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.

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。

经济政策不确定性、投资者情绪与银行系统性金融风险

在当今的全球经济环境中,银行业面临着诸多挑战。其中,经济政策的不确定性以及投资者情绪的波动,对银行业的稳定和发展产生了深远的影响。文章利用CoVaR模型,对2008—2022年间22家具有行业代表性的上市银行系统性金融风险进行了测度。通过构建回归模型,深入研究了在经济政策不确定性的背景下,投资者情绪如何影响银行的系统性金融风险的变化。研究表明:经济政策不确定性能显著增强银行系统性金融风险;并且投资者情绪在这一过程中发挥了明显的中介作用。据此,提出了应对策略,包括正确处理经济政策不确定性与银行系统性金融风险的关系,投资者应保持理性投资和建立公开透明的信息体系。

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.

谁主“沉浮”?——基于投资者理性变动对股市波动的影响分析

近年来我国股市波动加剧,引发了社会很大关注。本文以系统论的视角,将股市作为开放系统进行研究,综合分析内部因素与外部因素的交互作用,将股市系统的投资者按理性程度划分为三种类型:理性投资者、非理性投资者和有限理性投资者。通过仿真三类投资者在市场中的动态变化,演绎投资者理性结构对股市价格波动的影响,结果表明投资者的理性结构变动是一个动态均衡过程,非理性投资者也有在市场中存在的必要性,能够丰富市场的流动性。三类投资者在股市交易中理性程度的动态演绎也构成了股市发展的重要动力。未来将继续深入研究投资者的理性结构变动对股市波动的时变影响,将进行一系列的实证研究,以揭示更加复杂影响路径的生成机理。

媒体效应对银行系统性风险的影响

基于中国上市银行2007—2015年的季度面板数据,运用主成分分析法构建新的银行投资者情绪指标,采用SGMM和DGMM等模型估计银行系统性风险的媒体效应。结果发现:以投资者情绪为中介,银行系统性风险有显著的媒体效应,即媒体报道数量越少,投资者情绪越乐观,银行发生系统性风险的可能性越大;媒体报道数量越多,投资者情绪越悲观,发生银行系统性风险的可能性越小。同时,前期的银行系统性风险越大,当期的银行系统性风险也越大;前期的投资者情绪越高涨,当期的投资者情绪也呈现高涨状态。

IPO首日二级市场收益率研究

以2005-2010年期间中国对IPO实行询价制以来的671个IPO样本为研究对象,从信息不对称和投资者情绪两个方面对中国IPO首日二级市场收益率及影响该收益率的因素进行了研究,结果发现中国IPO的抑价主要是由一级市场的收益决定的,二级市场收益率主要受投资者情绪主导。进一步分析IPO公司的后市异常收益率后发现,其与首日二级市场收益率显著负相关,说明中国IPO公司的首日二级市场收益率确实是受投资者情绪主导的。

资产价格泡沫缘何周期性破灭?——基于市场情绪视角的结构性解释

动物精神理论认为,资产价格涨跌既能反映经济基本面变化,也会被市场情绪所驱动。市场情绪对资产价格的影响机理一直是行为金融学研究的重要议题。本文将市场情绪理论和泡沫计量方法相结合,采用单位根相关过程的贝叶斯计量方法,对资产价格与其内在价值之间的关系进行研究。结果表明:中国股票市场总体上能够较好地反映经济基本面变化。乐观情绪和悲观情绪对投资行为的影响机理存在本质差异。在乐观情绪下投资者预期持续分化,而悲观情绪下投资者行为一致性的持续期将大幅延长。由市场情绪主导的投资者集体性顺周期操作助推了股市周期性破灭泡沫的结构性分化,为绩优股带来缓慢收缩型泡沫隐患。因此,稳定金融市场应当加强对资本市场情绪的管控。

基于主成分分析的中国股市复合情绪指数的构建

从投资者情绪源指标监控体系中筛选出申万微利股指数、申万高市盈率指数、申万高市净率指数、新财富最佳分析师指数、每周基金账户开户数、每周IPO(initial public offering)数量6个指标,同时控制经济基本面因素的影响,应用主成分分析法构建中国股市的复合情绪指数。合理性检验、稳健性检验和预测能力检验的结果表明,该复合情绪指数能够较为准确地表征中国股市投资者情绪。

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股市场IPO抑价实证研究

以最新的数据作为研究样本,采用统计学上的研究方法——回归分析法,深入分析研究我国上市公司的IPO抑价率,探求其形成的原因和其影响因素。本研究将不仅能丰富现有的关于IPO抑价的理论假说,也能对完善我国IPO抑价的政策制定提供建议和借鉴,对提高我国上市公司的资本运作水平、更加有效地发挥股票回购协调公司权益结构的作用、完善上市公司治理、丰富我国资本市场的内涵会具有较大的理论价值和实践意义。

Light a lamp and look at the stock market

In this study,we investigate the impact of the light-a-lamp event that occurred in India during the COVID-19 lockdown.This event happened across the country,and millions of people participated in it.We link this event to the stock market through investor sentiment and misattribution bias.We find a 9%hike in the market return on the postevent day.The effect is heterogeneous in terms of beta,downside risk,volatility,and financial distress.We also find an increase(decrease)in long-term bond yields(price),which together suggests that market participants demanded risky assets in the postevent day.

