Developing an early warning model for company financial crises holds critical significance in robust risk management and ensuring the enduring stability of the capital market. Although the existing research has achieved rich results, the disadvantages of insufficient text information mining and poor model performance still exist. To alleviate the problem of insufficient text information mining, we collect related financial and annual report data from 820 listed companies in mainland China from 2018 to 2023 by using sophisticated web crawlers and advanced text sentiment analysis technologies and using missing value interpolation, standardization, and data balancing to build multi-source datasets of companies. Ranking the feature importance of multi-source data promotes understanding the formation of financial crises for companies. In the meantime, a novel Attention-based Fuzzy Neural Network (AFNN) was proposed to parse multi-source data to forecast financial crises among listed companies. Experimental results indicate that AFNN exhibits significantly improved performance compared to other advanced methods.

Many corporate executives believe blockchain technology is broadly scalable and will achieve mainstream adoption, yet there is little evidence of significant shareholder value creation associated with corporate adoption of blockchain technology. We collect a broad sample of firms that invest in blockchain technology and examine the stock price reaction to the “first” public revelation of this news. Initial reac- tions average close to +13% and are followed by reversals over the next 3 months. However, we report a striking differ- ence based on the credibility of the investment. Blockchain investments that are at an advanced stage or are con- firmed in subsequent financial statements are associated with higher initial reactions and little or no reversal. The results suggest that credible corporate strategies involving blockchain technology are viewed favorably by investors.

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.

This study examines the influence of ESG report textual similarity on stock price synchronicity within the Chinese A-share market. Using advanced textual analysis methods, including TF-IDF and LDA, we measure the textual similarity of ESG reports among industry peers. Our results reveal a positive association between ESG report textual similarity and stock price synchronicity, suggesting that ESG reports with high textual resemblance may not convey distinct market information. This research underscores the importance of textual distinctiveness in ESG reports and offers a fresh perspective on the role of non-financial information, particularly related to CSR, in stock pricing dynamics. By emphasizing the significance of ESG report textual distinctiveness, we contribute to the broader discourse on ESG disclosure behaviors and their implications for capital market efficiency.

This study examines the influence of ESG report textual similarity on stock price synchronicity within the Chinese A-share market. Using advanced textual analysis methods, including TF-IDF and LDA, we measure the textual similarity of ESG reports among industry peers. Our results reveal a positive association between ESG report textual similarity and stock price synchronicity, suggesting that ESG reports with high textual resemblance may not convey distinct market information. This research underscores the importance of textual distinctiveness in ESG reports and offers a fresh perspective on the role of non-financial information, particularly related to CSR, in stock pricing dynamics. By emphasizing the significance of ESG report textual distinctiveness, we contribute to the broader discourse on ESG disclosure behaviors and their implications for capital market efficiency.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

Digital economy promotes the modernization of industrial structure by influencing the rationalization and upgrading of industrial structure through technical level and factor level; while excessive credit expansion hinders the modernization of industrial structure. This paper uses panel data from 31 jurisdictions in China to conduct empirical analysis, and finds that digital economy development shows a year-on-year rising trend, and there is a large gap between different regions. The conclusion still holds after the robustness test and regional heterogeneity analysis, thus enriching the understanding of mechanisms and regional differentiation of digital economy, credit expansion on industrial structure modernization.

A lack of sufficient information about potential borrowers is a major obstacle to access to financing from the traditional financial sector. In response to the need for better information to prevent fraud, to increase access to finance and to support balanced sustainable development, countries around the world have moved over the past several decades to develop credit information reporting requirements and systems to improve the coverage and quality of credit information. Until recently, such requirements mainly covered only banks. However, with the process of digital transformation in China and around the world, a range of new credit providers have emerged, in the context of financial technology (FinTech, TechFin and BigTech). Application of advanced data and analytics technologies provides major opportunities for both market participants – both traditional and otherwise – as well as for credit information agencies: by utilizing advanced technologies, participants and credit reporting agencies can collect massive amounts of information from various online and other activities (‘Big Data’), which contributes to the analysis of borrowing behavior and improves the accuracy of creditworthiness assessments, thereby enhancing availability of finance and supporting growth and development while also moderating prudential, behavioral and conduct related concerns at the heart of financial regulation. Reflecting international experience, China has over the past three decades developed a regulatory regime for credit information reporting and business. However, even in the context of traditional banking and credit, it has not come without problems. With the rapid growth and development of FinTech, TechFin and BigTech lenders, however, have come both real opportunities to leverage credit information and data but also real challenges around its regulation. For example, due to fragmented sources of borrower information and the involvement of many players of different types, there are difficulties in clarifying the business scope of credit reporting and also serious problems in relation to customer protection. Moreover, inadequate incentives for credit information and data sharing pose a challenge for regulators to promote competition and innovation in the credit market. Drawing upon the experiences of other jurisdictions, including the United States, United Kingdom, European Union, Singapore and Hong Kong, this paper argues that China should establish a sophisticated licensing regime and setout differentiated requirements for credit reporting agencies in line with the scope and nature of their business, thus addressing potential for regulatory arbitrage. Further, there is a need to formulate specific rules governing the provision of customer information to credit reporting agencies and the resolution of disputes arising from the accuracy and completeness of credit data. An effective information and data sharing scheme should be in place to help lenders make appropriate credit decisions and facilitate access to finance where necessary. The lessons from China’s experience in turn hold key lessons for other jurisdictions as they move from credit information to credit data regulation in their own financial systems.

Whilst there is increasing research attention on predicting financial distress, the existing literature is subject to two specific limitations. The first is that a firm can experience a financial distress event (e.g., loan default, bankruptcy) more than once, yet most studies that model corporate financial distress prediction treat financial distress as occurring only once. This approach leads to an inefficient use of data with all subsequent events being ignored and subsequently a decrease in statistical power. Second, to account for the lack of independence between observations of repeated event data, the extant research utilising hazard analysis either has a separate analysis for successive distressed events or relies upon robust standard errors. In addition to a much smaller sample, a separate analysis yields the models that can be used to predict the survival of a distressed firm rather than the survival of a firm generally. The method of robust standard errors, while innocuous to one-time event data, ignores the possible downward bias in coefficient estimates for repeated event data. To address these two limitations, we treat financial distress as repeated events and apply more advanced methods (generalised estimating equations, random effects, fixed effects, and a hybrid approach) to account for the lack of independence between observations in discrete time hazard analysis. These different approaches are applied to a sample of listed companies in China over the 2007‒2021 period. We find that variables that are not statistically significant in models based on one-time events data become statistically significant in the models based on repeated events data, and that coefficient estimates are larger in their magnitude with more advanced methods than with the method of robust standard errors. We also find that among the advanced methods, a hybrid approach achieves substantially better out-of-sample prediction, particularly over a long-term horizon than other approaches. Our results remain robust in tests of robustness.

Declining worker bargaining power has been advanced as an explanation for dramatic generational changes in the U.S. macroeconomic environment such as the substantial decline in labor’s share of the national income, the loss of consumer purchasing power, and growing income and wealth inequality. In this paper, we investigate microeconomic implications by examining the effect of declining worker power on firm-level investment responses to a labor cost shock (mandated increases in the minimum wage). Over the past four decades, we find that investment-wage sensitivities go from negative to insignificant as management becomes less constrained and can pursue outside options. Consistent with drivers of weakening worker power, investment-wage sensitivity changes are more significant for firms that are more exposed to globalization, technological change, and declining unionization.