Investor sentiments and stock marketsduring the COVID-19 pandemic

This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using variousmethods, including panel regression with fixed effects, panel quantile regressions, apanel vector autoregression (PVAR) model, and country-specific regressions. We proxyfor negative and positive investor sentiments using the Google Search Volume Indexfor terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significantrelationships between positive and negative investor sentiments and stock marketreturns and volatility. Specifically, an increase in positive investor sentiment leads toan increase in stock returns while negative investor sentiment decreases stock returnsat lower quantiles. The effect of investor sentiment on volatility is consistent acrossthe distribution: negative sentiment increases volatility, whereas positive sentimentreduces volatility. These results are robust as they are corroborated by Granger causalitytests and a PVAR model. The findings may have portfolio implications as they indicatethat proxies for positive and negative investor sentiments seem to be good predictorsof stock returns and volatility during the pandemic.

Investor Sentiment and Cross-Sectional Return after Share Issuance:Evidence from Seasonal Equity Offering in China Market

Our research on private placement of equity on China capital market reveals that firms prefer to equity financing when their stock price is overvalued and investor sentiment is high,following the market timing hypothesis.However,after private issuance,we document a significant positive abnormal return within three years.We believe firms choose to polish their financial statement before the exit of institutional investors and controlling shareholders.Through manipulation of discretional accruals,firms improve the profitability and market valuation,and help institutional investors and controlling shareholders obtain the abnormal return after private placement of equity.Nevertheless,such manipulation cannot be sustained and will do harm to other investors in the long-term.

An empirical examination of investor sentiment and stock market volatility: evidence from India

Understanding the irrational sentiments of the market participants is necessary for making good investment decisions.Despite the recent academic effort to examine the role of investors’sentiments in market dynamics,there is a lack of consensus in delineating the structural aspect of market sentiments.This research is an attempt to address this gap.The study explores the role of irrational investors’sentiments in determining stock market volatility.By employing monthly data on market-related implicit indices,we constructed an irrational sentiment index using principal component analysis.This sentiment index was modelled in the GARCH and Granger causality framework to analyse its contribution to volatility.The results showed that irrational sentiment significantly causes excess market volatility.Moreover,the study indicates that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns.The findings are crucial for retail investors as well as portfolio managers seeking to make an optimum portfolio to maximise profits.

DOES INVESTOR SENTIMENT PREDICT STOCK RETURNS? THE EVIDENCE FROM CHINESE STOCK MARKET

This paper examines the proxy variables of investor sentiment in Chinese stock market carefully, and tries to construct an investor sentiment index indirectly. We use cross correlation analysis to examine lead-lag relationship between the proxy variables and HS300 index. The results show that net added accounts （NAA）, SSE share turnover （TURN）, and closed-end fund discount （CEFD） are leading variables to stock market. The average first day return of IPOs （RIPO） and relative degree of active trading in equity market （RDAT） are contemporary variables, while number of IPOs （NIPO） is a lagging variable of stock market. Using the sentiment proxy variables with most possible leading order, and forward selection stepwise regression method, the empirical results on monthly stock returns reveal that three leading proxy variables can be used to form a sentiment index. And the out of sample tests prove that this sentiment index has good predictive power of Chinese stock market, and it is robust.

Prediction of Shanghai Stock Index Based on Investor Sentiment and CNN-LSTM Model

In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions.This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory(LSTM) and convolutional neural network(CNN).The model adopts an end-to-end network structure,using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure.The empirical part makes a comparative experimental analysis based on Shanghai stock index in China.By comparing the experimental prediction results and evaluation indicators,it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.

DEPENDENCE BETWEEN STOCK RETURNS AND INVESTOR SENTIMENT IN CHINESE MARKETS:A COPULA APPROACH

Using data of newly opened stock trading accounts in China as a proxy of investor sentiment index, the authors employ the time-varying copula-GARCH model with Hansen＇s skewed Student-t innovations to investigate the dynamic dependence between investor sentiment and stock returns. The empirical findings show that shifts in investor sentiment are asymptotically positively correlated to stock returns in extreme value situations in both A shares market and B shares market in China, that is to say, stock prices will increase （decrease） more when investors become more bullish （bearish）. Also, results show that the dependence between investor sentiment and stock returns is time-varying, which means that the traditional Pearson＇s correlation based on normal distribution is not enough to describe the relationship between stock market behavior and investor behavior.

