英文标题：
《Identifying long-term precursors of financial market crashes using
  correlation patterns》
---
作者：
Hirdesh K. Pharasi, Kiran Sharma, Rakesh Chatterjee, Anirban
  Chakraborti, Francois Leyvraz and Thomas H. Seligman
---
最新提交年份：
2018
---
英文摘要：
  The study of the critical dynamics in complex systems is always interesting yet challenging. Here, we choose financial market as an example of a complex system, and do a comparative analyses of two stock markets - the S&P 500 (USA) and Nikkei 225 (JPN). Our analyses are based on the evolution of crosscorrelation structure patterns of short time-epochs for a 32-year period (1985-2016). We identify \"market states\" as clusters of similar correlation structures, which occur more frequently than by pure chance (randomness). The dynamical transitions between the correlation structures reflect the evolution of the market states. Power mapping method from the random matrix theory is used to suppress the noise on correlation patterns, and an adaptation of the intra-cluster distance method is used to obtain the \"optimum\" number of market states. We find that the USA is characterized by four market states and JPN by five. We further analyze the co-occurrence of paired market states; the probability of remaining in the same state is much higher than the transition to a different state. The transitions to other states mainly occur among the immediately adjacent states, with a few rare intermittent transitions to the remote states. The state adjacent to the critical state (market crash) may serve as an indicator or a \"precursor\" for the critical state and this novel method of identifying the long-term precursors may be very helpful for constructing the early warning system in financial markets, as well as in other complex systems.
---
中文摘要：
复杂系统的临界动力学研究一直是一个有趣而又富有挑战性的课题。在这里，我们选择金融市场作为一个复杂系统的例子，并对两个股票市场——标准普尔500指数（美国）和日经225指数（JPN）进行了比较分析。我们的分析基于32年（1985-2016年）短时间时期的互相关结构模式的演变。我们将“市场状态”确定为具有类似关联结构的集群，其发生频率高于纯粹的偶然性（随机性）。关联结构之间的动态转换反映了市场状态的演化。利用随机矩阵理论中的幂映射方法来抑制相关模式上的噪声，并采用簇内距离法来获得“最佳”市场状态数。我们发现，美国有四个市场国家，日本有五个市场国家。我们进一步分析了成对市场状态的共现性；保持在相同状态的概率远远高于向不同状态的过渡。到其他状态的过渡主要发生在紧邻的状态之间，少数罕见的间歇过渡到远程状态。与临界状态（市场崩溃）相邻的状态可以作为临界状态的指标或“前兆”，这种识别长期前兆的新方法可能非常有助于构建金融市场以及其他复杂系统中的预警系统。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
--

---
PDF下载：
-->