Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index

We examine the quantile connectedness of returns between the recently developed S&P 500 Twitter Sentiment Index and various asset classes. Rather than a mean-based connectedness measure, we apply quantile-connectedness to explore connectedness of means and, especially, extreme left and right tails of distributions. Using mean-based connectedness measures, the level of return connectedness between the twitter sentiment index and all financial markets is a modest 46%. However, when applying a novel quantile-based connectedness approach, we find that levels of tail-connectedness are much stronger, up to 82%, at extreme upper and lower tails. This suggests that the impact of sentiment on financial markets is much stronger during extreme positive/negative sentiment shocks. Moreover, return connectedness measures are less volatile during extreme events. Net connectedness analysis shows that the Twitter sentiment index acts as a net transmitter of return spillovers, highlighting the leading role of investor sentiment on predicting other financial markets.     

Sentiment stocks

To study how investor sentiment at the firm level affects stock returns, we match more than 58 million social media messages in China with listed firms and construct a measure of individual stock sentiment based on the tone of those messages. We document that positive investor sentiment predicts higher stock risk-adjusted returns in the very short term followed by price reversals. This association between stock sentiment and stock returns is not explained by observable stock characteristics, unobservable time-invariant characteristics, market-wide sentiment, overreaction to news, or changing investor attention. Consistent with theories of investor sentiment, we find that the link between sentiment and stock returns is mainly driven by positive sentiment and non-professional investors. Finally, exploiting a unique feature of the Chinese stock market, we are able to isolate the causal effect of sentiment on stock returns from confounding factors.     

Which sentiment index is more informative to forecast stock market volatility? Evidence from China

In this paper, we investigate the predictive ability of three sentiment indices constructed by social media, newspaper, and Internet media news to forecast the realized volatility (RV) of SSEC from in- and out-of-sample perspectives. Our research is based on the heterogeneous autoregressive (HAR) framework. There are several notable findings. First, the in-sample estimation results suggest that the daily social media and Internet media news sentiment indices have significant impact for stock market volatility, while the sentiment index built by traditional newspaper have no impact. Second, the one-day-ahead out-of-sample forecasting results indicate that the two sentiment indices constructed by social media and Internet media news can considerably improve forecast accuracy. In addition, the model incorporating the positive and negative social media sentiment indices exhibits more superior forecasting performance. Third, we find only the sentiment index built by Internet media news can improve the mid- and long-run volatility predictive accuracy. Fourth, the empirical results based on alternative prediction periods and alternative volatility estimator confirm our conclusions are robust. Finally, we examine the predictability of the monthly sentiment indices and find that the two sentiment indices of social media and Internet media news contain more informative to forecast the monthly RV of SSEC, CSI800, and SZCI, however invalid for CSI300.     

Sensitivity to sentiment: News vs social media

We explore the rapidly changing social and news media landscape that is responsible for the dissemination of information vital to the efficient functioning of the financial markets. Using the sheer volume of social and news media activity, commonly known as buzz, we document three distinct regimes. We find that between 2011 and 2013 the news media coverage stimulates activity in social media. This is followed by a transition period of two-way causality. From 2016, however, changes in levels of social media activity seem to lead and generate news coverage volumes. We uncover similar evolution of lead-lag pattern between sentiment measures constructed from the tonality contained in textual data from social and news media posts. We discover that market variables exert stronger impact on investor sentiment than the other way around. We also find that return responses to social media sentiment almost doubled after the transition period, while return responses to news-based sentiment almost halved to its pre-transition level. The linkage between volatility and sentiment is much more persistent than that between returns and sentiment. Overall, our results suggest that social media is becoming the dominant media source.     

Does it really pay off for investors to consider information from social media?

This paper raises the question whether investors can learn something from social media sentiment that they do not already know from (existing) financial information disclosed by companies and financial analysts. Therefore, the relationship between financial information and Refinitiv’s MarketPsych social media sentiment index is explored. The paper introduces adjusted social media sentiment, which corrects social media sentiment for the impact of financial information such as earnings surprises, analyst forecast revisions, new dividends, and 8-K filings. It turns out that adjusted social media sentiment is related to subsequent short-term stock returns. This is particularly true for stocks with negative (adjusted) sentiment. Moreover, looking at long-term holding returns the paper does not find compelling evidence for reversals suggesting that (adjusted) social media sentiment reflects information about the prospects of the firm.     

Crowd wisdom and internet searches: What happens when investors search for stocks?

