本文選題：股票相關(guān)性 + 金融時(shí)間序列�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：2014年以來(lái)我國(guó)股市進(jìn)入一輪顯著的牛市行情,全國(guó)掀起一波火熱的炒股的浪潮,技術(shù)分析在投資者投資決策中的作用和需要日漸重要。股票相關(guān)性是研究股價(jià)或者收益率間的關(guān)系和行業(yè)分類的技術(shù)工具,它對(duì)股票市場(chǎng)系統(tǒng)性風(fēng)險(xiǎn)與資產(chǎn)組合的有效性的衡量具有重要價(jià)值。所以,個(gè)人與機(jī)構(gòu)投資者均把股票間的相關(guān)性作為一個(gè)重要標(biāo)準(zhǔn),以此權(quán)衡股市風(fēng)險(xiǎn)的大小與組建的的投資組合有效性。通常情況,對(duì)于股票相關(guān)性的衡量,國(guó)內(nèi)外學(xué)者用相關(guān)系數(shù)大小加以表示,股票相關(guān)系數(shù)越大,相關(guān)性就越強(qiáng)。然而在我國(guó)股票市場(chǎng)中顯著的牛市或熊市行情會(huì)引起不同行業(yè)的股票間過(guò)度相似的的同漲同跌現(xiàn)象,說(shuō)明單邊上漲或下跌行情中引起了股票間的超額聯(lián)動(dòng)效應(yīng),這將導(dǎo)致測(cè)算的股票間相關(guān)系數(shù)偏離真實(shí)值,影響投資者的投資決策、行業(yè)分類及投資組合有效性。因此,研究出能盡可能規(guī)避股票間的超額聯(lián)動(dòng)效應(yīng),測(cè)算出反映股票收益率間真實(shí)相關(guān)性的相關(guān)系數(shù)對(duì)于行業(yè)股票相關(guān)性的研究有非常重大的應(yīng)用價(jià)值。針對(duì)該問(wèn)題,本文首先研究了OLS、資本資產(chǎn)定價(jià)模型(CAPM)及不同股市行情特征下的股票相關(guān)性分析方法,并以上證A股行業(yè)股票的對(duì)數(shù)收益率為實(shí)證研究對(duì)象,通過(guò)大數(shù)據(jù)程序算法驗(yàn)證了股票市場(chǎng)中超額聯(lián)動(dòng)效應(yīng)存在的普遍性。其次,針對(duì)CAPM模型測(cè)算股票收益率存在的超額聯(lián)動(dòng)效應(yīng),本文將傳統(tǒng)的CAPM公式進(jìn)行相應(yīng)的變形剔除引起超額聯(lián)動(dòng)效應(yīng)的市場(chǎng)系統(tǒng)性風(fēng)險(xiǎn),構(gòu)造出改進(jìn)的股票相關(guān)性的測(cè)算模型;應(yīng)對(duì)金融時(shí)間序列下存在的超額聯(lián)動(dòng)效應(yīng),本文通過(guò)自動(dòng)依照成交量截取、人工截取、依照滾動(dòng)時(shí)間窗口的股票間相關(guān)系數(shù)截取等方法構(gòu)造盡可能規(guī)避OLS及行情特征下的超額聯(lián)動(dòng)效應(yīng)影響的新型行業(yè)股票間相關(guān)性測(cè)量方法。最后,文章以上證A股行業(yè)股票進(jìn)行實(shí)證研究,將盡可能規(guī)避股票間超額聯(lián)動(dòng)效應(yīng)的新型相關(guān)性測(cè)算方法計(jì)算的相關(guān)系數(shù)與傳統(tǒng)測(cè)算方式進(jìn)行橫向與縱向?qū)Ρ确治?驗(yàn)證出新型相關(guān)性測(cè)算方法的合理有效性。同時(shí),文章將盡可能規(guī)避超額聯(lián)動(dòng)效應(yīng)的股票間相關(guān)性的測(cè)量方法測(cè)算的新型相關(guān)系數(shù)應(yīng)用于復(fù)雜網(wǎng)絡(luò)對(duì)股票間的相關(guān)性的研究中。規(guī)避超額聯(lián)動(dòng)效應(yīng)的股票間相關(guān)性的新型測(cè)量方法更有利于提高投資者投資決策、投資組合選擇的有效性以及行業(yè)分類的準(zhǔn)確性。
[Abstract]:Since 2014, China's stock market has entered a remarkable bull market, and a wave of hot stock speculation has been launched all over the country. Technical analysis plays an increasingly important role and needs in investors' investment decisions. Stock correlation is a technical tool to study the relationship between stock price and yield and industry classification. It is of great value to measure the systematic risk and the efficiency of portfolio in stock market. Therefore, both individual and institutional investors take the correlation between stocks as an important criterion to weigh the stock market risk and the efficiency of the portfolio. In general, for the measurement of stock correlation, domestic and foreign scholars use the correlation coefficient to express it. The greater the stock correlation coefficient is, the stronger the correlation is. However, in China's stock market, a significant bull market or bear market will lead to excessive and similar simultaneous rise and fall among stocks in different industries, indicating that a unilateral rise or fall has caused an excess linkage effect between stocks. This will lead to the deviation of the correlation coefficient between the measured stocks from the real value, which will affect the investment decision, industry classification and portfolio efficiency of investors. Therefore, the study can avoid the excess linkage effect of stock as much as possible, and calculate the correlation coefficient which reflects the real correlation between stock returns. It is of great application value for the research of industry stock correlation. To solve this problem, this paper first studies OLS, capital asset pricing model (CAPMM) and stock correlation analysis methods under different market market characteristics, and takes the logarithmic return rate of A shares in Shanghai Stock Exchange as the empirical research object. The universality of excess linkage effect in stock market is verified by big data program algorithm. Secondly, in view of the excess linkage effect of CAPM model, the traditional CAPM formula is deformed to eliminate the market systemic risk of excess linkage effect, and an improved stock correlation calculation model is constructed. In order to deal with the excess linkage effect in financial time series, this paper, by automatically intercepting according to the transaction volume, manually intercepting, According to the method of interstock correlation coefficient interception of rolling time window, a new type of industry stock correlation measurement method is constructed to avoid the influence of excess linkage effect under OLS and market characteristics as much as possible. Finally, the article carries on the empirical research with the Shanghai Stock Exchange A share industry stock, carries on the horizontal and the longitudinal contrast analysis to the correlation coefficient and the traditional calculation method which avoids the excess linkage effect of the stock as far as possible. The validity of the new method is verified. At the same time, this paper applies the new correlation coefficient to the study of the correlation between stocks based on complex networks. A new measurement method to avoid the correlation between stocks with excess linkage effect is more helpful to improve the investment decision, the validity of portfolio selection and the accuracy of industry classification.
【學(xué)位授予單位】：哈爾濱工業(yè)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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