Abstract:The report of the 20th National Congress of the Communist Party of China has clearly stated that it is necessary to improve modern financial supervision and strengthen the financial stability guarantee system. Despite the dramatic volatility of global capital markets in recent years and the increasing fragility of the financial system, there is still little literature that combines the new perspective of good and bad volatility to predict systemic risk. Most previous studies rely on lowfrequency financial data, such as daily, weekly, or monthly data, ignoring the rapid intraday evolution of financial volatility. In view of these facts, based on a systematic and comprehensive summary of existing good and bad volatility indicators, this paper uses intraday highfrequency yield data from China’s stock market to measure the good and bad volatility related indicators and investigates the potential relationship between good and bad volatility, stock returns, and systemic risk. Then, dominance analysis is used to investigate the contribution of different good and bad volatility indicators to China’s systemic risk and to further explore the good and bad volatility drivers of systemic risk in different industries. Finally, this paper further points out that if we do not distinguish the intrinsic nature of financial market volatility, it may lead to misjudgment of future systemic risk exposure. On this basis, this paper puts forward relevant policy suggestions to provide an important reference for China to take the initiative to prevent and resolve systemic risks and build a financial risk firewall.

Abstract:Considering the random factors involved in estimating modelfitting parameters, the classical linear regression model should be adjusted to improve its predictive performance. Therefore, this paper proposes an Lmultiplier model based on SignaltoNoise Ratio adjustment, which links the optimally fitted model to the best predictive model. The paper provides an explicit solution for the optimal Lmultiplier, and considering the presence of autocorrelation in the predictive variables, presents an improved expression for L. Subsequently, this paper discusses two estimation methods for the Lmultiplier and compares their respective advantages and disadvantages. Empirically, the theoretical validity of the Lmultiplier model has been confirmed by applying the modified Lmultiplier model to the problem of predicting stock returns. The results show that: 1)Compared to the baseline linear regression model, the model modified with the Lmultiplier has higher predictive accuracy; 2)When the noise is higher, the historical sample size is smaller, or the information content of the predictive variable is lower, the adjustment intensity of the Lmultiplier is greater, resulting in a more pronounced improvement in prediction; 3)For investors with meanvariance utility, this improvement in predictive performance can lead to an enhancement in investment returns. These findings also withstand a series of robust tests.

Abstract:Building upon the real options theory, this study investigates the valuation and real effects of specialized bankruptcy courts using a multiperiod differenceindifferences model, aiming to reveal a new channel through which insolvency regimes can allocate resources more efficiently. The research findings indicate that the establishment of bankruptcy courts significantly enhances the equity value of liquidationoption firms due to strengthened creditor protection. Mechanism tests reveal that bankruptcy courts notably expedite the execution of liquidation options, prompting underperforming firms to reduce their investment scale in a timely manner. Further tests reveal that bankruptcy courts also accelerate the expansion of investment scale for wellperforming firms through market competition effects, thereby boosting the equity value of their growth options. Our findings highlight that the bankruptcy system, by facilitating the timely exit of distressed firms, contributes to improving resource allocation efficiency.

Abstract:Since the revision of the Company Law in 2018, the regulatory authorities have strongly supported listed companies in repurchasing stocks, with the intention of making listed companies the backbone for maintaining stock market stability. However, some domestic media and investors believe that share repurchases will result in a transfer of wealth from bondholders to shareholders. To this end, the paper introduces the idea of asset value decomposition into the Merton model, demonstrates the mechanism by which share repurchases affect the interests of bondholders, and uses the data from Chinese listed companies to test the research propositions. The study found that after a listed company announced a share repurchase plan, bond prices, overall, showed a significant positive reaction. At the same time, default risk has been significantly reduced, and operating performance has also been remarkedly improved. It is further found that corporate bonds are more responsive to share repurchases than mediumterm notes and shortterm financing bonds, and the bond market reacts more positively to share buybacks by listed companies conducted for equity incentives and employee stock ownership plans. In addition, the paper finds that the positive signaling effect of share repurchase is not limited to the bond market but can also extend to commercial credit, banks, and other financial institutions, significantly improving the company’s access to commercial credit and reducing the overall financing cost. The paper not only enriches the relevant literature on share repurchase but also provides a policy basis for the supervision of share repurchases.

