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This study delves into the impact of reversals and investor attention on cryptocurrency returns before and during the COVID-19 pandemic. We employ the Two Stages Least Squares to analyze a sample of the top 20 cryptocurrencies from January 2016 to April 2021. Our results reveal that investor attention positively influences bitcoin returns in both periods, with a more pronounced effect during the pandemic. Conversely, reversals demonstrate a positive correlation with cryptocurrency returns before the outbreak but a negative relationship during the pandemic. Our robustness test further indicates that investor attention positively affects the returns of small and medium-cap cryptocurrencies, while reversals only exhibit positive consequences for small-cap cryptocurrencies. Additionally, our findings highlight stablecoins as a safe haven during the epidemic. The results suggest that investor attention has little influence on the returns of stablecoins, indicating that these coins are primarily resistant to market sentiment due to their inherent stability. The negative impact of the pandemic on the crypto market demonstrates a downward trend through each wave. Despite aligning with attention-induced price pressure and behavioral finance hypotheses, our results do not support efficient market theory or the notion of heterogeneity among investors. This research provides valuable insights for investors and policymakers in devising effective strategies for the cryptocurrency market. [ABSTRACT FROM AUTHOR]

Today, with a growing emphasis on sustainable economic development, corporate environmental, social and governance (ESG) performance is attracting increasing attention and favor from investors. This triggers a question: can good ESG performance of listed companies mitigate the "up and down" of the stock market by drawing investor attention? This paper utilizes the data from China's A-share listed companies from 2011 to 2020, with investor attention as a mediating variable, to explore how the ESG performance of listed companies influences abnormal stock price volatility. The findings suggest that stronger ESG performance of listed companies significantly reduces abnormal stock price volatility, in which investor attention plays a partial mediating role. This paper confirms the robustness of the findings through multiple robustness and endogeneity tests. Heterogeneity analysis reveals that listed companies with good ESG performance during the growth period are more likely to significantly mitigate abnormal stock price volatility. Similarly, firms that maintain commendable ESG performance in bear markets significantly reduce abnormal stock price volatility. These findings enrich the theoretical research on the impact of ESG performance on abnormal stock price volatility, provide empirical evidence for listed companies to emphasize ESG investment and encourage investors to consider ESG ratings. Additionally, the study provides a new perspective for government agencies to utilize corporate ESG performance to maintain the sound development of the capital market. [ABSTRACT FROM AUTHOR]

This work explores if behaviour-based asymmetries are likely to impact deal valuation in the life sciences by examining positive public sentiment as a proxy for market behaviour when negotiating under asymmetric conditions to examine heterogeneity in research & development collaboration (RDC) deal data. We use public sentiment as a proxy for behaviour along with stage of development-based RDC deal data to search for latent classes in the deal data using finite mixture modelling. The analysis reveals a nuanced picture: public sentiment emerges as a significant predictor of deal value, but only for approximately 15% of the data set. This subset exclusively includes firms in the Preclinical stage, where projects have moved past discovery but are yet to commence human studies. Interestingly, the research finds that sentiment's impact on deal valuation is particularly pronounced in this stage, suggesting heightened market sensitivity. With recent research demonstrating that knowledge asymmetry and behaviour impact valuation volatility, we take this further by capturing latent classes within the data which demonstrates how behaviour is most influential in deal pricing considerations. We argue that our research demonstrates the impact of asymmetry and market behaviour on a subset of RDCs where products are known, but likelihood of success is difficult to determine. [ABSTRACT FROM AUTHOR]

