Impact Of Sentiment Research Articles

The Impact of Investor Sentiment on Housing Prices and the Property Stock Index Volatility in South Africa

Abstract While prior studies have examined the predictive effect of macroeconomic and country risk components on property stock index dynamics, limited explanations exist in the literature regarding the time-varying effect of investor sentiment on housing prices. Accordingly, this study assesses the impact of investor sentiment on housing properties’ returns and the effect of investor sentiment on the conditional volatility of housing price indices under different market conditions, using GARCH, GJR-GARCH, E-GARCH and Markov-switching VAR models. We found investor sentiment to significantly impact the risk premium of the property returns, where property returns increased with positive changes in investor sentiment, and conditional volatility of property returns decreased with the same changes in investor sentiment. Investor sentiment exerts positive predictive influences on the prices of small and medium houses, in both bullish and bearish market conditions but does not affect the large housing market segment. This makes the implementation of risk-related diversification across small and medium real estate portfolios more effective than large real estate portfolios. Our findings show that investor sentiment is a plausible driver of mass investor redemption actions under conditions of uncertainty.

Stock Prediction by Polyglot Sentiment Analysis on Twitter

Research in the economics field has extensively documented the impact of media sentiments on the stock market. Sentiment analysis, as a tool to predict equity prices, has been popularized in the past years. Recently, Twitter has received a lot of attention due to the diversity of opinions on social media platforms. A common obstruction to sentiment analysis is the resource gap between English and other languages. This pilot study examines the effect of polyglotism in tweets concerning bilingual companies and develops a model that extracts sentiments from polyglot tweets to make price predictions. Results suggest that taking non-English tweets into consideration decreases errors in price predictions and that the random forest models have higher performance than linear regression models. The results of this pilot study need to be confirmed with larger sets of data.

Investor Sentiment and Cash Conversion Cycle: The Mediating Role of Macroeconomic, Financial, and Real Activity Uncertainties

We explore the impact of investor sentiment on the cash conversion cycle (CCC) and examine how macroeconomic, financial, and real activity uncertainties mediate this relationship. Our study focuses on U.S. publicly traded firms and reveals that investor sentiment is negatively associated with CCC. Furthermore, our findings suggest that the negative correlation is primarily attributable to negative (pessimistic) sentiment, indicating that an unfavorable outlook of investors has a significant impact on CCC. We argue that the cost stickiness theory helps explain the divergent reactions of firms with positive and negative CCCs to sentiment. Additionally, we show that economic uncertainty partially mediates the relationship between sentiment and CCC. Specifically, when there are considerable economic uncertainties, managers tend to take a more proactive approach toward their CCC. Overall, our study provides valuable insights into the factors that influence firms’ CCC and highlights the need for firms to take into account investor sentiment and appropriately address uncertainties in their management of CCC.

Like, Comment, and Share on TikTok: Exploring the Effect of Sentiment and Second-Person View on the User Engagement with TikTok News Videos

TikTok—the world’s most downloaded app since 2020, has become a place for more than silly dancing and lip-syncing. TikTok users are increasingly turning to TikTok for news content. Meanwhile, news publishers are embracing TikTok to reach a younger audience. We aim to examine the content strategy adopted by the most-followed news publishers on TikTok and how effective their TikTok strategy is in spurring audience engagement in terms of liking, commenting, and sharing. This study retrieved 101,292 TikTok news videos as of November 22, 2022. With the help of computer vision, natural language processing, and sentiment analysis, we found that TikTok news videos containing negative sentiment and more second-person view shots are associated with significantly higher audience engagement. In addition, this study demonstrated that the TikTok video features and engagement levels differ between the news publishers and other TikTok creators. Moderator analysis shows that both the effect of negative sentiment on engagement and the effect of the second-person view on engagement are moderated by the TikTok account type. The impact of negative sentiment and second-person view on engagement behaviors becomes smaller or even insignificant for news publisher TikTok videos. Theoretical and practical implications are discussed in this study.

Investor sentiment and firm capital structure

We provide novel evidence of the role of investor sentiment in determining firms' capital structure decisions from three perspectives: leverage ratio, debt maturity and leverage target adjustment. We find that when investor sentiment is high, firms increase their leverage ratios, supporting our contention that high investor sentiment increases firms' debt capacity and facilitates the use of an aggressive leverage policy. Debt maturity is shorter in high sentiment periods, implying that firms are confident about future earnings and use shorter debt maturity to signal their financial solvency. Leverage target adjustment is slower in low sentiment periods, indicating higher costs of external finance. Furthermore, the sentiment-leverage relationship sensitivity is greater for financially constrained firms. Our extended analysis determines that leverage-increasing firms generate lower stock returns subsequent to a period of high sentiment, offering practical insights into the economic consequences of increasing leverage in high sentiment periods on corporate value for investors. Our research advances the understanding of the impact of investor sentiment on firms' financing decisions and stock returns.

