本文選題：復(fù)雜網(wǎng)絡(luò)　切入點(diǎn)：股票市場　出處：《南京信息工程大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,對中國股票市場行為的研究逐漸成為熱點(diǎn)。眾所周知,中國股票市場波動性大。一般來說,股票市場的波動行為總是與股票間的復(fù)雜關(guān)系息息相關(guān)。傳統(tǒng)的方法是利用多元統(tǒng)計(jì)模型對股票之間的波動關(guān)聯(lián)性進(jìn)行研究,然而在利用這些模型進(jìn)行研究時(shí)往往受到“維數(shù)災(zāi)難”的限制,較難進(jìn)行有效的實(shí)證。復(fù)雜網(wǎng)絡(luò)理論的誕生方便了我們對資產(chǎn)價(jià)格聯(lián)動特性的研究。 本文從系統(tǒng)科學(xué)的角度出發(fā),基于復(fù)雜網(wǎng)絡(luò)理論對股票市場中資產(chǎn)價(jià)格聯(lián)動與市場穩(wěn)定問題進(jìn)行研究,論文的主要工作和創(chuàng)新成果如下： (1)以上海市場2001年1月2日至2011年3月11日期間的501只股票的日收盤價(jià)為樣本,采用互信息衡量股票價(jià)格之間的相互關(guān)系,并采用滑動窗口的方法構(gòu)建出相應(yīng)的2063個(gè)全連通的上海市場股票動態(tài)關(guān)聯(lián)網(wǎng)絡(luò)。從每個(gè)股票網(wǎng)絡(luò)中提取最大生成樹,分析了樹的平均路徑長度,中心節(jié)點(diǎn)影響力和p值隨時(shí)間的變化情況,又進(jìn)一步對上海股市不同時(shí)期最大生成樹的變化特性進(jìn)行了詳細(xì)分析。實(shí)證結(jié)果表明：2005年8月8日、2007年10月17日和2008年12月25日左右的這三段時(shí)期是上海股票市場的轉(zhuǎn)折時(shí)期,在轉(zhuǎn)折時(shí)期,股票市場穩(wěn)定性變差,最大生成樹拓?fù)浣Y(jié)構(gòu)變得松散,股票節(jié)點(diǎn)間的分離程度變大,中心節(jié)點(diǎn)的影響力減小,樹的結(jié)構(gòu)從星型變?yōu)殒湢?對應(yīng)的度分布也不再是冪律分布。同時(shí),對最大生成樹的單步和多步存活率進(jìn)行分析,實(shí)證發(fā)現(xiàn)：在短期的演化過程中,股票間的緊密關(guān)系不容易被打破；相反,在長時(shí)間的演化過程中,往往不存在一直都是緊密聯(lián)系的股票節(jié)點(diǎn)對。 (2)以2008年12月25日這個(gè)股票市場轉(zhuǎn)折點(diǎn)為背景,將它作為轉(zhuǎn)折前期和轉(zhuǎn)折時(shí)期的分界點(diǎn),構(gòu)建轉(zhuǎn)折前期和轉(zhuǎn)折時(shí)期兩個(gè)行業(yè)關(guān)聯(lián)網(wǎng)絡(luò),并采用兩種方法對網(wǎng)絡(luò)拓?fù)涮匦赃M(jìn)行分析。一方面,對原始網(wǎng)絡(luò)進(jìn)行閾值化處理,實(shí)證發(fā)現(xiàn)在轉(zhuǎn)折時(shí)期,網(wǎng)絡(luò)的聚類系數(shù)明顯減小,凝聚度變差,網(wǎng)絡(luò)變得松散,進(jìn)一步通過對網(wǎng)絡(luò)的k核分解,發(fā)現(xiàn)轉(zhuǎn)折時(shí)期處于最深核的節(jié)點(diǎn)與轉(zhuǎn)折前期相比發(fā)生了明顯變化,紡織服飾行業(yè)和電子行業(yè)成為最深核節(jié)點(diǎn),這與當(dāng)時(shí)我國的經(jīng)濟(jì)現(xiàn)象十分吻合。另一方面,提取原始網(wǎng)絡(luò)的最大生成樹,實(shí)證發(fā)現(xiàn)轉(zhuǎn)折時(shí)期節(jié)點(diǎn)間的分離程度變大,中心節(jié)點(diǎn)影響力減小,轉(zhuǎn)折前期與中心節(jié)點(diǎn)緊密相連的行業(yè)在轉(zhuǎn)折時(shí)期全部發(fā)生變化。比較以上兩種方法,雖然都能揭示行業(yè)網(wǎng)絡(luò)的特性,得到相似的實(shí)驗(yàn)結(jié)果,但是通過最大生成樹來觀察行業(yè)間聯(lián)動性的變化則更為直觀。 以上結(jié)論不僅能幫助我們進(jìn)一步了解股票市場資產(chǎn)價(jià)格聯(lián)動與市場穩(wěn)定的關(guān)系,還能對股票投資風(fēng)險(xiǎn)管理提供一定的指導(dǎo)意義。
[Abstract]:In recent years, the research on the behavior of Chinese stock market has gradually become a hot spot. As we all know, the volatility of Chinese stock market is great. Generally speaking, The volatility behavior of stock market is always closely related to the complex relationship between stocks. However, the use of these models is often limited by "dimensionality disaster", so it is difficult to carry out effective demonstration. The birth of complex network theory facilitates us to study the property of asset price linkage. From the point of view of system science, this paper studies the linkage of asset prices and market stability in stock market based on complex network theory. The main work and innovative results of this paper are as follows:. Using the daily closing price of 501 stocks in the Shanghai market from January 2nd 2001 to March 11th 2011 as a sample, using mutual information to measure the interrelationship between stock prices, A sliding window method is used to construct 2063 fully connected dynamic