摘要翻译：
本文分析了印度国家证券交易所(NSE)201只交易频繁的股票价格波动之间的相互关系。我们使用1996年至2006年期间的每日收盘价，该期间恰逢自由化后市场迅速转型的时期。NSE的互相关矩阵($\mathbf{C}$）的特征值分布与发达市场（如纽约证券交易所(NYSE)）相似：大多数特征值落在由互不相关的时间序列构造的随机矩阵的期望范围内。在少数几个偏离大宗的最大特征值中，最大的特征值与整个市场的运动有关。在纽约证券交易所，出现在最大和大宗之间的中间特征值与集团内部相互作用强的特定业务部门相关联。然而，在印度市场上，这些偏离的特征值相对来说很少，而且更接近于整体。我们认为，这是由于市场中相对缺乏明显的行业特征，股票的走势主要受整体市场趋势的影响。通过显式构造市场中的相互作用网络，首先从未过滤的相关矩阵中生成最小生成树，然后在从数据中过滤掉市场模式和随机效应后使用改进的生成图的方法来说明这一点。这两种方法都表明，与发达市场相比，相对缺乏属于同一业务部门的共同移动股票集群。这与新兴市场往往比发达市场更相关的普遍信念是一致的。
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英文标题：
《Uncovering the Internal Structure of the Indian Financial Market:
  Cross-correlation behavior in the NSE》
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作者：
Sitabhra Sinha and Raj Kumar Pan
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最新提交年份：
2007
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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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一级分类：Physics        物理学
二级分类：Other Condensed Matter        其他凝聚态物质
分类描述：Work in condensed matter that does not fit into the other cond-mat classifications
在不适合其他cond-mat分类的凝聚态物质中工作
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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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。
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英文摘要：
  The cross-correlations between price fluctuations of 201 frequently traded stocks in the National Stock Exchange (NSE) of India are analyzed in this paper. We use daily closing prices for the period 1996-2006, which coincides with the period of rapid transformation of the market following liberalization. The eigenvalue distribution of the cross-correlation matrix, $\mathbf{C}$, of NSE is found to be similar to that of developed markets, such as the New York Stock Exchange (NYSE): the majority of eigenvalues fall within the bounds expected for a random matrix constructed from mutually uncorrelated time series. Of the few largest eigenvalues that deviate from the bulk, the largest is identified with market-wide movements. The intermediate eigenvalues that occur between the largest and the bulk have been associated in NYSE with specific business sectors with strong intra-group interactions. However, in the Indian market, these deviating eigenvalues are comparatively very few and lie much closer to the bulk. We propose that this is because of the relative lack of distinct sector identity in the market, with the movement of stocks dominantly influenced by the overall market trend. This is shown by explicit construction of the interaction network in the market, first by generating the minimum spanning tree from the unfiltered correlation matrix, and later, using an improved method of generating the graph after filtering out the market mode and random effects from the data. Both methods show, compared to developed markets, the relative absence of clusters of co-moving stocks that belong to the same business sector. This is consistent with the general belief that emerging markets tend to be more correlated than developed markets.
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PDF链接：
https://arxiv.org/pdf/0704.2115