A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

A Study on Interaction between Investor Sentiments, China＇ s Monetary Policy and Stock Market

This paper builds the structure of the vector autoregression( SVAR) model short-term constraints and studies the interactive mechanism of investor sentiment,monetary policy and stock market from 2008 to 2016. The result finds that investor sentiment, currency liquidity and stock market gains a significant asymmetric effect. First,the interaction effects of investor sentiment and stock market are positive feedback mechanism, and investor sentiment significantly affects the stock market in the short term. Furthermore,monetary policy and stock market has a positive role in promoting each other. Finally, investor sentiment shows negative feedback mechanism of monetary policy.

企业ESG与资本市场表现——来自股票流动性的证据

在经济社会发展绿色化、低碳化态势下,ESG理念在资本市场中的地位日益凸显。文章基于2015—2021年我国A股上市公司数据,研究ESG表现对企业股票流动性的影响。研究发现,ESG表现显著提升了企业股票流动性,该效应在国有企业、大规模企业以及市场化程度较高的地区企业中更显著。机制分析表明,企业ESG有利于提升投资者情绪和信息透明度,进而促进股票流动性。进一步研究发现,企业良好ESG表现所带来的股票流动性显著提升了企业价值和企业创新。发掘企业ESG在金融市场的作用效果,为活跃资本市场提供新的经验证据。

经济政策不确定性、投资者情绪与银行系统性风险传染

面对经济政策不确定性和投资者情绪,银行业需承担应对经济衰退、化解银行风险和实现经济高质量稳定增长的责任。本文利用MVMQ-CAViaR模型测度了2008—2021年所有上市银行的系统性风险,构建面板门限回归模型和面板平滑转换模型,研究在投资者情绪转换作用下经济政策不确定性影响银行系统性风险多重传染的边际效应结构变化。研究表明:投资者情绪具有显著的区制转换效应,使经济不确定性影响银行系统性风险多重传染的净边际效应,以指数转换模式发生结构变化。据此提出正确处理经济政策不确定性与银行系统性风险的关系,投资者要保持理性情绪以及监管部门要加强监管的建议。

政策不确定性的市场效应:基于错误定价视角

中国经济转型过程中,政府通过制定和实施经济政策对经济发展发挥积极推动作用,但政策调整潜在的不确定性可能对资产价格产生影响。采用2003—2020年沪深A股上市公司季度数据实证研究政策不确定性对股票错误定价的影响及其作用机制。结果表明,政策不确定性上升总体上倾向于修正股票错误定价,且高估样本和低估样本呈现明显的非对称性:政策不确定性上升将修正高估股票的错误定价,而加剧低估股票的错误定价。投资者情绪和市值管理是主要作用机制,且投资者情绪机制占主导作用。进一步考察异质性的结果表明,政策不确定性对股票错误定价的影响主要表现在2007年之后、民营企业和规模较小的企业;货币和财政两类内部政策不确定性均会增大高估股票的错误定价程度,减小低估股票的错误定价程度,而贸易和汇率与资本项目两类外部政策不确定性的影响则相反。因此,需要充分认识预期管理在金融市场监管中的重要性,关注政策不确定性对资产错误定价的影响;科学制定经济政策调整的时间窗口,降低政策变动潜在的不确定性,缓解其对资产价格的影响。

上市公司互动式信息沟通能够降低股票错误定价吗?

