中国A股市场高交易量收益溢价的研究

美国学者研究发现,极端的股票交易量往往包含股票的价格信息,本文通过中国沪深A股股票市场进行验证,对股票在经历过极端的股票交易量即高交易量和低交易量后股票收益率进行研究,这样做的目的是研究两者之间的关系,结果发现交易量确实含有股票价格变动的信息,因此也证明了中国股市的确存在这种现象.为了排除盈利公告对高交易量收益溢价效应的影响,本文采用的样本剔除了包含盈利公告的区间,同样,这个结果也不能用系统性风险揭示,因为经历过极端股票交易量后的贝塔值没有显著的差异类似地,Mendelson(1987)认为低流动性带来的超常收益的观点也被否定.本文提出可见性假设,认为高交易量收益溢价效应是由于交易量的变化影响到了可见性的变化,从而使影响到了投资者的理解和判断,最终导致收益率的变化,本文通过做空来验证高交易量收益溢价效应的假设,从而论证了假设.     

中国股市行业动量效应研究

动量效应是行为金融研究的一个热点问题,并且对投资者也是一种主动管理的投资方式.本文根据申银万国研究所制定的一级行业分类指数,从行业动量的角度对2001年-2009年的中国股市进行了研究.研究结果表明,中国股市在短中期存在明显的行业动量效应,其中部分投资组合能获得显著的超额收益,具有较强的投资参考意义.并且,本文通过对行业动量实际交易策略进行实证检验,结果表明行业动量效应投资策略可以获得明显的超额收益,即使考虑到交易费用和卖空限制,只买入赢家组合的投资策略依然可以获得较高的超额收益.     

中国A股市场高交易量收益溢价的研究

美国学者研究发现,极端的股票交易量往往包含股票的价格信息,本文通过中国沪深A股股票市场进行验证,对股票在经历过极端的股票交易量即高交易量和低交易量后股票收益率进行研究,这样做的目的是研究两者之间的关系,结果发现交易量确实含有股票价格变动的信息,因此也证明了中国股市的确存在这种现象。为了排除盈利公告对高交易量收益溢价效应的影响,本文采用的样本剔除了包含盈利公告的区间,同样,这个结果也不能用系统性风险揭示,因为经历过极端股票交易量后的贝塔值没有显著的差异类似地,Mendelson(1987)认为低流动性带来的超常收益的观点也被否定。本文提出可见性假设,认为高交易量收益溢价效应是由于交易量的变化影响到了可见性的变化,从而使影响到了投资者的理解和判断,最终导致收益率的变化,本文通过做空来验证高交易量收益溢价效应的假设,从而论证了假设。     

中国汇市、股市和债市相依结构实证研究——基于资本账户开放加快推进前后的对比分析

金融市场间收益率的相依结构是规避风险发生的理论基础。随着资本账户开放加快推进,研究中国汇市、股市和债市相依结构的变化对于维持金融市场健康平稳发展显得尤为重要。文章首先基于GJRGARCH模型过滤不同金融市场收益率序列,然后采用经验分布估计方法,建立边际分布序列,最后选取最佳Copula函数,建立了汇市、股市和债市间的二元联合分布模型。研究发现,随着资本账户开放的加快推进,第一,中国汇市、股市和债市间的尾部相依性降低;第二,中美两国股市间和债市间的相依性明显增强;第三,中美两国股市间和债市间的相依性比中国金融市场间的相依性强。本文研究结论帮助我国投资者在进行资产管理和金融监管部门制定政策时,对金融市场的变化做好事先的评判。     

行为模型新解及其实证检验

在以信息逐步扩散和投资者有限理性为主要假设的行为模型中，特定信息交易者和市场信息交易者的比例对股价行为有着重要影响：当特定信息交易者占多数时，个股收益更容易呈现正自相关；当市场信息交易者占多数时，个股收益更容易呈现负自相关。该模型可以解释成熟股市中存在基于总收益的动量效应，而中国股市中不存在基于总收益的动量效应，仅存在基于公司特定收益的动量效应；并解释了市场平均收益呈现负自相关等。另外，实证分析支持了传统的CAPM和APT定价模型中的带越小，动量效应越显著的结论。     

