عباسعلی پورآقاجان

Cluster fluctuations and fractal structures are the two important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creating and expanding these two features in financial markets remains challenging. In the present study, using factor-based model design and considering a new interactive mechanism called multilevel convergence, the process of forming cluster fluctuations according to the fractal structure of financial markets is investigated. Virtual agents' trade in different groups according to market performance and their mass behavior is measured at three levels of stock, segment and market. The results, in addition to providing new insights into the space-time correlations of financial markets, show that multilevel convergence is one of the microscopic mechanisms of microstructure of such markets. In other words, multilevel collective behavior is an important factor in the occurrence of cluster fluctuations, price bubbles and market fractals and therefore should be considered in interpreting the concept of risk and defining risk management strategies from this perspective.

Although theoretical and empirical literature regarding the stylized facts shows evidence of their correlations to herding behavior in financial markets, the causes of such phenomena are still unknown. Using an agent-based model strengthened by the competition co-evolution algorithm (STGP) technique, this study provides laboratory evidence on capital market dynamics and analyses the behavioral foundations of stylized facts such as fat tails, leverage effects, and volatility clustering. The simulated stock markets consist of two groups; the “Best agents”, which are a small portion of artificial agents, and the “Residual agents”, which are the main group of artificial agents. The best performance in terms of breeding fitness returns is the main feature of the “Best agents”. More, the size of the “Best Agents” group is specified as 2.5%, 5%, 10% &20% of the total population size. An agent-based model consists of two portions, a two thousand population of trader agents that each has its decision-making strategy, and a virtual market that creates the trading strategies. Then the model evolved step by step using a feed with real quotes of the financial instruments by Adaptive Modeler. A training period is considered 2500 bars (started in November 2003), and the test period started in December 2013. The observation shows that the herding behavior in the price series created by the “Residual agents” is less than the “Best agents” series. Therefore, the greater diversity of trade strategies as the genetic differences of artificial agents leads to less herding. The observations exhibit that the volatility clustering, leverage effects, and nonlinear dependence are more likely to experience in the price series generated by “Best gents”. Furthermore, observations indicate that if the population is well diversified in terms of trading strategies, the efficiency of the market increases.