不同市态下投资者情绪与股市收益及其波动的互动关系研究

发布时间：2018-01-04 17:11

本文关键词：不同市态下投资者情绪与股市收益及其波动的互动关系研究　出处：《南京理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：不断出现的市场异象和日渐成熟的行为金融理论,使得以有效市场和投资者完全理性为基础假设的传统金融理论体系,受到了来自现实和理论两方面的挑战。现有的诸多实证研究结果表明,各国市场的投资者情绪表现虽有所不同,但股票市场价格在一定程度上都受到了投资者情绪的影响。1986年,Black提出著名的噪声交易者假说,1990年,Delong、Shleifer、Summer和Waldman正式将投资者情绪引入股票价格决定模型,建立了 DSSW噪声交易模型。1990年上海证券交易所成立,中国证券市场发展迅速,但仍是一个典型的不成熟的新兴市场,存在诸如,市场交易信息不对称,投资者结构失衡,个人投资者市场分析能力有限,"政策市"特征显著等问题。研究投资者情绪与股市收益的互动关系,在理论方面,有助于完善行为金融理论体系,更好地了解我国证券市场的现状,为解决我国证券市场现有的诸多问题提供有益参考;在现实意义方面,有助于我国政策制定者及时掌握市场动向,尽可能减轻"政策市"的影响,有助于市场监管者采取更有效地监管措施,完善我国的金融市场体制,同时有助于提高投资者自身对证券市场全面分析的能力,促进我国股票市场的良性发展。在综合整理总结现有研究成果的基础上,本文借鉴何兴强和周开国的研究思路与方法,根据Pegan和Sossounov的非参数诊断思想,划分出我国股票市场的牛、熊市周期,并以此作为后续实证研究的数据基础。本文选取交易量、换手率、市场流动性和涨跌比四个指标作为投资者情绪的代理指标,运用主成分分析方法,构建投资者综合情绪指标。在实证分析部分,本文采用向量自回归(VAR)模型,运用Granger因果检验、脉冲响应分析和方差分解,分别研究股市整体数据、四次熊市样本数据和四次牛市样本数据,检验不同市态下投资者情绪与股市收益的相互关系;采用TARCH-M(1,1)模型,以股市整体、大牛市和大熊市三个数据样本为基础,检验不同市态环境下股票市场投资者情绪对股票收益及其波动的影响,最后整理相关研究结论,并提出我国股票市场发展改革的相关政策建议,总结本次研究的不足之处,以期对后续的研究提供有益的贡献和参考。
[Abstract]:The emerging market anomalies and the increasingly mature behavioral financial theory make the traditional financial theory system based on the efficient market and investors' complete rationality. It is challenged by both reality and theory. Many empirical results show that investor sentiment is different in different countries. However, the stock market price is influenced by investor sentiment to some extent. In 1986, Black put forward the famous noise trader hypothesis and Delong in 1990. Shleifern Summer and Waldman formally introduced investor sentiment into the stock price determination model. The DSSW noise trading model was established. In 1990, the Shanghai Stock Exchange was established. China's stock market has developed rapidly, but it is still a typical immature emerging market, such as. This paper studies the interactive relationship between investor sentiment and stock market returns in theory, such as asymmetric market information, unbalanced investor structure, limited market analysis ability of individual investors and obvious characteristics of "policy market". It is helpful to perfect the behavioral finance theory system, better understand the present situation of our country's securities market, and provide beneficial reference for solving many problems existing in our country's securities market. In the aspect of practical significance, it is helpful for Chinese policy makers to grasp the market trend in time, to minimize the influence of "policy city", and to help market regulators to take more effective supervision measures. To improve the financial market system of our country, at the same time to improve the ability of investors to analyze the stock market comprehensively, and to promote the healthy development of the stock market in our country. This paper draws lessons from the research ideas and methods of he Xingqiang and Zhou Kaiguo, according to the non-parametric diagnosis idea of Pegan and Sossounov, divides the bull market and bear market cycle of our country stock market. This paper selects four indicators of trading volume, turnover rate, market liquidity and ratio of rise to fall as proxy indicators of investor sentiment, using principal component analysis method. In the part of empirical analysis, we use vector autoregressive (VAR) model, Granger causality test, impulse response analysis and variance decomposition. The whole stock market data, four bear market sample data and four bull market sample data are studied to test the relationship between investor sentiment and stock market returns under different market conditions. The model is based on three data samples: the stock market as a whole, the bull market and the bear market. This paper examines the impact of investor sentiment on stock returns and their fluctuations under different market conditions, finally collates the relevant research conclusions, and puts forward relevant policy recommendations for the development and reform of China's stock market. To sum up the shortcomings of this study, in order to provide useful contributions and references to the follow-up research.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F832.51

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