Sentiment Measures Research Articles

News Sentiment, News Intensity, and Price Movement of Indonesia's 45 Most Liquid Stock Index

This study analyzed the sentiment of the constituent news of the LQ45 index on its index movement by considering the news intensity. Two Sentiment analysis methods are used in this study. The Loughran and McDonald's dictionary as a measure of financial sentiment, then TextBlob sentiment analysis as a measure of general sentiment. We found that news intensity decreased during the financial crisis. Interestingly, news intensity increased significantly after the Covid-19 crisis. Then the constituent news sentiment has a relatively strong correlation with the index movement. Through linear regression analysis, the evidence suggests that the effect of sentiment on index returns is more significant when news intensity increases. Derived from these findings, we concluded that news sentiment is one of the unignorably price-forming factors in further analysis.

Out of sight, out of mind: The impact of lockdown measures on sentiment towards refugees

ABSTRACT How did COVID-19 related movement restrictions impact sentiment toward refugees? Existing theories offer conflicting answers. On the one hand, contact theories suggest that movement restrictions might reduce casual interactions with refugees, leading to less negative sentiments. On the other hand, integrated threat theories suggest refugees may be perceived as a security threat and blamed for these movement restrictions in the first place. To gauge the effect of movement restrictions, we investigate the effect of physical isolation on sentiments toward refugees in Turkey by using a novel dataset. We use Google Mobility Reports’ measurements of movement and our measures of sentiments toward refugees using refugee-related tweets from Turkey. Statistical analysis shows that xenophobic sentiment generally decreased during the pandemic. Our study shows that different types of reduced mobility correlate with increased sympathy toward refugees: the more people stay at home, the more positive sentiments toward refugees they exhibit on Twitter. We conclude by proposing two possible causal mechanisms for these findings. The findings suggest that the absence of casual contact with refugees may yield less negative sentiment, and/or that a rally around the flag mechanism yields unprecedented levels of social solidarity in response to the pandemic.

The Impact of Investor Sentiment on Stock Prices in The Developed and Developing Countries

The study of causal relationship of investor sentiment on stock prices has got more and more attention. This paper examines the impact of investor sentiment on investment behavior and thus on stock prices in four different countries including two developed countries (United States and United Kingdom), and two developing countries (China and India). Due to different levels of development, different countries have different measures of consumer sentiment. Therefore, the author enumerates in detail the measures of sentiment index for each studied country. By comparing representatives of investor indices, the index accuracy was analyzed as a function of market spacing. In addition, the differences between the conclusions of developing countries and developed countries are also compared. Meanwhile, the mutual influence of investor sentiment was summarized among developed countries. The empirical findings show that the study about exploring the causal relationship of the investor sentiment on the stock prices plays an important role in formulating detailed investor strategies. In other words, investors who are familiar with the impact of emotions on stock prices can be more successful in stock investing and their investment risk can be reduced than those who are not.

Infant food users' perceptions of safety: A web-based analysis approach.

This paper aims to explore consumer beliefs about health hazards in infant foods by analyzing data gathered from the web, focusing on forums for parents in the UK. After selecting a subset of posts and classifying them by topic, according to the food product discussed and the health hazard discussed, two types of analyses were performed. Pearson correlation of term-occurrences highlighted what hazard-product pairs are most prevalent. Ordinary Least Squares (OLS) regression performed on sentiment measures generated from the texts provided significant results indicating positive or negative sentiment, objective or subjective language, and confident or unconfident modality associated with different food products and health hazards. The results allow comparison between perceptions obtained in different countries in Europe and may lead to recommendations concerning information and communication priorities.

Structural racism and homophobia evaluated through social media sentiment combined with activity spaces and associations with mental health among young sexual minority men

