Under China’s transition toward high-quality financial development, this study investigates the tail dependence among media sentiment, investor attention, and stock market returns using the M-Clayton Copula. Comparative model fitting tests demonstrate that the M-Clayton Copula outperforms both single Copula models and M-Copula models in characterizing asymmetric negative dependence structures. It is particularly effective in capturing both upper-lower and lower-upper tail dependencies, thereby providing a more comprehensive analysis of their interdependencies. The empirical results reveal three key findings. First, a significant positive tail dependence exists between media sentiment and stock market returns, suggesting that they tend to move together under extreme conditions, with notable asymmetry in the strength of co-movements. Second, media sentiment and investor attention exhibit negative tail dependence, with the lower-upper tail dependence coefficient exceeding its upper-lower counterpart, suggesting higher probability of rising investor attention following media sentiment decline than vice versa. Third, a pronounced negative tail dependence emerges between stock market returns and investor attention, particularly showing strong lower-upper tail correlation, implying substantial likelihood of increased investor attention subsequent to market downturns.

The growing emphasis on Environmental, Social, and Governance (ESG) principles, particularly in corporate sectors, shapes investment trends and operational strategies, whose shift is supported by the increasing role of media in monitoring and influencing corporate ESG performance, thereby driving the energy innovation. Therefore, based on reported events from Baidu News and patent text information of Chinese A-share listed companies from 2012 to 2022, this study innovatively applied machine learning and text analysis to measure ESG news sentiment and corporate energy innovation indicators. Combing with reputation, stakeholder, and agency theories, we find that a good reputation conveyed by positive ESG textual sentiments in the media significantly promotes corporate energy innovation, and the effect is mainly realized through alleviating financing constraints and agency problems and promoting green investment. Further analysis shows that ESG news sentiment promotes corporate energy innovation mainly among private firms, non-growth-stage firms, high-energy-consuming firms, and regions with better green finance development and higher ESG governance intensity. From the perspective of ESG news content and information content, greater ESG news attention can also exert an energy innovation incentive effect, in which the incentive effect exerted by positive media sentiment in the environmental (E) and social (S) dimensions, as well as excellent attention, is more robust. This study provides new insights for promoting green and low-carbon development and understanding the external governance role of media in corporate ESG development.

In this study, we investigate the relationship between news media sentiment and management earnings forecasts. Using Ashare listed companies in China from 2007 to 2022, we find a negative relationship between media sentiment and the propensity of firms to issue management earnings forecasts. We also find that media sentiment is associated with the precision and accuracy of these forecasts. Overall, our study offers new insights into the underlying motivations and the quality of management earnings forecasts.

This paper selects 346 green bonds issued in China from 2016 to 2021 as the sample, and the Propensity Score Matching (PSM) method is employed to confirm the existence of ‘green premium’ in the Chinese bond market. On this basis, data on internet media sentiment and print media sentiment are collected from ‘Sina Weibo’ and ‘China Important Newspaper Full Text Database’ by both Web Crawler Technology and Textual Analysis Methods to explore the impact and the mechanism of heterogeneous media sentiments on the ‘green premium’. The results show that both the optimism of internet media and print media can significantly promote the ‘green premium’ of green bonds, and the influence of print media sentiment on the ‘green premium’ is greater than that of internet media sentiment. In addition, the Bootstrap method verifies the mediating effect of print media sentiment in the influence of internet media sentiment on ‘green premium’, indicating that print media sentiment is an important transmission path. Moreover, the results of the heterogeneity test show that the more optimistic the media is, the more significant the ‘green premium’ effect is in the regions with higher institutional environments and financial subsidy policies. The ‘green premium’ of green bonds is most pronounced for higher levels of institutional environment and green bond preferential policies.

A deep learning method is applied to predict stock portfolio allocation in the Chinese stock market. We use 6 original price and volume series as benchmark model settings and further explore the model's predictive performance with social media sentiment. Our results show that our model can achieve a high out-of-sample Sharp ratio and annual return. Moreover, social media sentiment could increase the performance for both Sharp ratio and annual return while reducing annual volatility. We provide an end-to-end stock portfolio allocation model based on deep neural networks.