摘要
研究的本质是什么?研究的本质是寻找规律。研究股票市场的本质是寻找股票市场的运行规律,即股票市场股价波动规律。Fama(1970)提出了有效市场理论,他认为股票市场的股价服从随机游走过程,无法找到股票市场股价运行规律。但是,现实世界中存在周末效应、公告效应、量价关系等,上述效应实际上就是股票市场股价波动规律。
动态量价关系究竟是什么?如何确定动态量价关系研究的逻辑起点?理论上如何确定中国A股股票市场个股异常交易量度量方法?实证分析研究中又采用何种方法度量中国A股股票市场个股异常交易量?如何验证中国A股股票市场显著的个股动态量价关系?哪些经济变量又决定了中国A股股票市场动态量价关系?回答上述问题,就是本文所要研究的全部内容。
动态量价关系是指股票组合或个股出现异常交易量后,预期收益与即期异常交易量之间的逻辑关系。回答动态量价关系的研究逻辑起点,则需要梳理动态量价关系理论解释框架与交易量的理论度量方法,包括CGW(1993)、Wang(1994)、Wang(2002)等人构建的对称信息与不对称信息条件下量价关系的解释框架以及Wang (2000)构建的个股或组合交易量的理论度量方法。在此基础上,进一步梳理动态量价关系实证分析框架,包括CGW(1993)构建的对称信息条件下股票市场量价关系、Wang(2002)与G. Andrew Karoly (2006)构建的实证研究模型,这就是本文研究动态量价关系的逻辑主线。在此基础上,再根据中国A股市场所具有的特殊约束条件,借鉴Wang(2002)与G. Andrew Karoly (2006)的实证研究框架检验中国A股股票市场动态量价关系并寻找决定中国A股股票市场动态量价关系的经济变量。
实证研究中,本文分析了中国A股股票市场自第一支股票上市以来的所有符合条件的个股动态量价关系,分析过程中由于创业板的样本较少,因此实证研究中也尚未研究分析创业板市场的个股动态量价关系,实证研究中分析时期为1990年12月19日至2010年12月31日。根据中国A股股票市场制度变迁与市场格局的约束条件,将样本进行划分研究。最后,得到以下三个结论：①制度变迁与市场格局不影响中国A股股票市场个股动态量价关系；②不对称信息程度是决定中国A股股票市场个股动态量价关系的主要经济变量；③中国A股股票市场的股价出现反转后持续的时间相对于美国市场持续的时间更长。
根据本文的研究逻辑主线,全文共分七章。
第一章,绪论主要介绍了中国A股股票市场个股动态量价关系研究背景、中国A股股票市场个股量价关系研究意义与目的、研究逻辑思路、论文结构安排以及本文研究创新与不足。
第二章,梳理了学术界中相关动态量价关系的理论与实证研究分析框架。
本章主要梳理了CGW(1993)、Wang(1994)、Wang(2002)构建的股票市场动态量价关系的理论研究与实证研究模型。CGW(1993)认为先前量价关系的研究文献将交易量作为外生变量,但资产交易量取决股票价格波动,因此他构建对称信息条件下股票市场量价关系,在此基础上推导出了量价关系的逻辑关系,即股票市场出现异常交易量后,未来一段时间内股价将发生反转。Wang(1994)认为,CGW(1993)仅仅研究了对称信息条件下股票市场量价关系,因此他构建新的模型研究分析信息不对称条件下股票市场量价关系,并且解释了不同信息不对称程度下股票市场量价关系。JiangWang(2002)则认为,Wang(1994)研究模型解释了信息不对称条件下股票市场量价关系,但Wang(1994)的理论模型设计过于复杂,在此基础上他简化了量价关系研究模型,并且通过Wang(2002)研究模型研究分析了信息不对称条件下股票市场个股动态量价关系。
在此基础上梳理了CGW(1993)构建的对称信息条件下股票市场个股动态量价关系的实证研究模型,构建了模型以后则使用美国综合指数验证动态量价关系。但现实世界中的股票市场却是不对称信息的市场,那么在不对称信息条件下的动态量价关系如何验证呢?哪些经济变量决定了信息不对称条件下动态量价关系呢?