资本市场本质上是一个信息市场,信息是决定上市公司股票价值最重要的因素之一。随着信息技术的运用和市场成熟度的提高,上市公司与投资者之间的信息传递模式正在由“单向式信息披露”向“互动式信息沟通”转变。本文考察了我国上市公司与投资者新型互动式信息沟通行为对股票市场定价效率的影响。研究发现:上市公司与投资者的互动式信息沟通行为越积极,即回复率越高、回复越及时,公司股票的错误定价程度越低;公司与投资者的积极互动沟通行为主要通过减少投资者异质信念、增加股价信息含量和改善公司信息环境来降低股票错误定价水平;进一步结合我国股票市场特征发现,这种互动沟通产生的定价效率优化效应在投资者情绪较强、机构投资者持股较少的公司中更加显著。本文的研究结论证实了我国交易所监管下互动沟通平台的市场有效性,同时也为进一步优化市场资源配置的效果提供了新的经验证据。

基于深度学习的投资者情绪挖掘及其对股价崩盘风险的影响

本文基于网络爬虫挖掘东方财富股吧中个股的发帖文本,搭建卷积神经网络和长短时记忆神经网络特征融合模型(LSTM-CNN),对样本股的股吧发帖文本进行情感识别,构建投资者情绪指标并分析了其对股价崩盘风险的影响效应及其机制。实证发现,当期投资者情绪对下一期股价崩盘风险存在显著的正向影响效应,投资者情绪高涨加剧了未来股价崩盘风险;不同市场环境下,情绪对股价崩盘风险影响具有不对称性,熊市状态下投资者情绪对崩盘风险的正向影响效应更为明显。进一步的异质性分析表明,规模较小、股权集中度较低、卖空限制大、公司所在地市场化水平低的样本公司中投资者情绪对股价崩盘风险的影响更为明显。此外,我们发现股票流动性是投资者情绪影响股价崩盘风险的一个重要的中介变量。研究结论有助于从投资者情绪视角来解释股价崩盘风险的形成机理,丰富了对股价崩盘风险影响因素的认识。

投资者情绪是否会影响股票定价效率?——来自股票社区的文本证据

股票定价效率是衡量股票市场有效性的重要指标,更高的定价效率有利于促进资本市场资源的合理配置,更好地服务实体经济高质量发展。本文挖掘东方财富的投资者社区的文本发帖信息,利用机器学习方法分析文本情绪状态,构建投资者情绪指标并分析其对股票定价效率的影响。研究表明,投资者情绪与股票定价效率之间存在显著正相关关系,也即乐观的投资者情绪会带动股票定价效率的提升。这种影响效应是通过乐观情绪降低了信息不对称程度,进而提升了股价信息含量,并与定价效率的机制路径产生作用。进一步看,随着卖空限制的降低,股票定价效率对情绪的敏感程度会增大;良好的信息环境会降低情绪对股票定价效率的影响。研究结论为从个体投资者情绪视角透视我国股票市场运行效率以及网络媒体信息监管的必要性提供了证据。

加密数字货币市场模糊性溢价检验及其驱动因素研究

在加密数字货币市场与现代金融经济联结程度不断深化的背景下,本文聚焦同时具备高创新性与高风险性的加密数字货币市场模糊性溢价问题进行研究,利用不确定概率条件下的预期效用理论(EUUP)测度模糊性,借助比特币的日内5分钟高频交易数据、向量自回归及规则回归的方法检验加密数字货币市场模糊性溢价的存在,并分析其驱动因素。研究发现,加密数字货币市场确实存在模糊性溢价,且其大小和方向受市场有利收益概率的影响;从市场整体状况来看,投资者情绪对市场的模糊性溢价有着很强的解释力。区分市场收益状况的异质性分析说明,若市场状况较好,则恐惧情绪会对其模糊性溢价产生显著抑制作用;若市场状况较差,则贪婪情绪会对其模糊性溢价产生显著抑制作用。本文从市场监管及投资者行为等角度提出政策建议,为加密数字货币市场的稳定发展与风险防范提供了新的解读。

