In the current stage, while fully leveraging the role of the market, advancing the internationalization of the renminbi (RMB) in a steady and prudent manner requires more policy support. As a significant measure in the process of RMB internationalization, the People's Bank of China (PBOC) has actively promoted the establishment of RMB clearing banks in recent years, achieving significant progress. By the end of 2023, 33 RMB clearing banks had been authorized in 31 countries and regions. The continuous optimization of the overseas RMB clearing network will facilitate the efficient global circulation of the RMB, providing more convenient service conditions for the use of RMB in cross-border trade payments. Since the inception of the RMB clearing bank model, existing literature has explored the role of RMB clearing banks from multiple dimensions. However, these studies have two major limitations. First, the role of RMB clearing banks in promoting RMB internationalization is often described qualitatively, lacking in-depth theoretical frameworks and empirical analysis. Second, most studies focus on the impact of RMB clearing banks on specific economic and financial indicators, such as payments and trade, without comprehensively examining the underlying logic and mechanisms affecting the international use of the RMB. Therefore, exploring whether RMB clearing banks can effectively enhance the level of RMB internationalization becomes an important but unresolved issue.
This paper examines whether the establishment of RMB clearing banks by the People's Bank of China in foreign countries or regions facilitates the use of RMB in these countries, thereby promoting the settlement of cross-border trade in RMB. In the empirical analysis, this paper utilizes data related to cross-border trade RMB settlement between China and other countries. It considers countries and regions that signed RMB clearing bank memorandum agreements with the People's Bank of China from January 2009 to December 2020 as the treatment group, and countries and regions that never signed such agreements during the sample period as the control group. To address potential endogeneity issues arising from reverse causality and omitted variables, this study employs a staggered Difference-in-Differences (DID) approach and controls for as many economic, financial, and institutional factors as possible.
This paper makes several contributions to the literature. First, this paper draws on cross-border trade RMB settlement data from the People's Bank of China's RCPMIS database as its primary sample and conducts a robustness check using SWIFT data on actual RMB transactions. Unlike prior studies, it combines official statistical settlement figures with high-precision transaction-level data and employs cross-validation between these two sources to enhance the credibility and reliability of its conclusions, thereby overcoming the perspective limitations caused by insufficient data granularity in earlier research. Second, this paper extends the literature on determinants of cross-border trade RMB settlement. Previous research has largely focused on the domestic macro-environment, exchange-rate volatility, or trade structure, while the role of cross-border financial infrastructure has received scant attention. Exploiting the quasi-natural experiment presented by the establishment of RMB clearing banks, we empirically examine how such infrastructure affects RMB settlement in cross-border trade and unpack its underlying mechanisms. Our findings reveal an asymmetric impact of RMB clearing banks on import versus export settlement and validate two key channels, offshore RMB financing availability and bilateral exchange-rate fluctuations, through which this infrastructure operates. These results indirectly substantiate the policy rationale for establishing RMB clearing banks and enrich the research on cross-border trade RMB settlement. Third, recognizing the China International Payment System (CIPS) as another critical piece of cross-border financial infrastructure, this paper incorporates the interaction between RMB clearing banks and CIPS into its analytical framework. We systematically explore how their joint operation promotes RMB settlement in cross-border trade and demonstrate that CIPS membership amplifies the positive effects of RMB clearing banks, indicating a clear synergistic effect. The conclusions furnish new theoretical underpinnings and empirical evidence to guide the continuous development of RMB-internationalization infrastructure and the prudent, steady advancement of RMB internationalization.

Faced with domestic and international economic downturn pressures and the need for structural transformation, balancing short-term “growth stabilization” with long-term “structural adjustment” has become a core challenge for macroeconomic policy. The shift of corporate investment from the real economy to financial assets not only weakens the investment incentives of the real sector and exacerbates industrial hollowing-out but may also amplify financial risks, thereby hindering high-quality economic development. As President Xi emphasized, “The real economy is the foundation of finance, and finance is the lifeblood of the real economy.” This highlights that how the financial system serves the real economy and guides capital back to productive investment is a key pathway to achieve an organic integration of growth stabilization and structural adjustment. In this context, green finance, as an essential component of supply-side structural reforms, carries significant theoretical and practical importance in promoting corporate reorientation toward the real sector and tapping new growth momentum through institutional innovation and market guidance.
