The scale of excess reserves held by the banking system is a core variable for understanding the central bank's monetary policy transmission mechanism, market interest rate formation, and the stability of the financial system. Particularly in the context of China's monetary policy framework transitioning towards price-based regulation and the increasing complexity of the banking system's liquidity environment, an in-depth analysis of the micro-level drivers of excess reserves holds significant theoretical value and practical relevance. However, existing research predominantly focuses on the experiences of developed economies, leaving the reserve decision-making mechanisms within China's unique institutional context underexplored.
This paper aims to systematically investigate the driving mechanisms and heterogeneous patterns of excess reserve holdings among Chinese commercial banks, following a framework of “theory construction-empirical identification-mechanism deepening”. Theoretically, this paper draws on the concept of convenience yield, innovatively introducing the notion of “convenience yield of excess reserves” into the bank liquidity management framework. It derives a log-linear reserve demand equation that incorporates interest rate spreads, deposit scale and structure, and bond holdings, thus laying a micro-founded theoretical basis for the empirical analysis.
Empirically, the paper primarily employs panel fixed-effects models based on semi-annual panel data of 42 A-share listed Chinese commercial banks from 2010 to 2024 (data sourced from CEIC and Wind databases). To precisely identify the complex effects of monetary policy, the model introduces a central bank announcement dummy variable and its interaction term with the interest rate spread, aiming to disentangle “price shocks” from “information shocks”. Simultaneously, to capture the intertemporal effects of banks' asset allocation and mitigate endogeneity, lagged two-period bond holdings are used. Furthermore, this paper constructs a interbank bond market liquidity model and complements the analysis with subgroup and subperiod regressions to fully explore heterogeneous mechanisms.
The empirical investigation yields four core findings:
First, banks' precautionary motives are primarily driven by the instability of their liability structure. Demand deposits are the dominant driver of excess reserve holdings (coefficient approx. 0.55～0.60), while the impact of time deposits is insignificant, confirming that reserve decisions are made to hedge the liquidity risk from more volatile liabilities.
Second, banks' asset allocation strategies exhibit significant intertemporal effects and persistence. Lagged one-year (two-period) bond holdings are significantly negatively correlated with current excess reserves, revealing the “stickiness” of banks' yield-seeking behavior rather than a simple risk-hedging motive.
Third, central bank announcements, via the “information shock” channel, significantly alter banks' responses to interest rate signals, but the effect is highly “state-dependent”. During non-announcement periods, rising interest rates lead banks to increase reserves (precautionary motive dominates). However, during announcement windows, this relationship systematically reverses, with banks tending to release reserves in pursuit of higher yields. Yet, this communication effectiveness is not unconditional: the “information effect” of announcements is most significant during policy tightening phases, while during early monetary easing periods and high-uncertainty periods, the role of central bank communication is significantly weakened.
Fourth, liquidity management paradigms differ fundamentally across bank sizes. Small and medium-sized banks' behavior aligns with the classic “risk-cost” trade-off model, with their reserve decisions responding to deposits, asset allocation, and interest rate signals. In contrast, large banks, as systemic payment nodes, have reserve holdings primarily driven by functional demand, exhibiting significant “passivation” (insensitivity) to short-term interest rate signals representing opportunity costs.
These findings carry important implications for optimizing the monetary policy framework and liquidity regulation system. First, policy operations must shift from an aggregate-focused to a structure-sensitive perspective, fully considering the heterogeneity of the banking system, particularly the “structural rigidity” of large banks' reserve demand, while optimizing liquidity support frameworks for smaller banks. Second, the effectiveness of forward guidance should be enhanced by establishing a “state-dependent” communication strategy. Policymakers should flexibly adjust communication content and methods based on real-time market liquidity status and policy stances to maximize expectation-stabilizing effects. Third, the liquidity regulation framework should reflect bank heterogeneity and intertemporal risks. This requires moving beyond static, point-in-time assessments to establish intertemporal macroprudential monitoring of banks' balance sheet strategies, while considering differentiated weights reflecting banks' systemic importance and payment-clearing roles.

Interest rate risk (IRR) in commercial banks is widely recognized as a potential threat to financial stability. It can cause operating losses, erode banks' solvency, and weaken monetary policy transmission (Gomez et al., 2021). The Global Financial Stability Report notes that over one-quarter of countries and regions exhibit deficiencies in measuring and managing banks' interest rate risk. The failure of Silicon Valley Bank (SVB) underscored these concerns. According to the Federal Reserve's postmortem analysis, SVB altered key assumptions in its risk models, leading to an underestimation of interest rate risk and an inappropriate reduction of its derivative positions. This episode highlights two core elements of sound risk management in commercial banks: reliable risk metrics and prudent hedging strategies.
Accurate measurement forms the foundation of prudent IRR management. Repricing gap is the difference between interest-rate-sensitive assets and liabilities that are repriced within one year. It quantifies the sensitivity of a bank's net interest income to changes in market rates. Repricing gap is a commonly used, income-based indicator of IRR. Theoretically, the product of the one-year repricing gap and market rate changes predicts variations in net interest income.In practice, however, when the interest rates applied at the repricingof bank assets or liabilities do not adjust in line with movements in marketinterest rates, the repricing gap cannot accurately capture the response of netinterest income to market rate fluctuations. Thus, the effectiveness of this measure depends on how market-oriented the pricing of bank assets and liabilities is.
