發(fā)布時(shí)間：2018-03-13 12:36

  本文選題：復(fù)雜網(wǎng)絡(luò)　切入點(diǎn)：社團(tuán)結(jié)構(gòu)　出處：《華南理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：在經(jīng)濟(jì)一體化的今天，各個(gè)行業(yè)間的經(jīng)濟(jì)合作越來越緊密，彼此間的影響越來越密切。作為國民經(jīng)濟(jì)晴雨表的證券市場，其安全問題越來越突出，風(fēng)險(xiǎn)管理難度越來越大。在這樣一個(gè)大背景下，研究各個(gè)行業(yè)股票之間的關(guān)聯(lián)關(guān)系有著迫切的理論和現(xiàn)實(shí)意義。 隨著1998年《“小世界”網(wǎng)絡(luò)的群體動(dòng)力行為》和《隨機(jī)網(wǎng)絡(luò)中標(biāo)度的涌現(xiàn)》兩篇開創(chuàng)性論文的發(fā)表，復(fù)雜網(wǎng)絡(luò)理論研究已經(jīng)成為學(xué)術(shù)界的研究熱點(diǎn)之一。十余年來，國內(nèi)外學(xué)者利用復(fù)雜網(wǎng)絡(luò)理論并結(jié)合其它學(xué)科進(jìn)行交叉研究取得了可觀的研究成果。已有學(xué)者證明股票市場是一個(gè)復(fù)雜系統(tǒng)，區(qū)別于傳統(tǒng)金融學(xué)基于有效市場假說（EMH）的研究方法，利用復(fù)雜網(wǎng)絡(luò)理論研究股票市場是從宏觀的角度研究其拓?fù)浣Y(jié)構(gòu)和整體特性。利用這一個(gè)全新的角度研究中國股票市場具有重要的現(xiàn)實(shí)意義。 經(jīng)過2008年金融危機(jī)后，在2011年至2013年間，，中國股市整體上仍然一路下挫。為了進(jìn)一步研究中國股票市場的內(nèi)部結(jié)構(gòu)，探討其運(yùn)行機(jī)制，本文筆者利用復(fù)雜網(wǎng)絡(luò)理論并運(yùn)用R軟件及其igraph軟件包來研究中國股票市場，并做了以下工作：1.結(jié)合復(fù)雜網(wǎng)絡(luò)理論，利用最小生成樹算法（MST）構(gòu)建了中國股票關(guān)聯(lián)網(wǎng)絡(luò)模型。然后探索性分析了所有股票在不同時(shí)間段內(nèi)的收益率分布情況，發(fā)現(xiàn)整體上呈現(xiàn)尖峰厚尾的分布。2.利用Newman快速算法對股票關(guān)聯(lián)網(wǎng)絡(luò)進(jìn)行社團(tuán)劃分，并通過對比各個(gè)社團(tuán)內(nèi)股票間的平均相關(guān)系數(shù)，發(fā)現(xiàn)股市在嚴(yán)重下跌時(shí)的平均相關(guān)系數(shù)明顯大于股市穩(wěn)定時(shí)，且行業(yè)聚集程度明顯加大。3.通過比較分析網(wǎng)絡(luò)中各個(gè)節(jié)點(diǎn)的度、介數(shù)、接近度和特征向量，發(fā)現(xiàn)金融和能源行業(yè)的股票在網(wǎng)絡(luò)中處于中心位置，這也和現(xiàn)實(shí)中該兩個(gè)行業(yè)在經(jīng)濟(jì)體系中占有重要地位的情況相吻合。此外，還發(fā)現(xiàn)兩個(gè)重要的統(tǒng)計(jì)特征，一個(gè)是具有高“介數(shù)中心性”的節(jié)點(diǎn)是連接多個(gè)社團(tuán)的重要的節(jié)點(diǎn)；另一個(gè)是同時(shí)具有高“介數(shù)中心性”與高“特征向量中心性”的節(jié)點(diǎn)具有強(qiáng)相關(guān)性。
[Abstract]:In today's economic integration, the economic cooperation among various industries is getting closer and closer, and the influence of each other is becoming more and more close. As a barometer of the national economy, the security problems of the securities market are becoming more and more prominent. Risk management is becoming more and more difficult. Under such a background, it is of urgent theoretical and practical significance to study the relationship between stocks in various industries. With the publication of two groundbreaking papers in 1998, "the dynamic behavior of small World" Network and "the emergence of scale in Random Networks", the theory of complex networks has become one of the research hotspots in academic circles for more than ten years. Scholars at home and abroad have made considerable achievements by using the theory of complex network and combining with other disciplines to carry out cross-research. Some scholars have proved that the stock market is a complex system. Different from the traditional approach of finance based on efficient market hypothesis (EMH), Using complex network theory to study the stock market is to study its topological structure and overall characteristics from the macroscopic angle. It is of great practical significance to use this new angle to study the stock market in China. After the financial crisis of 2008, between 2011 and 2013, the Chinese stock market as a whole continued to fall. In order to further study the internal structure of China's stock market and discuss its operating mechanism, In this paper, the author uses the complex network theory and R software and igraph software package to study the Chinese stock market, and does the following work: 1. Combining with the complex network theory, Based on the minimum spanning Tree algorithm (MST), a Chinese stock correlation network model is constructed, and then the yield distribution of all stocks in different time periods is analyzed. It is found that the whole distribution of peak and thick tail. 2. Using Newman fast algorithm to divide the stock association network, and by comparing the average correlation coefficient among the stocks in each community, It is found that the average correlation coefficient of the stock market in the severe decline is obviously higher than that in the stable stock market, and the degree of industry aggregation is obviously increased .3.Through comparing and analyzing the degree, the medium, the proximity and the characteristic vector of each node in the network, Found that stocks in the financial and energy sectors are at the centre of the network, which is consistent with the fact that the two industries are important in the economic system. Two important statistical features have also been found. One is that the node with high "centricity" is an important node connecting multiple communities, the other is that the node with high "centricity" and "eigenvector centrality" has strong correlation.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.51;O157.5

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