本文選題：投資者關(guān)注度 + 收益　； 參考：《哈爾濱工業(yè)大學》2017年碩士論文

【摘要】：20世紀80年代以來,隨著金融市場上一些現(xiàn)象無法利用傳統(tǒng)金融學解釋的現(xiàn)象出現(xiàn)時,人們開始對傳統(tǒng)的金融理論進行重新思考,并試圖運用更加合理的方法去解釋市場當中存在的“異象”。其中部分學者從心理學的視角,對金融市場當中的個體投資者進行仔細研究。通過觀察個體投資者的行為表現(xiàn),將得到的數(shù)據(jù)加入到傳統(tǒng)的金融模型當中,發(fā)現(xiàn)可以得到能夠更好的解釋市場的現(xiàn)象。從此,衍生出一系列結(jié)合心理學和金融學的理論,行為金融學進而廣泛被大家熟知與接受。有限注意作為心理學當中重要的一部分,在近些年當中,隨著互聯(lián)網(wǎng)大數(shù)據(jù)的跨越式發(fā)展,投資者關(guān)注度的數(shù)據(jù)成為研究股票市場上量化分析的有力工具。由于投資者的注意力被認為是一種稀缺資源,因而當投資者把自身的注意力分配給某些公司時,勢必會對其股票產(chǎn)生一定的影響,同時,由于噪聲投資者的存在,這種現(xiàn)象很可能在短期之內(nèi)被放大,從而影響股票的收益和波動。本文在第一章首先對投資者關(guān)注度相關(guān)研究進行梳理,并確定研究的目標、意義以及所選擇的研究方法;在第二章當中總結(jié)金融市場資產(chǎn)定價的相關(guān)理論及發(fā)展脈絡,同時提出行為金融學當中與投資者關(guān)注度相關(guān)的理論,為后文的實證分析打下理論基礎;在第三章中,對本文所涉及的變量的來源進行介紹,并對本文的模型選取的方法進行一般性概括;第四章為本文的實證部分,將投資者對股票的影響分解為對個股的影響及對市場的影響兩大部分,對個股的影響集中在對個股的收益分析,分別選擇用多元回歸及面板數(shù)據(jù)分析兩種角度,對市場的分析則為投資者關(guān)注度對市場的收益和波動影響兩部分內(nèi)容展開。本文通過對反映投資者關(guān)注度的指標進行深刻挖掘,選擇利用和訊網(wǎng)及東方財富網(wǎng)股吧的數(shù)據(jù)投資者關(guān)注度進行較為直觀的刻畫,最大程度上減小市場上的噪音,在此基礎上利用該數(shù)據(jù)結(jié)合股票指數(shù)及個股的相關(guān)財務數(shù)據(jù)研究投資者關(guān)注度對個股收益及對市場波動的影響,探究其內(nèi)在的影響機制,為投資者在研究股票表現(xiàn)時提供一定的參考。
[Abstract]:Since the 1980s, with the emergence of some phenomena in the financial market that cannot be explained by traditional finance, people began to rethink the traditional financial theory. And try to use a more reasonable method to explain the market exist in the "vision." From the perspective of psychology, some scholars carefully study individual investors in financial markets. By observing the behavior of individual investors, we add the data to the traditional financial model and find that the phenomenon can be better explained. Since then, a series of theories combining psychology and finance have been developed, and behavioral finance has been widely known and accepted. Limited attention is an important part of psychology. In recent years, with the development of Internet big data, the data of investor's attention has become a powerful tool to study the quantitative analysis in stock market. Because investors' attention is regarded as a scarce resource, when investors allocate their own attention to some companies, it will inevitably have a certain impact on their stocks. At the same time, because of the presence of noise investors, This phenomenon is likely to be amplified in the short term, affecting stock returns and volatility. In the first chapter of this paper, the author firstly combs the related research of investor's attention, and determines the goal, significance and research method of the research. In the second chapter, it summarizes the related theories and development context of asset pricing in the financial market. At the same time, it puts forward the theory of behavioral finance related to investor concern, which lays a theoretical foundation for the empirical analysis later. In chapter 3, the source of variables involved in this paper is introduced. The fourth chapter is the empirical part of this paper, the impact of investors on the stock is divided into two parts: the impact on the stock and the impact on the market. The influence of individual stock is concentrated on the income analysis of individual stock, the multiple regression and panel data are used to analyze the two angles, and the analysis of market is carried out in two parts: the investor's attention to the return and the fluctuation of the market. Through the deep excavation of the index reflecting investor's attention, this paper chooses to use the data investor's attention of Hexun net and Dongfang fortune net stock bar to describe the data investor's attention degree directly, so as to reduce the noise in the market to the greatest extent. On this basis, using the data combined with the stock index and the relevant financial data of individual stocks to study the impact of investor attention on individual stock returns and market volatility, and to explore its internal impact mechanism. For investors in the study of stock performance to provide a certain reference.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F832.51

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