With China's ongoing interest rate liberalization reform, both the formation of market rates and banks' pricing behavior have undergone major transformations. Pricing benchmarks for assets and liabilities have shifted from administratively regulated rates to market-determined ones. Historically, benchmark deposit and lending rates served as key monetary policy instruments. As interest rate liberalization reform deepened, the floating ranges around these benchmarks widened. The ceiling on lending rates and floor on deposit rates were removed in 2004, and controls on lending (deposit) rates were fully lifted in 2013 (2015). During this transition, the People's Bank of China (PBOC) moved from direct rate controls to a market-based framework, using monetary policy tools to guide market rates indirectly. Since October 2015, benchmark deposit and lending rates have remained unchanged and effectively phased out.
Currently, deposit rates are largely set by financial institutions, subject to industry self-discipline mechanisms that cap rates to ensure fair competition. Lending rates are independently determined and anchored to the Loan Prime Rate (LPR), now the principal benchmark for loan pricing. These shifts in pricing mechanisms of banks have increased the sensitivity of their interest income and expenses to market rate fluctuations. Theoretically, this should enhance the repricing gap's effectiveness as a measure of IRR. Hence, examining how the repricing gap performs across different stages of the reform helps test its theoretical validity and provides empirical evidence to improve risk models and hedging strategies for financial institutions.
China is currently in the deepening stage of interest rate liberalization reform, yet empirical evidence on the effectiveness of the repricing gap and the hedging performance of interest rate derivatives remains limited. Using annual reports of China's commercial banks from 2010 to 2020, this study conducts a comprehensive empirical analysis and yields three main findings. First, the average repricing gap during the sample period is negative and trending downward, indicating that liabilities reprice faster than assets. This asymmetry helps ease the pressure of narrowing net interest margins when market rates decline. Second, the link between the repricing gap and the sensitivity of net interest margins to market rates becomes significant only after 2015, when benchmark rates ceased to be adjusted. This suggests that the gap's effectiveness in capturing interest rate risk has strengthened with the reform. Third, banks' usage of interest rate derivatives reduces the sensitivity of net interest margins to market rate fluctuations, confirming their value in managing interest rate risk. These findings offer empirical insights for optimizing banks' risk management frameworks and establishing a long-term mechanism for financial stability.
The paper's marginal contributions are threefold. First, it extends the literature on identifying and measuring IRR in commercial banks. Focusing on the core issue of indicator effectiveness, it goes beyond prior domestic studies that mainly compare measurement methods qualitatively by providing empirical evidence on their sensitivity, precision, and applicability. Second, within the context of China's interest rate liberalization reform, the paper demonstrates empirically that the effectiveness of the repricing gap depends on the mechanism of interest rate formation. From a micro-level perspective, it explains how the marketization of rates improves measurement accuracy, enriching empirical research on IRR. Third, conditional on effective risk measurement, it evaluates the actual hedging performance of interest rate derivatives, verifying their role in mitigating banks' exposure to interest rate fluctuations and contributing to the literature on derivative-based risk management.

In recent years, the United States has continuously escalated and intensified its sanctions against China, exerting substantial negative impacts on the regular operation, strategic decision-making, and sustainable development of Chinese enterprises. These sanctions, ranging from export restrictions and investment prohibitions to technology bans, have created a complex and uncertain external environment for Chinese firms. In this context, an important yet underexplored question arises: to what extent have Chinese firms been affected by US sanctions, and through which channels can they mitigate such adverse impacts？ Addressing these questions is essential not only for understanding the micro-level financial consequences of international sanctions but also for evaluating the effectiveness of domestic policy interventions aimed at stabilizing corporate financing and maintaining economic resilience.
This study systematically identifies Chinese A-share listed firms exposed to US sanctions between 2003 and 2021, based on sanction announcements issued by various departments of the US federal government—such as the Department of Commerce, the Department of the Treasury, and the Department of State—and matches these with firm-level financial and ownership data. By constructing a comprehensive and matched firm-level dataset that integrates sanction information with corporate financial characteristics, this paper empirically investigates the causal impact of US sanctions on firms' debt-financing costs and explores the potential mitigating effects of government policy responses. The empirical design relies on a difference-in-differences framework, enabling robust identification of the sanction shock effects while additionally controlling for firm fixed effects and industry-specific trends. The empirical results reveal that exposure to US sanctions significantly increases corporate debt financing costs. This implies that sanctions tighten credit conditions and heighten risk perceptions among investors and financial institutions, thereby increasing firms' financing pressure. Meanwhile, the study also finds that proactive government responses—such as fiscal subsidies, industrial support programs, tax relief measures, and preferential loan policies—can effectively alleviate the surge in financing costs induced by sanctions. These results highlight the critical role of domestic policy interventions in cushioning external shocks and stabilizing firms' financing environments under conditions of international geopolitical uncertainty. Further heterogeneity analyses show that the magnitude of the sanction effect on debt financing costs varies significantly across firms depending on several key characteristics. Specifically, firms facing high-intensity sanctions or maintaining close ownership ties with sanctioned entities experience more pronounced increases in financing costs. Conversely, enterprises with higher audit quality or stronger product competitiveness are better positioned to resist the negative effects of sanctions. These firms tend to signal greater information transparency and financial reliability to creditors, thereby reducing information asymmetry and enhancing market confidence. Mechanism analyses further demonstrate that US sanctions primarily increase debt financing costs through the deterioration of market perception rather than direct. Sanctions act as a negative external signal that amplifies information asymmetry and undermines investors' and lenders' confidence in the sanctioned firms' creditworthiness. This erosion of trust elevates perceived risk, leading to higher required risk premiums and tighter financing constraints. Hence, the transmission mechanism operates not only through trade and investment restrictions but also through the reputational and informational channels embedded in capital markets.
