英文标题：
《Evidence of Self-Organization in Time Series of Capital Markets》
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作者：
Leopoldo S\\\'anchez-Cant\\\'u, Carlos Arturo Soto-Campos, Andriy Kryvko
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最新提交年份：
2017
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英文摘要：
  A methodology is developed to identify, as units of study, each decrease in the value of a stock from a given maximum price level. A critical level in the amount of price declines is found to separate a segment operating under a random walk from a segment operating under a power law. This level is interpreted as a point of phase transition into a self-organized system. Evidence of self-organization was found in all the stock market indices studied but in none of the control synthetic random series. Findings partially explain the fractal structure characteristic of financial time series and suggest that price fluctuations adopt two different operating regimes. We propose to identify downward movements larger than the critical level apparently subject to the power law, as self-organized states, and price decreases smaller than the critical level, as a random walk with the Markov property.
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中文摘要：
开发了一种方法，以确定股票价值从给定最高价格水平的每一次下跌作为研究单位。研究发现，价格下跌量的临界水平将随机游动下的细分市场与幂律下的细分市场分开。该层被解释为自组织系统的相变点。在所有研究的股票市场指数中都发现了自组织的证据，但在对照合成随机序列中没有发现。研究结果部分解释了金融时间序列的分形结构特征，并表明价格波动采用了两种不同的运行机制。我们建议将明显服从幂律的大于临界水平的向下运动识别为自组织状态，将小于临界水平的价格下降识别为具有马尔可夫性质的随机游动。
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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
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