英文标题：
《Optimal decision for the market graph identification problem in sign
  similarity network》
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作者：
V.A. Kalyagin, P.A. Koldanov, P.M. Pardalos
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最新提交年份：
2015
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英文摘要：
  Investigation of the market graph attracts a growing attention in market network analysis. One of the important problem connected with market graph is to identify it from observations. Traditional way for the market graph identification is to use a simple procedure based on statistical estimations of Pearson correlations between pairs of stocks. Recently a new class of statistical procedures for the market graph identification was introduced and optimality of these procedures in Pearson correlation Gaussian network was proved. However the obtained procedures have a high reliability only for Gaussian multivariate distributions of stocks attributes. One of the way to correct this drawback is to consider a different networks generated by different measures of pairwise similarity of stocks. A new and promising model in this context is the sign similarity network. In the present paper the market graph identification problem in sign similarity network is considered. A new class of statistical procedures for the market graph identification is introduced and optimality of these procedures is proved. Numerical experiments detect essential difference in quality of optimal procedures in sign similarity and Pearson correlation networks. In particular it is observed that the quality of optimal identification procedure in sign similarity network is not sensitive to the assumptions on distribution of stocks attributes.
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中文摘要：
市场图的研究在市场网络分析中受到越来越多的关注。与市场图相关的一个重要问题是从观察中识别它。市场图识别的传统方法是使用基于成对股票之间皮尔逊相关性的统计估计的简单程序。最近引入了一类新的市场图识别统计方法，并证明了这些方法在Pearson相关高斯网络中的最优性。然而，所得到的方法仅对股票属性的高斯多元分布具有较高的可靠性。纠正这一缺陷的方法之一是考虑由不同的股票成对相似性度量生成的不同网络。在这种背景下，一个新的、有前途的模型是符号相似网络。本文研究了符号相似网络中的市场图识别问题。介绍了一类新的市场图识别统计方法，并证明了这些方法的最优性。数值实验检测符号相似性和皮尔逊相关网络中优化过程质量的本质差异。特别指出，符号相似网络中最优识别过程的质量对股票属性分布的假设不敏感。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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