Impact Of Sentiment Research Articles

Nexus between Twitter-based sentiment and tourism sector performance amid COVID-19 pandemic

The outbreak of the COVID-19 pandemic and the steps taken to contain its spread resulted in a decline in tourism sector stock prices. Using linear and quantile regressions, we examine the impact of Twitter-based investor sentiment for COVID-19 and Twitter-based sentiment towards uncertainty on the performance of tourism stocks. The findings indicate a heterogenous effect of tweets and Twitter economic uncertainty on tourism sector equity returns with a major impact on the lower quantiles.

The Impacts of The Systematic, Idiosyncratic Risks and Market Sentiment on China A-Shares Performances

The purposes of this paper are (i) to examine 3 driving factors affecting China A-shares market performance; namely systematic risk, idiosyncratic risk, and market sentiment, and (ii) to investigate the relationship between state-owned enterprise (SOE) & non-SOE and stock returns. In addition, the study also analyze normal condition and the impacts of Sino-US trade war and Covid-19 pandemic. This study employs monthly data which is divided into two parts namely (i) 2004-2020 period and (ii) 2018-2020 period. Multiple classic asset pricing models are employed to investigate the impacts of the 3 driving factors on stock returns. The results showed that these 3 driving factors exert significant influence on China A-shares in 2004-2020, However, the impact of market sentiment is weak during the period 2018-2020. Furthermore, market risks, firm size and B/M factor show great impacts on both SOE and non-SOE, profitability factor affecting non-SOE stock return is more important than investment which improves SOE stock return. This study proposes that investors and companies pay more attention to systematic risk and idiosyncratic risk, which potentially have greater impact on the stock market and to reduce unnecessary economic losses.

The Impacts of The Systematic, Idiosyncratic Risks and Market Sentiment on China A-Shares Performances

The purposes of this paper are (i) to examine 3 driving factors affecting China A-shares market performance; namely systematic risk, idiosyncratic risk, and market sentiment, and (ii) to investigate the relationship between state-owned enterprise (SOE) & non-SOE and stock returns. In addition, the study also analyze normal condition and the impacts of Sino-US trade war and Covid-19 pandemic. This study employs monthly data which is divided into two parts namely (i) 2004-2020 period and (ii) 2018-2020 period. Multiple classic asset pricing models are employed to investigate the impacts of the 3 driving factors on stock returns. The results showed that these 3 driving factors exert significant influence on China A-shares in 2004-2020, However, the impact of market sentiment is weak during the period 2018-2020. Furthermore, market risks, firm size and B/M factor show great impacts on both SOE and non-SOE, profitability factor affecting non-SOE stock return is more important than investment which improves SOE stock return. This study proposes that investors and companies pay more attention to systematic risk and idiosyncratic risk, which potentially have greater impact on the stock market and to reduce unnecessary economic losses.

The impact of different sentiment in investment decisions: evidence from China’s stock markets IPOs

In this study, we used data on China’s initial public offerings (IPOs), market volatility and macro environment before and after two stock crashes during 2006–2016 to investigate how different investor sentiment affects IPO first-day flipping. The empirical results show that the expected returns of allocated investors are affected by sentiment, with allocated investors having higher psychological expectations of future returns during an optimistic bull market and their optimism discouraging first-day flipping, while higher risk-free interest rate levels and rising broad market indices also discourage first-day flipping and tend to sell in the future. The pessimistic bear market during which allocated investors have lower psychological expectations of future returns, their pessimism will promote first-day flipping, and the increase in the risk-free rate level will also promote first-day flipping, which is the opposite of the optimistic bull market, indicating that their risk aversion has increased and they tend to sell on the same day. We also found an anomaly that the greater the decline in the broad market index during a pessimistic bear market, the more inclined the allocated investors are to sell in the future when the broad market index rises in an attempt to gain higher returns. These findings help explain and understand the impact of market and macro index fluctuations on investor behavior under different investor sentiments.