中国股市行业动量效应研究

动量效应是行为金融研究的一个热点问题,并且对投资者也是一种主动管理的投资方式。本文根据申银万国研究所制定的一级行业分类指数,从行业动量的角度对2001年-2009年的中国股市进行了研究。研究结果表明,中国股市在短中期存在明显的行业动量效应,其中部分投资组合能获得显著的超额收益,具有较强的投资参考意义。并且,本文通过对行业动量实际交易策略进行实证检验,结果表明行业动量效应投资策略可以获得明显的超额收益,即使考虑到交易费用和卖空限制,只买入赢家组合的投资策略依然可以获得较高的超额收益。     

我国股市行业间的收益与波动溢出效应研究——基于VAR模型构造溢出指数

已有的文献多是通过脉冲响应来刻画溢出效应,该方法得到的溢出效应不具有连续性,在实际应用中有一定的缺陷.而本文则是通过构建溢出指数的方法来衡量我国股市行业间的收益与波动的溢出效应,它能够从溢出指数走势特征中提取股市对信息的反应,可以辅助投资者预判市场走势,做好资产配置准备.研究发现我国股市行业间的收益与波动溢出指数的突变特征明显,收益溢出指数的突变点多是局部高点,而波动溢出指数的突变点多是局部低点.     

中国股市月份效应和节口效应实证研究

本文利用上证综合指数每日收益率的数据,使用虚拟变量和GARCH模型对中国股市月份效应和节日效应进行检验,发现中国股市一月效应和春节效应非常显著,投资者可以利用这些市场异常现象获取超额收益.最后本文对市场异常现象做一些简单的解释.     

新华富时中国A25指数ETF期权多空比值与上证综指关系研究

本文选取新华富时中国A25指数BTF认活期权、认购期权交易量比值和上证综指日收益率为研究对象,考察这两个时间序列的联动关系及两个市场的波动溢出关系,力图证明该ETF期权认购期权交易量与认沽期权交易量的比值能够在一定程度上预测中国上证综指的走势.向量自回归模型表明,多空比值与指数收益率之间存在长期均衡关系,且多空比值是收益率的格兰杰因.之后提取VECM中的误差修正项,带入双变量EC-EGARCH-M模型,结果表明,两市场存在时间序列波动聚集性、非对称性和波动溢出效应.最后,本文利用前述结果总结并建立指数投资策略.在样本时间范围内,该类投资策略能够获得比大盘更高的收益.     

中国A股市场大交易量与收益率关系的研究

利用技术分析制定股票投资策略是投资者主要采用的方法之一,而对交易量与收益率两者之间关系的研究又是技术分析的基础。我们认为,大交易量能更好地预测未来股票的收益。本文通过对中国A股市场代表不同规模股票的指数实证研究发现,不同指数在大交易量形成后的检验期里反应是不同的。代表大盘股的指数存在明显的"大交易量溢价效应";而代表小盘股的指数几乎不存在这种效应。我们还进一步的发现,这种"大交易量溢价效应"只发生在指数上涨了10%-20%的情况下。最后,我们给出了相关的投资策略。     

Corporate governance and stock performance: The case of COVID-19 crisis

This paper analyzes the impact of COVID-19 on firm-level stock behaviors (including stock price volatility, trading volume and stock returns). Using US data, this paper examines whether confirmed cases (and deaths) of COVID-19 or COVID-19-associated online searches affect stock behaviors. The results show that our five COVID-19 proxies are all positively associated with stock price volatility and trading volume and negatively associated with stock returns. This paper further investigates the mitigating effect of corporate governance (viz., board and ownership structures) in this COVID-19 crisis. Overall, the results suggest that good corporate governance can mitigate the impact of COVID-19 on stock price volatility and trading volume but may not help to enhance stock returns. This paper also considers key policies used to tackle the COVID-19 pandemic and finds that government intervention plays an important role in stabilizing stock markets in this COVID-19 crisis.     