BackgroundResearch suggests that structural racism and homophobia are associated with mental well-being. However, structural discrimination measures which are relevant to lived experiences and that evade self-report biases are needed. Social media and global-positioning systems (GPS) offer opportunity to measure place-based negative racial sentiment linked to relevant locations via precise geo-coding of activity spaces. This is vital for young sexual minority men (YSMM) of color who may experience both racial and sexual minority discrimination and subsequently poorer mental well-being. MethodsP18 Neighborhood Study (n = 147) data were used. Measures of place-based negative racial and sexual-orientation sentiment were created using geo-located social media as a proxy for racial climate via socially-meaningfully-defined places. Exposure to place-based negative sentiment was computed as an average of discrimination by places frequented using activity space measures per person. Outcomes were number of days of reported poor mental health in last 30 days. Zero-inflated Poisson regression analyses were used to assess influence of and type of relationship between place-based negative racial or sexual-orientation sentiment exposure and mental well-being, including the moderating effect of race/ethnicity. ResultsWe found evidence for a non-linear relationship between place-based negative racial sentiment and mental well-being among our racially and ethnically diverse sample of YSMM (p < .05), and significant differences in the relationship for different race/ethnicity groups (p < .05). The most pronounced differences were detected between Black and White non-Hispanic vs. Hispanic sexual minority men. At two standard deviations above the overall mean of negative racial sentiment exposure based on activity spaces, Black and White YSMM reported significantly more poor mental health days in comparison to Hispanic YSMM. ConclusionsEffects of discrimination can vary by race/ethnicity and discrimination type. Experiencing place-based negative racial sentiment may have implications for mental well-being among YSMM regardless of race/ethnicity, which should be explored in future research including with larger samples sizes.

Investigating the Victorian Government Schools’ Parent Opinion Survey validity: Pursuing a dependable measure of parent sentiment

Investigating the Victorian Government Schools’ Parent Opinion Survey validity: Pursuing a dependable measure of parent sentiment

Is sentiment the solution to the risk–return puzzle? A (cautionary) note

The risk–return relationship in stock markets is often found to be negative or non-existent, in contrast with fundamental finance theories. In this note we investigate if one proposed solution to this puzzle, which states that high irrational investor sentiment disrupts the otherwise positive risk–return nexus, is robust across popular sentiment proxies and therefore empirically comprehensively validated. We find that it is not robust, as most individual sentiment proxies fail to support the hypothesised negative impact of sentiment on the risk–return relationship. Only when a common component of individual proxies is extracted to form a single sentiment measure do we find robust support for the notion that high sentiment impedes rational asset pricing.

Ambiguity and asset prices: a closer look in an emerging market

PurposeThis study empirically documents the effect of ambiguity on stock returns in a major emerging market along with the ambiguity attitudes under various market conditions.Design/methodology/approachAmbiguity is measured as the volatility of return probability distributions extracted from high frequency intraday data via a method developed by Brenner and Izhakian (2018). The impact of ambiguity is then tested on stock market returns.FindingsThe results show that ambiguity is a priced factor in Turkish stock market with a positive premium that is distinct from risk premium. In contrast with the findings in the US market, the investors in Turkey show an increasing level of ambiguity aversion as expected probability of favorable returns deviate from the mean value. The investors are effectively ambiguity neutral in lateral markets. The results are robust to testing with higher moments, sentiment measures and under recession conditions.Originality/valueThis study contributes to empirically documenting ambiguity and ambiguity aversion in a major emerging market along with the opportunity to observe international differences in ambiguity attitudes.

A sentiment analysis approach to the prediction of market volatility.

Prediction and quantification of future volatility and returns play an important role in financial modeling, both in portfolio optimisation and risk management. Natural language processing today allows one to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. We found that there is evidence of correlation between sentiment and stock market movements. Moreover, the sentiment captured from news headlines could be used as a signal to predict market returns; we also found that the same does not apply for volatility. However, for the sentiment found in Twitter comments we obtained, in a surprising finding, a correlation coefficient of -0.7 (p < 0.05), which indicates a strong negative correlation between negative sentiment captured from the tweets on a given day and the volatility observed the next day. It is important to keep in mind that stock volatility rises greatly when the market collapses but not symmetrically so when it goes up (the so-called leverage effect). We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modeling, based on Latent Dirichlet Allocation, in order to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modeling even on modest (essentially personal) architecture our classifier achieved a directional prediction accuracy for volatility of 63%.

