This paper, based on data from Chinese A-share listed companies between 2009 and 2019, employs the implementation of the "Shanghai-Hong Kong Stock Connect" as a landmark event of capital market liberalization, utilizing a difference-in-differences model to empirically examine the impact of market openness on firms' cross-region investment behavior and its underlying mechanisms. The findings indicate that: (1) the launch of the "Shanghai-Hong Kong Stock Connect" has significantly promoted the establishment of cross-provincial and cross-border subsidiaries by the companies involved; (2) capital market liberalization influences firms' cross-region investment through three dimensions: finance, governance, and stakeholders. In terms of finance, the openness alleviated financing constraints and improved stock liquidity; in governance, it pressured companies to adopt more digitalized and transparent governance structures to accommodate cross-regional expansion; in the stakeholder dimension, it attracted the attention of external investors, accelerating their understanding of firms and alleviating the trust issues associated with cross-region expansion. (3) The effect of capital market liberalization on promoting cross-border investments by private enterprises is particularly pronounced, and this effect is further strengthened as the quality of corporate information disclosure improves. Firms with higher levels of product diversification benefit more from market liberalization, accelerating their overseas expansion. (4) Capital market liberalization has elevated the level of cross-region investment, thereby significantly fostering innovation and improving investment efficiency. The conclusions of this study provide fresh empirical evidence for understanding the microeconomic effects of China's capital market liberalization, the intrinsic mechanisms of corporate cross-region investments, and their economic consequences.

This paper investigates the high-frequency dynamics of macroeconomic expectations and disagreement among professional forecasters. We propose a novel mixed-frequency estimation approach that integrates daily asset returns with quarterly expectation data from the Survey of Professional Forecasters. Our findings indicate that consensus forecasts are updated efficiently according to Bayes' rule, independent of prior forecasts. By employing "representative forecasters" as proxies for real-world agents, we derive a simple yet intuitive evolution equation for disagreement, revealing that changes in disagreement are primarily driven by different interpretations of new information. Furthermore, we reconstruct daily series of expectations and disagreement concerning macroeconomic growth, achieving impressive R2 values of 93.3% and 84.5% against the true quarterly series.

Climate change and the escalation of extreme weather events jeopardize every corner of the globe. This paper investigates the impact of extreme high temperatures on the spread of newly issued municipal corporate bonds (MCBs) in China, which serves as a crucial instrument for local governments to meet the financial demands. We find that relative to the reference temperature range of 16 ◦C–20 ◦C, the issuing spread of MCBs increases by 2.48 basis points for each extra day where the mean temperature surpasses 32 ◦C. The findings highlight the risk-increasing effects of extreme temperatures in financial markets.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese public news articles to predict stock returns. Based on representativeness and influences, we consider seven LLMs: BERT, RoBERTa, FinBERT, Baichuan, ChatGLM, InternLM, and their ensemble model. We show that news tones and return forecasts extracted by LLMs from Chinese news significantly predict future returns. The value-weighted long-minus-short portfolios yield annualized returns between 35% and 67%, depending on the model. Building on the return predictive power of LLM signals, we further investigate its implications for information efficiency. The LLM signals contain firm fundamental information, and it takes two days for LLM signals to be incorporated into stock prices. The predictive power of the LLM signals is stronger for firms with more information frictions, more retail holdings and for more complex news. Interestingly, many investors trade in opposite directions of LLM signals upon news releases, and can benefit from the LLM signals. These findings suggest LLMs can be helpful in processing public news, and thus contribute to overall market efficiency.

This paper investigates the supervisory incentive effects of employee stock ownership plans based on a corporate risk-taking perspective using data from a sample of Chinese A-share listed companies from 2006-2021. The results show that employee stock ownership plans significantly enhance corporate risk-taking. The specific mechanism is that employee stock ownership plans reduce the two-tier agency costs between shareholders and managers and managers and employees, alleviate corporate financing constraints, and thus enhance the level of corporate risk-taking. It is also found that employee stock ownership plan enhances the level of corporate risktaking with high quality, because employee stock ownership plan not only promotes R&D investment which is beneficial to corporate value growth, but also reduces excessive investment and high debt which are detrimental to corporate value, and the corporate risk-taking is of higher quality and more substantial value effect. In addition, differences in the institutional design of employee stock ownership plans have different effects on corporate risk-taking: employee stock ownership plans that are leveraged, highly discounted, with longer lock-up periods and duration, and entrusted to third-party institutions have a stronger effect on corporate risk-taking; employee subscriptions can promote corporate risk-taking more than executive subscriptions; employee stock ownership plans in China do not have the problem of "free-riding There is no "free-rider" problem in China's employee stock ownership plan. The larger the issuance ratio of the employee stock ownership plan, the greater the number of participants, and the larger the scale of capital, the better the implementation effect.