Search engines and social media have become popular among investors as tools for finding and sharing information. The investor social media gathers a large amount of investor-generated content (IGC), which reflects the crowd wisdom of investors, while search engines help investors increase their chances of finding them. In this study, we integrate investor search behavior data from the Baidu Index and investor crowd wisdom data from Eastmoney Guba to assemble a unique data set at the daily level. We then describe and quantify crowd wisdom from investor-generated content (IGC) using three dimensions (IGC average sentiment, IGC sentiment volatility, and IGC increased volume) to investigate the impact of crowd wisdom in the relationship between investors' Internet searches and next-day stock returns. In our empirical analysis, we find that IGC average sentiment strengthens the relationship between investors' Internet searches and next-day stock returns, while IGC sentiment volatility and IGC increased volume have negative effects. These moderating effects are also moderated by institutional investor attention, search terminal preference, and content reading volume. These findings help to explain the value and impact of crowd wisdom when investors search for stock information through the Internet.     

Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.     

“I just like the stock”: The role of Reddit sentiment in the GameStop share rally

This paper investigates the role played by the social media platform Reddit in the events around the GameStop (GME) share rally in early 2021. In particular, we analyze the impact of discussions on the r/WallStreetBets subreddit on the price dynamics of the American online retailer GameStop. We customize a sentiment analysis dictionary for Reddit platform users based on the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis package and perform textual analysis on 10.8 million comments. The analysis of the relationships between Reddit sentiments and 1-, 5-, 10-, and 30-min GameStop returns contribute to the growing body of literature on “meme stocks” and the impact of discussions on investment forums on intraday stock price movements.     

Tehran stock exchange prediction using sentiment analysis of online textual opinions

We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.     

Intraday online investor sentiment and return patterns in the U.S. stock market

We implement a novel approach to derive investor sentiment from messages posted on social media before we explore the relation between online investor sentiment and intraday stock returns. Using an extensive dataset of messages posted on the microblogging platform StockTwits, we construct a lexicon of words used by online investors when they share opinions and ideas about the bullishness or the bearishness of the stock market. We demonstrate that a transparent and replicable approach significantly outperforms standard dictionary-based methods used in the literature while remaining competitive with more complex machine learning algorithms. Aggregating individual message sentiment at half-hour intervals, we provide empirical evidence that online investor sentiment helps forecast intraday stock index returns. After controlling for past market returns, we find that the first half-hour change in investor sentiment predicts the last half-hour S&P 500 index ETF return. Examining users’ self-reported investment approach, holding period and experience level, we find that the intraday sentiment effect is driven by the shift in the sentiment of novice traders. Overall, our results provide direct empirical evidence of sentiment-driven noise trading at the intraday level.     

Risk sentiment and firms’ liquidity in the French market

This paper investigates the impact of risk sentiment on market liquidity by using panel data. We use six risk word lists; uncertain, weak model, negative, legal, opportunity, and environmental & social responsibility word lists to measure the risk sentiment. Concerning the liquidity proxies, we use three measures, quoted spread, effective spread, and adverse selection component. The results indicate that an intensive risk tone and uncertain information in annual reports lead to decreased liquidity. In addition we find that risk sentiment variable impacts the liquidity but not vice versa.     

Enhancing the government accounting information systems using social media information: An application of text mining and machine learning

This study demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations.     

Social media effect,investor recognition and the cross-section of stock returns

Investor recognition affects cross-sectional stock returns. In informationally incomplete markets, investors have limited recognition of all securities, and their holding of stocks with low recognition requires compensation for being imperfectly diversified. Using the number of posts on the Chinese social media platform Guba to measure investor recognition of stocks, this paper provides a direct test of Merton's investor recognition hypothesis. We find a significant social media premium in the Chinese stock market. We further find that including a social media factor based on this premium significantly improves the explanatory power of Fama-French factor models of cross-sectional stock returns, and these results are robust when we control for the mass media effect and liquidity effect. Finally, we find that investment strategies based on the social media factor earn sizable risk-adjusted returns, which signifies the importance of the social media premium in portfolio management.     

Does investor sentiment predict bitcoin return and volatility? A quantile regression approach

The study investigates hypotheses relating to the effect of investor sentiment on predicting bitcoin returns and volatility. Using moments quantile regression, we present robust empirical evidence for the period 2017–2021. Our findings demonstrate that investor interest and emotions are significant predictors of bitcoin returns and volatility, while VIX and Bitcointalk.org forum are the most suitable predictors for representing investor emotions and interest, respectively. The findings also indicate a nonlinear relationship between investor sentiment and bitcoin returns and volatility, with predictable power changing based on the market conditions. Thus, the study enriches existing literature by providing empirical evidence to affirm the viability of behavioral finance theories in the bitcoin market and complements investors with more information to seek profits in different market conditions.     

