發(fā)布時(shí)間：2018-01-12 23:03

  本文關(guān)鍵詞：基于投資者情緒和宏觀經(jīng)濟(jì)情況的股市波動(dòng)分解　出處：《山西財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 波動(dòng)成分模型 宏觀經(jīng)濟(jì)信息 投資者情緒 文本挖掘 網(wǎng)絡(luò)爬蟲(chóng)

【摘要】：我國(guó)股市一直以來(lái)被冠以“政策市”、“散戶(hù)市”的頭銜,這樣的說(shuō)法在一定程度上有其合理性,我國(guó)市場(chǎng)投資者以散戶(hù)居多,廣大股民接受的投資教育有限,但也不能就此否認(rèn)股市波動(dòng)在很大程度上反映了宏觀經(jīng)濟(jì)運(yùn)行情況這一事實(shí)。實(shí)際上,隨著學(xué)界對(duì)股市研究的不斷深入,行為金融學(xué)認(rèn)為投資者的情緒影響其交易行為,進(jìn)而對(duì)市場(chǎng)走勢(shì)造成影響。而傳統(tǒng)金融理論認(rèn)為股價(jià)是圍繞其內(nèi)在價(jià)值上下波動(dòng)這一論斷依然有其合理性。與此同時(shí)越來(lái)越多的人意識(shí)到投資者情緒和宏觀經(jīng)濟(jì)波動(dòng)可能是推動(dòng)股市波動(dòng)背后的主要力量。然而,將長(zhǎng)、短期波動(dòng)結(jié)合起來(lái)考慮缺乏合適的實(shí)證方法。基于以上考慮,本文采用了近年來(lái)受到廣泛關(guān)注的GARCH-MIDAS模型作為本文的實(shí)證框架,綜合考慮股市波動(dòng)的長(zhǎng)短期兩個(gè)層面的作用機(jī)制。同時(shí)在投資者情緒代理變量的確定過(guò)程中采用了學(xué)界常用到的股市換手率作為比較基準(zhǔn),同時(shí)筆者嘗試通過(guò)結(jié)合網(wǎng)絡(luò)爬蟲(chóng)和文本挖掘提高投資者情緒這一短期因素對(duì)模型的貢獻(xiàn)度,綜合運(yùn)用Python、R語(yǔ)言編程實(shí)現(xiàn)網(wǎng)絡(luò)文本的的信息采集、文本處理、清洗、分詞等研究步驟,構(gòu)建了直接來(lái)源于網(wǎng)絡(luò)文本的投資者情緒指數(shù)。通過(guò)一系列的理論和實(shí)證研究,本文得到如下結(jié)論:第一,在波動(dòng)分為長(zhǎng)期與短期兩種成分這一認(rèn)識(shí)上,國(guó)內(nèi)外股市波動(dòng)的表現(xiàn)是一致的:股市波動(dòng)包含長(zhǎng)期成分和短期成兩個(gè)層面,且宏觀經(jīng)濟(jì)信息中包含股市波動(dòng)長(zhǎng)期成分的驅(qū)動(dòng)力量。同時(shí),我國(guó)股票市場(chǎng)的短期波動(dòng)成分能通過(guò)投資者情緒得到很好的解釋。第二,通過(guò)網(wǎng)絡(luò)爬蟲(chóng)和文本挖掘相結(jié)合的方式構(gòu)建情緒指數(shù)不失為良策,為后續(xù)研究的指標(biāo)構(gòu)建極大的拓寬了信息收集渠道。第三,情感詞典在情緒指數(shù)的構(gòu)建當(dāng)中具有不可替代的重要作用。情感詞典選詞越全面、篩選越具有針對(duì)性,最終構(gòu)建的投資者情緒指數(shù)就越具有代表性。特別是涉及到專(zhuān)業(yè)領(lǐng)域的情感分析,需要相關(guān)領(lǐng)域的豐富語(yǔ)料以及科學(xué)合理的情緒指數(shù)構(gòu)建方法。第四,GARCH模型與MIDAS(混頻數(shù)據(jù)抽樣)模型結(jié)合而構(gòu)建的GARCH-MIDAS模型能有效拓展模型研究的縱深,同時(shí)也能夠最大程度上挖掘混頻數(shù)據(jù)的信息,且在我國(guó)股市波動(dòng)成分模型研究中的應(yīng)用是有效且適用的。
[Abstract]:The stock market of our country has always been labeled as "policy city" and "retail market". To a certain extent, it is reasonable to say that Chinese market investors are mostly retail investors, and the investment education received by the vast number of investors is limited. But we can not deny the fact that the fluctuation of stock market reflects the macroeconomic situation to a great extent. In fact, with the deepening of the academic research on stock market. Behavioral finance believes that investors' emotions affect their trading behavior. The traditional financial theory that the stock price fluctuates around its intrinsic value is still reasonable. At the same time, more and more people are aware of investor sentiment and macro economy. Volatility may be the main force behind the volatility of the stock market. The combination of long and short term volatility is lack of appropriate empirical methods. Based on the above considerations, this paper uses the GARCH-MIDAS model which has received extensive attention in recent years as the empirical framework. Comprehensive consideration of the stock market volatility of the long-term and short-term mechanisms of the two levels of action. At the same time, in the process of determining investor sentiment proxy variables, the stock market turnover rate commonly used in academia as a comparison benchmark. At the same time, the author tries to improve the contribution of investor sentiment to the model by combining web crawler and text mining, and synthetically uses Python R language to realize the information collection of network text. Text processing, cleaning, word segmentation and other research steps, directly derived from the Internet text investor sentiment index. Through a series of theoretical and empirical research, this paper obtains the following conclusions: first. In the understanding that volatility is divided into long-term and short-term components, the performance of domestic and foreign stock market volatility is consistent: stock market volatility includes long-term components and short-term into two levels. And the macroeconomic information contains the driving force of long-term stock market volatility. At the same time, the short-term volatility of China's stock market can be well explained by investor sentiment. Second. Through the combination of web crawler and text mining, it is a good way to construct emotional index, which greatly broadens the channel of information collection. Emotion dictionary plays an irreplaceable role in the construction of emotion index. The final construction of investor sentiment index is more representative, especially involving the professional domain of emotional analysis, need related to the field of rich corpus and scientific and reasonable emotional index construction method. 4th. The GARCH-MIDAS model constructed by combining GARCH model with midas (mixed frequency data sampling) model can effectively extend the depth of the model research. At the same time, it can mine the information of mixing data to the maximum extent, and the application in the research of volatility component model of stock market in our country is effective and applicable.
【學(xué)位授予單位】：山西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F832.51

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