在此基础上继续梳理不对称信息条件下个股动态量价关系的实证研究模型以及信息不对称程度对中国A股股票市场动态量价关系的影响效应。
不对称信息条件下个股动态量价关系的实证研究模型包括Wang(2002)构建的时间序列模型研究个股的动态量价关系,并且构建了横截面模型验证信息不对称变量对个股动态量价关系的影响效应,包括买卖差价、上市公司规模、分析师跟踪数量三个变量对上市公司个股动态量价关系的影响效应。实.证分析结果表明,信息不对称是决定动态量价关系的主要经济变量。若信息不对称程度越高,个股出现异常交易量后股价发生反转的可能性越小；若信息不对称程度越低,个股出现异常交易量后股价发生反转的可能性越大。
G. Andrew Karolyi (2006)借鉴Wang(2002)构建的不对称信息模型验证了全球部分地区股票市场动态量价关系,并且构建经济环境变量验证了信息不对称对股票市场个股量价关系的影响。实证研究结果表明,信息环境是决定股票市场个股动态量价关系的主要变量；若信息环境越好,个股出现异常交易量后股价发生反转的可能性越小；若信息、环境越差,个股出现异常交易量后股价发生反转的可能性越大。
第三章,股票市场交易量与股价的度量方法,即研究股票市场动态量关系则需要构建相应的模型度量股票市场个股的交易量与个股股价的度量方法。
本章梳理了Wang(1998)、Wang(2000)、Wang(2009)构建的个股交易量的度量方法方法理论分析框架以及交易量的计量模型。本文实证研究过程中使用的交易量为个股成交量与流通股数量的比值。
实证研究过程中所研究的量价关系是动态量价关系,因此本文在实证研究过程中,股票市场股价的度量则选择个股收益率的作为度量变量,个股收益率在本文则是使用取对数后的个股收盘价的差分作为个股收益率的度量变量。
第四章,实证研究模型与经济变量的选择以及经济变量的度量。
本章梳理了Wang(1993)、Wang(2002)、G. Andrew Karoly(2006)等人构建的实证分析模型以及实证研究中经济变量的选择与度量,他们的实证研究模型中经济变量包括异常交易量、信息不对称变量,在他们的模型中信息不对称变量主要是上市公司规模、买卖差价、分析师跟踪数量、信息披露环境等变量。
在实证研究中同样借鉴了Wang(2002)构建的信息不对称条件下个股动态量价关系的实证分析框架以及信息不对称对动态量价关系的影响。
异常交易量采用Wang(2002)构建的估计模型度量股票市场的异常交易量。
信息不对称变量的代理变量本文选择了上市公司规模与私有信息套利程度作为度量变量,上市公司规模与私有信息套利程度在研究过程中经过的一定的处理。处理上述变量的思路是将基数数列转化为序数数列。
第五章,本章主要是实证研究数据的来源与数据分段及数据统计特征描述。
本文实证分析中的数据来源于WIND与CSMAR两个数据库,本文实证分析的数据区间为1990年12月19日至2010年12月31日,此区间的样本包括除创业板以外的所有A股上市公司。
中国A股股票市场是一个制度变迁剧烈的市场,在这种条件下为了验证不同制度条件下中国A股股票市场的动态量价关系以及信息不对称对动态量价关系的影响。本文则根据中国A股股票市场的发展历史将数据进行分段,数据分段的主要依据是中国实行涨跌停制度、股权分置改革两个制度性的变迁。
除了将数据划分为不同制度条件下的样本,本文为了验证不同市场根据条件下股票市场的动态量价关系,本文根据牛市、熊市的特点将数据继续分段,最后将数据分为8个子样本。数据分段后,本章则开始描述数据的统计特征。
第六章,本章主要是实证研究结果的分析。
本章主要分析了动态量价关系实证研究结果,即中国A股票市场个股存在比较显著的个股动态量价关系。
在此基础上进一步研究分析了信息不对称对中国A股股票市场动态量价关系的影响。实证分析结果表明,上市公司规模越小则意味着信息不对称程度越高,信息不对称程度越高,个股出现异常交易量后,股价发生反转的可能性则较大。反之,则反是。
实证研究了上市公司规模对个股动态量价关系的影响后,本章继续研究了私有信息套利程度对股票市场动态量价关系的影响。实证分析结果表明,私有信息套利程度越大,个股出现异常交易量后,股价发生反转的可能性则越大。反之,则反是。
研究了上市公司规模、私有信息套利程度对个股动态量价关系的影响后,本文借鉴了Wang(2002)的实证研究模型进行了稳健性检验。稳健性检验结果与上述研究结果相同。
此外,本文继续研究了中国A股股票市场出现异常交易量后,股价则发生反转的时间长度。实证研究结果表明,中国A股股票市场放量后,大盘股大约持续的时间是3-5天,小盘股的时间更长一些,大约是10天左右。
第七章,研究结论与研究展望。
本章总结了中国A股股票市场的量价动态关系,包括不同样本区间中国A股市场个股动态量价关系以及不对称信息程度对中国A股股票市场个股动态量价关系影响效应,实证研究结果表明中国A股股票市场上个股动态量价关系并未受到制度变迁与市场格局的影响,并且不对称信息程度是影响中国A股股票市场个股动态量价关系的主要经济变量。
最后,本文提出了中国A股股票市场量价关系未来进一步研究的方向。
本文主要研究创新表现在：
1、使用度量中国A股股票市场的私有信息套利程度,并且即将私有信息套利程度作为中国A股股票市场的信息不对称程度的度量变量。
2、美国股票市场放量后,股价反转持续时间大约是2天左右。但是,中国A股股票市场放量后,股价反转持续时间大约是3-5天。
3、本文研究了不同制度条件下中国A股股票市场个股动态量价关系,包括“涨跌停”制度、股权分置改革制度变迁前后中国A股股票市场个股动态量价关系。
The aim of study is finding the principle of market. Especially, finding the discipline of volatility of stock price. Fama(1970) proposed the EMH, which suggests that the stock price is random walk. Therefore, it is impossible to find the rules of volatility for stock price. But, in the real world, we do find the weekend effect, announcement effect. These effects suggests there is a discipline in the stock market., and we can find it.
