The paper proposes a computational adaptation of the principles underlying principal component analysis with agent based simulation in order to produce a novel modeling methodology for financial time series and financial markets. Goal of the proposed methodology is to find a reduced set of investor s models (agents) which is able to approximate or explain a target financial time series. As computational testbed for the study, we choose the learning system L FABS which combines simulated annealing with agent based simulation for approximating financial time series. We will also comment on how L FABS s architecture could exploit parallel computation to scale when dealing with massive agent simulations. Two experimental case studies showing the efficacy of the proposed methodology are reported.

翻译：本文建议对主要组成部分分析所依据的原则进行计算调整,与代理模拟相结合,以便为金融时间序列和金融市场制定新的模型方法。拟议方法的目标是找到一套简化的投资者模型(代理),能够大致或解释一个目标财务时间序列。作为研究的计算测试,我们选择学习系统L FABS, 该系统将模拟Anneing与代理模拟结合,以模拟接近财务时间序列。我们还将评论L FABS结构在处理大规模代理模拟时如何利用平行计算到规模。报告两个显示拟议方法有效性的试验性案例研究。