Causal and Dynamic Relationships among Stock Returns,Return Volatility and Trading Volume: Evidence from Emerging markets in South-East Asia

This paper examines the causal and dynamic relationships among stock returns, return volatility and trading volume for five emerging markets in South-East Asia—Indonesia, Malaysia, Philippines, Singapore and Thailand. We find strong evidence of asymmetry in the relationship between the stock returns and trading volume; returns are important in predicting their future dynamics as well as those of the trading volume, but trading volume has a very limited impact on the future dynamics of stock returns. However, the trading volume of some markets seems to contain information that is useful in predicting future dynamics of return volatility.     

THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME,RETURNS AND VOLATILITY

This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume-price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume-price change slope for negative returns is smaller than the slope for non-negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non-normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.     

Does volume help in predicting stock returns? An analysis of the Australian market

This paper presents an analysis of the relationship between trading volume and stock returns in the Australian market. We test this hypothesis by using data from a sample of firms listed on the Australian stock market for a period of 5 years from January 2001 to December 2005. We explore this relationship by focusing on the level of trading volume and thin trading in the market. Our results suggest that trading volume does seem to have some predictive power for high volume firms and in certain industries of the Australian market. However, for smaller firms, trading volume does not seem to have the same predictive power to explain stock returns in Australia.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

The Endogeneity of Trading Volume in Stock and Bond Returns: An Instrumental Variable Approach

This paper investigates the joint determination of trading volume and returns. Our approach follows from the argument that trading activity depends on security returns, thus resulting in a reverse causality from returns to trading activity. Using exogenous instruments for security trading activity, we estimate a system of two‐stage simultaneous equations to better model the return‐volume relationship. Our results confirm that returns and trading volume are determined simultaneously in both stock and corporate bond markets and that conclusions about the direction and significance of causality between volume and returns can be reversed once one corrects for the endogeneity of volume.     

The bivariate GARCH approach to investigating the relation between stock returns,trading volume,and return volatility

We use a bivariate GJR-GARCH model to investigate simultaneously the contemporaneous and causal relations between trading volume and stock returns and the causal relation between trading volume and return volatility in a one-step estimation procedure, which leads to the more efficient estimates and is more consistent with finance theory. We apply our approach to ten Asian stock markets: Hong Kong, Japan, Korea, Singapore, Taiwan, China, Indonesia, Malaysia, the Philippines, and Thailand. Our major findings are as follows. First, the contemporaneous relation between stock returns and trading volume and the causal relation from stock returns and trading volume are significant and robust across all sample stock markets. Second, there is a positive bi-directional causality between stock returns and trading volume in Taiwan and China and that between trading volume and return volatility in Japan, Korea, Singapore, and Taiwan. Third, there exists a positive contemporaneous relation between trading volume and return volatility in Hong Kong, Korea, Singapore, China, Indonesia, and Thailand, but a negative one in Japan and Taiwan. Fourth, we find a significant asymmetric effect on return and volume volatilities in all sample countries and in Korea and Thailand, respectively.     

Do regulatory policies affect the flow of information in emerging markets?

In a previous paper we established that volatility is best explained by contemporaneous rather than lagged trading volume in the Egyptian stock exchange (EGX). The main objective of this paper is to investigate the effects of regulatory policies - namely the switch from price limit to circuit breaker - on the dynamic relationship between trading volume and stock returns volatility in the EGX. Using daily returns data for 20 actively traded companies as well as the EGX30 market index, the Generalised Method of Moments (GMM), results show that the volume-volatility relationship is not only endogenous but is also structurally altered by the switch.     

Revisiting the empirical linkages between stock returns and trading volume

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.     

Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects

This paper provides empirical support for the notion that Autoregressive Conditional Heteroskedasticity (ARCH) in daily stock return data reflects time dependence in the process generating information flow to the market. Daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns, which is an implication of the assumption that daily returns are subordinated to intraday equilibrium returns. Furthermore, ARCH effects tend to disappear when volume is included in the variance equation.