Overall, this study makes several contributions to the existing literature. First, it provides systematic empirical evidence on how international economic sanctions affect firm-level debt financing costs in the context of an emerging economy, filling an important research gap at the intersection of international political economy and corporate finance. Second, it identifies the crucial role of domestic policy countermeasures in mitigating the financial consequences of sanctions, thereby offering policy insights for enhancing economic resilience and maintaining financial stability. Third, by uncovering the role of market perception as a key transmission mechanism, the paper enriches the understanding of how geopolitical shocks are internalized within domestic capital markets. Taken together, these findings shed new light on the dynamic interactions between external shocks, corporate financing conditions, and government intervention, offering valuable implications for policymakers, regulators, and corporate managers seeking to navigate the complex landscape of global economic sanctions.

The disorderly expansion of shadow banking poses a potential threat to economic and financial stability. Consequently, mitigating financial risks and promoting financial deleveraging have become key objectives of the New Asset Management Regulations. Using this reform as a quasi-natural experiment, this paper employs a difference-in-differences model to investigate whether stringent financial regulation can drive the dynamic adjustment of corporate capital structures and thereby facilitate structural deleveraging. The findings show that, following the implementation of the regulations, non-financial firms with higher involvement in shadow banking activities exhibit faster dynamic adjustments in their capital structures—that is, their actual capital structure converges more quickly toward the target structure. Mechanism analysis further reveals that the regulations foster this adjustment by alleviating financing constraints and improving the efficiency of financial resource allocation. Distinguishing between the directions of the dynamic adjustment, the study identifies an asymmetric effect: over-leveraged firms significantly accelerate downward adjustments, while under-leveraged firms do not exhibit significant upward adjustments. This suggests that the regulations promote structural deleveraging at the micro firm level, specifically by driving over-leveraged firms to lower their leverage. Additional evidence indicates that, prior to the implementation of the regulations, firms' participation in asset management activities was primarily motivated by profit-seeking behavior and the desire to maintain bank-firm relationships.
The reform of the New Asset Management Regulations provides compelling empirical evidence for strengthening the financial regulatory framework and enhancing the allocation efficiency of financial resources. From the unique perspective of dynamic capital structure optimization, this study sheds light on how regulatory gaps distort corporate investment and financing behavior, as well as the structural implications of stringent financial regulation at the micro level. The conclusions of this paper yield important policy implications.
First, policymakers must fully recognize the incentive mechanisms underlying non-financial firms' engagement in shadow banking and their potential economic consequences. The analysis shows that, before the regulations, firms' participation in shadow banking was not precautionary but rather driven by profit-seeking motives and the maintenance of bank-firm relationships. Regulatory loopholes created opportunities for low-cost, lightly constrained, and seemingly risk-free arbitrage, thereby inducing a strong tendency toward shadow banking at the firm level. At the micro level, this inclination distorted firms' resource allocation decisions in two major ways.
One way is resource misallocation and deviation from core business activities. Arbitrage opportunities encouraged firms to channel substantial resources into highly leveraged and structurally complex financial products rather than productive activities in the real economy. This weakened their capacity for core business development and eroded long-term competitiveness. To mitigate such risks, policies should encourage firms to expand investment in the real economy, particularly in technological innovation and productive activities. Targeted instruments such as tax incentives and financial support can help guide corporate funds toward long-term development and core operations, rather than short-term speculative arbitrage.
The other way is idle capital circulation and risk accumulation. When firms, acting as “capital suppliers,” became deeply embedded in the shadow banking system, their capital operations increasingly took on short-term and complex forms, creating multilayered financial chains. As a result, funds circulated repeatedly within the financial system rather than flowing into the real economy. This reduced the efficiency of financial resource allocation, raised financing costs for the real sector, and—more importantly—heightened systemic fragility and amplified potential systemic risks. To address these challenges, policies should strengthen information disclosure requirements for both firms and financial institutions, enhance the transparency of financial products, curb idle capital circulation, and ensure that funds are directed toward the real economy, thereby fostering a virtuous cycle between financial development and economic growth.
