基于网络化数据挖掘技术的银行间资金流网络研究

发布时间：2018-06-02 14:26

  本文选题：银行网络 + 复杂网络　； 参考：《西南财经大学》2012年硕士论文

【摘要】：当今世界,随着金融创新不断加快,金融体系也变得日益复杂,各种金融产品、金融工具与全球金融市场中的各种金融机构,甚至金融机构以外的其它机构都错综复杂的联系在一起。这意味着现代金融体系具有其组成部分之间相互高度链接的特征,这种链接可以用复杂网络的形式形象而直观地表现出来,这就是所谓的金融网络。 2008年的金融危机使很多国家的经济遭受重创,金融危机的爆发,引发人们对整个金融体系安全的思考。现有的研究表明,仅凭微观层面管理的努力(微观审慎管理)很难实现整个金融体系的稳定。于是人们开始探索新的宏观调控策略和系统性风险管理办法。金融危机之后,国际金融体系改革的核心内容之一就是大力提倡对金融体系实施宏观审慎监管。从这个角度来看,应该把经济活动、金融市场以及金融机构的行为当作一个整体来考虑,从全局的角度去评估和防范金融体系的风险。欧洲央行出版的《金融稳定评论》(2010)中指出：由于金融体系中各组成部分之间相互链接的特性,研究现代金融需要引入复杂网络分析方法。 从小的生物神经网络到大的电信电力网络,再到我们熟悉的Internet,由于它们的节点(结点)庞大,链接关系复杂,我们称之为复杂网络。在复杂网络分析方法中,由于节点之间链接的原因,对某个节点来说,相邻节点的变化对其有重要影响,甚至会受到没有与其直接链接节点的影响。因此,我们不能单独去分析一个或者几个节点的特征和行为,应该把有着“关系”的所有节点作为一个整体来考虑。 传统数据挖掘的目的是寻找海量数据中隐藏的规律,而复杂网络理论的发展也是希望发现网络中节点之间联系的规律,所以从根本上来说,用复杂网络的理论和方法去分析现实中的复杂系统,也是一种数据挖掘模式,网络化数据挖掘由此产生。 网络化数据挖掘,就是将网络拓扑作为一种知识表示方式,将大规模原始数据对象及其关系抽象为网络拓扑的形式,采用传统的数据挖掘思想,结合复杂网络的理论和方法对复杂网络的拓扑属性和结构特征进行分析和挖掘,发现蕴涵在其中的、反映网络中联系规律的知识和信息。 金融网络是众多复杂网络中的一种,因此可以使用网络化数据挖掘的方法来分析金融网络。它的优点就是,可以从全局的角度去分析系统,而不是孤立的去分析某个节点,这就有助于对金融体系更深入的认识,对于合理布局金融基础设施、预测和评估金融风险、健全金融监管体制等方面都有着重大的意义。与欧美国家相比,我们在使用复杂网络方法对金融市场、银行系统、支付体系等方面的研究,做的还远远不够(欧阳卫民,2010)。 银行体系作为金融系统的主要组成部分,它正常高效的运行对于一个国家金融系统的稳定和健康发展意义重大。国内外已经有很多学者从不同的角度对银行网络作了大量的研究,包括使用大额支付系统信息对银行与银行、银行与其它非金融机构之间的资金流动规律的研究,希望能够对潜在的流动性危机的进行预警和揭示系统性风险的传染规律。学者们普遍认为,使用支付系统中的数据构建银行间资金流网络,最及时、最直接,也最准确。 本文基于网络化数据挖掘技术,利用大额支付系统中的数据,构建银行间资金流网络(包括交易业务笔数网络和业务金额网络)的加权复杂网络拓扑模型,并对其进行了研究。包括分析了网络的拓扑性质——即两个网络的边权和节点强度统计特征和分布规律；在对网络结构测度的研究发现,网络结构属于异配模式且整个网络具有较低的聚集系数；对网络进行社区发现结果表明,五大国有控股商业银行有着紧密的联系,形成一个比较稳定的社区。 概括来讲,本文主要研究了银行间资金流网络的可视化、拓扑性质、网络结构测度和网络的社区发现。详细的内容如下。 第一部分,基于复杂网络理论对银行网络研究的综述。 首先对复杂网络理论进行了评述,包括复杂网络的基本概念、统计特征量以及在金融银行网络上应用的研究,然后介绍了本文重点研究的网络结构——加权网络。