Analyze the Behavioral Foundation of Stylized Facts Using Agent-Based Simulation and STGP Algorithm

Saeid Sarkamaryan, A. Jafari, Abbasali Pooraghajan
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Abstract

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.
基于agent仿真和STGP算法分析程式化事实的行为基础
尽管关于风格化事实的理论和实证文献显示了它们与金融市场羊群行为相关的证据,但这种现象的原因仍然未知。利用竞争协同进化算法(STGP)技术强化的基于主体的模型,本研究提供了资本市场动态的实验室证据,并分析了诸如肥尾、杠杆效应和波动性聚类等风格化事实的行为基础。模拟的股票市场由两组组成;“最佳代理”是人工代理的一小部分,“剩余代理”是人工代理的主要群体。在育种适应度回报方面的最佳表现是“最佳代理”的主要特征。此外,“最佳代理人”群体的规模被指定为总人口的2.5%、5%、10%和20%。一个基于agent的模型由两部分组成,一个是2000个拥有自己决策策略的交易agent,另一个是创建交易策略的虚拟市场。然后,使用Adaptive Modeler提供的金融工具的真实报价,模型逐步发展。训练期为2500杠(从2003年11月开始),测试期从2013年12月开始。观察发现,“剩余代理人”所产生的价格序列中的羊群行为小于“最佳代理人”系列。因此,由于人工主体的遗传差异,贸易策略的更大多样性导致较少的羊群效应。观察结果表明,波动性聚类、杠杆效应和非线性依赖在“最佳代理”产生的价格序列中更有可能出现。此外,观察表明,如果人口在交易策略方面多样化,市场的效率就会提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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