This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.

Introduction: Amid China’s pursuit of its “dual carbon” objectives and the escalating emphasis on Environmental, Social, and Governance (ESG) disclosure, corporate environmental responsibility has emerged as a critical regulatory and market concern. However, mounting institutional and stakeholder pressures have incentivized some firms to engage in greenwashing—strategically overstating or misrepresenting their environmental commitments—to sustain legitimacy and competitive positioning.Methods: This study empirically investigates the impact of greenwashing on corporate financial performance using panel data from 157 publicly listed Chinese public health firms between 2020 and 2022. A mediation model is utilized to identify the mechanism role of organizational legitimacy in affecting the relationship between greenwashing and corporate financial performance.Results: The findings reveal that greenwashing significantly undermines organizational legitimacy, which, in turn, leads to negative financial repercussions. Firms that overstate their environmental commitments experience diminished stakeholder trust, regulatory scrutiny, and reputational damage, ultimately eroding their financial performance.Discussion: By analyzing the indirect pathways through which greenwashing influences firm performance, this research advances theoretical discourse on corporate environmental accountability and legitimacy-based performance dynamics. Furthermore, the study offers actionable insights for public health enterprises, emphasizing the necessity of transparent and verifiable environmental communication to mitigate reputational risks and ensure sustainable financial outcomes. Additionally, it provides empirical evidence to inform policymakers in refining regulatory frameworks that enhance transparency in environmental reporting and foster substantive sustainability practices.

The present study aims to investigate the dynamic information spillover relationship between Chinese carbon and stock markets, as well as the impact of extreme weather shocks exerted on this relationship. The method adopted is the least absolute shrinkage and selection operator–vector autoregressive–Diebold-Yilmaz spillover approach so that the degree and direction of risk spillovers among markets can be assessed simultaneously. Empirical results reveal that there is a high level of extreme risk spillover among markets in comparison to return spillover. The carbon market receives return spillover from high-polluting sectors, but it will turn into a risk transmitter under extreme risk conditions. Weather shocks significantly affect extreme risk spillover among markets and may lead to spillovers from the carbon market to low-polluting sectors. The portfolio strategy constructed based on the identified information spillover relationship is shown to achieve higher average returns than strategies focusing on a single carbon or stock market sector. This paper is among the first to integrate carbon markets and 38 stock sector indices for different pollution intensities, comprehensively exploring their dynamic interrelationships under extreme weather threats. The corresponding practical and policy implications for investors and regulators are also provided along with these findings.