Comprehensive evaluation and identification of the critical regulatory determinants of carbon emission efficiency (CEE) are very important for China’s low-carbon transition. Accordingly, this paper first employs an undesirable global super-hybrid measure approach to calculate the CEE of China’s iron and steel industry (ISI). We then further use spatial error and threshold regression models to examine the spatial and non-linear effects of heterogeneous environmental regulations on CEE, respectively. Our empirical results show that (1) CEE varies significantly across China’s regions, with the eastern region having the highest CEE score, followed by the western and central regions, with the northeast region ranking the lowest; (2) command-and-control and market-incentive regulations both promote CEE, whereas the public participation approach does not significantly contribute to performance gains; (3) all three types of environmental regulations exhibit a non-linear threshold effect on CEE; (4) openness level, technological progress, and industrial concentration enhance efficiency gains, while urbanization level exerts a negative impact on CEE. Our findings have important implications for the design of environmental regulations.

Digital transformation (DT) is an ongoing and costly process that requires careful planning and the motivation of top executives (CEOs). This research analyze the CEOs compensation as a motivation to embrace DT by reducing agency issue. We determine the extent of DT through a textual analysis method and utilize data from Chinese publicly traded companies spanning the period between 2007 and 2020. Our study findings are threefold, (a) we observe a positive relationship between CEOs' pay gap and DT, highlighting the significant role CEOs compensation plays in encouraging CEOs to adopt digitalization, (b) we find that managerial shareholding significantly enhances this relationship, (c) we note that the relationship between CEOs pay gap and DT is more pronounced in state-owned enterprises compared to non-stateowned enterprises. Additionally, we discover through channel analysis that agency cost and audit quality mediate the relationship between CEOs pay gap and DT potentially by reducing the agency problem between CEOs and shareholders. These findings are vital for comprehending the pay practices and behaviors of corporate executives regarding digitalization in China. Importantly, the study results remain robust when considering instrumental variables (IV), propensity score matching (PSM), and alternative techniques.

Purpose – This study aims to investigate whether firms purchase directors’ and officers’ liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high. Design/methodology/approach – This study uses D&O insurance data from Chinese listed firms between 2003 and 2019 to conduct regression analyses to examine the association between D&O insurance and EPU. Findings – The results show that government EPU, despite being an exogenous factor, increases the likelihood of firms’ purchasing D&O insurance, and this effect is more pronounced when firms are exposed to great share price crash risk and high litigation risk, suggesting that firms intend to purchase D&O insurance possibly due to the accentuated stock price crash risk and litigation risk associated with EPU. In addition, the results indicate that the effect of EPU on the D&O insurance purchase decision is moderated by the provincial capital market development and internal control quality. Practical implications – The study highlights the role of uncertain economic policies in shareholder approval of D&O insurance purchases. Originality/value – The study enriches the literature on the determinants of D&O insurance purchases by documenting novel evidence that country-level EPU is a key institutional factor shaping firms’ decisions to purchase D&O insurance.

Software and accounting advances have led to a rapid expansion in and proliferation of loyalty tokens, typically bundled as part of product price. Some tokens, such as in the airline industry, already account for tens of billions of dollars and are a major contributor to revenues. An open question is whether, as technology evolves, firms will have a strong incentive to make loyalty tokens tradable, raising regulation issues, including with monetary and banking authorities. This paper argues that for the vast majority of tokens, issuing firms have a strong incentive to make them non-tradable. The core incentive for token issuance here is that an issuer can earn a higher rate of return on the ``float'' (tokens issued but not yet used) than its retail customers can, much like a bank. Our main finding is that an issuer earns higher revenue by making tokens non-tradable even though the consumer would be willing to pay a higher price for tradable tokens. We further show that an issuer with stronger market power tends to allow more frequent token redemption, and its revenue is more token-dependent. We test the model's predictions with data on airline mileage and hotel reward programs and document consistent empirical results that align with our theory.

In the digital era, the information value of online prices, characterized by weak price stickiness and high sensitivity to economic shocks, deserves more attention. This paper integrates the high-frequency online inflation rate into the dynamic Nelson-Siegel (DNS) model to explore its relationship with the term structure of interest rates. The empirical results show that the weekly online inflation can significantly predict the yield curve, particularly the slope factor, while the monthly official inflation is predicted by yield curve factors. The mechanism analyses indicate that, due to low price stickiness, online inflation is more responsive to short-term economic conditions and better reflects money market liquidity, thereby having predictive power for the yield curve. Specifically, online inflation for non-durable goods and on weekdays shows stronger predictive power for the slope factor. The heterogeneity in price stickiness across these categories explains the varying impacts on the yield curve.