The humanizing effect of market interaction

The quality and quantity of intergroup contact affects how outgroups are perceived. Positive interaction tends to have a humanizing effect of moral inclusion. Negative interaction instead tends towards dehumanization and moral exclusion. One avenue of intergroup contact that has been empirically underexplored is interaction in a market. Do markets generate moral sympathy, or do they allow us to ignore or deny the moral status of others? We create a measure of moral sentiment that captures the frequency, valence, and type of moral language used about an outgroup. We match our novel sentiment data to dyadic measures of market interaction to test if markets act as a (de)humanizing force. We find a positive relationship between market interaction and the use of (1) moral, (2) virtuous (but not vice), (3) bridging, and (4) bonding language to talk about a contacted outgroup. Our results suggest market interaction has a humanizing effect.

Supply side sentiments and international travel: a novel dynamic simulation of policy options for business and investor sentiments

ABSTRACT The tourism literature widely documents how emotions, mood and optimism drive the demand for tourism. However, the literature is mute on whether sentiment plays a role in the supply side. We use a forward-looking forecasting model to investigate the interplay between various sentiment measures and travel. The simulation robustly accounts for the effect of trade, real GDP, and foreign direct investment on travel services. The result shows a long-term relationship between travel services and customer sentiment, hence, an increase in the bull-bear spread (BBS), in the short run, has a negative influence on travel services, but this link fades in the long run. Consumer confidence, on the other hand, has a positive impact on travel services in both the short and long run. Our findings advocate the consideration of sentiments when modelling travel services export. The forecasting model also reveals that travel services will decline in the future.

Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market

In this study, we construct an investor sentiment indicator (SsPCA) to predict stock volatility in the Chinese stock market by applying the scaled principal component analysis (sPCA). As a new dimension reduction technique for supervised learning, sPCA is employed to extract useful information from six individual sentiment proxies and obtain the common variations to characterize the investor sentiment (SsPCA). The empirical results indicate that SsPCA is a significant and powerful volatility predictor both in and out of sample. We also employ the partial least squares (PLS)-based investor sentiment index, three extra sentiment measures in past studies, and six individual sentiment proxies for comparison, and find SsPCA outperforms them on predicting stock volatility in the Chinese stock market. More importantly, the predictability of SsPCA remains significant before and after the famous financial crises (the sub-prime mortgage crisis and Chinese stock market turbulence) and the spread of the pandemic (COVID-19). Additionally, our findings imply that SsPCA still plays an essential role in predicting sock volatility after considering the leverage effect. The robustness of SsPCA in volatility forecasting is further verified in various industry indices of the Chinese stock market. Finally, we state that the strong predictability of SsPCA is highly related to its dimensionality reduction. Our results indicate that SsPCA is a robust volatility predictor from various aspects and performs better compared with existing sentiment indicators.

News coverage vs sentiment: evaluating German residential real estate markets

PurposeTo investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.Design/methodology/approachUsing methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.FindingsThe empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment.Originality/valueTo the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.

The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19

This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.

Linguistic measures of personality in group discussions.

This investigation sought to find the relationships among multiple dimensions of personality and multiple features of language style. Unlike previous investigations, after controlling for such other moderators as culture and socio-demographics, the current investigation explored those dimensions of naturalistic spoken language that most closely align with communication. In groups of five to eight players, participants (N = 340) from eight international locales completed hour-long competitive games consisting of a series of ostensible missions. Composite measures of quantity, lexical diversity, sentiment, immediacy and negations were measured with an automated tool called SPLICE and with Linguistic Inquiry and Word Count. We also investigated style dynamics over the course of an interaction. We found predictors of extraversion, agreeableness, and neuroticism, but overall fewer significant associations than prior studies, suggesting greater heterogeneity in language style in contexts entailing interactivity, conversation rather than solitary message production, oral rather than written discourse, and groups rather than dyads. Extraverts were found to maintain greater linguistic style consistency over the course of an interaction. The discussion addresses the potential for Type I error when studying the relationship between language and personality.

Term premium dynamics in an emerging market: Risk, liquidity, and behavioral factors

This paper contributes to the fixed income research by identifying determinants of term premium in an emerging market’s treasury bond yields with particular attention on ambiguity. We use Nelson–Siegel yield curves generated from daily bond price quotes as input to construct a three-factor affine term structure model which decomposes observed yields into risk-neutral and term premium components. We also construct an ambiguity index using intraday FX return data following Brenner and Izhakian (2018). Our analyses suggest that a combination of factors representing market risk, credit risk, liquidity, ambiguity, and investor sentiments can explain majority of the variation in term premia. Explanatory power of credit risk measures are found to increase while those of volatility, ambiguity, and sentiment measures diminish with the maturity horizon. The results imply that ambiguity aversion of bond investors is a major determinant of the shape of the yield curve as it drives the premia for short end of the yield curve lower in line with the expectation of flight-to-safety behavior.

