论文标题
非零温度平衡理论框架中的金融危机
Financial Crisis in the Framework of Non-zero Temperature Balance Theory
论文作者
论文摘要
金融危机被称为崩溃,导致金融资产价值突然损失很大程度上,并且不时地发生了令人惊讶的。为了发现金融网络的特征,在许多研究中都考虑了股票的成对相互作用,但是股票的牢固相关性及其在危机中的集体行为的存在使我们解决了高阶互动。因此,在这项研究中,我们在平衡理论框架中通过三胞胎互动调查了金融网络。由于检测高阶相互作用在理解股票的复杂行为中的贡献,因此我们利用了高阶相互作用的订单参数。查看从$ s \&p500 $获得的金融市场的真实数据,通过平衡理论的镜头来寻求在危机附近和远离危机的不同时期内的网络结构,这揭示了网络的结构性差异的存在,与不同时期相对应。在这里,我们解决了两个众所周知的危机《大回归》(2008年)和19020年的共衰退。结果表明,在金融网络中,有序结构形成了危机,而股票的行为远离危机。危机中股票有序结构的形成使网络抵抗疾病。有序结构抵抗施加疾病(温度)的电阻可以测量危机强度并确定网络过渡的温度。有一个临界温度,$ t_ {c} $,用统计力学和平均场方法的语言,上面的有序结构突然破坏,并发生一阶相变。危机越强,临界温度越高。
Financial crises are known as crashes that result in a sudden loss of value of financial assets in large part and they continue to occur from time to time surprisingly. In order to discover features of the financial network, the pairwise interaction of stocks has been considered in many research, but the existence of the strong correlation of stocks and their collective behavior in crisis made us address higher-order interactions. Hence, in this study, we investigate financial networks by triplet interaction in the framework of balance theory. Due to detecting the contribution of higher-order interactions in understanding the complex behavior of stocks we take the advantage of the orders parameters of the higher-order interactions. Looking at real data of financial market obtained from $S\&P500$ through the lens of balance theory for the quest of network structure in different periods of time near and far from crisis reveals the existence of a structural difference of the network that corresponds to different periods of time. Here, we address two well-known crises the Great regression (2008) and the Covid-19 recession (2020). Results show an ordered structure forms on-crisis in the financial network while stocks behave independently far from a crisis. The formation of the ordered structure of stocks in crisis makes the network resistant against disorder. The resistance of the ordered structure against applying a disorder (temperature) can measure the crisis strength and determine the temperature at which the network transits. There is a critical temperature, $T_{c}$, in the language of statistical mechanics and mean-field approach which above, the ordered structure destroys abruptly and a first-order phase transition occurs. The stronger the crisis, the higher the critical temperature.