The challenge to analyze the synchronization between stock market returns has  calling the atention of economists, physics and mathematical researchers due to  the complex nature of its dynamical colletive behavior. While some pair relations among the stock markets can be explained by basic, relatively simple rules, the  global features that spans from the whole financial system may be hard to describe, very often presenting long-correlations, cascading effects, non-stationarity, and many others.  In this work we used, what we called “thermos-comparative analysis”, based on thermodynamical and statistical concepts, to quantify the relative  performance between different stock market indexes. We study a number of stock  indexes and examined the typical co-movements of the markets in different time  scales and examine some common features across the markets that may distinguish  their previous and/or upcoming dynamics.

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1. Confidence and the Self-Attribution Bias in an Artificial Stock Market