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.

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.

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.

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

用最新打开的存货的数据，做贸易作为投资者情绪索引的一个代理在中国说明，作者与 Hansens 雇用变化时间的 copula-GARCH 模特儿调查在投资者情绪和股票回来之间的动态依赖的扭曲的 Student-t 革新。在投资者情绪的移动断然 asymptotically 被相关到存货的实验调查结果表演在股票在中国出售的一个股票市场和 B 处于极端值状况回来，那是说，当投资者变得更行情看涨时，存货更定价愿望增加(减少)( 熊市) 。另外，结果证明在投资者情绪和股票回来之间的依赖是变化时间的，它意味着传统的皮尔森关联不基于正常分发是足够描述在证券市场行为和投资者行为之间的关系。

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。

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。

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。

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。

数字化转型对高管机会主义减持的影响研究

数字技术深度融入实体企业会颠覆并重塑企业高管的行为逻辑。以高管机会主义减持为切入点,使用沪深交易所A股上市企业相关数据,实证考察数字化转型对高管机会主义减持的影响效应与作用机制。研究结果显示:数字化转型对高管机会主义减持具有显著正向影响,具体表现数字化转型提升了高管机会主义减持金额与次数;作用机制检验发现,数字化转型促使高管机会主义减持的主要路径在于加剧信息不对称和推高投资者情绪;进一步地,机构投资者、分析师以及新闻媒体均发挥积极的治理作用,数字化转型加剧高管机会主义减持的效应会随着机构投资者持股比例、分析师关注度以及媒体关注度的增加而有所减弱。研究结论有助于全面客观认知企业数字化转型的经济效应,为数字经济时代有效预防高管减持乱象,进而促进资本市场高质量发展提供相关政策建议:企业在推进数字化转型的同时也要强化内外部治理机制建设;政府监管部门要强化对企业数字化转型相关信息披露的监管,合理引导投资者情绪以及外部相关主体的治理积极性;投资者也要不断提升自身金融素养,避免盲目追求热点的非理性行为。

融合媒体信息和信号分解的股票市场深度学习预测

对股票市场未来回报和风险的精确预测不仅能够帮助理性投资者更加合理有效地进行投资,也能够为政策制定者和投资者提供有用的指导。利用金融新闻标题文本,通过词嵌入模型和机器学习等文本分析方法,构建考虑新闻累积效应的投资者时闻累积情绪指数表征投资者情绪;以上证指数为例,采用变分模式分解(VMD)方法将指数波动数据分解为各种内在固有模式进行实证分析。最后,引入双向门控循环单元(BiGRU)作为深度学习模型进行股票预测。结果表明,投资者情绪指数显著影响上证指数波动,并且积极情绪和消极情绪的影响是不对称的;考量投资者情绪指标进行信号分解,能够有效提高股票的预测性能,相对于单纯分析股票时间序列的BiGRU预测模型,VMD-BiGRU模型的MAE,RMSE,RMSPE,MAPE等指标降低超过30%;在基准场景下,VMD-BiGRU模型性能优于多个计量经济模型和机器学习模型,对于收益率和波动率预测的MAE,RMSE,RMSPE,MAPE等指标普遍降低超过40%;模型在五粮液、工商银行、科大讯飞3只个股的推广中保持着同样稳定精确的预测效果。

多层网络视角下沪深港股票市场关联性演化研究

随着“沪港通”和“深港通”的开通,沪深港股票市场间关联性日趋紧密,而充分认识沪深港股票市场间关联性的演化特征,对维持沪深港股票市场的稳定具有重要的价值.本文从多层网络视角出发,同时考虑股票收益率间的相关性和投资者情绪间的相关性,构建沪深港股票市场多层网络,进而探究沪深港股票市场间的关联关系及演化过程.结果表明,“沪港通”开通后,沪深港股票市场收益率间的关联性以及投资者情绪间的关联性没有显著增强,但不同市场间收益率与投资者情绪的关联性显著增强.而“深港通”开通后,沪深港股票市场收益率间、投资者情绪间以及收益率与投资者情绪间的关联性均显著增强.此外,不同市场之间收益率与投资者情绪间的交互关系并不对称,沪深两市股票收益率与港市股票投资者情绪的关联性较高,而港市股票收益率与沪深两市股票投资者情绪的关联性却较低.

ESG评级分歧与企业经营风险

随着ESG意识的提高和可持续发展的重要性日益突显,ESG评级已成为学术界和实务界讨论的重点问题,但目前中国ESG评级标准并不统一,不同评级机构给出的评分有较大分歧,这是否会对企业经营风险产生影响?基于此,本文以2015—2022年中国A股上市公司为样本,使用华证、万德、富时罗素、商道融绿、盟浪、彭博6家评级机构的评级结果构建ESG评级分歧指标,实证检验了ESG评级分歧对企业经营风险的影响。结果显示,ESG评级分歧显著提高了企业经营风险,这一结论在经过一系列内生性稳健性检验后依然成立。机制检验发现,ESG评级分歧通过加剧信息不对称以及抑制投资者情绪进而提高企业经营风险。异质性分析表明,ESG评级分歧对企业经营风险的提升作用在非国有企业、信息质量较差的企业和高市场化企业更强。进一步研究发现,相比于ESG评级机构间分析差异,被评级企业本身ESG信息披露不规范是ESG评级分歧加剧企业经营风险的主要原因。研究结论为监管机构规范ESG评级标准以引导企业规范披露,推动中国ESG评级体系建设提供了重要借鉴。