Sentiment Impact of Public Health Agency communication Strategies on TikTok under COVID-19 Normalization: Deep Learning Exploration.

The accessibility of social media data has allowed researchers to measure official-public interactions during COVID-19. However, previous work analyzing official posts or public comments has failed to explore the link between the two. Therefore, this study investigates the relationship between the communication strategies of public health agencies (PHAs) on TikTok and public emotional/sentiment tendencies in COVID-19 normalization. This study uses the 2022 Shanghai city closure event as a public health communication case study in the context of COVID-19 normalization, using TikTok as a data source. We first analyze the communication strategies adopted by the PHA based on the Crisis and Emergency Risk Communication (CERC) model. Then, we classify the sentiment of public comments using the Large-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation (ERNIE) pre-training model. Finally, we explore the connection between PHA communication strategies and public sentiment tendencies. First, the public's sentiment tendencies differ at different stages. Therefore, appropriate communication strategies should be developed stage-by-stage. Second, the public's emotional disposition to different communication strategies varies: government statements, vaccines, and prevention and control programs are more likely to produce a friendly comment environment, while policy and new cases per day are more likely to produce unfavorable comment content. However, this does not mean that policy and new cases per day should be avoided; the judicious use of these two strategies can help PHAs understand the current issues causing public dissatisfaction. Third, videos with celebrity appearances can significantly increase positive public sentiment and, thereby, public participation. We propose an improved CERC guideline for China based on the Shanghai lockdown case.

The impact of investor sentiment on firms' green total factor productivity-facilitator or inhibitor?

Investor sentiment does not only have negative impacts. It may also improve green total factor productivity by invigorating funds. This research constructs a new indicator at the firm level to measure the green total factor productivity of firms. We research the effect of investor sentiment on firms' green total factor productivity using a sample of Chinese heavy polluters listed on Shanghai and Shenzhen A-shares between 2015 and 2019. Through a series of tests, the mediating role of agency costs and financial situations is confirmed. It is discovered that the digitization of businesses facilitates the effect of investor sentiment on the green total factor productivity of businesses. And when managerial competence reaches a certain threshold, the impact of investor sentiment on green total factor productivity is amplified. Tests for heterogeneity reveal that high investor sentiment has a larger impact on green total factor productivity in firms with superior supervision.

Resale HDB Price Prediction Considering Covid-19 through Sentiment Analysis

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon-based tool, VADER, is used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid-19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. In a micro sense, this paper demonstrates the use of sentiment analysis of Twitter data in urban economics. In a macro sense, the paper demonstrates the extent to which social media is able to capture the behavioral economic cues of a population.

Impact of public news sentiment on stock market index return and volatility

Impact of public news sentiment on stock market index return and volatility

Investigating the dynamics of crisis transmission channels: A comparative analysis

This paper analyzes the growing complexity of cross-market interdependence during financial crises. From macroeconomic, investor constraint, and quantitative easing policy perspectives, we investigate crisis transmission channels across major stock markets by comparing the 2008 global financial crisis, the 2020 COVID-19 crisis, and the 2022 Russo-Ukrainian crisis. We find that lower-tail contagion is mainly driven by the wealth effect and upper-tail contagion is mainly driven by the portfolio rebalancing, and shed light on the underlying dynamic mechanisms, such as credit spreads, risk aversion, economic policy uncertainty, and quantitative easing policy. Additionally, we show the impact of macroeconomic fundamentals, investor sentiment, and quantitative easing policy on lower-tail and upper-tail financial contagion.

Investor sentiment, stock returns, and the dependence between their quantiles: evidence from G7 countries

ABSTRACT This study uses a quantile-on-quantile regression framework to establish a link between investor sentiment proxied by market turnover and stock returns in G7 countries. The QQ analysis results reveal that the impact of investor sentiment on future stock returns is asymmetric and varies across quantiles of stock returns and sentiment. Overall, a significantly negative relationship is observed for all G7 countries. Interestingly, when the market is very bullish, and investors are pessimistic, investor sentiment positively influences future returns, except for Japan.

COVID-induced sentiment and the intraday volatility spillovers between energy and other ETFs

Did Covid19 induce market turmoil impact the intraday volatility spillovers between energy and other ETFs?. To examine this, we first estimate the realized volatility of ETFs using the 5-min high-frequency data. Next, we employ time-varying parameter vector autoregressions (TVP-VAR). Finally, we utilize the wavelet coherence measure to test the time-frequency impact of COVID-induced sentiment on the spillovers by employing investors' psychological and behavioural factors. We find that oil and stock markets are net transmitters while currency, bonds, and silver markets are net receivers. The wavelet analysis embarked significant impact of media coverage and fake news index towards shaping investors' pessimism for their investments. We proposed useful implications for policymakers, governments, investors, and portfolio managers.

