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The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

The purpose of this study is to investigate the influence of firm-specific investor sentiment on Brazilian companies’ accrual-based earnings management between 2010 and 2018. The paper aims to bring deeper insight into the relationship between the investor expectations and managers’ decision-making in an emerging market.

The authors use the quantitative approach and apply a multiple linear regression model to test the relationship among the abnormal accruals, the firm-specific investor sentiment index and the control variables. The final sample includes data from 175 companies, between 2010 and 2018.

These results reveal a negative association between firm-specific investor sentiment and accrual-based earnings management, which could mean that the risk propensity of managers to manipulate earnings increases when they face known losses in the capital market.

The research findings provide a valuable understanding of how emerging capital market expectations can influence managerial decisions, such as accrual-based earnings management. The geographical area of study was limited to only Brazil.

Previous studies on developed markets show that market-wide investor sentiment positively influences accrual-based earnings management. However, the present study shows that the firm-specific investor sentiment index has a significant and negative relationship with Brazilian companies’ earnings manipulation, whereas market sentiment indicates contradictory relationship in previous studies in the country.

El propósito de este estudio es investigar la influencia del sentimiento de los inversionistas a nivel de empresa en la manipulación contable de las empresas brasileñas entre 2010 y 2018. El documento pretende aportar una visión más profunda sobre la relación entre las expectativas de los inversores y la toma de decisiones de los gestores en un mercado emergente.

usamos el enfoque cuantitativo y aplicamos un modelo de regresión lineal múltiple para probar la relación entre las acumulaciones anormales, el índice de sentimiento de los inversores a nivel de empresa y las variables de control. La muestra final incluye datos de 175 empresas, entre 2010 y 2018.

Los resultados revelan una asociación negativa entre el sentimiento de los inversores a nivel de empresa y la manipulación contable basada em acumulaciones, lo que podría significar que la propensión al riesgo de los administradores a manipular las ganancias aumenta cuando enfrentan pérdidas conocidas en el mercado de capitales.

los resultados de la investigación proporcionan una valiosa comprensión de cómo las expectativas de los mercados de capitales emergentes pueden influir en las decisiones de gestión, como la manipulación contable basada en acumulaciones. El área geográfica de estudio se limitó únicamente a Brasil y, en consecuencia, los hallazgos y conclusiones del estudio tuvieron sus límites.

estudios anteriores sobre mercados desarrollados muestran que el sentimiento de los inversores a nivel de mercado influye positivamente en la manipulación contable. Sin embargo, el presente estudio muestra que el índice de sentimiento de los inversores a nivel de empresa tiene una relación significativa y negativa con la manipulación de las ganancias de las empresas brasileñas, mientras que el sentimiento del mercado indica una relación contradictoria en estudios anteriores en el país.

This study examines the investment horizon influence, mediated by market optimism, on earnings management based on accruals and real activities. Based on short-termism, the authors argue that earnings management increases in optimistic periods to boost corporate profits.

The authors analyzed non-financial Brazilian publicly traded firms from 2010 to 2020 by estimating industry-fixed effects of groups of short- and long-horizon firms to compare their behavior on earnings management practices during bullish moments. For robustness, the authors used alternate measures and trade-off analyses between earning management practices.

The findings indicate that, during bullish moments, companies prioritize managing their earnings through real activities management (RAM) rather than accruals earnings management (AEM), depending on their time horizon. The results demonstrate the trade-off between earnings management practices.

This study presents limitations when using proxies for earnings management and investor sentiment.

Investors and regulators should closely monitor companies' operations, especially during bullish market conditions to prevent fraud.

The study addresses investor sentiment mediation in the earnings management discussion, introducing the short-termism approach.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

This paper has analysed the impact of cultural dimensions, investor sentiment and uncertainty on bank stock returns. Also, the study examined the influences of the interaction between cultural dimensions and individual (private) sentiment (investor sentiment).

To meet the study's objectives, a two-step generalised method of moments estimator was applied to the study sample, which included 105 banks in the nine Middle East and North African region countries between 2010 and 2020.

The cultural dimensions of individualism and masculinity were found to have a positive and significant effect on banks' buy and hold stock return (BUH). At the same time, power distance and uncertainty avoidance were discovered to have negative effects. Besides, the findings revealed that the interactions of power distance, individual sentiment and uncertainty avoidance had positive and significant relationships with banks' BUH. However, individualism, individual sentiment and masculinity had inverse relationships with banks' BUH. Furthermore, the findings revealed that investor sentiment positively influenced banks' BUH. Finally, uncertainty influenced banks' BUH stock returns positively.