Since the establishment of the first batch of green finance reform and innovation pilot zones in 2017, local practices have provided a quasi-natural experiment for evaluating the effectiveness of green finance policies. Based on data from A-share listed companies from 2012 to 2023, this study employs a difference-in-differences approach to identify policy effects and conducts multiple robustness checks to ensure the reliability of the conclusions. It systematically examines the impact of pilot zone policies on corporate “de-financialization,” their transmission mechanisms, and economic consequences. The main findings are as follows. First, the pilot zone policies significantly promote corporate “de-financialization,” by reducing firms' propensity to allocate assets toward financial instruments. Second, mechanism analysis shows that the policies operate through two channels: (i) internal governance mechanisms: by incentivizing firms to increase green R&D and green investment; and (ii) external governance mechanisms: by enhancing institutional investors' green attention. Third, heterogeneity analysis indicates that this effect is more pronounced in firms with lower internal governance, more severe financing constraints, and higher public environmental attention. Fourth, economic consequence analysis demonstrates that policy-induced “de-financialization” not only improves firms' sustainable performance but also reduces stock price crash risk, thereby achieving a micro-level synergy between growth stabilization and structural adjustment. Fifth, spatial analysis reveals that the pilot zone policies exert significant spillover effects on other firms within the same province.
This study contributes in four key aspects. First, it identifies and systematically analyzes the “de-financialization” effect of green finance, expanding research boundaries on green finance from the perspective of growth stabilization and providing a new theoretical rationale for its development in the new era. Second, from the dual perspective of internal and external governance, it uncovers the transmission mechanisms of green finance policies, clarifying the critical roles of corporate green R&D investment and institutional investor attention, while conducting heterogeneity analysis based on governance quality, financing constraints, and public environmental awareness, offering micro-level evidence for differentiated policymaking. Third, the economic consequence analysis shows that promoting “de-financialization” through green finance can reduce stock price crash risk and enhance sustainable development performance, revealing the long-term value of these policies. Fourth, by comparing the effects across different pilot zones and examining intra-provincial spillovers, the study provides more comprehensive empirical evidence, enriching the literature on green finance policy effectiveness.
Based on the empirical results, this study proposes several policy recommendations: (i) leverage the “guiding role” of green finance reform and innovation pilot zones to deeply integrate green finance into local traditional financial infrastructure, establish green credit evaluation mechanisms, and gradually expand pilot zones based on lessons learned to ensure that policy benefits reach more real-sector firms; (ii) improve the green finance policy system by creating a long-term mechanism balancing “incentives and constraints,” providing differentiated support through fiscal subsidies, tax incentives, and green credit, while strengthening environmental information disclosure and penalizing greenwashing to promote the rational flow of capital to the real economy; and (iii) enhance market participation mechanisms by strengthening the governance role of institutional investors, and encourage cross-sector collaboration among securities, banking, funds and insurance institutions to jointly innovate green financial products, thereby improving resource allocation efficiency.

Green innovation has become a pivotal strategy for reconciling economic growth with environmental protection. While existing research has extensively explored the drivers of green innovation from institutional and firm-level perspectives, the role of top executives' intrinsic value orientations remains underexplored. This study addresses this gap by investigating how CEOs with a social value orientation, influence corporate green innovation. Drawing on social capital theory and stewardship theory, we posit that such CEOs foster green innovation through two primary mechanisms: alleviating external resource constraints and enhancing internal environmental consciousness.
We hypothesize that social-value-oriented CEOs bolster green innovation via a resource channel and an awareness channel. The resource channel suggests that these CEOs, often through participation in philanthropic organizations, build stronger relationships with key stakeholders such as governments, investors, and communities. This enhanced social capital facilitates access to critical resources by easing financing constraints and attracting high-skilled talents, thereby providing the necessary foundation for green innovation activities that are typically characterized by high costs and long cycles. Simultaneously, the awareness channel, rooted in stewardship theory, posits that these CEOs act as organizational stewards with a long-term vision. They are more likely to intrinsically integrate environmental sustainability into corporate strategies, foster a green culture, and exhibit greater tolerance for the risks associated with green research and development, thus creating an internal environment conducive to substantive innovation.
To test these hypotheses, we construct a unique dataset of Chinese A-share listed manufacturing companies from 2007 to 2022. Our key explanatory variable, Social-value-oriented CEO (SocialCEO), is a binary indicator that equals one if the CEO participates in at least one charitable organization, based on hand-collected data from China's official “Charity China” platform. Our primary measure of green innovation is the number of green invention patent applications, identified using the World Intellectual Property Organization's International Patent Classification Green List. We employ a fixed-effects panel regression model controlling for a comprehensive set of firm-level characteristics and CEO demographic variables. The baseline results provide robust evidence that firms led by social-value-oriented CEOs exhibit a significant increase in green invention patent applications.
The mechanism tests corroborate the proposed dual channels. We find that social-value-oriented CEOs significantly reduce corporate financing constraints and increase the proportion of high-skilled employees, validating the resource channel. Concurrently, they lead to higher environmental protection investments, more frequent green training, and a stronger corporate green culture, supporting the awareness channel.
