本文選題：股票網(wǎng)絡(luò)市場 + 閾值法��； 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：近年,大量的研究人員主要是通過復(fù)雜網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)以及中心性度量的方法來描繪復(fù)雜網(wǎng)絡(luò)中的基本特性。而在網(wǎng)絡(luò)信息化社會(huì)中,復(fù)雜網(wǎng)絡(luò)早已被廣泛的利用到各行各業(yè)中,其中金融證券市場便是復(fù)雜網(wǎng)絡(luò)的一個(gè)重要的討論方向,使得復(fù)雜網(wǎng)絡(luò)中的鏈路預(yù)測被提升為研究主體。在金融證券市場里股票價(jià)格的大幅波動(dòng)會(huì)產(chǎn)生較大的風(fēng)險(xiǎn)。這不僅打破市場帶來的均衡價(jià)值,甚至可能促使市場機(jī)能的徹底喪失。因此本論文將利用相關(guān)系數(shù)法與閾值法來構(gòu)建股票網(wǎng)絡(luò)市場,運(yùn)用局部結(jié)構(gòu)中心性來分析A股市中潛在影響力的股票,隨后結(jié)合鏈路預(yù)測中相似性指標(biāo)來衡量潛在影響力股票在未來相互連接的可能性程度。本論文主要工作是:首先主要是從拓?fù)浣Y(jié)構(gòu)中心性來衡量節(jié)點(diǎn)重要性的影響程度。其中通過比較了局部度量和全局度量的中心值,闡述其優(yōu)缺點(diǎn)。即本論文將運(yùn)用局部結(jié)構(gòu)中心法來分析A股市場中股票的影響力,其中它的精確性遠(yuǎn)遠(yuǎn)高于局部度量,而計(jì)算復(fù)雜性遠(yuǎn)遠(yuǎn)低于全局度量。其次,收集每日A股股票的收盤價(jià)格數(shù)據(jù),來做股票價(jià)格的對(duì)數(shù)相關(guān)系數(shù)計(jì)算,從而簡單粗略地反映股票之間相關(guān)性的強(qiáng)弱。隨之引入度量矩陣和閾值法來分析股票網(wǎng)絡(luò)市場模型,并得出本論文研究股票網(wǎng)絡(luò)市場模型的閾值為0.89較為合適。通過對(duì)閾值取值不同時(shí),分別運(yùn)用局部結(jié)構(gòu)中心法來分析股票網(wǎng)絡(luò)市場中潛在影響力的股票。最后,論文主要以股票節(jié)點(diǎn)之間的相似性越大連接可能性就越大為研究前提條件,以AUC為評(píng)價(jià)標(biāo)準(zhǔn)并利用控制變量法來比較局部信息相似性,分析出資源配置(RA)相似性指標(biāo)為最佳理想的相似性指標(biāo)�；赗A指標(biāo)與局部路徑相似性指標(biāo)中網(wǎng)絡(luò)的動(dòng)態(tài)演化,可知最優(yōu)相似性指標(biāo)是在不斷的發(fā)生變化。研究表明,通過對(duì)具體的網(wǎng)絡(luò)形態(tài)利用RA指標(biāo)與Katz指標(biāo)來對(duì)A股中股票進(jìn)行數(shù)值化分析,A股股票與有色金屬類潛在影響力的股票之間可能發(fā)生連接性,以此使得投資者達(dá)到規(guī)避風(fēng)險(xiǎn)效果。
[Abstract]:In recent years, a large number of researchers describe the basic characteristics of complex networks mainly through the topological structure of complex networks and the method of central measurement. In the network information society, the complex network has been widely used in various industries, in which the financial security market is an important discussion direction of the complex network, which makes the link prediction in the complex network to be promoted to the research subject. In the financial securities market, large fluctuations in stock prices will have greater risks. This not only breaks the equilibrium value of the market, it may even cause the complete loss of market function. Therefore, this paper will use the correlation coefficient method and threshold method to construct the stock network market, and use the local structure centrality to analyze the potentially influential stocks in the A-share market. Then the similarity index in link prediction is used to measure the possibility of potential impact stocks to be connected in the future. The main work of this thesis is as follows: firstly, the influence degree of node importance is measured from topology centrality. The central values of local and global metrics are compared, and their advantages and disadvantages are expounded. That is to say, this paper will use the method of local structure center to analyze the influence of stock in A-share market, in which its accuracy is far higher than that of local measurement, and the computational complexity is far lower than the global metric. Secondly, the daily closing price data of A shares are collected to calculate the logarithmic correlation coefficient of stock prices, so as to reflect the correlation between stocks roughly. Then the measurement matrix and threshold method are introduced to analyze the stock network market model, and it is concluded that the threshold value of this paper is 0.89. The local structure center method is used to analyze the potentially influential stocks in the stock network market by taking different threshold values. Finally, the paper mainly takes the similarity between stock nodes as the premise of the study, taking AUC as the evaluation standard and using the control variable method to compare the similarity of local information. The similarity index of resource allocation is the best ideal similarity index. Based on the dynamic evolution of the network between the RA index and the local path similarity index, it can be seen that the optimal similarity index is constantly changing. The results show that by using RA index and Katz index to numerically analyze the A-share stock, the connection between A-share stock and non-ferrous metal potentially influential stock may occur. In this way, investors to achieve risk-aversion effect.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51

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