Impact of interest rate surprises on Islamic and conventional stocks and bonds

This paper studies the impact of interest rate news surprises on Islamic and conventional stock and bond indices, using a dataset which covers interest rate announcements and forecasts, as well as stock and bond indices in three Islamic and eight non-Islamic countris. We find that interest rate surprises tend to have a smaller impact on the returns and volatility of Islamic than conventional bonds because Islamic bonds are structured to avoid explicit interest rates. However, interest rate surprises have about the same or bigger impact on the returns and volatility of Islamic relative to conventional stocks, despite the low amounts of cash and debt holdings of firms comprising Islamic stock indices.     

Twitter investor sentiment and corporate earnings announcements

We examine the impact of firm-specific investor sentiment (FSIS) on stock returns for negative and positive earnings surprises. Using a measure constructed from firm-specific tweets, we find that FSIS has a greater impact on stock returns for negative relative to positive earnings surprises. We further show that the impact of FSIS is greater for firms whose valuation is uncertain and difficult to arbitrage. Moreover, we provide evidence of return reversals over post-announcement periods. Our results highlight the importance of FSIS around earnings announcements.     

Retail Derivatives and Sentiment: A Sentiment Measure Constructed from Issuances of Retail Structured Equity Products

We use retail structured equity product (SEP) issuances to construct a new sentiment measure for large capitalization stocks. The SEP sentiment measure predicts negative abnormal returns on the SEP reference stocks based on a variety of factor models, and also predicts returns in Fama-MacBeth regressions that include a wide range of covariates. Consistent with our interpretation that SEP issuances reflect investor sentiment, aggregate SEP issuances are highly correlated with the Baker-Wurgler sentiment index. Tobit regressions reveal that proxies for attention and sentiment predict SEP issuance volumes, providing additional evidence consistent with the hypothesis that SEP issuances reflect sentiment.     

投资者情绪对我国股市行业收益率的动态影响作用——基于固定效应广义最小二乘法的研究

本文使用2005--2011年我国股市行业收益率数据并构造投资者情绪指标,利用VAR格兰杰因果检验和固定效应广义最小二乘法分析投资者情绪对我国股市的动态影响。实证结果发现,2005--2011年的两次股票市场大幅度涨跌中,我国投资者情绪和股票收益率存在双向因果关系;投资者情绪在3个月内会对股票收益率有正面的影响,此后12个月内其正向影响作用出现了明显的负向反转,其中具有较高账面市值比和占有较高经济地位的交通运输业、信息技术业和制造业等国家基础行业容易受到投资者乐观情绪的影响而出现大幅度涨跌。     

重大突发公共卫生事件下的金融市场反应

本文旨在评估金融市场对重大突发公共卫生事件的反应,尤其是上市公司所在地的公共治理能力是否会影响上市公司股票收益率。其中,城市公共治理能力以基于实时数据计算的防疫能力和复工复产能力指标来刻画。主要发现如下:第一,防疫能力会影响投资者情绪,但不会直接影响股票收益率;第二,所在地复工复产能力对股票收益率存在正向影响;第三,机制分析表明,经营基本面更容易受疫情影响的企业,如小企业、成长型企业、所在地数字金融基础设施较差的企业,其股票收益率对当地复工复产能力的反应更敏感。本文结论表明,在全国一盘棋的抗疫努力下,投资者对于战胜疫情有信心,短期内复工复产能力对金融市场更重要。从应对措施来看,短期内可对比较脆弱的企业实施精准果断的帮扶,长期内可考虑加强地区防疫能力建设和数字基础设施建设。     

Media,sentiment and market performance in the long run

This paper investigates the impact of media pessimism on financial market returns and volatility in the long run. We hypothesize that media sentiment translates into investor sentiment. Based on the underreaction and overreaction hypotheses [Barberis, N., A. Shleifer, and R. Vishny. 1998. “A Model of Investor Sentiment.” Journal of Empirical Economics 49 (3): 307–343], we suggest that media pessimism has an effect on market performance after a lag of several months. We construct a monthly media pessimism indicator by taking the ratio of the number of newspaper articles that contain predetermined negative words to the number of newspaper articles that contain predetermined positive words in the headline and in the lead paragraph. Our results indicate that media pessimism is associated with negative (positive) market returns 14–17 (24–25) months in advance and positive market volatilities 1–20 months in advance. Our results are statistically and economically significant. We find evidence for Granger causality of media pessimism on market performance. Our media pessimism indicator possesses additional predictive power for the Baker and Wurgler [2006. “Investor Sentiment and the Cross-section of Stock Returns.” Journal of Finance 61 (4): 1645–1680] investor sentiment index and the Chicago Board Options Exchange Market Volatility Index.