Heterogeneity analyses reveal boundary conditions for this effect. The positive impact is more pronounced in technology-intensive industries and non-heavily polluting industries. Furthermore, the effect is stronger when CEOs possess greater power, as indicated by CEO-chairman duality, or hold higher shareholdings, suggesting that decision-making authority and incentive alignment amplify their influence. Additional analyses yield nuanced insights. The promotive effect is primarily observed in substantive green innovation , indicating a focus on real technological advancement. Crucially, the interaction between social-value-oriented CEOs and green innovation is associated with a significant increase in long-term firm value, underscoring the economic sustainability of this strategic alignment.
This study contributes to the literature in several ways. It extends research on the antecedents of green innovation by introducing and validating CEOs' social value orientation as a novel and significant driver. It enriches upper echelons theory by delineating the specific resource-based and awareness-based pathways through which personal values translate into strategic environmental outcomes. Our findings also offer practical implications for corporate governance and policy-making, suggesting the value of fostering leadership with prosocial tendencies and creating supportive institutional environments for green transformation.

With the deepening of the global aging process, many countries are undertaking pension system reforms that tend to increase the uncertainty of future pension wealth. A primary trend in these reforms is the establishment of multi-pillar pension systems, a direction China has also emphasized in its national development strategy. This trend unfolds against the backdrop of China's persistently high savings rate, which has hovered around 45% since 2015. It is therefore crucial to investigate how pension uncertainty affects household savings and financial asset allocation. While existing literature has extensively studied the impact of changes in the level of pension wealth on household savings, research on how pension wealth uncertainty affects the structure of household financial assets remains scarce. This paper aims to fill this gap by leveraging the 2015 reform of the pension system for public sector employees in China as a policy experiment. This reform not only altered the benefit formula for public sector employees but also introduced an occupational annuity, thereby substantially increasing the uncertainty of their future pension wealth.
This study conducts an empirical analysis using the 2015 public sector pension reform. Before the reform, the pension wealth for public sector employees was highly predictable, primarily determined by their final year's salary and years of service. The reform abolished this system, establishing a framework consistent with that for enterprise employees and introducing a second-pillar “occupational annuity” operating on a funded, individual account basis. A key feature of this annuity is that employer and employee are invested in the capital market, with returns based on actual investment performance. Compared to the relatively stable growth of wages, this market oriented reform significantly increased the uncertainty associated with future pension income. We employ a difference-in-differences (DID) methodology, analyzing data from the China Household Finance Survey (CHFS) from 2011 to 2019. Households headed by public sector employees constitute the treatment group, while those headed by enterprise employees serve as the control group.
Our empirical results show that the reform caused public sector households to increase their savings rate by 6.1 percentage points. Simultaneously, the proportion of their financial assets held in risky instruments decreased by approximately 5.9 percentage points. To analyze the underlying mechanisms, we first rule out several potential alternative explanations. Regarding the wealth effect, our calculations show that while the reform did significantly reduce basic pension wealth, the newly established annuity compensated for this reduction, leaving the expected value of total lifetime pension wealth largely unchanged. Regarding the income effect, we find that although the pre-tax wages of public sector employees increased significantly post-reform, this increase was largely channeled into higher social security contributions, resulting in no significant change in their actual post-tax disposable income.
Conversely, this paper provides direct evidence for the mechanism through which increased uncertainty in pension wealth altered household asset allocation decisions. First, in provinces that had explicitly implemented market-based investment management for their occupational annuity funds, the changes in household savings and financial asset structure were more pronounced, suggesting that the uncertainty stemming from the annuity's operation is a key driver. Second, the reform's impact is concentrated among younger employees who face a higher degree of uncertainty. For older employees within a transitional period, who were protected by a provision of guaranted benefits no lower than those under the old system, the effect was not statistically significant.
The findings of this paper enrich the understanding of multi-pillar pension systems. The research indicates that under a market-based operation, increased uncertainty in pension wealth can crowd out household-level allocation to risky assets while simultaneously increasing household savings. These findings have important policy implications for improving China's multi-pillar pension system, reducing households' excessive savings motives, and informing the promotion of the third-pillar pension system. Policymakers need to leverage the long-term investment advantages of annuity funds to provide stable returns, and regulate and develop professional investment services to help households determine appropriate savings levels and investment structures.

Corporate violations undermine investor confidence, distort resource allocation, and ultimately erode the healthy functioning of capital markets. Venture capital (VC), as both a long-term companion and an external monitor of entrepreneurial firms, plays a crucial role in restraining opportunistic behavior and promoting compliance. In the context of China, where informal institutions are deeply ingrained in economic activities, examining the governance effects of syndicated investment networks is particularly significant. A syndicated investment network refers to a relational structure formed when multiple VC institutions, driven by motives of risk sharing and value co-creation, jointly invest in the same target firm. Compared with single-investor arrangements, syndication enables VCs to leverage information sharing and reputation-based constraints, thereby enhancing governance effectiveness, with central VCs exerting especially strong influence.