Second, regulators should continue to advance financial regulatory reforms by establishing a unified supervisory framework for all types of financial institutions and instruments, thereby effectively closing institutional gaps and addressing supervisory blind spots. The empirical results of this study demonstrate that the New Asset Management Regulations, as a representative example of stringent financial regulation, have been highly effective in curbing the expansion of shadow banking—particularly in prompting firms heavily involved in such activities to accelerate capital structure adjustments. This experience illustrates that closing regulatory loopholes, narrowing arbitrage opportunities, and strengthening rule-based constraints can safeguard financial stability at the macro level while simultaneously promoting structural deleveraging and dynamic capital structure optimization at the micro level. From a broader institutional perspective, the New Asset Management Regulations not only provide a regulatory model for managing shadow banking risks and advancing the standardized operation of financial markets but also offer a practical case for China's transition from “institution-based” to “function-based” and “behavior-based” financial regulation.

In the digital economy era, the deep penetration of data elements and rapid iteration of digital technologies have created unprecedented opportunities for corporate innovation, while simultaneously rendering cybersecurity risks a critical constraint on innovation. However, the academic literature has not reached a consensus on the impact of cybersecurity risks on firm innovation. From a risk-hedging perspective, Lattanzio and Ma (2023) argue that cybersecurity risks diminish the value of trade secrets and increase confidentiality costs, prompting firms to file more patents to protect intellectual property as a means of reducing reliance on trade secrets and hedging against data breach risks. Gomes et al. (2023) posit from a technological progress perspective that cybersecurity risks compel firms to prioritize internal cybersecurity defenses and increase research and development (R&D) expenditure in digital technologies. Conversely, He et al. (2020) find that firms experiencing cyberattacks tend to increase cash holdings as a precautionary measure against cybersecurity threats, resulting in reduced R&D investment. Wang et al. (2024) contend from an innovation cost perspective that cybersecurity risk prevention requires substantial capital investment to secure software, hardware, and network operations, thereby crowding out R&D funding.
Evidently, existing research provides insufficient guidance for corporate cybersecurity governance decisions. Regrettably, no studies have examined how cybersecurity risk governance affects firm innovation from a risk governance perspective. This paper argues that a primary source of the gap between theory and practice, and a key research challenge, lies in the inability of conventional text analysis methods to accurately capture corporate cybersecurity risk governance behavior. Listed firms typically disclose cybersecurity-related information in annual reports to signal their awareness of cybersecurity risks and corresponding governance measures to the market (Florackis et al., 2023). Consequently, scholars predominantly employ dictionary-based approaches, measuring cybersecurity risk exposure by the frequency of cybersecurity-related keywords in annual reports. However, keyword counts alone cannot determine whether firms have undertaken cybersecurity risk governance activities and may even lead to semantic misinterpretation. For instance, one listed firm's annual report states: “Although the overall security level of global cyberspace is improving, the global cybersecurity threat landscape remains severe, with data breaches, cyberattacks, critical vulnerabilities, and other cybersecurity incidents occurring frequently, seriously endangering internet users, corporate institutions, and even national security.” While this passage contains numerous cybersecurity-related terms such as “data breaches” and “cyberattacks,” semantic analysis reveals that the firm merely provides an objective description of global cybersecurity trends without addressing its own risk perception or governance actions.
To address this gap, this paper examines Chinese A-share listed companies from 2007 to 2021, employing large language models (LLMs) to construct a quantitative indicator of corporate cybersecurity risk governance and systematically investigate its impact on firm innovation. The findings reveal that cybersecurity risk governance significantly enhances innovation output, particularly promoting patent applications in cybersecurity technologies. Mechanism analysis demonstrates that cybersecurity risk governance influences innovation through three primary channels: First, improving data utilization efficiency, enabling firms to efficiently transform data assets into innovation outcomes while ensuring data security; Second, stabilizing collaborative innovation relationships by enhancing trust and cooperation with universities, research institutions, and business partners; Third, alleviating financing constraints by strengthening external investors' and creditors' confidence. Heterogeneity analysis indicates that this positive effect is more pronounced among firms operating in more complex digital environments, exhibiting higher technological dependence, and maintaining greater internationalization levels. Furthermore, from a supply chain perspective, this paper uncovers significant asymmetric spillover effects of corporate cybersecurity risk governance: upstream suppliers are incentivized to increase cybersecurity innovation investments, whereas downstream customers reduce their own innovation efforts by relying on core firms' security protections. This reveals the complex transmission mechanisms of cybersecurity governance in digital supply chains.
The main contributions of this paper are threefold. First, it proposes an LLM-based measurement method for corporate cybersecurity governance that effectively addresses semantic and expression ambiguity in Chinese texts, providing a novel technical approach for quantifying corporate risk governance behavior. Second, it elucidates the underlying mechanisms through which cybersecurity governance promotes firm innovation from a risk governance perspective, offering new evidence to resolve debates in the literature while providing clearer policy guidance. Third, it explores the vertical spillover effects of cybersecurity risks from a supply chain perspective, enriching the research framework on inter-firm network risk transmission and innovation interactions in the digital economy context.
