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.

When examining corporate governance in China, it is crucial to recognize the unique socioeconomicstructures and legal systems at play. The mechanisms of corporate governance theorized in the West might not necessarily have the same impact in China. In particular, given China’s distinct feature of the domestic economy and its socio-political structure, the results of introducing a hostile takeover system might not align with common anticipations that scholars and policymakers in China and elsewhere broadly share. In greater detail, this paper highlights the significant market imperfections in the Chinese economy, stemming from information asymmetry, imperfect product markets, and capital-market inefficiency. These market imperfections suggest that an active hostile takeover regime might not function effectively in China, as its disciplinary mechanism operates successfully in other advanced countries. Additionally, this paper underscores that due to China’s distinctive features—including its state-owned corporate landscape, the dominance of controlling shareholders in private corporations’ ownership structures, and its unique brand of socialism—the introduction of an active takeover regime could produce unintended consequences in the Chinese economy. Overall, challenging the prevailing perspective, I posit that within the Chinese hostile takeover framework, corporate governance is not as influential as one might assume.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

This paper explores the ethical dimensions of firm-level digitalisation and its impact on ESG metrics during a decade (2010-2020) of rapid technological progress, focusing on Chinese-listed companies. Utilising a text-based index to measure digitalisation, we find that while digitalisation positively influences ESG ratings, supporting resource-based and dynamic capability theories, its relationship with corruption reveals complex dynamics. Surprisingly, corruption strengthens digitalisation’s positive impact on ESG, raising concerns about technology being used to enhance ESG appearances artificially. A distinct difference emerges between state-owned enterprises (SOEs) and non-SOEs; SOEs use digitalisation more ethically and are less influenced by corruption, indicating a more responsible approach to technology adoption. Through examining cash holdings, internal controls, and audit fees, we unpack how corruption influences the digitalisation-ESG nexus. These insights underscore the need for policy that encourages ethical digitalisation and highlight the potential role of SOEs in leading the charge towards sustainable and ethical digitalisation.

The influence of informal institutions such as history and culture on corporate behavior has been widely recognized, but few studies have been analyzed from the perspective of the ruling party culture. Based on the data of the old revolutionary base areas (ORBA) in China, this paper makes an empirical test on the role of Red Culture in promoting enterprises green innovation. First, this paper finds that the stronger the Red Culture in the region where the enterprise is located, the higher the level of green innovation.Secondly, in the samples with high political sensitivity and less cultural conflict, the promoting effect of Red Culture is more obvious. This paper not only expands the relevant literature on the influence of informal system on enterprise green innovation, but also enriches the research on the influence of Chinese unique culture on enterprise management decision-making.

We investigate the impact of severe snowfall induced operational disruptions on insider trading. Applying geospatial analytics to an extensive dataset of snow cover, we conduct granular analyses of snowstorms across firms at establishment level. When analyzing a sample of firms that operate in snowfall-impacted areas, we find that corporate insiders significantly adjust their trading behavior during these events. These insiders not only predict lower future returns but also increase the size of their sales in response to snowfall crises. Further, we explore the salience and operational insights channels through which snowfall triggers informed insider sales. Our findings show that insiders residing in impacted regions, as well as senior insiders with unique operational insights, effectively avoid losses during these periods. The snow intensity test reveals that these phenomena are more pronounced for snowstorms of greater severity. We also provide direct evidence that establishments under severe snow strikes experience lower total sales volumes. Our study highlights the capacity of insiders to anticipate and respond to weather-related business risks.

Since the global financial crisis of 2007–2009, preventing financial crises has become one of the most important objectives of regulators and banks. Although previous studies have identified the phenomenon of risk contagion in the banking system, the underlying mechanisms of risk contagion are still unclear. This study delves into the multi-stage contagion mechanism of liquidity risk based on interbank lending linkages and clearing rules and introduces a new index to quantify bank liquidity risk. We find that the contagion of liquidity risk is primarily determined by the network structure of risk exposures between banks in default and is not significantly influenced by the lending relationships of banks that remain solvent. The empirical results suggest that banks with high risk should be prioritized for cash injections to improve system liquidity. These findings offer new insights into financial risk contagion and practical recommendations for regulatory authorities formulating intervention strategies and for banks conducting risk management.

This paper provides evidence on the cost of political interference on banks with Chinese Private Enterprise Survey data between 2002 and 2012. Using regional political turnovers as a proxy for political influence, we show that political motivations for future promotions distort the bank lending decisions and crowd out lending to private firms. Besides, firms with business connections are more sensitive to turnover, while political connections are not significantly affected. These lending distortions are more considerable where competition for future promotion is more intense and where incumbents have more influence over banks. Moreover, the effect is especially pronounced for small firms. As a result of reduced bank credit, firms’ total credit availability decreases and they have to cut investments. Overall, our results suggest that preferential lending to politically important sectors has negative spillovers and can lead to costly crowding-out of private sectors.