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.

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.

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.

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.

Network Forum Affects the Stock Market： An Empirical Analysis Based on Multivariate GARCH-BEKK Model

Based on the text of posing on the network forum, the authors establish a set of keyword dictionary to measure the long and short investors＇ sentiment effectively, then they investigate the mutual relations between investor sentiment and the trading market through a Multivariable BEKK-GARCH model of abnormal long and short investor sentiment, returns, and abnormal trading volume. The results show that abnormal sentiment of long investor has a negative impact on the returns and a positive impact on the abnormal trading volume; while abnormal sentiment of short investor has no impact on the return and a negative impact on the abnormal trading volume. Otherwise, there is the negative volatility effect from abnormal sentiment of long investor to returns and abnormal trading volume, the positive volatility effects from abnormal sentiment of short investor to returns, and no volatility effects from abnormal sentiment of short investor to abnormal trading volume. In addition, network forum investor sentiment is a factor affecting the trading market. The analysis of forum information plays a certain role in presenting market risk and improving efficiency in making investment decision.

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.

Intraday Market-Wide Ups/Downs and Returns

Using stock market data over 16 years for Chinese stock markets and over 3 years for U.S.stock markets,this study explores the explanatory power of early intraday market-wide up and down movements to the subsequent intraday returns within the same trading day.As compared to the closing of the previous trading day,we introduce two intraday market-wide up/down indicators in terms of the index return and the proportional difference in the numbers of stocks moving upwards to downwards at each minute.A time series analysis shows an economically and statistically significant positive relation between the intraday indicators and the subsequent intraday returns of the market indices.Intraday trading strategies that exploit this intraday relationship lead to monthly returns of 4.1%in the Chinese market and 2.8%in the U.S.market.In addition,the strategies are more profitable in markets with high activity of individual investors(i.e.,high trading value,low trading volume per transaction,small-cap,high B/M ratio,low institutional ownership,low price,and high number of shareholders).The results indicate that simple intraday market-wide up/down movements in the earlier trading affect the sentiment of retail investors,resulting in market movements in the same direction within the trading day.