Regarding policy implications, this paper argues that firms should regard cybersecurity governance as an integral component of innovation strategy rather than a mere cost burden. Governments should refine cybersecurity regulation and incentive mechanisms, encourage firms to strengthen cybersecurity risk governance, and construct collaborative security-innovation ecosystems. Future research may leverage cross-national data and incorporate institutional differences to further analyze the impact of cybersecurity risk governance on innovation.

Accelerating the development of the digital economy and promoting the deep integration of the digital economy with the real economy is an important path to drive the development of new quality productivity. Over the past decade, the scale of China's digital economy has continued to expand, with the added value of core digital industries accounting for about 10% of the country's GDP. New infrastructure construction (referred to as “new infrastructure”) is an indispensable public good for the high-quality development of the digital economy and is crucial for the intelligent and integrated development of industries. However, “new infrastructure” is characterized by high costs, long payback period, and significant externalities, which urgently requires long-term stable financial support and institutional guarantees. With the gradual expansion of the scale and investment fields of local government special purpose bonds, “new infrastructure” has also become a key support direction for special purpose bonds, mainly covering information infrastructure such as 5G base stations, big data centers, and industrial internet. Against this background, few articles have systematically studied whether the issuance and use of “new infrastructure” special purpose bonds can promote the development of the digital economy , and the underlying mechanisms.
Based on the panel data of prefecture-level cities from 2005 to 2021, this paper constructs a multi-period difference-in-differences model to empirically examine the impacts and mechanisms of the issuance and use of “new infrastructure” special purpose bonds on the development of the digital economy in cities. Compared with existing literature, the contributions of this paper are reflected in three aspects: First, it quantitatively identifies the impacts of “new infrastructure” special purpose bonds on the digital economy from the perspective of investment and financing, which is different from the traditional infrastructure financing assessment or the benefit analysis of “new infrastructure” itself. Second, it fills the gap in the research on the economic consequences of special purpose bonds in the field of new economic forms. Third, it focuses on the triple positive effects of financing, demand pull, and signal transmission, breaking through the research limitations of previous studies that focused on the implicit debt crowding-out effect, and simultaneously exploring heterogeneity from multiple dimensions such as regions, policies, and industrial agglomeration, deepening the understanding of the relationship between the two.
This paper finds that the issuance and use of “new infrastructure” special purpose bonds can significantly enhance the development of the digital economy in regions. Mechanism analysis shows that “new infrastructure” special purpose bonds fill the funding gap through the financing effect, accelerate the implementation of new infrastructure; expand the market for digital products through the demand-pull effect, promoting the accumulation of digital human capital and technological innovation; boost market confidence through the signal transmission effect, stimulating the vitality of digital entrepreneurship. The above effects are more significant in eastern regions, regions with high levels of new quality productivity, cities that are nodes of computing power networks, and regions with high levels of digital technology development. Moreover, strong government attention to the digital economy and digital industrial agglomeration can further enhance this effect.
The policy implications of this paper are as follows: First, moderately expand the issuance scale of “new infrastructure” special purpose bonds and optimize fund allocation; second, improve the institutional framework, establish an incentive mechanism for quota allocation and a full life cycle management system to ensure the precise and efficient use of funds; third, activate the synergy of multiple mechanisms, improve the mechanism for cultivating digital talents, encourage digital technological innovation, enrich application scenarios, and deepen the integration of the digital and real economies.

Green and low-carbon technological innovation is an important driving force for new quality productivity, and its market value not only acts as a key verification mechanism for economic transformation, but also continuously provides feedback and impetus for technological iteration. Non-listed enterprises are the main body of China's green and low-carbon technological innovation, but they face difficulties in discovering the value of R&D results and capital conversion due to the lack of a public market valuation system. Therefore, based on the unique perspective of green and low-carbon technology M&As for non-listed enterprises, this article provides empirical evidence for the relevant research on the discovery of technological innovation value in non-listed enterprises.
To accurately evaluate the market value of green and low-carbon technological innovation in non-listed enterprises, M&As of non-listed enterprises by listed companies as buyers is an ideal research scenario. Combined with open innovation theory and signaling theory, M&A is an important way for listed enterprises to quickly make up for the shortcomings of green and low-carbon technology capabilities, which can significantly reduce the uncertainty and sunk costs of innovation, enhance their competitiveness and market confidence, transmit sustainable development signals to other stakeholders, enhance investors' confidence, and ultimately have a positive impact on market value. Therefore, this article analyzes the market premium through the regression of the target company's green and low-carbon technological innovation on the CAR of the acquiring company.
