Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns

Manahov, Viktor; Hudson, Robert

doi:10.1016/j.physa.2013.05.029

Cited by 15 publications

(14 citation statements)

References 25 publications

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“…Heterogeneous agents' models are capable to capture the extreme movement in the asset price through their interaction and specification of diverse types of traders. Huang et al, (2012) and Manahov et al, (2013) prove that this hypothesis can do an improved job of modeling the price dynamics than rational expectations and efficient markets models. In particular, a model is able of duplicating the majority of the stylized facts such as asymmetric return, excessive volatility and volatility clustering, both statistically and quantitatively.…”

Section: Contributions To Literaturementioning

confidence: 91%

Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market

Rekik¹,

Boujelbène²

2015

International Journal of Financial Research

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In recent years, studying stock markets using multi-agent based models has grow into an important research area due to the fact that this line of attack replicates the nature of the financial market where varied traders with a mixture of expectations and diverse points of rationality network with each other through the market. The main aim of this paper is to include selected finding from behavioral finance into agent model hypothesis. In particular we revise the switching of the trading strategies such as the switching between, uninformed agents, informed agents, zero-intelligence and switchers traders. More precisely we focus on the relationship concerning switching and the excess volatility of the stock market prices and to addresses the question of why traders switch from one type to another.

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Section: Contributions To Literaturementioning

confidence: 91%

Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market

Rekik¹,

Boujelbène²

2015

International Journal of Financial Research

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“…The reason for this interest is that mimicking behaviour has a decisive impact on price dynamics, and it might offer an explanation of excessive volatility and creation of bubbles (e.g. (Manahov, 2013) and (Chang, 2014)). …”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to test those theoretical models directly (Manahov, 2013), because it is difficult to access at a time t to private information of traders, their investment strategies and interactions among them. Therefore, it is difficult to determine whether traders make similar decisions as they neglect their own information and imitate others, or because they have access to the same information they use to make their decisions.…”

Section: Introductionmentioning

confidence: 99%

“…In literature, there is a little work that uses ASMs to study the phenomenon of mimicking behaviour on financial markets. In (Manahov, 2013), we propose the study of herd behaviour through the use of an artificial financial market, where the market is populated by agents who are with learning behaviour represented by the genetic programming algorithm. Therefore, as in real markets, authors use statistical measures of price series generated by the experiments to quantify mimicking behaviour on the market.…”

Section: Introductionmentioning

confidence: 99%

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An Artificial Stock Market with Interactions Network and Mimetic Agents

Benhammada

Amblard²,

Chikhi

2017

Proceedings of the 9th International Conference on Agents and Artificial Intelligence

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18906The contribution was presented at ICAART 2017 :http://www.icaart.org/?y=2017 Abstract: Agent-based artificial stock markets attracted much attention over the last years, and many models have been proposed. However, among them, few models take into account the social interactions and mimicking behaviour of traders, while the economic literature describes investors on financial markets as influenced by decisions of their peers and explains that this mimicking behaviour has a decisive impact on price dynamics and market stability. In this paper we propose a continuous double auction model of financial market, populated by heterogeneous traders who interact through a social network of influence. Traders use different investment strategies, namely: fundamentalists who make a decisions based on the fundamental value of assets; hybrids who are initially fundamentalists, but switch to a speculative strategy when they detect an uptrend in prices; noise traders who don't have sufficient information to take rational decisions, and finally mimetic traders who imitate the decisions of their mentors on the interactions network. An experimental design is performed to show the feasibility and utility of the proposed model.

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“…A great deal of attention has been paid in literature to make use of FACTS technology in electric industry (RezaeeJordehi, 2015;Valle and Araujo, 2015;Balamurugan et al, 2015;Mohammadpour, et al, 2015;Ghorbani et al, 2015;Singh et al, 2015;Gasperic and Mihalic, 2015;Abdelsalam et al, 2015;Duong et al, 2014;Esmaili et al, 2014;Kumar and Kumar Mittapalli, 2014;Manahov and Hudson, 2013;Ravi and Rajaram, 2013 among others). The present paper aimed at determination of three important components of FACTS devices, i.e.…”

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confidence: 99%