本文關(guān)鍵詞：媒體信息內(nèi)容與中國(guó)股市中的股票收益的關(guān)系　出處：《哈爾濱工業(yè)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 媒體信息內(nèi)容 文本傾向性分析 股票收益 投資策略

【摘要】：隨著中國(guó)股票市場(chǎng)的蓬勃發(fā)展，投資者對(duì)媒體信息的關(guān)注度越來(lái)越高，而媒體信息對(duì)投資者的關(guān)注度和投資策略的影響也是越來(lái)越大。因?yàn)閭�(gè)人投資者的關(guān)注力和認(rèn)知能力的有限，他們只能選擇性的處理他們所獲得的全部信息里的一部分，所以相對(duì)于其他信息而言更權(quán)威的、更可信的媒體信息就成為了散戶投資者們的首選，進(jìn)而影響了股票收益。雖然人們認(rèn)同媒體信息對(duì)投資者的行為以及上市公司股票價(jià)格、收益的影響，但系統(tǒng)全面的研究媒體信息到底如何影響投資者和股票收益，國(guó)內(nèi)才剛剛開始，而對(duì)媒體信息內(nèi)容進(jìn)行文本傾向性分析，進(jìn)而探討其對(duì)上市公司股票收益的影響的研究更是少之又少，所以目前非常需要對(duì)該領(lǐng)域進(jìn)行更為全面和深入的研究。 本文首先采用篇章級(jí)的文本傾向性分析方法，對(duì)媒體信息進(jìn)行文本傾向性分類，分出積極正向的文章和消極負(fù)向的文章。分類算法采用本文提出的HMSA分類算法，同時(shí)與為K-最近鄰算法、最大熵分類算法和支持向量機(jī)分類算法進(jìn)行比較，實(shí)驗(yàn)結(jié)果表明，HMSA算法的分類效果最好，準(zhǔn)確率為79.71%，召回率達(dá)到78.00%，F(xiàn)值為78.85%。 在文本傾向性分類后，驗(yàn)證了媒體信息內(nèi)容對(duì)股票收益的影響機(jī)理，媒體信息首先對(duì)投資者的投資策略產(chǎn)生影響，進(jìn)而影響了股票的價(jià)格、成交量和收益率。通過分析發(fā)現(xiàn)積極正向的媒體信息可以有效的預(yù)測(cè)股票收益率的上漲和隨后的收益率下跌，而消極負(fù)向的媒體信息可以有效的預(yù)測(cè)股票收益率的下跌和隨后的收益率上漲。 在對(duì)影響機(jī)理驗(yàn)證結(jié)果的基礎(chǔ)上，研究了基于媒體信息文本傾向性指標(biāo)的投資策略獲利模式。由于媒體信息明顯地影響了股票收益，，構(gòu)建了基于媒體信息文本指標(biāo)的零投資組合的投資策略和不考慮該指標(biāo)的零投資組合的投資策略。通過比較，發(fā)現(xiàn)引入該指標(biāo)后可以獲得較高的收益回報(bào)，增加了投資者的獲利空間，對(duì)理論研究和實(shí)際投資都提供了有益的幫助。
[Abstract]:With the vigorous development of Chinese stock market, investors pay more and more attention to media information. The influence of media information on investors' attention and investment strategy is also increasing, because of the limited attention and cognitive ability of individual investors. They can only deal selectively with some of the information they get, so more authoritative and credible media information than other information is the preferred choice for retail investors. And then affect the stock returns, although people agree with the media information on the behavior of investors and listed companies stock prices, returns. However, the systematic and comprehensive research on how media information affects investors and stock returns is just beginning in China, and the text orientation analysis of media information content is carried out. Therefore, it is necessary to do more comprehensive and in-depth research in this field. Firstly, the text orientation analysis method is used to classify the media information. The classification algorithm adopts the HMSA classification algorithm proposed in this paper, and at the same time, it is the K-nearest neighbor algorithm. The maximum entropy classification algorithm is compared with the support vector machine classification algorithm. The experimental results show that HMSA algorithm has the best classification effect, the accuracy is 79.71 and the recall rate is 78.00%. F value is 78.85. After the text orientation classification, the paper verifies the influence mechanism of media information content on stock returns. Media information first affects investors' investment strategy, and then affects the stock price. The positive positive media information can effectively predict the rise of stock yield and the subsequent decline of yield. Negative media information can effectively predict the decline of stock yield and the subsequent increase in yield. Based on the results of verification of the influence mechanism, this paper studies the profit model of investment strategy based on the tendency index of media information text, because media information has a significant impact on stock returns. A zero-portfolio investment strategy based on the media information text index and a zero-portfolio investment strategy without considering the index are constructed. By comparison, it is found that the introduction of the index can achieve a higher return. It increases the profit space of investors and provides beneficial help for both theoretical research and practical investment.
【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】：2013
【分類號(hào)】：F832.51;TP391.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 魯臻;鄒恒甫;;中國(guó)股市的慣性與反轉(zhuǎn)效應(yīng)研究[J];經(jīng)濟(jì)研究;2007年09期

2 婁德成;姚天f ;;漢語(yǔ)句子語(yǔ)義極性分析和觀點(diǎn)抽取方法的研究[J];計(jì)算機(jī)應(yīng)用;2006年11期

3 徐軍;丁宇新;王曉龍;;使用機(jī)器學(xué)習(xí)方法進(jìn)行新聞的情感自動(dòng)分類[J];中文信息學(xué)報(bào);2007年06期

4 姚天f ;程希文;徐飛玉;漢思·烏思克爾特;王睿;;文本意見挖掘綜述[J];中文信息學(xué)報(bào);2008年03期

5 葉強(qiáng);張紫瓊;羅振雄;;面向互聯(lián)網(wǎng)評(píng)論情感分析的中文主觀性自動(dòng)判別方法研究[J];信息系統(tǒng)學(xué)報(bào);2007年01期

本文編號(hào)：1425321