What is the meaning of relationship between trading volume and stock prices? How to confirm the foundation of relationship between trading volume and stock prices? How to find the theory framework, which explains Dynamic volume-return relation of individual stocks. What are the determinants for this relationship? Such as abnormal trading volume, stock price and asymmetric information, and so on. This paper will try to answer these questions.
Volume-return relation is the dynamic volume-return relation of individual stocks. That means the relationship between volume and return is negative. In this paper, we build theory framework and the empirical framework to study this relationship.
Firstly, we studied CGW(1993), Wang(1994) and Wang(2002). CGW(1993) builds a framework to interpret the rule of Volume-return relation under the symmetric information background. Wang(2002) builds a framework to interpret the rule of Volume-return relation under the asymmetric information background. They find that the rule of Volume-return will not change with the information background.
Second part is empirical models, we use the data from CSMAR and WIND, the data includes all China A shares except the Chuanye Board.
Finally, we find the Volume-return relation in Chinese stock market. It is found that the Volume-return relation will not change with the information background.
This thesis includes seven chapters.
Chapter one is the preface, which introduces the research background, structure, purpose, methodology, innovation and drawbacks of our study.
Chapter two is a briefly introduce the definition of volume-return relation and current research progress on the topic. According to the theory by CGW(1993)、 Wang(1994)、 Wang(2002), if there is abnormal trading volume, we will find that expect return will reverse. These literature also studied further on why the Volume-return relation will change with the information background? They find that the information background will affect the nature of dynamic volume-return relation. If the difference of asymmetric information increases, the probability of reversing will increase.
Chapter three studies the literature on measurement of trading volume and stock price respectively. we adopt the return to measure stock prices and the turnover rate to measure the trading volume. In this study, the turnover rare equals the ratio of trading volume to liquidity shares. All variables are measures in log forms.
Chapter four presents the empirical framework of our study. Firstly, we choose the suitable variables. Wang(1993)、Wang(2002)、G.Andrew Karoly (2006) use bid-ask price, market value, number of analysts to measure the asymmetric information. In our model, we use the market model to measure r_square to measure the asymmetric information, and use the framework based Wang(2002) to measure the abnormal trading volume. The difference between the largest market value and the smallest market value is very significant, so we apply certain techniques to solve the problem.
Chapter five is the data summary. It gives details on sample period, selection of data types, selections of variables, and the summary statistics on data. Our data includes all A share listed firms from1990to2010. Also, due to the split share structure reformation in Chinese stock market, there are some structure breaks in our sample period. We have adjusted our sample data to solve this problem.
Chapter six analyze the regression results. We find there are significant volume-return relation in Chinese stock market. And we carry on to study the asymmetric information effects in the market.
Chapter seven is the conclusion. It is found that the volume-return relation in Chinese stock market is significant, and this significance will not change by the institutional factors and market conditions. In the end, we proposed three future research direction on the volume-return relation.
The main contributions of the thesis are as follows:
1. we use the r_square to measure asymmetric information, and then test the effect of asymmetric information.
2. The volume-return effect usually lasts for2days in U.S. market, but in China, it will last for3-5days.
3. we study the volume-return relation under Chinese market conditions. Some institutional factors in Chinese market have been considered in the research.
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