Fund-level Investor Sentiment and Mean-variance Relation: Evidence From Singapore-listed ETFs

This paper tests the influences of fund-level sentiment on the mean-variance relation in ETF market. We find that in low (high)-sentiment periods, the expected excess return is positively (negatively) related to the conditional variances. Sentiment traders undermine the otherwise positive risk-return tradeoff and even twist it into a negative one in high-sentiment periods. The impact of sentiment is stronger during the global pandemic.

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.

Impact of network investor sentiment and news arrival on jumps

This paper studied the influence of news announcements and network investor sentiment on Chinese stock index and index futures market jumps. A machine learning text analysis algorithm was employed to measure investor forum sentiment. It was found that news arrivals were an important reason for jump occurrences, jumps were significantly associated with network investor sentiment, and while occasionally the news and network investor sentiment resulted in simultaneous market jumps, they appeared to be relatively independent. The network investor sentiment time-lag and asymmetric effects were also tested, from which it was found that network investor sentiment had a significant asymmetric effect on the jumps, but time-lag effects had little influence. News announcements and the top 25% of the extreme network sentiments were found to explain more than 50% of the jumps, with extreme sentiments tending to increase the volatility of the news-related jumps and persistently influencing returns after the news-related jumps.

Assessing the impact of media sentiment on the returns of sukuks during the Covid-19 crisis

ABSTRACT The wavelet approach covering simultaneously the time and frequency domains is employed to study the impact of the Covid-19 coverage in mass media on the performance of the Dow Jones Sukuk investment grade total return indices. The overall coherence level for the media-coverage – sukuk pairs is found to increase with the investment horizon. Multiple time-frequency regions with low level of coherence, observable along the Covid-19 systemic crisis, imply attractive diversification attributes of investing in Islamic fixed-income securities especially in times of financial stress and turmoil. We investigate coherence and phase difference patterns, which differ for distinct maturity buckets of the Sukuk indices, further highlighting their potentiality for the downside risk hedge, workable under economic and financial distress.

Analysis and Prediction of Healthcare Sector Stock Price Using Machine Learning Techniques

Healthcare sector stocks are a very good opportunity for investors to obtain gains faster most of the time in a year and mostly during this COVID pandemic. Purchasing a healthcare stock of a certain company indicates that you hold a part of the company shares. Specifically, various examinations have been led to anticipate the development of financial exchange utilizing AI calculations, such as SVM and reinforcement learning. A collection of machine learning algorithms are executed on Indian stock price data to precisely come up with the value of the stock in the future. Experiments are performed to find such healthcare sector stock markets that are difficult to predict and those that are more influenced by social media and financial news. The impact of sentiments on predicting stock prices is displayed and the accuracy of the final model is further increased by incorporating sentiment analysis.

Cross-Market Investor Sentiment of Energy Futures and Return Comovements

Investor sentiment in the energy futures of a single market might not be able to capture cross-market effects well. This paper considers multiple energy futures markets (crude oil, gasoline, etc.) in constructing cross-market sentiment and returns to capture the sentiment characteristics and overall dynamics of the energy futures market. The results show that (i) cross-market investor sentiment and returns Granger-cause each other, (ii) cross-market investor sentiment has an important but asymmetric impact on the volatility of the energy futures market, and (iii) the asymmetric impact of cross-market investor sentiment on return volatility varies across segments of bear/bull energy markets.

Political sentiment and MAX effect

The well-known “MAX effect” documents that stocks with high maximum daily returns in the past month underperform those with low maximum daily returns. We show that such an effect varies with firm-level political sentiment. Among firms with low political sentiment, the usual MAX strategy gives a monthly risk-adjusted return of 1.52% and is statistically significant. However, the MAX effect weakens substantially or even reverses for firms with high political sentiment. Our findings provide novel guidance for trading on the MAX effect. Moreover, the results challenge the usual sentiment-based explanation for the MAX effect. Further evidence suggests that the prospect theory or investors’ underreaction to news may be consistent with our findings, although these channels cannot empirically explain the impact of political sentiment.