How does the COVID-19 pandemic shape the relationship between Twitter sentiment and stock liquidity of US firms?

Using a new investor sentiment metric derived from Twitter, this paper examines how the pandemic's death rate influences the impact of investor sentiment on stock liquidity. Recent literature remains inconclusive regarding the effect of COVID-19 information and investor sentiment on financial markets. Using panel smooth transition regression (PSTR) for daily data on 338 listed firms in the S&P500 from January 2, 2020, to May 26, 2021, the findings reveal that the impact of Twitter sentiment on stock liquidity is nonlinear and changes over time and across firms in the function of the pandemic's death rate in the US. The results exhibit a threshold level of 4.32%, above which investor sentiment boosts stock liquidity. The speed of the transition from low to high pandemic death rate regime occurred abruptly rather than smoothly. This translates to severe changes in investor perception and demonstrates that investors are rapidly updating their beliefs during the COVID-19 outbreak.

Nexus between corporate innovation and investor sentiment: empirical evidence from the US

PurposeThis study examines the connection between investor sentiment and corporate innovation in the United States, considering the magnitude of corporate information asymmetry, the implied cost of capital and the financial constraints.Design/methodology/approachThe authors employ a two-step GMM framework to examine the hypotheses of this study by utilizing annual data from 2001 to 2021 for US corporations.FindingsThe empirical evidence demonstrates a significant impact of investor sentiment on corporate innovation for firms with a lower information asymmetry and implied cost of capital than those with a higher information asymmetry and cost of capital. Although the financial constraint channel remained positive, it had little impact on the innovations of US corporations. Overall, the study's results show that companies make more valuable and high-quality patents when investors are optimistic.Practical implicationsThis research has policy implications for all managers, investors, analysts and state officers, particularly in the USA and other developed countries. Managers and investors of all types should predict the role of corporate innovation in increasing shareholder wealth.Originality/valueTo the authors' knowledge, this is the first study to examine the relationship between investor sentiment and corporate innovation in the United States, considering the extent of corporate information asymmetry, the implied cost of capital and the financial limitations. The study's empirical findings uniquely contribute to the existing literature on corporate innovation and investor sentiment in the current context.

Are Longer and More Negative Online Reviews More Helpful? - The Mediating Role of Consumers' Perceived Usefulness of Reviews

Purpose - This study investigates how review length and sentiment impact consumers' purchase intentions, using real online reviews as the data source. The study aims to understand how the length and tone of a review affect a potential buyer's decision-making process when considering a purchase.
 Design/methodology/approach - A 2 (comment length: long vs. short) × 2 (comment sentiment: positive vs. negative) × 2 (product type: practical vs. hedonic) experiment was conducted.
 Findings - Results indicate that longer reviews have a greater impact on consumers' perceived usefulness compared to short reviews, but do not affect purchase intentions. Review sentiment is found to have a stronger impact than review length, especially for negative sentiment. The study also suggests that consumers pay more attention to reviews of practical products, and that reviews have less influence on hedonic products.
 Research implications or Originality - The implications of these findings are relevant for both merchants managing reviews and consumers reviewing products.The results of this research could help businesses and marketers optimize their online review strategies to maximize their impact on consumer behavior.

Dynamic impacts of online investor sentiment on international crude oil prices

In recent years, international crude oil prices have been subject to unusually high fluctuations due to the ravages of the COVID-19 epidemic. Under such extreme market conditions, online investor sentiment can strengthen the correlation between oil price changes and external events. We use a (rolling-window) structural vector autoregression method to investigate the dynamic impact of online investor sentiment on WTI crude oil prices before and after the COVID-19 pandemic across multiple topics of price, supply, demand, and so on, which aims to explore the fluctuation mechanism driven by sentiment and the price changes triggered by public health events. The proposed aspect-level sentiment analysis approach can effectively distinguish and measure sentiment scores of different aspects of the oil market. Our results show that the constructed oil price prosperity index contributes 49.84% to the long-term fluctuations of WTI oil price, ranking first among the influencing factors considered. In addition, the peak value of impulse shocks to WTI oil prices rose from 6.47% to 8.40% during the period of dramatic price volatility caused by the epidemic. The results sketch the mechanisms by which investor sentiment can affect crude oil prices, which help policymakers and investors protect against extreme risks in the oil market.

