IPO发行价溢价异象与投资者情绪研究

以2003—2011年沪深两市所有A股作为样本进行研究可以发现,IPO发行价与IPO溢价之间存在显著的负相关关系,IPO发行价越低,IPO溢价现象越明显,称之为“发行价溢价异象”。采用受限因变量Tobit模型对投资者结构进行分析又可发现,IPO发行价越低,个人投资者持股比例越高,机构投资者持股比例越低．由此表明,正是由于低发行价股票投资以个人投资者为主,个人投资者易于受到投资者情绪影响,高估股票价值,增加非理性投资需求,寸导致了“发行价溢价异象”产生。     

行为金融视角下投资者情绪对财务重述行为的影响

基于行为金融理论,以2008～2016年我国A股上市公司为样本,实证检验了投资者情绪对财务重述行为的影响。研究发现:投资者情绪与公司财务重述行为正相关,即投资者情绪越高涨,财务重述行为发生的概率也越高。在进一步的分样本检验中发现,投资者情绪与上市公司财务重述行为的正相关关系只在如下子样本中显著:非国有的、第一大股东持股比例较低的、董事长未兼任总经理的、机构投资者持股比例较低的和由非十大会计师事务所审计的公司。研究结论对于理解投资者情绪与公司财务行为之间的关系提供了新的经验证据,同时也为监管部门更好地发挥其监督作用提供了一定的参考。     

反馈交易、交易诱导与资产价格行为

金融市场归根到底是人的市场,金融市场异象与人性紧密相关。情绪反馈和交易诱导是金融市场中最为普遍的现象,既是人性的体现,也会对资产价格行为造成显著影响。本文构建了一个投机者对具有反馈交易特征的非理性投资者进行信息推断和交易诱导的情绪反馈模型,对投机者的需求函数、资产价格的稳定性及时间序列特征进行了讨论。研究结果表明:(1)投机者同时具有套利需求和投机需求,当市场中"噪音"不足时,投机者会主动地制造"噪音",诱导非理性投资者;(2)投机者对非理性投资者的诱导行为可导致资产价格的过度波动;(3)基本面交易者具有稳定市场的作用,并决定了不同市场动量的强弱。本研究为理解金融市场中的价格操纵行为和复杂性提供了参考。     

投资者情绪、有限套利与股价异象

同美国市场投资者情绪与截面股票未来收益短期正相关、长期负相关的现象不同,中国A股市场两者之间短期内就呈现负相关特征。本文认为这主要与中国A股市场以个人投资者为主体且做空套利受限严重有关,因此与以机构投资者为主体的美国市场存在明显差别。考虑到A股独特的市场交易结构与投资者行为特征,本文提出非主力资金买卖不平衡指标(BSI),通过捕捉散户的资金流向实现对个股投资者情绪的测算。实证结果表明,股票组合收益率与非主力资金BSI指标存在明显的单调递减特征,且无法由通常衡量投资者情绪的换手率因子所解释,与换手率的市场异象也有显著差异,后者表现出换手率特别高的股票组合收益明显更低的非对称特征。本文的理论与实证也进一步指出,有限套利是A股市场投资者情绪异象的主要原因之一,在套利受限更为严重的股票组合中,投资者情绪与股票收益截面负相关的现象更为明显。同时,市场极端情绪和极度乐观情绪下的投资者情绪股价异象更为明显。     

中国股票市场投资者交易偏好及其对股价波动的影响

本文利用2005～2008年深圳A股市场的数据,对各类投资者的交易偏好及其差异、交易偏好与股价波动的关系进行了实证研究。结果表明：（1）机构投资者比个人投资者更偏向于交易大盘股、高价股、低市盈率股和非ST股。各类个人和机构之间的交易偏好存在显著的差异,其中月末平均持股市值在100万元以下的个人投资者对小盘股、低价股、高市盈率股、ST股的偏好程度比其他个人投资者高;（2）无论是按规模、价格、市盈率、是否ST股对股票进行分类后利用Panel固定效应模型,还是应用一般回归模型进行分析,结果都表明,个人交易偏好程度高的股票的波动大于偏好程度低的股票,而机构投资者则相反;在大部分同类型股票中,个人和机构交易量的增加均会强化股价波动,但机构投资者交易量变化对收益波动的影响系数远大于个人。个人投资者的交易偏好及其对波动的影响显示其面临的风险可能较大．加强投资者教育、培养专业化理财队伍并鼓励个人通过专业机构进行投资显得尤为重要。     