Using a sample of Chinese A-share listed firms from 2007 to 2022, we employ the partially observable binary probit model to jointly estimate firms' probability of corporate violations and the likelihood of regulatory detection, thus providing a more comprehensive assessment of the governance role of syndication network centrality. Empirical results demonstrate that higher syndication network centrality significantly reduces the likelihood of corporate violations while simultaneously increasing the probability of detection. These effects remain robust after multiple sensitivity checks. The mechanisms at work are twofold: first, the information channel, whereby central VCs facilitate the flow of information and alleviate information asymmetry across firms; second, the governance channel, whereby central VCs, often acting as lead investors, exert stronger monitoring pressure through ongoing oversight, reputation spillovers, and exit threats.
Further analyses reveal that syndication network centrality exerts significant governance effects on both disclosure-related violations and operational violations: it reduces firms' inclination to engage in such practices and increases the probability that violations will be uncovered. However, centrality does not significantly constrain misconduct committed by top executives, reflecting the persistent challenges of addressing leadership-level opportunism. Moreover, higher syndication centrality mitigates the severity of misconduct when it does occur and shortens the time between violation and regulatory detection, highlighting its deterrent effect. Importantly, we find that these governance effects dissipate when syndication networks fracture, such as when central VCs exit. The withdrawal of such investors not only raises the probability of misconduct but also reduces the likelihood of timely detection.
Theoretically, this study enriches the literature on corporate violations by introducing the role of syndication networks into the broader framework of external governance mechanisms complementing prior research that has primarily focused on independent directors, media exposure, and short-selling mechanisms in constraining misconduct. By contrast, we highlight the embeddedness of venture capital within syndication structures and quantify its governance function, thereby extending the understanding of how financial intermediaries contribute to corporate compliance. Empirically, our findings provide robust evidence on the disciplinary role of venture capital in emerging markets and underscore the importance of network centrality as a key governance factor. Practically, our results offer valuable insights for policymakers and regulators: strengthening institutional arrangements that foster sustained VC participation in syndication networks may help curb corporate violations, enhance detection, and contribute to the modernization of capital market governance.

In recent years, small and micro enterprises (SMEs) have faced sustained pressure on their production and operations due to multiple domestic and international factors, including reduced orders, intensifying competition, and rising costs. Inclusive finance, as a key institutional arrangement for promoting the development of the real economy and safeguarding the survival and growth of small and micro enterprises, has drawn considerable attention to its policy effectiveness. To alleviate the financing difficulties of SMEs, the Chinese government has introduced a series of policies centered on inclusive SME credit. Based on this, this paper constructs a panel dataset covering the period from 2009 to 2021 to examine the impact of inclusive micro and small business lending on the market entry behavior of these enterprises. It then proposes policy recommendations from an inclusive finance perspective to enhance the entrepreneurial vitality and operational resilience of micro and small businesses, providing empirical support for a comprehensive assessment of the effectiveness of inclusive micro and small business lending policies.
First, this paper constructs a theoretical model of enterprise market entry incorporating financing constraints and costs, systematically analyzing the specific mechanisms through which the establishment of micro-enterprise branches influences SME market entry. Second, empirical tests using a staggered difference-in-difference (DID) method reveal that the establishment of small and micro branches (SMBs) significantly promotes SMEs market entry: counties or districts with SMBs exhibit a roughly 25.44% higher SMEs market entry rate than those without. Mechanism tests indicate that SMBs alleviate SMEs' financing constraints and reduce their financing costs, forming a transmission pathway that facilitates market entry. Heterogeneity analysis reveals that this promotional effect is more pronounced in enterprises with higher quality, greater initial capital requirements, and technology-intensive industries, as well as in eastern regions and areas with lower fiscal decentralization. Additional analysis further confirms that the establishment of SMBs significantly enhances the employment absorption capacity of SMEs thereby effectively increasing the regional employment scale.
Compared with existing research, the marginal contributions of this study are primarily reflected in three aspects: First, at the theoretical level, it systematically explains the impact of inclusive microcredit on SMEs market entry and its underlying mechanisms by integrating theoretical models with empirical evidence. This research addresses the insufficient focus on SMEs “market entry” behavior of existing literature, enriching the theoretical framework of SMEs credit while responding to academic debates and skepticism regarding the effectiveness of inclusive microcredit policies. It provides robust empirical evidence from the Chinese context supporting the proposition that “inclusive finance empowers SMEs”. Second, at the methodological level, given the gradual establishment of MSME branches, the study employs a staggered DID approach. By appropriately controlling various fixed effects and exogenous variables, and integrating econometric methods such as instrumental variables and double machine learning, it effectively mitigates endogeneity issues. This provides more robust causal identification evidence for how inclusive micro credit promotes SME market entry. Third, at the data level, we collected and compiled micro data from the EPS China Industrial and Commercial Enterprise Database to measure SME market entry. This data selection avoids sample selection bias potentially encountered in questionnaire-based datasets like the China SMEs Survey or National Tax Survey, while also overcoming measurement errors arising from using SMEs Board or ChiNext-listed companies as proxies for micro and small enterprises. It enables accurate and comprehensive depiction of the spatiotemporal evolution of SMEs across Chinese counties before and after the establishment of SMBs at a finer granularity.