This article selects successful M&A events for non-listed companies initiated by Shanghai and Shenzhen A-share listed companies in 2013-2022 as the initial sample. The information of M&A events, stock returns of listed companies and financial data are all from the CSMAR database, and the industrial and commercial information and patent data of both M&A parties are respectively from Qichacha database and the patent retrieval system of the China National Intellectual Property Administration. The research results indicate that there is a significant market premium for green and low-carbon technological innovation in non-listed enterprises, with clean energy patents being particularly prominent. Further analysis based on the dual heterogeneity of M&A entities and transaction types reveals that vertical M&As with large state-owned enterprises with a strong R&D foundation but a green technology gap as buyers exhibit higher premiums. In the long run, technology M&A triggers dynamic innovation synergy, resulting in long-term growth of green and low-carbon technology patent output and total factor productivity of the acquiring companies, demonstrating the dynamic value-added effect of knowledge transfer.
Based on the research findings, this article proposes the following policy recommendations. Firstly, strengthen the deep integration of the innovation chain and industrial chain of green and low-carbon technologies, especially clean energy technologies. Secondly, through vertical integration, strengthen the industrial chain synergy of green and low-carbon technologies. Thirdly, improve the incentive mechanism for knowledge transfer and strengthen the dynamic innovation synergy effect of technology M&As.
The marginal contribution of this article is mainly reflected in the following aspects. Firstly, starting from the M&A scenario, this article extends the price discovery function of green finance to the market value evaluation of green and low-carbon technological innovation of non-listed enterprises. Secondly, this article systematically reveals the boundary conditions and path selection of the market premium of the green and low-carbon technological innovation from the perspective of the dual heterogeneity of M&A entities and transaction types, enriching the research on the influencing factors of the realization of technological innovation value. Thirdly, this article further approaches from the perspective of dynamic innovation collaboration, revealing how green technology M&As can achieve economic value transformation by improving patent output and production efficiency, elucidating the inherent mechanism of this market value realization, and deepening the understanding of the dynamic value-added effect in the process of green technology knowledge transfer. The current analysis still has room for further extension. For example, we can further explore the mechanism of the synergistic effect of green and low-carbon technological innovation, and reveal the transmission mechanism of knowledge transfer and resource restructuring. In addition, in the future, attention can be paid to other technology trading models, such as strategic alliances and technology licensing, to build a more comprehensive framework for discovering the value of green and low-carbon technological innovation.

Data, as a new factor of production, underpins digitalization, networking, and intelligent transformation. It has rapidly integrated into all facets of production, distribution, circulation, consumption, and social service management, profoundly reshaping how a nation produces, lives, and governs. To better leverage the value of public data, Chinese local governments have launched data openness platforms, piloting centralized and standardized provision of public data resources. Since 2012, these platforms have been progressively established, aggregating vast amounts of data from government and societal entities. By the end of 2022, 208 public data platforms had been launched, covering 23 provinces (excluding municipalities directly under the central government). A critical question remains: can—and how does—public data openness help market entities improve decision-making efficiency？ Clarifying this is essential to unlocking the potential of public data in China.
Using Chinese A-share listed firms from 2007 to 2022 as the sample, the paper employs provincial public data openness as a quasi-natural experiment and finds that such openness enhances firms' investment-price sensitivity. Mechanism analyses show that public data openness increases the idiosyncratic information content in stock prices by boosting analyst coverage and institutional investor research, thereby strengthening market feedback effects. Consistent with the “feedback effect” literature, the positive impact of public data openness on investment efficiency is more pronounced among firms with poorer ex-ante information environments (e.g., lower turnover, less informed trading) and stronger feedback channels (e.g., lower financing constraints, less insider information). Furthermore, heterogeneous effects are observed under types of data opened: when the data is more relevant to business operations, the enhancement effect is stronger, and the higher the quality of the data, the greater its role in improving investment efficiency. Finally, the study confirms that public data openness, as a policy tool, improves listed firms' profitability and long-term value through information feedback of the capital market.
This paper makes three contributions. First, it reveals an indirect mechanism through which public data affects corporate decisions via capital market feedback, offering a new perspective on how data openness improves investment efficiency and extending the literature on public data and investment decisions. Existing studies have mainly focused on the direct effects of data openness on investment levels. Few examine investment efficiency, and those that do overlook the role of capital markets. By opening this “black box,” this study provides a new theoretical lens for understanding how public data openness optimizes resource allocation.
Second, leveraging China's staggered public data platform launches as a quasi-natural experiment, this study provides empirical evidence that public data openness increases the private information content in stock prices in the A-share market. Theoretically, expanding public information supply may either “crowd in” or “crowd out” private information production (Goldstein & Yang, 2017), yet empirical evidence remains limited. This paper offers timely empirical insights from a unique institutional setting.
Third, the findings offer a capital-market-based perspective for evaluating the comprehensive effects of government information disclosure policies. The findings highlight the importance of considering both direct and indirect (capital market) effects when designing and evaluating public policies. This research suggests that public data openness not only has direct effects but also significantly shapes corporate behavior through information channels. This highlights the need for policymakers to adopt a more comprehensive approach that accounts for both direct policy impacts and indirect transmission through capital markets, thereby enabling a more accurate and complete understanding of policy outcomes.