机构投资者对上市公司绩效影响的实证分析

机构投资者既不同于个人投资者也不同于国家股东,它有"退出"和"呼吁"两种选择。正因为如此,机构投资者能否有效监督经理人员引起了较多争议。文章利用1999年至2003年328家上市公司的资料和工具变量方法,在控制一些条件变量的基础上,对机构投资者持股、对董事会规模、公司红利和公司业绩影响进行了分析。回归结果表明:机构投资者的持股比例不仅与董事会规模、董事会和高管层的重叠人数显著正相关,而且与公司的会计业绩显著正相关,但对公司红利有显著性的负面影响。因此,大力培养机构投资者并提高其持股比例,对提高我国上市公司业绩有重要意义。     

投资者情绪对中国 IPO首日收益率影响的实证分析

以2009年1月～2014年3月上证交易数据为样本,利用主成分分析法构建基于中国国情的投资者情绪综合指数（ CICSI ）,并采用VAR模型、格兰杰因果关系检验和脉冲响应等方法考查投资者情绪综合指数与中国IPO首日收益率之间的动态关系。结果表明,投资者情绪对IPO首日收益率有显著的正向影响;同时,IPO首日收益率的波动也会对投资者情绪波动有显著的冲击,但产生的影响在时间上会相对滞后。实证分析结果有助于了解投资者的行为特征、决策的过程及影响机制,对于投资者的管理与引导、定价效率的提高、相关机制的完善具有现实意义。     

基于投资者情绪的资本资产定价理论研究

笔者构建了基于投资者情绪的资产定价理论模型,就投资者情绪对风险资产定价有系统性的影响进行研究。研究发现股票均衡价格可以分解成理性部分和情绪部分,投资者情绪导致价格偏离内在价值并导致高的波动率和价格泡沫。基于信息的动态情绪资产定价模型和连续时间情绪资产定价模型,对以模糊数学为基础构建基于投资者情绪的行为投资组合,提供了可行的研究方向。     

投资者情绪、股票市场流动性和经济增长

采用2000—2009年的GDP、股票市场流通市值以及交易值的季度数据,从实证角度对此时段的投资者情绪、股票市场流动性和资本化率进行计量分析,通过ADF单位根检验、Johansen协整检验及Granger因果检验研究投资者情绪、股票市场流动性和股票市场发展之间的关系,进而揭示了投资者情绪影响经济增长的途径。结果显示,投资者情绪和股票市场流动性是经济增长的格兰杰原因。     

我国股票投资者情绪指数构建的研究

本文利用主成份分析法,以换手率、封闭式基金折价率、异常交易量、新增开户数、IPO发行量为代理变量构造投资者情绪指数.进而采用多元回归方法对我国股票市场上证综合指数股票收益率与投资者情绪相关性进行研究.研究结果表明本文所构造的投资者情绪能够在一定程度上反映投资者情绪.     

Dynamic sentiment asset pricing model

Conventional wisdom suggests that the equilibrium stock price is not affected by investor sentiment, and the equilibrium price at an early time is higher than the one at a later time. In contrast to this wisdom, we present a dynamic asset pricing model with investor sentiment and we find that investor sentiment has a significant impact on the equilibrium stock price. The equilibrium stock price, which is affected by pessimistic sentiment at time 0, may be lower than the one at time 1. Moreover, consistent with the reality stock market, our model shows that time varying sentiments can lead to various price changes. Finally, the model could offer a partial explanation for the financial anomaly of high volatility.     

A nonlinear Granger causality test between stock returns and investor sentiment for Chinese stock market: a wavelet-based approach

In this article, we re-examine the causality between the stock returns and investor sentiment in China. The number of net added accounts is used as a proxy for investor sentiment. To mimic the different investment horizons of market participants, we use the wavelet method to decompose stock returns and investor sentiment into time series with different frequencies. Additionally, we test for nonlinear causal relationships based on Taylor series approximation. Our results indicate that there is a one-directional linear causality from stock returns to investor sentiment on the original series, while there is a strong bi-directional nonlinear causality between stock returns and investor sentiment at different timescales.     