Based on research findings, this paper proposes the following policy recommendations. First, strengthen SMEs financial services within small and medium financial institutions and deepen the development of specialized institutions. Building on the “localized and specialized” positioning of small and medium financial institutions, encourage them to increase the deployment of specialized institutions for micro and small enterprises, reversing the tendency to favor large clients. Second, improve the mechanism for coordinated government and market efforts to establish a long-term micro and small enterprise financial mechanism characterized by “willingness to lend, ability to lend, and expertise in lending”. Simultaneously, deepen rural financial reforms to bridge the “last mile” of policy implementation, achieving an organic integration of an “effective government” and an “efficient market”. Third, implement targeted financial support strategies to optimize credit resource allocation. Financial resource deployment should balance inclusiveness and structural focus, ensuring basic coverage while prioritizing sectors aligned with national strategic priorities, such as technological innovation and manufacturing, and underdeveloped regions.

Report on the Work of the Government in 2025 places significant emphasis on technological and digital advancement. This prioritization occurs within a historical context defined by a deepening technological and industrial transformation. The digital technology innovation serves as a core role for change and updates at an unprecedented pace. Enterprises, in this framework, are the principal architects of digital technological progress. Consequently, a critical academic challenge is to investigate the constitutive factors within the “black box” of enterprise digital technology innovation and to systematically unravel its intrinsic driving mechanisms. Digital technology innovation, marked by high barriers, significant value density, and low substitutability, demands greater investments and bears higher risks and longer cycles than its traditional counterparts. These demands impose considerable decision-making risks on managers, potentially suppressing their innovation motivation and hindering related activities. Thus, cultivating managerial perseverance in innovation and bolstering their risk tolerance and resilience are important for enhancing corporate digital technology innovation capabilities.
Finance plays a key role within the technological innovation However, little attention has been paid to whether finance can mitigate ex-ante managerial decision risks and alleviate managerial constraints in the digital innovation process. Directors' and Officers' (D&O) insurance serves as a risk-diversification tool, and encourages managers to pursue innovation through risk absorption and oversight. Given the high-risk nature of digital technology innovation, it remains unclear whether D&O insurance can effectively promote innovation of firms and support breakthrough advancements. Moreover, how insurance should empower enterprises in digital innovation remains an open question. To date, few studies have directly connected insurance with enterprise digital innovation, leaving theoretical propositions untested. Thus, this study examines the effect of insurance on digital technology innovation, and explores related channels.
Using data from Chinese listed firms (2010-2023), this study employs textual analysis of public documents to identify digital technology innovation and precisely constructs a corresponding measurement metric. The results show that D&O insurance significantly promotes enterprise digital technology innovation. This finding remains robust after addressing endogeneity concerns, replacing indicator variables, and narrowing the research window. Mechanism analysis reveals that D&O insurance promotes enterprise digital technology innovation by mitigating managerial risk exposure, improving corporate governance, elevating investment and financing efficiency, and optimizing the human capital structure. FinTech services significantly strengthen the positive effect, while well-established intellectual property protection systems and digital infrastructure further create multiple overlapping reinforcement effects.
Further extended analyses and heterogeneity tests deepen the understanding of how the insurance industry facilitates digital technology innovation. The promoting effect of D&O insurance on digital technology innovation is stronger among enterprises with longer duration and higher coverage of insurance. This pattern demonstrates significant time-accumulation and intensity-deepening effects. Moreover, the positive effect is further characterized by a “crossover effect”. Specifically, the D&O insurance encourages enterprises to pursue cross-border R&D patenting and guides non-digital firms toward digital transformation. Heterogeneity analysis shows that the positive effect of D&O insurance is more pronounced for high-tech, highly-competitive enterprises, as well as large-scale, state-owned, or technology-intensive enterprises. The positive effect is more significant for listed enterprises in northern regions, helping to narrow the overall “high in the south, low in the north” gap in the scale of digital technology innovation. These findings highlight the importance of targeted, precise, and graduated approaches in strengthening the financial role in promoting enterprise digital technology innovation.
This study offers three key contributions: First, this study provides robust empirical evidence from Chinese listed enterprises. It confirms that D&O insurance promotes digital technology innovation and exhibits time-accumulation and coverage-deepening effects. This finding thereby delineates the specific role of insurance within a national innovation strategy. Second, the study builds a precise measurement of digital technology innovation and then designs a two-tiered indicator system that captures both lower-layer driving innovation and application scenario innovation. It also distinguishes two dimensions of enterprise digital technology innovation, deepening and cross-boundary innovation. Third, this study constructs an integrated framework linking innovation decisions, managerial incentives, and risk management. The results provide a new theoretical foundation for managerial decision-making in high-risk digital innovation.