Corporations face multifaceted environmental uncertainties (EU) in today's dynamic global economic environment. Such uncertainties profoundly affect corporate operations and pose significant challenges to their environmental, social, and governance (ESG) performance. Therefore, using data from Chinese A-share listed corporations from 2009 to 2021, we empirically analyze the impact of the EU on ESG performance. The results demonstrate that the EU significantly negatively impacts ESG performance, indicating that corporations frequently find it difficult to implement and maintain high-standard ESG policies and practices effectively. Additionally, they reveal that the EU inhibits the improvement of ESG performance by increasing corporate financing constraints (KZ). Lastly, this study explores the role of government subsidies (GOV), investor attention (IA), and geopolitical risks (GPR) as moderating variables. We discover that GOV can mitigate the negative impact of the EU on KZ because they provide additional resources that help corporations maintain their ESG in uncertain environments. Furthermore, IA can reduce the adverse impact of KZ on ESG. Positive moderating effects result from ESG issues; the capital they provide effectively reduces corporate KZ, thus enabling corporations to maintain good ESG performance despite operating in highly uncertain environments. However, due to the increased asymmetry of information in economic markets caused by geopolitical tensions, GPR exacerbates the negative impact of the EU on ESG performance, thus leading to an increase in KZ. These findings offer new perspectives on understanding how these moderating effects affect corporate ESG strategies. [ABSTRACT FROM AUTHOR]

This study examines the influence of investor attention and Chief Executive Officers (CEOs) power on Corporate Social Responsibility (CSR) within Vietnamese family businesses. Unlike most of the past literature, this study further investigates the potential moderating effects of CEOs' power on the relationship between investor attention and CSR. Utilizing the dynamic system Generalized Method of Moments (GMM), this study analyzes a dataset comprising 116 Vietnamese family businesses from 2005 to 2020. The findings reveal an inverted U-shape between CEO power and CSR within family businesses; meanwhile, investor attention demonstrates a negative impact on CSR. Moreover, the results report that CEO power is a moderating factor in the relationship between investor attention and CSR. These results are consistent with various theoretical frameworks, including agency theory, overinvestment, career concern, career horizon, and conflict-resolution hypotheses. Finally, our study offers management implications to foster the sustainable development of CSR within family businesses, particularly within emerging markets. [ABSTRACT FROM AUTHOR]

In March 2020, the outbreak of COVID-19 precipitated one of the most significant stock market downturns in recent history. This paper explores the relationship between public sentiment related to COVID-19 and stock market fluctuations during the different phases of the pandemic. Utilizing natural language processing and sentiment analysis, we examine Twitter data for pandemic-related keywords to assess whether these sentiments can predict changes in stock market trends. Our analysis extends to additional datasets: one annotated by market experts to integrate professional financial sentiment with market dynamics, and another comprising long-term social media sentiment data to observe changes in public sentiment from the pandemic phase to the endemic phase. Our findings indicate a strong correlation between the sentiments expressed on social media and market volatility, particularly sentiments directly associated with stocks. These insights validate the effectiveness of our Sentiment(S)-LSTM model, which helps to understand the evolving dynamics between public sentiment and stock market trends from 2020 through 2023, as the situation shifts from pandemic to endemic and approaches new normalcy. [ABSTRACT FROM AUTHOR]

To explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises. [ABSTRACT FROM AUTHOR]

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market. [ABSTRACT FROM AUTHOR]

This paper examines the linkage between Chinese stock market volatility and investor attention fluctuation. In Heterogeneous autoregressive (HAR) model, first, we analyzed the linkage between both decomposed and undecomposed stock market realized volatility and investor attention fluctuations across full-sample and two-year moving window sub-samples. Second, we compare the predictive power of four models in short-, medium-, and long-term volatility forecasting. Empirical results show large positive attention fluctuation amplified Chinese stock market volatility after the outbreak of COVID-19, and negative small attention fluctuation significantly stabilized stock market volatility before COVID-19, and the impact dwindled in after COVID-19. The model incorporating decomposed realized volatility and decomposed attention fluctuation performs better in volatility Forecasting. This research underscores a shift in the dynamics between stock market volatility and investor attention fluctuations, and investor attention fluctuation improves the volatility forecasting accuracy of the Chinese stock market. [ABSTRACT FROM AUTHOR]