Does investor sentiment dynamically impact stock returns from different investor horizons? Evidence from the US stock market using a multi-scale method

This article uses the investor sentiment index to investigate the Granger causality between investor sentiment and stock returns for the US economy using a multi-scale method. To focus on the local analysis of different investor horizons, bivariate empirical mode decomposition is used to decompose time series of investor sentiment and stock returns at different timescales. We employ the linear and nonlinear integrated Granger causality method to examine the causal relationship of decomposed series on similar timescales. The results indicate both strong bilateral linear and nonlinear causality between longer-term investor sentiment and stock returns. However, there is no strong evidence for correlation of stock returns and investor sentiment on shorter timescales.     

Does mixed-frequency investor sentiment impact stock returns? Based on the empirical study of MIDAS regression model

We examine whether mixed-frequency investor sentiment affects stock returns. In line with recent evidence from China, we find that the aggregate effect and the individual effect of mixed-frequency investor sentiment are statistically significant, and mixed-frequency investor sentiment is more important than the low-frequency one. Moreover, mixed-frequency investor sentiment, which is mixed by high-frequency data, can be more important than the market premium.     

Sentiment asset pricing model with consumption

We present an asset pricing model by incorporating investor sentiment. The sentiment equilibrium price could be decomposed to the rational term and the sentiment term, and the investor sentiment has a systematic and significant impact on the risky asset price. In the model, the sentiment term has a wealth-weighted average structure and the investor's wealth proportion could amplify the sentiment shock on the asset price. The model could offer a partial explanation of some financial anomalies in the stock market: the phenomenon of savings transfer to the stock market, pricing bubble and high volatility.     

投资者情绪、股票错误估值与上市公司投资简

以剩余收益估值模型衡量股票错误估值程度,基于公司层面测度投资者情绪,利用非平衡面板数据模型检验投资投资者情绪如何通过证券市场传导到上市公司从而影响其实际投资行为。实证研究发现,投资者情绪主要通过直接的迎合渠道而非间接的股权融资渠道影响上市公司投资行为,且公司投资在迎合渠道上表现出对投资者情绪的“反迎合机制”,此外不同特征的公司其投资对投资者情绪的敏感度存在横截面差异。     

我国股市波动的非对称性和杠杆效应研究

本文选取2006年1月4日到2008年12月31日期间上证综合价格指数日收益率和收益波动率的数据,建立二者变量指标的GARCH模型、AGARCH模型、EGARCH模型,对我国牛熊市轮替过程中股票市场波动的非对称性和杠杆效应进行实证分析。结果发现,股改后牛熊市期间我国股票市场的波动表现出显著的长记忆性、非对称性和杠杆效应,股票市场波动性对"利好"和"利空"消息呈现出不平衡性反应,我国股票市场出现了强市恒强、弱市恒弱现象。最后,从投资者心理预期、过度反应与反应不足、投资者构成和交易机制等方面对该结论进行了分析。     

Threshold effect in the relationship between investor sentiment and stock market returns: a PSTR specification

This article verifies whether the hypothesis of heterogeneous agent modelling and the behavioural heterogeneity framework can reproduce recent stylized facts regarding stock markets (e.g. the 1987 crash, internet bubble, and subprime crisis). To this end, we investigate the relationship between investor sentiment and stock market returns for the G7 countries from June 1987 to February 2014. We propose an empirical non-linear panel data specification based on the panel switching transition model to capture the investor sentiment-stock return relationship, while enabling investor sentiment to act asymmetrically, non-linearly, and time varyingly according to the market state and investor attitude towards risk. Our findings are twofold. First, we show that the hypotheses of efficiency, rationality, and representative agent do not hold in reproducing stock market dynamics. Second, investor sentiment affects stock returns significantly and non-linearly, but its effects vary with the market conditions. Indeed, the market appears predominated by fundamental investors in the first regime. In the second regime, investor sentiment effect is positively activated, increasing stock returns; however, when their overconfidence sentiment exceeds some threshold, this effect becomes inverse in the third regime for a high threshold level of market confidence and investor over-optimism.