接下来对使用复杂网络的理论研究银行网络的现状进行了评述,同时指出了目前研究的成果和不足之处。 第二部分,对网络化数据挖掘理论的综述。 主要介绍的是本文使用的技术和理论知识。使用传统数据挖掘的思想,结合复杂网络的理论和方法,就是网络化数据挖掘技术。在本部分评述了传统的数据挖掘和网络化数据挖掘的理论、模式和挖掘流程。最后对两者的理论和方法作了比较和总结。 第三部分,对本文采用数据的描述及其可视化的研究。 本文研究数据来自中国现代化支付系统的大额支付系统,所以首先对支付体系、支付系统和中国现代化支付系统作了相关评述,然后对本文使用数据的来源、整体特征作了简单的描述和初步的分析,最后使用复杂网络的可视化方法分别展现业务金额网络和业务笔数网络拓扑图型。在这部分中,还对可视技术的兴起、发展和可视化算法以及可视化技术在复杂网络领域的应用进行了简单评述。 第四部分,研究大额支付系统银行间资金流网络的拓扑性质和结构测度。 第一阶段,对业务笔数网络和业务金额网络的拓扑性质进行分析,着重研究两个网络的静态统计特征量：边权重和节点强度。先简单描述了边权重和强点强度的分布特征,然后针对两个网络估计了它们的边权分布的幂指数和节点强度分布的幂指数。 第二阶段即网络结构测度,研究了两个方面：网络的匹配模式和聚集程度。借鉴许多学者对复杂网络的研究思路,使用设置阈值的方法,分析网络结构在不同的边权阈值下匹配模式的变化情况,然后通过计算业务金额网络和业务笔数网络的加权聚集系数了解整个网络的聚集程度,同样使用设置阈值的方法研究加权网络的聚集系数的变化情况,并对其变化的趋势作了相应的分析和解释,同时也计算了无权网络的聚集系数变化情况,对两类网络进行对比分析。 第五部分,进一步深入挖掘银行间资金流网络,研究网络的社区结构。 社区发现是复杂网络一个重要的研究方向。首先对社区发现作简单评述,总结社区发现和传统聚类分析的相似和不同之处,其次针对社区形成过程和算法物理背景对社区发现的研究现状作简单评述,然后对本文使用的两种社区发现算法：谱聚类算法和可重叠层次结构算法的原理作了详细介绍,并使用两种算法对资金流网络进行社区发现,发现网络中隐藏的社区结构,最后对划分结果作相应的解释和总结。 本文的创新性突出在以下两个方面。 一是把传统的数据挖掘的思想和复杂网络理论结合起来,研究了银行间资金流网络。全文的流程是：原始数据经过初步预处理→构建网络拓扑→数据展现(可视化)→分析网络特性→发现结构特征→对发现的结构特征再可视化描述。运用了一套完整的网络化数据挖掘流程,对复杂网络的研究和分析有更全面的认识。 二是使用社区发现算法对银行间资金流网络作社区划分。国内外大量研究表明,社区发现理论已经运用到各种复杂网络之中,许多学者从不同的方法和角度研究了银行网络的结构和特征。但就我所知,把社区发现的方法运用到银行网络,特别是银行间支付业务的资金流网络中的研究是非常少的。
[Abstract]:In today ' s world , as financial innovation is accelerating , the financial system becomes increasingly complex , and financial products , financial instruments and other institutions outside the global financial market are inextricably linked . This means that modern financial systems have a highly linked feature between its components , which can be visualized in the form of complex networks , which is the so - called financial network .