Sentiment, Attention, and Earnings Pricing

We find that investor sentiment restrains the predictability of earnings news on announcement returns but the constraining effect of sentiment on the predictive power of earnings news diminishes as sentiment falls. We document that investor attention works as an important channel in the relation between investor sentiment and announcement returns. Investor attention enhances the immediate price reaction to earnings news by curbing the impact of sentiment on the predictive power of earnings news. Our findings reflect the joint effect of attention and sentiment on the source of excess returns documented in the prior earnings-based market anomaly literature.

Investor sentiment, corporate social responsibility, and financial performance: Evidence from Japanese companies

Utilizing data from 367 Japanese companies from 2005 to 2019, this paper investigates the relationship between investor sentiment, corporate social performance (CSP), and financial performance (FP). The empirical results obtained from the panel fixed effects and instrumental variable regressions reveal a positive impact of firm-specific sentiment on future FP. Conversely, the market sentiment effect is mixed as it positively influences Tobin's Q but negatively influences the return on assets (ROA). The impact of CSP, measured by environmental, social, and governance (ESG) scores, on FP is similar to that of market sentiment. Specifically, we find that an effective ESG performance in the prior year, both the overall performance and the performance of the individual aspects, can escalate firm value but diminish firm profitability in the following year. We claim that the CSP–FP relationship weakens under the moderating influence of investor sentiment.

Information Asymmetry and Analyst Forecast in Market-Wide Investor Sentiment

This study investigates the role of market-wide investor sentiment on the level of information asymmetry and analyst forecast error. The role of market-wide investor sentiments in the valuation and forecasts by analysts is still uncertain. In addition to this, the role of market-wide investor sentiment in influencing information asymmetry between the market participants is a pertinent question. This study attempts to close this gap by answering the impact of market-wide investor sentiment on information asymmetry and analyst forecasting dispersion. The study utilized the data of public listed firms in the USA and employed multiple regression fixed effect model to estimate the results. The results concluded that investor sentiment increases the information asymmetry and analyst forecast errors. That confirms that optimistic investor sentiment brings in the noise of trading and unsophisticated trading mechanism in the stock market, thereby questioning the classical theory of finance, which only relies on the fundamental analysis. The study provides important insights for the investors and analysts to incorporate behaviors and sentiments while undertaking valuation and trading decisions.

A Literature Review of Credit Ratings

The behavioural finance literature focuses on the impact of investor sentiment on the underlying value of individual stocks. Credit ratings can be viewed as a link between borrowers and issuers that reduce information asymmetry in the financial system through an independent view of creditworthiness. The purpose of this study was to determine the variables related to credit ratings. This research is a literature study, which uses 21 articles as study material. The results of this study indicate the variables related to credit ratings, both influencing and being influenced. This study did not find credit ratings used as a moderating variable, which can be used as research material in the future.

Sentiment and Emotion-Aware Multi-Modal Complaint Identification

The expression of displeasure on a consumer's behalf towards an organization, product, or event is denoted via the speech act known as complaint. Customers typically post reviews on retail websites and various social media platforms about the products or services they purchase, and the reviews may include complaints about the products or services. Automatic detection of consumers' complaints about items or services they buy can be critical for organizations and online merchants since they can use this insight to meet the customers' requirements, including handling and addressing the complaints. Previous studies on Complaint Identification (CI) are limited to text. Images posted with the reviews can provide cues to identify complaints better, thus emphasizing the importance of incorporating multi-modal inputs into the process. Furthermore, the customer's emotional state significantly impacts the complaint expression since emotions generally influence any speech act. As a result, the impact of emotion and sentiment on automatic complaint identification must also be investigated. One of the major contributions of this work is the creation of a new dataset- Complaint, Emotion, and Sentiment Annotated Multi-modal Amazon Reviews Dataset (CESAMARD), a collection of opinionated texts (reviews) and images of the products posted on the website of the retail giant Amazon. We present an attention-based multi-modal, adversarial multi-task deep neural network model for complaint detection to demonstrate the utility of the multi-modal dataset. Experimental results indicate that the multi-modality and multi-tasking complaint identification outperforms uni-modal and single-task variants.