In recent years, convertible bonds have become a significant refinancing channel for Chinese listed firms, with their market prominence increasingly highlighted. Compared to traditional bonds and equities, convertible bonds offer greater flexibility in contractual terms, and their complex clauses shape the dynamic balance between their debt and equity features. Specifically, the debt-like nature of convertible bonds is manifested in the bondholders' claim to the face value and fixed interest payments before conversion, while their equity-like nature is reflected in holders' rights to share in the residual value after conversion. However, firms may issue convertible bonds not merely for financing purposes but also to exploit speculative opportunities that promise short-term gains. As an emerging market, China exhibits relatively weaker protection for creditors. The development of bankruptcy legal frameworks, a direct reflection of creditor protection, focuses on building a robust legal system and an efficient judiciary to effectively safeguard creditors' legitimate rights and interests during firm liquidation. Nevertheless, the impact of bankruptcy legal framework development on convertible bond issuance and its underlying motivations remains unexplored. Therefore, in the context of China's increasingly refined bankruptcy legal system, this study aims to investigate the impact of enhanced creditor protection mechanisms on firm convertible bond issuance behavior and its underlying mechanisms.
Based on a quasi-natural experiment of the bankruptcy court establishment in Chinese prefecture-level cities, this study empirically examines the impact of creditor protection on convertible bond issuance from the perspective of bankruptcy legal system development. The findings reveal that after the establishment of bankruptcy courts, local firms are more likely to use convertible bonds for financing, and the equity-like nature of these bonds significantly increases. Furthermore, the mechanism tests show that the “backdoor equity” and “risk shifting” motivations are the primary channels through which bankruptcy courts affect the attributes of convertible bonds. The heterogeneity analysis shows that the establishment of bankruptcy courts has a more pronounced impact on promoting the issuance of equity-like convertible bonds in firms with weak internal governance, inadequate external monitoring, lower creditor protection levels, and in regions with relatively weaker financial regulation. Finally, further tests demonstrate that after the establishment of bankruptcy courts, issuing equity-like convertible bonds reduces investment efficiency, encourages firms to increase investments in speculative financial assets, and decreases funding for R&D projects, providing evidence for the strategic motivations behind convertible bond issuance.
The marginal contributions of this study are threefold. First, in the context of the Chinese market, this study enriches the literature on the economic consequences of creditor protection. By analyzing the impact of bankruptcy courts on the strategic issuance of convertible bonds, it reveals how firms may circumvent legal constraints through the design of financial instruments following the strengthening of creditor protection. This finding not only supplements the discussion on the potential negative effects of creditor protection but also provides practical insights into the dynamic interplay between policy and firm strategies in emerging markets. Second, this study extends the literature on the microeconomic effects of bankruptcy court establishment. By leveraging the exogenous shock of bankruptcy court establishment, it reveals how the development of bankruptcy legal frameworks influences firm strategic choices in convertible bond issuance through the enhancement of creditor protection mechanisms. The findings provide a novel theoretical perspective on the role of bankruptcy courts in financial markets and offer valuable insights for improving bankruptcy legal systems. Third, this study deepens the understanding of the relationship between the legal environment and the contractual design of convertible bonds. It provides a theoretical foundation and policy recommendations for policymakers to refine the design of bankruptcy judicial systems, optimize financial instrument innovation, and promote high-quality development of the financial sector.

Public mutual funds not only fulfill the function of value discovery and creation in the secondary market, but also serve as an important vehicle for Chinese residents to achieve common prosperity by obtaining property income through capital market participation. However, in recent years, both academia and industry have found a significant performance gap between funds and fund investors in the fund market. Explaining the performance gap of fund investors is of great significance for further optimizing the capital market's institutional mechanisms and achieving healthy accumulation of national wealth.
Existing research mainly explains the fund-investor performance gap from the perspective of investors' own behavioral biases, arguing that investors' performance-chasing behavior of buying high and selling low is the main reason for their lower performance. This paper argues that fund managers' trading behavior is also an important cause of this phenomenon. Even if investors possess sufficient rationality, the performance comovement formed by funds imitating each other's holdings will weaken investors' learning ability and reduce their performance.
Based on this idea, this paper constructs a multi-period game model that includes heterogeneous fund abilities and rational investors, starting from fund managers' trading behavior under the assumption of investor rationality. In the model, low-ability fund managers may adopt imitation strategies, following the positions of high-ability funds, thereby generating holding concentration and performance comovement. Although fund investors can rationally learn from funds' historical performance and infer fund ability, such imitation-induced performance comovement dilutes the information content embedded in historical performance. This leads to a mismatch between fund capital flows and fund ability, ultimately creating a performance gap between funds and investors.
To test the above logic, this paper uses daily historical performance data and quarterly holdings data of actively managed Chinese funds from 2007 to 2022 from CSMAR, and constructs performance-comovement networks and portfolio-holdings networks based on Jaccard distance between funds. Based on funds' positions in the holding network and performance comovement network, combined with fund capital flow and investor performance data, the paper finds: First, the holding network between funds creates performance comovement, and for funds with lower ability, the performance comovement caused by holding imitation is more intense when the market transitions to a stable period. Second, the performance comovement formed through the holdings network reduces fund investors' learning ability, manifested as a diminished convexity in the flow-performance relationship. Finally, due to the weakening of investors' learning ability, low-ability funds obtain more future capital inflows through performance comovement, causing a mismatch between fund capital flows and ability, ultimately widening the performance gap between funds and investors. This evidence effectively supports the paper's argument. In addition, the paper also conducts an in-depth analysis of the heterogeneous characteristics and transmission mechanisms of performance comovement from the perspectives of risk-return characteristics, network diffusion effects, and sources of holding concentration.