Algorithmic trading refers to trading technology that uses computer algorithms to automate trading decisions, submit orders, and manage order execution. It includes portfolio selection, trading strategies, execution strategies, and other elements, and has become a significant trading method in the securities market. Currently, program trading in the securities market often adopts algorithmic trading approaches.
In April 2024, the State Council issued the “Guidelines on Strengthening Regulation, Forestalling Risks and Promoting the High-Quality Development of the Capital Market”, which explicitly called for the formulation of regulations on program trading supervision and strengthened oversight of high-frequency algorithmic trading. In May, the China Securities Regulatory Commission (CSRC) introduced the “Provisions on the Administration of Program Trading in the Securities Market (Trial)” to provide institutional foundation for algorithmic trading regulation. In line with the political and people-centered nature of financial work and the philosophy of serving the people through finance, further efforts are needed to protect the legitimate rights and interests of the vast number of small and medium-sized retail investors in China's stock market. Improving the regulatory framework for algorithmic trading requires comprehensive theoretical and empirical research on its market impact and mechanisms, tailored to the realities of China's capital market.
This paper uses high-frequency trading data and order book data from China's A-share market between 2015 and 2023 to develop an algorithmic trading intensity indicator. It empirically examines the dual impact of algorithmic trading on stock market stability and information efficiency. The main findings are as follows: First, algorithmic trading reduces bid-ask spreads in the stock market while also decreasing price ranges and realized volatility, thereby enhancing market stability by improving market liquidity and suppressing market volatility. Second, algorithmic trading reduces the intensity of informed trading and short selling, thereby lowering market information efficiency by crowding out traditional informed traders and short sellers. Third, during periods of market turbulence, algorithmic trading enhances market stability through stronger liquidity support and suppression of price volatility; however, it simultaneously reduces the information content and speed of information reaction in the market, leading to a more significant decline in information efficiency. Fourth, algorithmic trading does not significantly impact the commonality risk of individual stock liquidity, indicating that it does not increase liquidity commonality risk. Fifth, algorithmic trading exhibits an asymmetric suppression effect on “good volatility” and “bad volatility” in the stock market. Specifically, compared to “good volatility” triggered by positive information, algorithmic trading shows a weaker stabilizing effect in response to “bad volatility” caused by negative information. Additionally, this paper analyzes the heterogeneous impact of algorithmic trading on market stability and information efficiency from perspectives such as company's ownership, size, and quality of information disclosure. The study finds that for non-state-owned listed companies, small and medium-sized listed companies, and listed companies with lower-quality information disclosure, the effect of algorithmic trading in enhancing market stability is accompanied by a more pronounced reduction in information efficiency.
The possible contributions of this paper are mainly in the following two aspects: (1) Previous studies have shown that algorithmic trading enhances market information efficiency in mature capital markets but exacerbates market volatility and, in extreme cases, leads to rapid depletion of market liquidity, thereby threatening market stability. This paper, however, reveals the dual impact of algorithmic trading on China's capital market, that is, enhancing market liquidity, reducing volatility, and thereby improving market stability, while simultaneously reducing market information efficiency. Thus, in terms of theoretical implications, the findings provide new empirical evidence on the market impact of algorithmic trading. In terms of practical implications, the empirical results offer a quantitative basis for improving algorithmic trading regulatory rules. (2) This paper enriches the academic discussion on the impact of securities trading models on capital markets. While existing literature has largely focused on the effects of listed company characteristics, institutional mechanisms, and market systems on capital markets, this study reveals the dual impact of algorithmic trading, an artificial intelligence-driven securities trading method, on both market stability and informational efficiency. These findings provide a new perspective for understanding the role of artificial intelligence technology in capital markets.
Based on the findings of this paper, the following policy recommendations are proposed to fully leverage the positive role of algorithmic trading in supporting market stability while optimizing the market information environment to mitigate its negative impact on information efficiency: First, regulatory authorities should strengthen the management of algorithmic trading transparency to enhance market comprehensibility and predictability of algorithmic trading behaviors. Second, given the significant differences in the impact of algorithmic trading on stability under different market conditions, policymakers need to establish a market stability regulatory framework specifically tailored to algorithmic trading. Third, since algorithmic trading may delay price convergence to intrinsic value, regulatory agencies should strengthen the protection and monitoring of market information efficiency.

The Chinese stock market, characterized by rapid expansion, high retail investor participation, significant information asymmetry, and substantial volatility, frequently exhibits overreaction phenomena. Financial analysts, as key information intermediaries, play a crucial role in shaping market-wide expectations. This study investigates their overreaction through the novel diagnostic expectations theory. Rooted in the psychological concept of “representativeness,” this theory argues that individuals, when interpreting new information, overweight outcomes that are more representative of a given state. This cognitive shortcut leads to systematic prediction errors and overreaction, a behavior particularly relevant in China's uncertain and competitive market environment, where analysts may be encouraged to rely on such heuristics. This research, therefore, aims to quantify this bias and explore its implications for asset prices and market sentiment.