correlation networks in Shanghai stock market. The maximum spanning tree is extracted from each stock network and the average path length of the tree is analyzed. The influence of the center node and the change of p value over time, Furthermore, the variation characteristics of the largest spanning tree in different periods of Shanghai stock market are analyzed in detail. The empirical results show that the three periods about August 8th 2005, October 17th 2007 and December 25th 2008 are the turning points of Shanghai stock market. In the transition period, the stability of the stock market becomes worse, the topological structure of the maximal spanning tree becomes loose, the degree of separation between the stock nodes becomes larger, the influence of the central node decreases, and the structure of the tree changes from star to chain. The corresponding degree distribution is no longer a power-law distribution. At the same time, the one-step and multi-step survival rates of the maximal spanning tree are analyzed. It is found that the tight relationship between stocks is not easily broken in the short-term evolution process; on the contrary, In the long-term evolution process, there is often no stock node pair which has been closely related. Based on the turning point of the stock market in December 25th 2008, this paper regards it as the dividing point between the early turning period and the turning period, and constructs two related networks of industries in the early turning period and the turning period. Two methods are used to analyze the network topology. On the one hand, the threshold value of the original network is analyzed. It is found that in the transition period, the clustering coefficient of the network decreases obviously, the cohesion becomes worse, and the network becomes loose. Further, by decomposing the k-core of the network, it is found that the nodes in the deepest core in the transition period have changed obviously compared with the earlier transition period, and the textile and clothing industry and the electronics industry have become the deepest core nodes. On the other hand, by extracting the largest spanning tree of the original network, it is found that the separation between nodes becomes larger and the influence of the central node decreases during the transition period. The industries that are closely connected to the central nodes in the early transition period have all changed during the transition period. Comparing the above two methods, although the characteristics of the industry network can be revealed, similar experimental results can be obtained. But it is more intuitionistic to observe the change of inter-industry interaction through the maximal spanning tree. These conclusions can not only help us to understand the relationship between the linkage of asset prices and market stability, but also provide some guidance for stock investment risk management.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.51

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相關(guān)期刊論文 前1條

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