Physica A 387 (2008) 2535–2546 www.elsevier.com/locate/physa The effects of behavioral and structural assumptions in artificial stock market Xinghua Liu a,b,∗ , Shirley Gregor c , Jianmei Yang a a School of Business Administration, South China University of Technology, Guangzhou, 510641, China b School of Information Management, Shandong Economic University, Ji’nan, 250014, China c College of Business and Economics, The Australian National University, Canberra ACT 0200, Australia Received 16 October 2007; received in revised form 20 December 2007 Available online 9 January 2008 Abstract Recent literature has developed the conjecture that important statistical features of stock price series, such as the fat tails phenomenon, may depend mainly on the market microstructure. This conjecture motivated us to investigate the roles of both the market microstructure and agent behavior with respect to high-frequency returns and daily returns. We developed two simple models to investigate this issue. The first one is a stochastic model with a clearing house microstructure and a population of zero- intelligence agents. The second one has more behavioral assumptions based on Minority Game and also has a clearing house microstructure. With the first model we found that a characteristic of the clearing house microstructure, namely the clearing frequency, can explain fat tail, excess volatility and autocorrelation phenomena of high-frequency returns. However, this feature does not cause the same phenomena in daily returns. So the Stylized Facts of daily returns depend mainly on the agents’ behavior. With the second model we investigated the effects of behavioral assumptions on daily returns. Our study implicates that the aspects which are responsible for generating the stylized facts of high-frequency returns and daily returns are different. c  2008 Elsevier B.V. All rights reserved. PACS: 02.50.Le; 05.65. +b; 89.65.Gh; 89.75.Fb Keywords: Stylized facts; Artificial stock market; Behavioral and structural assumptions; Minority game; Market clearing frequency 1. Introduction Agent-based models of complex adaptive systems are attracting significant interest in many disciplines. An important area receiving much attention is agent-based computational finance (ACF), which gives a new approach providing deep insights into the dynamics of security markets [1]. Researchers in agent-based computational finance have built artificial stock markets (ASM) that reproduce characteristic behavior (stylized facts) of regular markets, such as heavy tails of the (unconditional) distribution of daily and hourly returns, excess volatility, volatility clustering, and volume/volatility correlation. ∗ Corresponding author at: School of Business Administration, South China University of Technology, Guangzhou, 510641, China. E-mail address: xhliu58@126.com (X. Liu). 0378-4371/$ - see front matter c  2008 Elsevier B.V. All rights reserved. doi:10.1016/j.physa.2008.01.025