Relative Impact of the U.S. Energy Market Sentiments on Stocks and ESG Index Returns: Evidence from GCC Countries

In this study, we provide empirical evidence on the relative impact of energy market sentiments on the stock and ESG index returns in the U.S. and Gulf Cooperation Council (GCC) economies. Specifically, we study movements in four distinct categories of energy sentiments (natural gas, crude oil, RBOB gasoline, and heating oil) displayed by professional investors and investigate their relative impact on ESG investments and stock returns in the U.S. and GCC economies. We employ the recently developed automatic time series forecasting methodology Autometrics to examine the postulated relationships. The results of the regression models suggest that there is a significant negative impact of stock sentiments and a positive impact of energy sentiments on the S&P 500 returns. However, in the case of the U.S. energy companies' returns, there are significantly higher effects of only energy market sentiments (mainly crude oil and RBOB gasoline). In the case of the GCC stock markets, there are significant positive impacts of crude oil sentiments and the S&P 500 of varying degrees of strength. The most significant impact of crude oil sentiments is observed in UAE and Saudi Arabia and is almost of the same magnitude as those on the U.S. energy companies' returns. These results are consistent with arguments provided in behavioral finance studies that investors prefer bigger profits over social returns during bullishness and step back from social investing when better investment opportunities are available. Also, ESG investing may be preferred during bearishness by utilitarian investors to generate abnormal returns during such lean periods.

A Behavioural Appraisal of Regulatory Financial Reforms and Implications for Corporate Management

AbstractFinancial markets play a vital role in shaping corporate behaviour, impacting corporate financial decisions ranging from investment and mergers/acquisitions to payout policies and management renumeration. Financial markets, however, are prone to irrational sentiments to trade, driving prices away from fundamental values, with the potential to distort corporate decisions and, hence, corporate efficiency. It is important, therefore, to examine the extent to which regulatory reforms help mitigate the influence of irrationality in financial markets. To this end, we examine the consequences of the mandatory adoption of International Financial Reporting Standards (IFRS) in Europe through the behavioural lens of investor sentiment. In country‐level analyses, we find the impact of irrational sentiment on stock markets to have significantly diminished post‐IFRS. In global pooled analyses, we compare the change in the sentiment–return relationship in countries adopting IFRS with the change in a set of non‐adopting countries to account for stock market trends: weakening of the impact of irrational sentiment on stock prices is greater in IFRS‐adopting countries. Results are robust to a battery of alternative tests and explanations. We provide strong support, therefore, for the success of IFRS in its aim of improving market efficiency, with important implications for corporate management.

Investor reaction to ESG news sentiment: evidence from South Africa

PurposeUtilising a database that distinctly classifies firm-level ESG (environmental, social and governance) news sentiment as positive or negative, the authors examine the information flow between the two types of ESG news sentiment and stock returns for 20 companies listed on the Johannesburg Stock Exchange between 2015 and 2021.Design/methodology/approachThe authors use Shannonian transfer entropy to examine whether information significantly flows from ESG news sentiment to stock returns and a modified event study analysis to establish how stock prices react to changes in the two types of ESG sentiment.FindingsUsing Shannonian transfer entropy, the authors find that for the majority of the companies studied, information flows from the positive ESG news sentiment to stock returns while only a minority of the companies exhibit significant information flow from negative ESG news sentiment to returns. Furthermore, the study’s findings show significantly positive (negative) abnormal returns on the event date and beyond for both upgrades and downgrades in positive ESG news sentiment.Originality/valueThis study is among the first in an African context to investigate the impact of ESG news sentiment on stock market returns at high frequencies.

Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment

We use a quantile machine learning (random forests) approach to analyse the predictive ability of newspapers-based macroeconomic attention indexes (MAIs) on eight major fundamentals of the United States on the realized volatility of a major commodity-exporting emerging stock market, namely South Africa. We compare the performance of the MAIs with the performance of a news sentiment index (NSI) of the US. We find that both fundamentals and sentiment improve predictive performance, but the relative impact of the former is stronger. We document how the impact of fundamentals and sentiment on predictive performance varies across the quantiles of the conditional distribution of realized volatility, and across different prediction horizons. Specifically, fundamentals matter more at the extreme quantiles at short horizons, and at the median in the long-run. In addition, we report several robustness checks (involving sample period and alternative definitions of realized volatility), and indicate that the obtained results for South Africa also tend to carry over to other emerging countries such as, Brazil, China, India, and Russia. Our results have important implications for investors with volatility being an input for portfolio allocation decisions. In addition, with stock market variability also capturing financial uncertainty, its accurate prediction based on US fundamentals and sentiment also has a role in policy design to prevent possible collapse.