Abstract:The financial statement data of 703 listed companies in China’s Ashare manufacturing, wholesale and retail industries for the year 2006-2022 show that the average value of return on assets of listed companies is 3%, which is slightly higher than the bank riskfree rate (2%) for the same period. Using data from the balance sheets and income statements of listed companies, as well as the notes to the annual financial reports, this paper analyzes the impact of customer concentration and the bullwhip effect on a company’s return on assets from the perspective of operations management, based on theories of inventory management and resource dependence. It is found that customer concentration is positively correlated with return on assets and bullwhip effect is negatively correlated with return on assets. Moreover, the effect of customer concentration on return on assets operates through its impact on inventory turnover and the effect of bullwhip effect on return on assets also operates through inventory turnover. The managerial insights for firms’decision makers are that they should appropriately increase customer concentration or invest resources to mitigate the bullwhip effect to accelerate inventory turnover and improve return on assets. For companies’investors, by following companies’news to detect changes in customer concentration, the bullwhip effect, and inventory turnover, they can predict the trend of the companies’return on assets and improve their investment decisions.

Abstract:Under the global wave of intelligence, intelligent manufacturing has become the only way for China’s manufacturing industry to transition from “manufacturing” to “intelligent manufacturing”. Taking the promotion of China’s intelligent manufacturing demonstration project as a quasinatural experiment, this paper uses a PSMDID approach to identify the casual effect of intelligent manufacturing on corporate investment efficiency and to examine its underlying mechanisms. The results show that the implementation of intelligent manufacturing significantly improves corporate investment efficiency, and this finding remains robust after a series of robustness tests. Mechanism analysis reveals that smart manufacturing reshapes the internal and external information environments of firms, enabling a twoway empowerment of firms’investment efficiency by improving internal information acquisition and attracting external attention. In addition, heterogeneity analysis shows that the positive effect of intelligent manufacturing on corporate investment efficiency is more obvious in nonstateowned enterprises, enterprises with higher employee knowledge, enterprises with weaker industry competition degree, during nondeclining period, and in regions with lower levels of digital economy development and better legal environment. Further research shows that intelligent manufacturing inhibits the overinvestment behavior of enterprises but has no obvious effect on underinvestment. Moreover, intelligent manufacturing will also have a positive impact on the labor capital investment efficiency of enterprises. This paper takes corporate investment efficiency as the breakthrough point to evaluate the microlevel effects of intelligent manufacturing, providing a useful reference for enterprises to implement intelligent manufacturing and improve investment efficiency.

Abstract:The regulatory issue of whether listed companies should be required to publish quarterly financial statements has sparked debates in developed capital markets in recent years. This study utilizes the Chinese National Equities Exchange and Quotations’requirement that firms in the Innovative Tier should disclose quarterly reports since 2018 as a natural experiment to conduct regression discontinuity analysis. Our findings indicate that mandatory quarterly reporting enhances investment efficiency among firms in the Innovation Tier by improving information transparency, enabling minority shareholders to better fulfill their monitoring and governance roles. Further analysis reveals that the positive impact of mandatory quarterly reporting on investment efficiency is more pronounced in firms with higher R&D activities, whereas the effects are more modest in firms with higher audit quality, a more developed financial market, and a stronger legal environment. Additionally, mandatory quarterly reporting primarily curbs excessive investments in relatedparty M&A transactions by firms. This paper provides empirical evidence for considering convergence and differences in information disclosure rules between developed and emerging markets.