Firms and Collective Reputation: a Study of the Volkswagen Emissions Scandal

AbstractThis paper uses the 2015 Volkswagen (VW) emissions scandal as a natural experiment to provide evidence that collective reputation externalities are economically significant. Using a combination of difference-in-differences and demand estimation approaches, we document a spillover effect from the scandal to the non-VW German auto manufacturers. The spillover amounts to an average drop of $2,057 in consumer valuations of these manufacturers’ vehicles and a 34.6% reduction in their annual sales. We substantiate our interpretation that the estimates reflect a reputation spillover using data on internet search behavior and direct measures of consumer sentiment from Twitter.

Has COVID-19 changed carer's views of health and care integration in care homes? A sentiment difference-in-difference analysis of on-line service reviews

Closer integration of health and social care is a policy priority in many countries. The COVID-19 pandemic has reinforced the necessity of joining up health and social care systems, especially in care home settings. However, the meaning and perceived importance of integration for residents’ and carers’ experience is unclear and we do not know whether it has changed during the pandemic.Using unique data from on-line care home service reviews, we combined multiple methods. We used Natural Language Processing with supervised machine learning to construct a measure of sentiment for care home residents' and their relatives’ (measured by AFINN score). Difference-in-difference analysis was used to examine whether experiencing integrated care altered these sentiments by comparing changes in sentiment in reviews related to integration (containing specific terms) to those which were not. Finally, we used network analysis on post-estimation results to assess which specific attributes stakeholders focus on most when detailing their most/least positive experiences of health and care integration in care homes, and whether these attributes changed over the pandemic.Reviews containing integration words were more positive than reviews unrelated to integration in the pre-pandemic period (about 2.3 points on the AFINN score) and remained so during the first year of the pandemic. Overall positive sentiment increased during the COVID-19 period (average by +1.1 points), mainly in reviews mentioning integration terms at the beginning of the first (+2.17, p-value 0.175) and second waves (+3.678, p-value 0.027). The role of care home staff was pivotal in both positive and negative reviews, with a shift from aspects related to care in pre-pandemic to information services during the pandemic, signalling their importance in translating integrated needs-based paradigms into policy and practice.

Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Customer opinion is an important aspect in determining the success of a company or service provider. By determining the sentiment of the existing opinion, the company can use it as an evaluation material to improve the quality of the service or product provided. Sentiment analysis can be used as a measure of opinion sentiment with input data in the form of a corpus which will be classified into positive or negative classes to obtain the level of customer satisfaction with a product or service. Aspect-based sentiment analysis can be used by companies to analyze more specifically and find out what aspects need to be improved. In this research, an aspect-based sentiment analysis was conducted on Telkomsel users on Twitter. The data used is 16,992 tweets from users who discuss several aspects such as Telkomsel's services and signals in Twitter. In this research Word2Vec was used for feature expansion to minimize vocabulary mismatch caused by limited words in tweets. The results showed that Word2Vec, Synthetic Minority Oversampling Technique (SMOTE), and Boosting algorithm combination with Logistic Regression classifier achieve highest accuracy of 95.10% for signal aspect and using hyperparameters makes the service aspect get the highest accuracy of 93.34%.&#x0D;

Anti-market sentiment and corporate social responsibility: Evidence from anti-Jewish pogroms

This paper examines the impact of anti-market culture on a firm's environmental and social (E&S) outcomes. We use the varying degree of intensity of historical anti-Jewish pogroms in 20 European countries as a quasi-exogenous measure of anti-market sentiment. We show that the historical occurrence of anti-Jewish pogroms in Eastern Europe during the period 1800–1927 generated an enduring anti-market sentiment that continues to influence the E&S performance of present-day firms. This relationship is more pronounced for firms with better corporate governance, which rejects the bad governance view on corporate social responsibilities. Taken together, our results add new evidence in support of the view that corporate social responsibility is responsive to institutional differences and may reflect the presence of an anti-market ideology that has historical roots.