In recent years, the concept of corporate social responsibility has gained more attention from investors, and green innovation has become a key factor in China's economic growth. Despite this, regional disparities still remain, and the impact of corporate social responsibility on green innovation in local and surrounding areas is worth exploring. This article uses a Spatial Durbin Model to analyze the spatial spillover effect and mechanism of corporate social responsibility on green innovation of A-share listed companies in China from 2010 to 2020. The results show that corporate social responsibility behavior motivated by "tools" has a negative effect on local enterprises' green innovation, while also having a negative spillover effect on surrounding areas, thus affecting the spatial pattern of green innovation. Further research suggests that fulfilling corporate social responsibility can attract investors, alleviate financing constraints, and change corporate resource investment strategies, thus promoting cross-regional resource flow and enhancing green innovation. The results of this study remain consistent when the matrix and variables are changed. This article provides new insights into corporate social responsibility and green innovation, and offers policy recommendations to improve green innovation in enterprises. [ABSTRACT FROM AUTHOR]

Market liquidity can reflect whether financial market conditions are favorable and is the primary concern for investors when making investment decisions. Therefore, investors' psychological perception and confidence in the quality of products (assets) are particularly important. Using 264 of China's online loan platforms from August 2017 to November 2018, we investigate the impact of the negative psychological perceptions of investors on platform liquidity. The empirical results suggest that the negative psychological perceptions of investors reduce platform liquidity and increase platform liquidity risk. Using the Baidu Search Index to measure investor sentiment, we find that the negative psychological perceptions of investors affect platform liquidity by affecting investor sentiment, which provides a good channel for explaining the main conclusions. Heterogeneity analysis shows that the impact of the negative psychological perceptions of investors on platform liquidity is smaller in high-quality platforms with higher market share and higher registered capital. Meanwhile, we also find that the impact of negative psychological perceptions of investors is greater in private platforms, after the rectification policy, with positive net inflow, and in first- and second-tier cities and coastal cities. Precautionary financial regulatory policies are necessary, not punishment ex post. The research findings of this article can assist investors, platform managers, and regulatory agencies in identifying the liquidity characteristics of platforms, which can contribute to strengthening market liquidity management and financial risk control and provide some reference and support for formulating sustainable development policies in the financial industry. [ABSTRACT FROM AUTHOR]

COVID-19 affected the world's economy severely and increased the inflation rate in both developed and developing countries. COVID-19 also affected the financial markets and crypto markets significantly, however, some crypto markets flourished and touched their peak during the pandemic era. This study performs an analysis of the impact of COVID-19 on public opinion and sentiments regarding the financial markets and crypto markets. It conducts sentiment analysis on tweets related to financial markets and crypto markets posted during COVID-19 peak days. Using sentiment analysis, it investigates the people's sentiments regarding investment in these markets during COVID-19. In addition, damage analysis in terms of market value is also carried out along with the worse time for financial and crypto markets. For analysis, the data is extracted from Twitter using the SNSscraper library. This study proposes a hybrid model called CNN-LSTM (convolutional neural network-long short-term memory model) for sentiment classification. CNN-LSTM outperforms with 0.89, and 0.92 F1 Scores for crypto and financial markets, respectively. Moreover, topic extraction from the tweets is also performed along with the sentiments related to each topic. [ABSTRACT FROM AUTHOR]

Our study analyzed the impact of African swine fever (ASF) news on the Korean meat market using sentiment analysis. We applied a neural network language model (NNLM) to generate a sentiment index indicating whether the news had a positive or negative impact on consumer expectations. We analyzed 24,143 news articles to estimate the impulse responses of meat price variables to sentiment shocks. Our study contributes significantly to agricultural economics as it applies NNLM to generate a sentiment index. The empirical results indicated that ASF news sentiment has a substantial impact on meat prices in Korea, and there is evidence of substitution effects among different types of meat. ASF news has a positive impact on the price of pork, negative effects on beef and chicken prices, and a greater impact on the price of chicken than beef. The findings imply that the effect of ASF news on demand outweighs its impact on supply in the pork market, whereas the effect on supply surpasses the effect on demand in the beef and chicken market. We believe our methods and results will inspire discussions among applied economists studying consumer behavior in this specific market and could encourage the application of big data analysis to the agricultural economy. [ABSTRACT FROM AUTHOR]

