger sensitivity to new information from investor neighbors. Secondly, stock price volatility becomes more moderate with increasing sensitivity to new information from investor neighbors, under the condition of a smaller fundamental contagion coefficient or smaller investor sensitivity to new information. Thirdly, with the increasing fundamental contagion coefficient, stock price volatility may strongly increase under the condition of smaller investor sensitivity to new information and smaller sensitivity to new information from investor neighbors, or slightly increase under the condition of smaller investor sensitivity to new information and larger sensitivity to new information from investor neighbors, or slightly decrease under the condition of larger investor sensitivity to new information and smaller sensitivity to new information from investor neighbors, or show a little change under the condition of larger investor sensitivity to new information and larger sensitivity to new information from investor neighbors.

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“…These models have been constructed, in general, in one of two ways: models in which the agents do not use a particular set of strategies, but rather participate in the market in a random fashion, and models in which the agents follow different specific strategies inspired in actual strategies used by participants of real markets, as we do in this work. The first type of models usually make use of market trading structures similar to those used in real markets, such as double auction order books, and as a consequence, the price formation is directly driven by the offers (to buy and sell) supplied by the agents [ 12 – 21 , 21 , 22 ]. latter type of models usually have prices adjusted in a stochastic manner [ 23 – 26 ].…”

Section: Introductionmentioning

confidence: 99%

A detailed heterogeneous agent model for a single asset financial market with trading via an order book

Navarro¹,

Larralde²

2017

PLoS ONE

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We present an agent based model of a single asset financial market that is capable of replicating most of the non-trivial statistical properties observed in real financial markets, generically referred to as stylized facts. In our model agents employ strategies inspired on those used in real markets, and a realistic trade mechanism based on a double auction order book. We study the role of the distinct types of trader on the return statistics: specifically, correlation properties (or lack thereof), volatility clustering, heavy tails, and the degree to which the distribution can be described by a log-normal. Further, by introducing the practice of “profit taking”, our model is also capable of replicating the stylized fact related to an asymmetry in the distribution of losses and gains.

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Impact of information cost and switching of trading strategies in an artificial stock market

Cited by 7 publications

References 33 publications

Reproduce stylized facts of artificial financial market and comparison with real data

Reproduce stylized facts of artificial financial market and comparison with real data

Investor Behavior and Risk Contagion in an Information-Based Artificial Stock Market

A detailed heterogeneous agent model for a single asset financial market with trading via an order book

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