量化交易、投资者情绪与市场波动——基于东方财富股吧贴文的情感分析

随着我国资本市场的逐步完善和成熟,市场参与者对于高效、科学的投资方法需求日益增长,为量化交易提供了广阔的发展空间。为了更好地了解量化交易对证券市场的影响,从量化交易对投资者情绪与市场波动影响的角度进行研究,以期为我国资本市场的稳健发展和投资者综合素质的提高提供理论指导。基于量化交易、投资者情绪及市场波动的理论逻辑,运用事件研究法、最优标度回归,分析2023年1月至9月量化交易相关数据。研究结果表明,量化交易对股票市场具有显著影响;量化交易存在一定程度的“砸盘”效应,投资者对该现象存在不理性行为;不同量化交易策略之间存在显著的效应差异;把事件个股按照可否进行融资融券区分,得出量化交易的影响也存在显著差异。据此,提出通过加大监管力度、增强量化公司风险管理能力和提高投资者综合素质的相关建议,稳定量化交易对证券市场的冲击,维护投资者利益。

投资者情绪与股价同步性研究

以2018-2021年沪深证券交易所A股上市公司为样本,研究投资者情绪对股价同步性的影响及作用机制。研究表明:投资者情绪与股价同步性显著正相关,支持了“信息效率”说;高涨的投资者情绪能够通过提升融资约束加剧股价同步性。进一步研究发现,投资者情绪对股价同步性的积极作用在证券监管力度较低、会计稳健性较低与股权激励较高的企业中更为明显。从优化信息获取渠道、健全公司内部信息公开披露机制、强化监管部门监督职能等方面对抑制股价同步性提出了建议。

公司股利迎合行为:研究述评与展望

股利政策是上市公司财务决策的重要内容,基于行为金融学视角分析公司股利迎合行为具有重要的理论意义和实践意义。从内涵、动机、表现和经济后果等方面系统梳理并评述了公司股利迎合行为的现有研究成果,并提炼了需要进一步着重研究的问题。首先,结合投资者非理性股利偏好,厘清和归纳了投资者非理性行为的内涵和衡量方法。其次,从动机、影响因素、回购场景等方面总结了公司的现金股利迎合行为,并结合股票拆分、高送转等多种方式归纳了公司的股票股利迎合行为。再次,从市场反应、信息质量、未来业绩、公司风险等角度阐释了公司股利迎合行为的经济后果。最后,从投资者非理性股利情绪指标的构建、现金股利迎合中的委托代理问题、股票股利迎合行为的差异性以及互联网环境下公司股利迎合的经济后果等多个方面指出了关于公司股利迎合行为的未来研究方向,为后续的理论研究和监管政策的优化完善提供了指引和启示。

个人投资者情绪与权益资本成本——基于行为金融视角的分析

文章基于行为金融视角,以沪深A股上市公司为研究样本,实证探究了个人投资者情绪与权益资本成本之间的关系、具体机制路径以及会计信息透明度和机构持股比例对个人投资者情绪与权益资本成本间关系产生的异质性影响。实证结果发现,个人投资者高涨情绪能够显著降低权益资本成本;进一步研究发现,股票流动性是个人投资者情绪影响权益资本成本的机制路径,且个人投资者高涨情绪对权益资本成本的降低效应在未来期间会出现反转;异质性检验发现,高持股比例的机构投资者和较高的会计信息透明度能帮助个人投资者建立投资信心,强化个人投资者高涨情绪与权益资本成本间的负相关关系,同时也能作为“定心丸”弱化个人投资者高涨情绪对未来期间权益资本成本的负面影响。研究结论拓展了个人投资者情绪经济后果以及权益资本成本影响因素的相关研究,为证监会相关部门、企业和大型金融服务机构合理引导个人投资者情绪以保障股市正常运行提供了现实启示。

投资者情绪、市场流动性与股市崩盘风险

抑制股市崩盘风险是各国监管机构防范金融系统性风险,促进资本市场健康发展的关键问题。本文将投资者情绪、市场流动性和股市崩盘风险三者科学联系在一起,研究逻辑关系和作用机理。选取2010—2020年1867家样本公司,运用多元回归分析法,验证得出市场流动性和投资者情绪对股市崩盘风险具有正向刺激作用,进而从不同主体角度提出相应的对策建议。