The Impact of Two Kinds of Responses to COVID-19 on the Stock Prices of Multinational and Local Enterprises

<p>Ever since COVID-19 became popular around the world, there are two kinds of measures taken by governments to respond to the impact on stock markets, including controlling the number of new cases and promulgating economic policies to stimulus economy. This paper studies how the two measures, controlling the spread of COVID-19 and formulating economic policies, impact stock returns in United States and what are the differences. Additionally, this paper analyses if these two measures have the same impact on multinational companies and local companies. By using CAPM and descriptive statistical analysis, the researchers find that economic stimulus is effective in short term but controlling the spread of pandemic plays the key part in long-term economic growth, international trades make a difference in reducing risk during COVID-19. Finally, to understand is the impact of investor sentiment on stock prices the reason for these two responses to changes in stock returns, Fama-French three factor with sentiment factor added is used to explore the relationship between investor sentiment and stock returns. The conclusion is that changes in investor sentiment are positively correlated with the excess return of the portfolio.</p>

The Value of “Brand” in the Chinese Stock Market: The Impact of Brand Attention on Stock Performance and the Moderation Role of Investor Sentiment

Using a database of more than 1.1 million comments from the largest Chinese stock discussion forum Eastmoney, we explore the value of “brand” in the Chinese stock market by empirically investigating the impact of brand attention on stock performance and the moderation role of investor sentiment, as well as the heterogeneity of these effects across companies with different brand values. It finds that brand attention has a positive impact on stock returns and stock trading volume. Investor sentiment has a positive impact on stock returns while having a negative impact on stock trading volume. Investor sentiment positively moderates the impact of brand attention on stock returns, but negatively moderates the impact of brand attention on stock trading volume. The impact of brand attention and investor sentiment on stock performance varies across companies with different brand values. It is more pronounced in companies with high brand value than in companies with a relatively low brand value, while the negative effect of investor sentiment on stock trading volume is lower in companies with high brand value, which highlights the importance of brand-building to improve stock performance.

Psychological factors of Canadian and Mexican tourists and the US tourism sector

This paper investigates the impact of psychological factors of Canadian and Mexican tourists on the US tourism sector. Using the data of 1996–2019, the study uses vector autoregression models and the spillover analysis to perform the investigation. The paper discovers that high insecurity of tourists significantly reduces tourist arrivals, passenger fare receipts, and expenditure of tourists in the US. Also, tourist inflows are highly influenced by insecurity during terrorist attacks, natural disasters, and the financial crisis of the US. It is found that high sentiment of Canadian and Mexican tourists increases their outbound travel to the US, but the impact of sentiment is relatively stronger for the Canadian tourists. Results show that the tourist inflows from Canada and Mexico are influenced by low sentiment during recessionary periods of Canada and Mexico, respectively. The paper finds no robust evidence of mood and nationalism-based retaliation of tourists for traveling to the US.

How Government Information Release Affect Stock Market during Dramatic Public Health Shocks? The Intermediating Role of Public Sentiment

During dramatic public health shocks, the impact of anti-epidemic policies on public sentiment is uncertain, and public sentiment has a significant impact on the stock market. But the relationship between the three is not clear. This paper uses government information release to reflect anti-epidemic policies, uses personal weibo to calculate public sentiment, and analyzes the relationship between Government information release, public sentiment and stock market. Through the Bi-LSTM classification model analysis about 200,000 microblog data during COVID-19, and build panel data regression model. The results show that: public sentiment have a significant positive impact on stock returns; Government information release have a significant positive impact on public sentiment; public sentiment play a intermediating role between government information release and stock market; Influenced by the enterprise size and the industry it belongs to, there is heterogeneity in public sentiment's impact.