Textual analysis and the Entropy-TOPSIS method are used in this research to create a measure of corporate environmental protection, and multiple regressions are used to find out how digitalization affects corporate environmental protection. The research sets up a theoretical framework for how corporate digitalization affects environmental protection and looks into how external financing constraints and an organization's own financial position play a role in the middle. The research then looks at how outside factors like the business environment of the market and the level of competition in the industry affect the relationship. Using a threshold regression approach, the research also examines the change in the impact of digitalization on environmental protection after investor sentiment crosses the threshold from the distinct perspective of investor sentiment. Our research provides theoretical support for environmental protection by corporations and government policy direction. [ABSTRACT FROM AUTHOR]

This study uses Non-linear Predictive Regression Analysis to analyze the effect of investor sentiment on the returns of the selected developing equity markets, including Brazil, South Africa, Indonesia, India, China, Russia, and Pakistan. The Principal Component Analysis is applied to construct an Investor Sentiment Index. In most selected countries, investor sentiment substantially affects contemporaneous market returns, and this effect remains persistent in the short term. However, it becomes less prominent over time. It suggests that stakeholders should give importance to the investors' sentiments while making investment decisions. [ABSTRACT FROM AUTHOR]

In this study, using AI, we empirically examine the irrational behaviour, specifically attention-driven trading and emotion-driven trading such as consensus trading, of retail investors in an emerging stock market. We used a neural network to assess the tone of messages on social media platforms and proposed a novel Hype indicator that integrates metrics of investor attention and sentiment. The sample of messages, which are written in Russian with slang expressions, was retrieved from a unique dataset of social network communication of investors in the Russian stock market. Applying different portfolio designs, we evaluated the effectiveness of the new Hype indicator against the factors of momentum, volatility, and trading volume. We found the possibility of building a profitable trading strategy based on the Hype indicator over a 6-month time horizon. Over short periods, the Hype indicator allows investors to earn more by buying stocks of large companies, and over «longer» periods, this indicator tends to perform better for illiquid stocks of small companies. As consensus trading tends to produce negative returns, the investment strategy of 'Go against the crowd' proves rewarding in the medium term of 3 months. [ABSTRACT FROM AUTHOR]

This paper studies the relation of information cost, retail investor sentiment and asset pricing. Our motivation to study this model is to learn why retail investors could move asset price away from fundamental values. In the model, the institutional investors are pessimistic and the retail investors are optimistic, the ratio of the expected utility of informed and rational but uninformed institutional investors increases first and then decreases as the cost of information increases. In addition, a large number of retail investors promoted substantial increases in stock prices. This model provides part of the explanation for the unusually high stock price of Game Stop in early 2021 that retail investors cliqued and confronted institutional investors. [ABSTRACT FROM AUTHOR]

This paper presents new updated evidence on the initial public offering (IPO) aftermarket performance for 144 public listed firms on the Colombo Stock Exchange from 1991 to 2017. We found that average aftermarket returns are always lower than 1%. On average, buy and hold abnormal returns are negative in a short period, and abnormal returns gradually become positive over a longer period (12.46% in 3 years). Further, aftermarket returns are positively related to investor sentiment and the annual volume of listings while being negatively related to initial returns, which is consistent with the divergence of opinion hypothesis. We suggest that investors should hold their subscriptions of IPO shares for a prolonged time, usually exceeding two years, as the dynamic of shares rewards the investors with positive abnormal returns in the long run. [ABSTRACT FROM AUTHOR]