The potential marginal contributions of this paper are: First, in terms of research perspective, this paper starts from fund trading on the market supply side and provides a new explanation for the fund-investor performance gap, providing a complementary micro-mechanism explanation to existing research. Second, asset return comovement is a core issue in securities portfolio theory, but existing research mainly focuses on stock and bond markets in developed countries such as Europe and the United States. This paper supplements the understanding of asset return comovement from a fund perspective. Third, this paper has important policy implications for strengthening financial regulation and building a high-quality capital market.

Enhancing investors' welfare is a long-standing imperative for promoting the high-quality development of the mutual fund industry. With the rapid advance of digital technologies, fund distribution channels are undergoing a profound digital transformation. Yet, mutual funds differ fundamentally from ordinary consumer goods: they entail substantial cognitive demands and uncertain returns, making investor education and guidance by distribution platforms essential. This paper studies a major online fund distributor's introduction of a “Recommended Funds” advisory service. The service selects a small set of high-quality funds from a universe of thousands to reduce investors' search costs and provides continuous monitoring, interpretation, and follow-up guidance aimed at facilitating long-term investment. Although these features are designed from an investment advisory perspective to help investors select funds and allocate assets more systematically, advisory services inherently exhibit negative network externalities. On a large-traffic digital platform, uniform advisory signals can unintentionally generate diminishing returns to scale and reduced managerial efficiency.
We examine this issue using an entropy balancing procedure to assign weights to treated (recommended) and matched control funds to ensure comparability on observable fund characteristics and prior performance. Taking July 2020, the launch date of the recommended-fund list, as the treatment event, we implement a difference-in-differences design to compare net flows and fund performance between recommended funds and matched controls over the two years before and after the service rollout.
Our main findings are as follows. First, the introduction of the recommendation list leads to a sharp increase in flows into recommended funds. On average, recommended funds experience quarterly asset-growth rates 11.3% higher than matched funds, indicating that unified digital sales guidance exerts a substantial effect on investor fund choices and persistently attracts inflows.
Second, recommended funds exhibit significantly lower subsequent abnormal performance. Their quarterly Jensen alphas decline by 1.2% relative to matched funds. Further, when we sort recommended funds by the magnitude of inflows, the performance deterioration is concentrated among funds experiencing larger inflows. This pattern confirms that investor crowding triggered by the advisory service is the primary mechanism driving performance decline.
Third, we study the underlying channels from the perspective of fund managers. Larger assets under management can erode performance through multiple channels. We examine adjustments in portfolio rebalancing ability, managerial activeness, and trading impact costs. The evidence shows that reduced short-term rebalancing flexibility and increased passive holdings are the dominant channels through which performance deteriorates for recommended funds.
From an academic perspective, this paper uses the mutual fund distribution setting to uncover a distinctive challenge in the digitalization of investment advisory services. The classic advisory literature primarily focuses on how agency conflicts and behavioral biases affect client outcomes (e.g., Hackethal et al., 2012; Hoechles et al., 2017; Linnainmaa et al., 2021). A growing strand of literature examines how technology-enabled advisory tools can improve investor welfare (e.g., D’Acunto et al., 2019; Hao et al., 2022; Rossi & Utkus, 2024; Bianchi & Brière, 2024). However, it has not recognized a key distinction between advisory services and ordinary products: investment advice exhibits negative network externalities. Unlike standard goods and services, the utility of an advisory service declines as more clients follow the same recommendation. Consequently, uniform investment guidance delivered through large digital platforms can impair the performance and value creation of the advised products, representing an inefficient form of advisory digitalization.
This paper also provides causal evidence on how increases in fund scale reduce performance. A large strand of literature studies the relationship between fund size and returns. Chen et al. (2004) first documented a negative relation between fund performance and lagged fund size. Subsequent studies have largely followed the same setting and employed more sophisticated empirical techniques, yet they have not fully addressed endogeneity concerns (Pástor et al., 2015; Zhu, 2018). Reuter & Zitzewitz (2021) exploited a plausibly exogenous setting but failed to find consistent negative scale effects. Leveraging the release of a recommended-fund list by a major distribution platform as an exogenous shock, this paper provides clean evidence that scale expansion not only depresses fund performance but also undermines the fund's value creation.
From a practical standpoint, the results deepen our understanding of individual investors' fund selection behavior and offer important implications for regulating fund distribution on the digital platform. Financial regulation in the digital era must guard not only against the moral hazard of distributors but also against the unintended consequences of well-intentioned advisory practices. On large-traffic digital platforms, centralized advisory guidance can accelerate diminishing returns to scale, ultimately lowering investors' perceived service quality and damaging the reputation of financial institutions.