Important implications for participants in the financial sector and governments may be learnt from this study's conclusions. Due to cultural biases, this study's findings suggested that investors overreact in the stock market.

Additionally, this research comprises one of the few studies that have overviewed the link between classical and behavioural finance in MENA countries with distinctive cultural characteristics.

This study aims to investigate the asymmetric impact of daily announcements regarding COVID-19 on investor sentiment in the stock market.

This study uses a Non-Linear Autoregressive Distribution Lag (NARDL) model that relies on positive and negative partial sum decompositions of the Coronavirus indicators. Five investor sentiments had been used and the analysis is conducted on the full sample period from 24th February 2020 to 25th March 2021.

The results show that new cases have a greater impact on investor sentiment compared to daily announcements of new deaths related to COVID-19. In addition to revealing a significant impact of new COVID-19 new cases and new death announcements on a daily basis on investor sentiment over the short- and long-term, this paper also highlights the nonlinearity and asymmetry of this relationship in the short and long run. Investors' sentiments are more affected by negative news regarding Covid 19 than positive news.

Financial markets have been severely affected by COVID-19 pandemic. This study is the first to measure the extent of reaction of investors to positive and negative announcements of COVID-19. Interestingly, this study examines the asymmetric effect of daily announcements on new cases and new deaths by COVID-19 on investor sentiments and derive many implications for portfolio managers.

This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

The dissemination of misleading and false information through media can jeopardize a company’s reputation, thus posing a threat to its stock and performance. Institutional investors are known to influence capital markets. Therefore, this paper investigates whether institutional investors engage in shaping the media sentiment stock nexus, stabilize company stocks and enhance performance.

We first investigate the effect of media sentiment on market reactions by using panel regression models. To examine the role of institutional investors, we design a quasi-experiment by exploiting the Financial Crisis of 2008 and go further by examining the heterogeneity across levels of institutional ownership. Due to risk-averse, investors may respond asymmetrically to pessimistic and positive sentiment. Accordingly, we split the sample into two sub-types, good news and bad news, based on keywords representing positive or negative content.

We find supportive evidence that institutional investors have impacts on how the markets react to media news, and the impacts are heterogeneous in the face of bad and good news. We conjecture that institutional investors act as a stabilizer of stock prices through media sentiment management.

This paper confirms the distinctive effects of institutional investors on capital markets, and uncovers the behind-the-scenes intervention and possible causal link running from institutional investors to media sentiment management. It contributes to the broad field of institutional investors' behavior, media news involvement in capital markets and market efficiency.

Based on the theoretical framework, this paper analyzes the sentiment-herding relationship in emerging stock markets (ESMs). First, it aims to examine the effect of investor sentiment on herding. Second, it seeks the direction of causality between sentiment and herding time series.

The present study applies the Exponential Generalized Auto_Regressive Conditional Heteroskedasticity (EGARCH) model to capture the volatility clustering of herding on the financial market and to investigate the role of the investor sentiment on herding behaviour. Then the vector autoregression (VAR) estimation uses the Granger causality test to determine the direction of causality between the investor sentiment and herding. This study uses a sample consisting of stocks listed on the Shanghai Composite index (SSE) (348 stocks), the Jakarta composite index (JKSE) (118 stocks), the Mexico IPC index (14 stocks), the Russian Trading System index (RTS) (12 stocks), the Warsaw stock exchange General index (WGI) (106 stocks) and the FTSE/JSE Africa all-share index (76 stocks). The sample includes 5,020 daily observations from February 1, 2002, to March 31, 2021.

The research findings show that the sentiment has a significant negative impact on the herding behaviour pointing out that the higher the investor sentiment, the lower the herding. However, the results of the present study indicate that a higher investor sentiment conducts a higher herding behaviour during market downturns. Then the outcomes suggest that during the crisis period, the direction is one-way, from the investor sentiment to the herding behaviour.

The findings may have implications for universal policies of financial regulators in EMs. We have found evidence that the Emerging investor sentiment contributes to the investor herding behaviour. Therefore, the irrational investor herding behaviour can increase the stock market volatility, and in extreme cases, it may lead to bubbles and crashes. Market regulators could implement mechanisms that can supervise the investor sentiment and predict the investor herding behaviour, so they make policies helping stabilise stock markets.

The originality of this paper lies in investigate the sentiment-herding relationship during the Surprime crisis and the Covid-19 epidemic in the EMs.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.