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.

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.

Herding and investor sentiment after the cryptocurrency crash:evidence from Twitter and natural language processing

Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors?Using natural language processing techniques applied to Twitter data,this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors.The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors.In particular,cryptocurrency enthusiasts’tweets became more neutral and,surprisingly,less negative.This result appears to be primarily driven by a deliberate,collectivist effort to promote positivity within the cryptocurrency community(“wagmi”).Considering the more nuanced emotional content of tweets,it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors.Moreover,cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash,with a relative increase in tweet frequency of approximately one tweet per day.An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.

Investor sentiment and the holiday effect in the cryptocurrency market:evidence from China

This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market,using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1,2017 to July 1,2022.The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods.Additionally,we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns.However,when positive investor sentiment prevails in the cryptocurrency market,the holiday effect weakens,implying that positive investor sentiment attenuates the holiday effect.Robustness tests based on the Bitcoin market generate consistent results.Moreover,this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon.These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market.

Do US states’responses to COVID-19 restore investor sentiment?Evidence from S&P 500 financial institutions

This paper specifically investigates the effects of US government emergency actions on the investor sentiment–financial institution stock returns relationship.Despite attempts by many studies,the literature still provides no answers concerning this nexus.Using a new firm-specific Twitter investor sentiment(TS)metric and performing a panel smooth transition regression for daily data on 66 S&P 500 financial institutions from January 1 to December 31,2020,we find that TS acts asymmetrically,nonlinearly,and time varyingly according to the pandemic situation and US states’responses to COVID-19.In other words,we uncover the nexus between TS and financial institution stock returns and determine that it changes with US states’reactions to COVID-19.With a permissive government response(the first regime),TS does not impact financial institution stock returns;however,when moving to a strict government response(the overall government response index exceeds the 63.59 threshold),this positive effect becomes significant in the second regime.Moreover,the results show that the slope of the transition function is high,indicating an abrupt rather than a smooth transition between the first and second regimes.The results are robust and have important policy implications for policymakers,investment analysts,and portfolio managers.

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.

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.

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.

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.

地缘政治风险与中资企业债券融资成本——情绪传递与避险效应

中国作为全球系统重要性国家,外部环境变化必然冲击国内债券市场,投资者情绪起落传递金融风险.本文选取2007—2022年中国非金融上市公司发行债券数据验证地缘政治风险对企业融资成本的影响.研究发现,此类风险带来的冲击扩大企业债券信用利差,投资者乐观情绪与低违约避险效应缓解了冲击,国有企业债券违约强化负面信息传递,并且企业应对措施因其特性和货币金融环境不同而异,良好的经济基本面是应对地缘政治冲击最有效的平滑器.为增加企业财务韧性,建议宏观上应健全风险预警机制,完善资本市场体系,合理引导预期,同时要加强企业信息披露机制,建立信誉累积的长效机制.

上市银行市值管理相关问题的思考与路径优化

科学精准的市值管理是“增强资本市场内在稳定性”“强本强基”的重要内容,也是商业银行实现高质量发展的战略任务。随着市值管理考核的全面推开和逐渐细化,商业银行将在稳市场、稳预期上起到表率作用,关注市场表现、强化市值管理,增强投资者对市场的信心和信任,吸引更多的投资者特别是中长期资金参与市场。文章在明晰商业银行市值管理战略站位与关键内核的基础上,探究估值折价背后的盈利可持续性,深挖我国资本市场中的中国特色和时代特征双重加持下的市值管理新挑战,为现阶段商业银行市值管理工作的进一步优化提供可行性建议。

媒体关注、投资者情绪与企业资金期限错配

为缓解企业债务“爆雷”背后的资金期限错配,文章以2008—2022年中国A股上市公司为研究样本,分析媒体关注、投资者情绪与企业资金期限错配三者的关系。研究发现:媒体关注能缓解企业资金期限错配,投资者情绪会抑制媒体关注对企业资金期限错配的缓解作用;机制分析表明,媒体关注通过缓解融资约束和抑制管理层短视来降低企业资金期限错配;异质性分析显示,媒体关注对企业资金期限错配的缓解作用在非负面媒体报道、长期借款占比低、非国有以及内部控制质量高的企业中更显著。研究结论为缓解企业资金期限错配提供理论依据和政策参考,有助于降低市场系统性金融风险,更好支撑实体企业高质量发展。

突发公共卫生事件下投资者情绪对股票市场的影响研究

2019年12月底,由美国暴发的新冠疫情传入中国,疫情对全球、全国的经济造成了显著影响,全球股市也进入寒冬。股市是经济的晴雨表,在中国的股票市场中,有超过九成的投资者属于个人投资者,个人投资者的情绪反映与股票市场的涨跌息息相关。基于此,论文选取换手率、成交量、腾落比例、融资融券余额和百度搜索指数这5个单项指标通过主成分分析法建立投资者情绪综合指标。同时选取相关指标构建疫情综合指标,并通过VAR模型、Granger因果检验、脉冲响应分析、方差分解等方法考察二者与股票收益率之间的关系,同时,提出建议。

Can Twitter Sentiment Gives the Weather of the Financial Markets?

Finance 3.0 is still in its infancy.Yet big data represents an unprecedented opportunity for finance.The massive increase in the volume of data generated by individuals every day on the Internet offers researchers the opportunity to approach the question of financial market predictability from a new perspective.In this article,we study the relationship between a well-known Twitter micro-blogging platform and the Tunisian financial market.In particular,we consider,over a 12-month period,Twitter volume and sentiment across the 22 stock companies that make up the Tunindex index.We find a relatively weak Pearson correlation and Granger causality between the corresponding time series over the entire period.

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。

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。

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。

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。