Tian Qiang, Shang Pengjian, Feng Guochen
Department of Mathematics, School of Science, Beijing Jiaotong University, No. 3 of Shangyuan Residence, Haidian District, Beijing 100044, People's Republic of China.
Physica A. 2014 Dec 15;416:183-191. doi: 10.1016/j.physa.2014.08.055. Epub 2014 Aug 30.
The paper mainly applies the information categorization method to analyze the financial time series. The method is used to examine the similarity of different sequences by calculating the distances between them. We apply this method to quantify the similarity of different stock markets. And we report the results of similarity in US and Chinese stock markets in periods 1991-1998 (before the Asian currency crisis), 1999-2006 (after the Asian currency crisis and before the global financial crisis), and 2007-2013 (during and after global financial crisis) by using this method. The results show the difference of similarity between different stock markets in different time periods and the similarity of the two stock markets become larger after these two crises. Also we acquire the results of similarity of 10 stock indices in three areas; it means the method can distinguish different areas' markets from the phylogenetic trees. The results show that we can get satisfactory information from financial markets by this method. The information categorization method can not only be used in physiologic time series, but also in financial time series.
本文主要应用信息分类方法来分析金融时间序列。该方法通过计算不同序列之间的距离来检验它们的相似性。我们应用此方法来量化不同股票市场的相似性。并且我们使用此方法报告了1991 - 1998年（亚洲货币危机之前）、1999 - 2006年（亚洲货币危机之后且全球金融危机之前）以及2007 - 2013年（全球金融危机期间及之后）美国和中国股票市场的相似性结果。结果显示了不同股票市场在不同时间段相似性的差异，并且这两次危机之后两个股票市场的相似性变得更大。此外，我们还获得了三个地区10个股票指数的相似性结果；这意味着该方法能够从系统发育树中区分不同地区的市场。结果表明，通过此方法我们可以从金融市场中获取令人满意的信息。信息分类方法不仅可以用于生理时间序列，也可用于金融时间序列。