Since the financial crisis in 2008 , the economy of many countries has been hit hard , and the financial crisis broke out , raising people ' s thinking about the security of the whole financial system . One of the key elements of the reform of the international financial system is to advocate the macro - prudential regulation of the financial system .

From a small biological neural network to a large telecommunications power network and to the Internet we are familiar with , because of their large nodes ( nodes ) and complex link relations , we call it a complex network . In the complex network analysis method , because of the link between nodes , the changes of adjacent nodes have important influence on them , and even be affected by no direct link nodes . Therefore , we cannot analyze the characteristics and behaviors of one or several nodes separately , and all nodes with " relation " should be considered as a whole .

The purpose of the traditional data mining is to find the hidden rules in the mass data , and the development of the complex network theory is to discover the law of the connection between nodes in the network , so the complex system in reality is analyzed by the theory and method of complex networks .

The network data mining is to use the network topology as a knowledge representation mode , abstract the large - scale raw data objects and their relationships into the form of network topology , adopt the traditional data mining thought , analyze and excavate the topological properties and structural characteristics of complex networks in combination with the theory and method of complex networks , and find out the knowledge and information in which the contact laws in the network are reflected .

The financial network is one of many complex networks , so the network data mining method can be used to analyze the financial network . It has the advantages that the system can be analyzed from the global perspective , rather than the isolated analysis of a certain node , which can help to realize the deeper understanding of the financial system , and it is far from enough for the rational distribution of financial infrastructure , forecasting and evaluation of financial risks and sound financial supervision system .

As the main component of the financial system , the banking system plays a very important role in the stability and healthy development of a country ' s financial system .

In this paper , based on the network data mining technology , the weighted complex network topology model of inter - bank fund flow network ( including transaction service pen number network and service amount network ) is constructed by using the data in large amount payment system , and the topology property of the network , i.e . , the edge weight and the node strength statistical characteristic and distribution rule of the two networks are analyzed .
In the study of network structure measure , it is found that the network structure belongs to the heterogeneous mode and the whole network has lower aggregation coefficient ;
The results of community discovery on the network show that the five state - owned commercial banks have close ties to form a more stable community .

Generally speaking , this paper mainly studies the visualization , topological property , network structure measure and community discovery of inter - bank fund flow network . The detailed contents are as follows .

The first part , based on the complex network theory to the bank network research overview .

Firstly , the complex network theory is reviewed , including the basic concept of complex network , the quantity of statistics and the research applied in the network of financial bank , then introduces the network structure _ weighting network of the research in this paper . Then the present situation of the bank network is reviewed with the theory of complex network , and the achievements and shortcomings of the current research are pointed out .

The second part is a review of the theory of networked data mining .

This part reviews the theory , model and mining flow of traditional data mining and networking data mining , and compares and summarizes the theories and methods of the two .

In the third part , the description of the data used in this paper and its visualization are studied .

This paper makes a brief description of the payment system , payment system and China ' s modern payment system , and then makes a brief description and preliminary analysis of the source and the whole characteristics of the data used in this paper . Finally , the author makes a brief comment on the rise , development and visualization algorithms of the visual technology and the application of the visualization technology in the complex network .

The fourth part is to study the topological property and the structure measure of the inter - bank fund flow network of large amount payment system .

In the first stage , the topological properties of the network and the service amount network are analyzed , and the static statistical feature quantity of the two networks is emphatically studied : the edge weight and the node strength . Firstly , the distribution characteristics of the edge weight and the strong point intensity are briefly described , and then the power index and the power exponent of the strength distribution of the nodes are estimated for the two networks .

In the second phase , network structure measure , two aspects are studied : matching mode and degree of aggregation of network .

In the fifth part , the inter - bank fund flow network is further explored , and the community structure of the network is studied .

Community discovery is an important research direction of complex network . Firstly , the author makes a brief comment on community discovery , summarizes the similarities and differences between community discovery and traditional cluster analysis , and then makes a brief comment on the research status quo of community discovery and traditional cluster analysis . Secondly , two kinds of community discovery algorithms used in this paper are introduced in detail . Two kinds of algorithms are used to find out the hidden community structure in the network , and finally explain and summarize the results of the division .

The innovative features of this article are highlighted in the following two aspects .

First , combining the traditional theory of data mining with the complex network theory , the paper studies the inter - bank capital flow network . The whole process is : the raw data is pre - processed 鈫，

本文编号：1969087