Abstract:The quantitative credit evaluation of enterprises is a cornerstone of establishing a more efficient market economy supervision system in China. However, the credit evaluation of medium, small, and micro enterprises has been challenging due to the lack of public business and financial data. Based on the observation that there are a large number of open and unstructured business registration data of medium, small, and micro enterprises on the Internet, this paper proposes to mine unconventional and unstructured data such as enterprise names, investment relationships between enterprises, and business scope to improve the credit evaluation results. Specifically, this paper proposes two data representation methods. The first uses a Gated Recurrent Neural Network to extract sequential information from the business registration text and transform the text into numerical data.The second uses a Graph Attention Network to encode the graph structure formed from the investment relationships into a numerical space. As a result, the heterogeneous information can be easily fused by merging the numerical vectors. Since the interpretability of credit evaluation models is crucial in financial applications, this paper further proposes interpretable solutions for the textminingbased credit evaluation model and for identifying the credit risk transmission path. The experimental results based on 68 504 enterprises revealed that both enterprise names and investment relationships contain credit information that cannot be identified in traditional numerical data. The results showed that business registration information can be used as a useful supplement to the current enterprise credit evaluation system, which is valuable in dealing with the scarcity of credit information for medium, small, and micro enterprises.

Abstract:Accelerating the application of carbon capture, utilization and storage (CCUS) technology in coalfired power plants is of great significance for promoting global green and lowcarbon development. However, given the high cost of the technology, it is urgent to explore the optimal choice of CCUS operation modes. This paper constructs a differential game model for CCUS operation of coalfired power plants. Considering the influence of government incentives and knowledge accumulation, this paper compares and analyzes the differences in cost control and carbon emission reduction benefits among four operation modes, integration, operator, joint venture and outsourcing and further verifies the findings with case studies. The study finds that the choice of business model is affected by government subsidies and carbon trading policies. Knowledge accumulation plays a key role in CCUS technology adoption and cost optimization, and the choice among four different modes is affected by the economy, policy and technical environment of the country or region. This study offers both theoretical and practical guidance for the investment decisionmaking of coalfired power generation enterprises and serves as a basis for the government to improve industrial policy.

Abstract:The continuous iteration and upgrading of information technology have given rise to a series of new business model, among which the C2C sharing model is particularly typical. Against this background, this paper explores the impact mechanisms of the sharing market across various sales model, where the platform assumes dual responsibilities for product sales and sharing transactions. The aim is to furnish online channel participants with theoretical insights into their decisionmaking processes. The results show that: 1) The rise of the sharing economy, On the one hand, enhances consumers’ motivation to purchase, generating a positive valueenhancing effect; on the other hand, it leads to a decrease in consumers’ price sensitivity, triggering a negative cannibalization effect. 2) When the C2C transaction cost is exogenous, if both the unit production cost of the product and the C2C transaction cost are low, the sharing economy will result in a loss of firms’ profits. Conversely, the sharing economy can benefit the firms. 3) When the C2C transaction cost is endogenously determined by the platform, the sharing economy will inevitably enhance firms’ profits under the wholesale model. Conversely, under the agency model, the sharing economy may negatively impact firms’ profits. 4) As the unit production cost increases, the optimal transaction cost decreases under the wholesale model. In contrast, under the agency model, the optimal transaction cost initially increases and then decreases with the rise in unit production cost. Furthermore, the agency model reshapes the impact mechanism of unit production cost on firms’ profits, potentially leading to higher profits for both the manufacturer and the platform as the unit production cost increases.

Abstract:With the increasingly diversified healthcare service workflows and coalition scenarios in China, healthcare data is undergoing rapid expansion while demonstrating increasingly prominent crossdomain characteristics. Crossdomain healthcare data governance has emerged as an essential approach for maximizing data value and enhancing risk management. However, in the multiparty healthcare coalition scenarios, organizations are limited by factors such as divergent interests and differences in technological capabilities, thus encountering significant challenges in establishing sustainable and stable cooperation in crossdomain healthcare data governance. This paper focuses on the management dynamics of multiparty cooperation in the Internet of Healthcare Systems (IHS) environment, systematically summarizing healthcare coalition scenarios. It investigates the evolutionary processes and influencing factors of tripartite cooperative strategies in crossdomain healthcare data governance, with simulation analysis conducted through a case study of the RuijinLuwan healthcare consortium. Building on this foundation, a multiscenario adaptable and dynamically iterative generalpurpose cooperation mechanism is proposed based on Action Design Research (ADR) theory, providing theoretical insights for achieving sustainable and stable cooperation in crossdomain healthcare data governance in the IHS environment.