To enhance the reliability of our estimation, this paper refines the existing Simulated Method of Moments (SMM) methodology for quantifying the degree to which an analyst's judgment is distorted by the representativeness heuristic. Our primary methodological contributions are twofold. First, we introduce a Block-Bootstrap procedure to construct confidence intervals for the estimated parameters. This non-parametric approach addresses the potential non-normal distribution of parameter estimates and strengthens the statistical robustness of our findings. Second, to mitigate the subjective bias associated with specific moment condition selection for the estimation, we systematically traverse all possible combinations of valid moment conditions. This ensures that our results are not contingent on an arbitrary choice of model specifications, thereby providing a more robust and statistically sound estimate of the diagnostic expectations parameter in China.
Our research yields several key findings. We find strong evidence of overreaction among Chinese stock market analysts. The baseline estimation for the key parameter measuring this bias is not only statistically significant but also more than double the value estimated for the U.S. market, indicating a substantially stronger influence of this cognitive bias in China. The bias is more pronounced for firms with higher risk profiles, specifically those with poor liquidity, high financial leverage, and low institutional ownership. After decomposing forecasts into a rational component and a bias component, we find this bias has significant economic consequences. It drives a time-varying relationship with future stock returns, where a long-term negative correlation is consistent with the classic overreaction hypothesis and subsequent price reversal. Moreover, the bias exhibits an inverted U-shaped relationship with future market sentiment, with the effect being more pronounced and rapid in online media compared to traditional print media.
This study makes several contributions to the literature. First, it enhances the estimation of the diagnostic expectations model by incorporating Block-Bootstrap and a systematic traversal of moment conditions, improving the robustness and reliability of the empirical results. Second, it provides strong evidence for the applicability of diagnostic expectations theory in the Chinese context, a crucial emerging market, quantifying a key behavioral bias that drives analyst behavior. Most importantly, by decomposing forecasts and linking the bias component to subsequent market outcomes, it offers a novel channel for understanding the relationship between analyst cognitive biases, long-term stock return predictability, and the dynamics of market sentiment. These findings provide valuable insights into understanding overreaction for investors seeking investment strategies and for regulators concerned with market stability.

Share pledging serves as a crucial financing method for controlling shareholders in China's listed companies. By pledging shares, controlling shareholders can obtain financing from financial institutions using their holdings as collateral. However, share pledging carries high-risk characteristics. If a company's stock price declines rapidly or the borrower fails to repay the loan upon maturity, the controlling shareholder may face the risk of forced liquidation of pledged shares, potentially leading to a loss of control. A sharp decline in stock prices can easily trigger discounted share sales and risk contagion, thereby evolving into systemic risks. Therefore, clarifying the economic motivations and governance mechanisms behind controlling shareholders' share pledging holds significant practical importance for preventing financial risks and promoting high-quality development of listed companies.
We innovatively integrate prospect theory with the behavioral theory of firms to propose a corporate behavior framework that accounts for decision-maker heterogeneity. Since both prospect theory and the behavioral theory of firms originated in Western countries where corporate ownership structures are dispersed and management serves as key decision-makers, existing literature has primarily investigated reference point effects from a managerial perspective. However, China's corporate landscape features high ownership concentration, with controlling shareholders of listed companies often serving as de facto controllers, whose risk preferences may also be influenced by performance reference points. Theoretical analysis demonstrates that when corporate performance falls below the reference point, controlling shareholders operate in a loss domain, leading to increased risk appetite. This paper accordingly proposes that when corporate performance falls below the reference point, controlling shareholders exhibit enhanced risk-seeking tendencies, increasing the likelihood of engaging in high-risk financing through share pledging.
This study selects A-share listed companies in China from 2000 to 2022 as its sample, using the median performance of firms in the same industry with identical ownership types as the reference point to empirically test the impact of corporate performance relative to this benchmark on controlling shareholders' share pledging. The findings indicate that when corporate performance falls below the reference point, both the probability and proportion of share pledging by controlling shareholders increase significantly. To alleviate endogeneity concerns, this paper employs regression discontinuity estimation, sample matching methods, and the exclusion of other reference.points, with the results remaining robust. In analyzing economic motivations, this paper categorizes pledging behaviors from the perspective of individual controlling shareholders into three types of motivations: first, investment motivation, where controlling shareholders use funds obtained from pledging to invest in other projects or financial markets with higher returns; second, tunneling motivation, where controlling shareholders use pledging to expropriate listed companies or transfer profits to safeguard personal gains; and third, propping motivation, where controlling shareholders use pledging to raise funds for listed companies to alleviate financing constraints. Empirical results demonstrate that when performance falls below the reference point, high pledge ratios are often associated with external investments or tunneling behaviors, while low pledge ratios more frequently reflect objectives supporting corporate development. Regarding the governance effects of external shareholders, both minority investor supervision and state capital participation suppress controlling shareholders' share pledging motivations, while institutional investors adopt a contingent approach, promoting propping motivations while inhibiting tunneling motivations.