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.

Trading stocks following sharp movements in the USDX, GBP/USD, and USD/CNY

We hypothesized that sharp movement in the USDX,GBP/USD,and USD/CNY might result in stock market fluctuations owing to heightened investors’sentiments.The subsequent performance of trading stocks right after such sharp movements in exchange rates is seldom explored in existing studies.We examined the historical data of the constituent stocks of the DJ 30,FTSE 100,and SSE 50 indexes and found that the share prices were more volatile after sharp movements in the CNY,even though the currency is less volatile because of China’s exchange rate policy.However,for the USD and GBP,share prices of the DJ 30 and FTSE 100,respectively,rose after sharp appreciation and depreciation of the currencies.

股票发行注册制改革、投资者情绪和IPO抑价

随着注册制改革的全面推行,发行制度和交易制度的变革如何影响IPO股票定价成为多方关注的重点。以创业板注册制改革作为准自然实验,构建DID模型,探寻投资者情绪对IPO股票定价的影响发现,相对于核准制主板市场,投资者参与注册制创业板IPO股票“打新”的热情上升,投资者情绪加剧了注册制创业板IPO股票抑价。同时,相对于主板市场,注册制创业板IPO股票表现出更高的信息含量,这说明,注册制改革后,IPO抑价的变化不能由信息不对称解释。进一步分析显示,在投资者情绪推升股票短期估值的情况下,注册制创业板IPO股票表现出更显著的长期反转。分析师关注度高、乐观程度高、意见分歧大的股票,IPO抑价受注册制的影响更大。投资者结构更理性的科创板股票没有表现出更高的投资者情绪和IPO抑价,说明投资者情绪影响了创业板IPO定价。上述结论意味着,在注册制改革过程中,政府、企业和投资者不仅需要了解上市公司相关信息的披露情况,还需要密切关注股票投资者情绪变化,促进资本市场和实体经济的稳定发展。

中国波动率指数iVX与投资者情绪的双向互动关系

本文构建了包含宏观经济和股票市场两个层面的投资者情绪指数体系,采用分布滞后模型和联立方程模型,分析了不同层面上情绪对中国波指iVX的影响以及两者的互动关系.研究发现,与仅考虑情绪对iVX影响的单方程结果相比,考虑了双向互动机制下的联立方程模型能发掘更多信息.结果显示,第一,在iVX运行期间和停止发布后,iVX对同期宏观层面情绪均具有表征作用;第二,在iVX运行期间和停止发布后,iVX可以反映同期的股票市场层面的投资者情绪;第三,在iVX运行期间,股票市场情绪会受到iVX变化的显著影响,但这种影响在iVX停止发布后消失,表明投资者情绪在一定程度上受到股市指标的干扰,而宏观情绪在两段期间内均不会受到iVX变化的影响.由于我国股市还处于不断发展和完善的阶段,本文的研究对于证实iVX的实践价值、推进和维护股票市场指标发布和运行具有重要意义.

投资者情绪对中国上市金融机构系统性风险的影响

2007-2009年的金融危机之后,金融机构的系统性风险受到广泛关注,本文利用CoVaR方法度量了我国上市金融机构的系统性风险。考虑到投资者的非理性行为可能会对上市金融机构系统性风险产生影响,本文考察了投资者情绪对系统性风险的影响。研究结果表明,投资者情绪对未来的系统性风险有显著的正向影响,这意味着较高的投资者情绪往往伴随着较高的系统性风险,这种正向影响在熊市中表现更为明显,引入投资者情绪这一指标也有助于提高模型对金融机构系统性风险的预测能力。

注册制、投资者情绪与IPO抑价

注册制改革作为我国股票市场新股发行制度市场化进程的重要环节,其降低IPO抑价率的效用也备受关注。本文基于2018年1月-2021年3月主板和创业板新股IPO数据,使用PSM-DID等计量方法对注册制与IPO抑价率的关系进行了研究。实证结果表明,注册制的实施加剧了IPO抑价程度,投资者情绪对于IPO抑价率有显著的正增长效应。同时,注册制的实施与投资者情绪的交互作用也对于提高IPO抑价率具有显著的作用。此外,注册制实施之后,具有炒作概念(低市盈率、低募资额、低中签率)的新股,其炒作现象反而更加严重,导致相关新股IPO抑价率的显著提高。因此,对于投资者关于价值投资等理性投资理念的教育依然任重而道远。