The main contributions of this paper are threefold: First, by integrating prospect theory with the behavioral theory of firms, it proposes a corporate behavior framework incorporating decision-maker heterogeneity, revealing changes in risk preferences of controlling shareholders as core decision-makers when performance deviates from reference points, thereby enriching the research paradigm of the behavioral theory of firms. Second, it identifies the economic motivations through which performance reference points influence share pledging and clarifies the relationship between share pledging ratios and different motivations, providing new insights for identification and categorized regulation. Third, it reveals the governance effects of external shareholders on share pledging, demonstrating that minority investor supervision and state capital participation suppress share pledging motivations, while institutional investors exercise contingent governance. These findings provide empirical evidence for improving the share pledging risk governance system in China.
Overall, we find that corporate performance reference points serve as crucial psychological and behavioral benchmarks explaining controlling shareholders' share pledging behavior. When corporate performance falls below the reference point, controlling shareholders' risk preferences increase, leading them to favor financing through share pledging. Their behavioral motivations include investment motives, tunneling motives and propping motives. This paper provides policy implications for regulatory authorities to improve categorized regulation, strengthen external supervision, and prevent systemic risks.

The evolution of the mutual fund industry, along with growing retail investor participation, has made mutual funds a significant investment vehicle for individual investors in China. However, a persistent issue in China's mutual fund market is the significant gap between the actual returns of fund investors and the returns of the funds. Behavioral biases, or deviations from optimal investment decisions, are primary drivers of this phenomenon, and existing studies have explored investor biases from various perspectives. Central to all financial activities, information flow makes the news media a vital channel for market-relevant news that guides asset allocation choices. Understanding how news media affect fund investors in the process of obtaining and using financial information, particularly reports on fund managers, is crucial to addressing behavioral biases. However, few studies have employed micro-level trading data to investigate the origins and mechanisms of these biases from the perspective of news media, especially regarding media coverage of fund managers.
In light of this, using micro-level trading data from individual investors provided by a major brokerage firm, this paper examines the impact of media attention of fund managers on individual investors' trading behaviors and explores the underlying mechanisms from the perspective of limited attention bias. Empirical findings reveal that media coverage about fund managers significantly affects net subscription behaviors. Specifically, funds managed by fund managers with more media coverage experience higher net subscription rates among individual investors. If a fund manager was reported by the media last month, the average net subscription rate of investors is 2.4% higher than that of funds whose managers were not reported. For every 1% increase in the number of media coverage, the average net subscription rate of investors increases by 1.2%. In terms of the mechanisms underlying this effect, media coverage of fund managers enhances the salience of the highlighted managers in investors' choice sets. This effect increases net subscription behaviors primarily through drawing investor attention. Additionally, media coverage of fund managers does not predict future fund returns, and the trading behaviors driven by such coverage fail to generate excess returns. Finally, in terms of heterogeneity, investors without professional experience, male investors, middle-aged and older investors, those with lower asset levels, and those with higher trading frequency are more susceptible to media coverage of fund managers, leading to increased net buying of funds managed by covered managers. Funds with higher past returns, smaller size, and shorter duration exhibit stronger investor response to such media coverage. Articles published in media outlets with higher impact and those with shorter length exert a greater influence on investors' net purchase behavior, and media coverage of star fund managers has a more pronounced effect.
Compared to the existing research, this paper has three marginal contributions. First, this paper provides more detailed micro-level evidence for literature on factors affecting investors' subscription and redemption decisions. Prior research has primarily relied on net inflow of funds as a proxy for individual investor behavior. This paper constructs a high-frequency, accurate monthly net subscription rate metric using all fund transaction records from clients of a leading brokerage firm. It reveals that media coverage of fund managers significantly influences individual investor behavior. Second, the study extends the understanding of media influence on financial market participants. While existing research has focused on media's effect on asset prices, few have explored how media coverage impacts individual investor behaviors directly. By focusing on the role of media coverage of fund managers, this study highlights the catagory-learning feature of investor attention. Third, this paper offers a novel perspective to explain the significant underperformance of investor returns compared to fund returns in China's fund market. Existing literature focuses on traditional behavioral biases such as the disposition effect and gender bias. In contrast, this study finds that media coverage of fund managers triggers investors' irrational attention through the salience effect, driving net purchase behavior that does not predict future fund performance and ultimately leads to investor losses. Therefore,it provides a theoretical basis for enhancing investor education and promoting high-quality development of the industry.