As China's population structure and economic structure undergo a rapid transition, its labor market is also seeing significant changes, with more severe structural unemployment as its principal contradiction. Structural unemployment is caused by a mismatch between the labor supply structure and labor demand structure, which makes it difficult both for workers to find jobs and for firms to hire workers. This paper focuses on the role of the artificial intelligence (AI) revolution and well-functioning government in structural transformation and structural unemployment, offering a new perspective on structural unemployment and related policy implications. The literature on structural transformation and AI has largely overlooked structural unemployment and home production. This paper is the first to focus on explaining structural transformation and structural unemployment from the perspective of AI and home production.
We present a multi-sector general equilibrium dynamic model with structural changes in employment and production, in which time is allocated between the market and home. In the model, high-skill and low-skill labor are employed in skill-intensive and unskilled-intensive industries, respectively, in the market and home. We show that households' time allocation between the market and home determines the labor supply structure and employment structure and, in turn, the industrial structure. The employment structure and industrial structure simultaneously affect the labor demand structure and time allocation. Moreover, AI technology may initiate a revolution in the production processes in the market and home. Thus, the key insight for understanding the relationship between AI and structural unemployment is that AI simultaneously promotes capital deepening in the market and home, which alters the transitional path of employment structure and production structure, resulting in a considerable impact on structural unemployment.
We find that changes in labor supply may aggravate the mismatch between the demand structure of high-skill and low-skill labor and the supply structure of labor with high and low levels of education, causing more severe structural unemployment. The AI revolution simultaneously promotes the structural transformation of production in the market and home, which may cushion the negative impact of structural unemployment. More specifically, because high-skill labor has a comparative advantage in skill-intensive industries, when the elasticity of substitution between industries is low and the elasticity of smart machines and labor in unskilled-intensive industries is high, the AI revolution in unskilled-intensive industries speeds up the process of machines replacing labor, and increases the relative price of skill-intensive industries. As a result, both high-skill and low-skill labor are drawn from unskilled-intensive industries to skill-intensive industries, leading to higher relative demand for high-skill labor; thus, the process of structural transformation helps tackle structural unemployment. Similarly, because low-skill labor has a comparative advantage in home, when the elasticity of smart durables and labor in home is high, the AI revolution in home speeds up the process of durables replacing labor and increases the opportunity cost of home production. As a result, both high-skill and low-skill labor are drawn from home to the market, leading to a greater relative supply of low-skill labor, which also helps tackle structural unemployment. If the rapid structural transition of the labor supply and the slow development of AI technology intensify structural unemployment, the government should invest in digital infrastructure and new infrastructure in unskilled-intensive industries and home, which would effectively tackle structural unemployment by accelerating the AI revolution and structural transformation.
To tackle structural unemployment and promote structural transformation, we derive the following policy implications from our findings. First, the government should support and guide research and development into AI technology in labor-intensive industries and unskilled-intensive industries, conforming to the law of technological revolution in AI and new digital technology. Second, the government should initiate a technological revolution in home production using AI technology and increase the demand for smart durables, which may be an effective tool to simultaneously expand domestic demand and deepen supply-side structural reforms. Third, the government should comprehensively strengthen the construction of digital infrastructure and new infrastructure, build a modern infrastructure system, and promote the integration of the digital economy and real economy. Fourth, the government should strengthen the strategic layout of talent, improve the system of lifelong vocational training, and build a talent team with a rational structure and high quality to achieve a better match between the labor supply structure and skill demand structure.

Climate change has become a major challenge to all of humankind, with profound impacts on the financial and economic system. As an extreme weather event occurring with high frequency and intensity, typhoons not only cause great damage to economic activities but also have a powerful impact on the financial sector, which in turn triggers a series of chain reactions and feedback behaviors. The literature on the impact of typhoon disasters on banks' risk behavior is still insufficient. Researchers face certain difficulties when applying existing climate risk indicators to study typhoon disasters. Additionally, the literature on typhoon research has tended to focus on the post-disaster resumption problems of real enterprises and the operation of the capital market, with less research on the impact on the banking sector, which is an important participant in economic operations. China's financial system is still dominated by the bank-led indirect financing system, and commercial banks are the first to bear the brunt of climate disasters.
In view of this, this paper empirically examines the impact of typhoon disasters on banks' risk behavior by taking 295 local commercial banks in China during 2010-2019 as research objects, matching the operational data of 3452 A-share listed companies, and combining these with the Holland wind field model to calculate a typhoon destructive power index at the city level. The paper also examines the mediating effects of corporate total factor productivity and corporate fixed asset losses, and it further analyzes the resulting series of chain reactions and feedback behaviors, including changes in banks' risk appetite and credit decisions. This paper's empirical analyses yield several findings. First, typhoon disasters significantly raise banks' non-performing loan ratios (NPLRs) and increase banks' credit risk, and they raise banks' NPLRs significantly more in coastal areas than in inland areas. Second, the rise in corporate fixed asset losses and the decline in corporate total factor productivity levels are important channels through which typhoon disasters affect banks' NPLR ratios. Third, further analyses show that the process by which typhoon disasters' effect on financial institutions is transmitted is not monolithic. Typhoon disasters first hit the real economy, then are transmitted to the financial system, and finally swing back to the real economy. Specifically, after being affected by a typhoon disaster, the production and operation of enterprises fall into “economic difficulties,” which are transmitted to the bank level, making banks' NPLR increase (i.e., an increase in passive risk-taking). This further affects banks' future credit decision-making, such as causing them to tighten the extent of credit and reduce their appetite for risk (i.e., a decrease in active risk-taking). This, in turn, feeds back to the enterprise level, increasing the cost of financing for enterprises and ultimately magnifying the impact of the typhoon disaster on overall economic and financial activities.
The possible innovations of this paper are as follows. First, the paper empirically tests the impact of typhoon disasters on the credit risk of China's commercial banks by using indicators of typhoons' destructive force, and verifies the hypothetical mechanism by which typhoon disasters affect bank credit risk. This enriches research on climate finance and the empirical literature on the association between bank risk and other climatic disasters in China. Second, this paper analyzes the “feedback loop” characteristic of typhoon disasters, i.e., the series of feedback behaviors that are brought about by typhoon disasters and impact bank risk, which provides theoretical support for the financial industry to assess climate risk and a reference for the design of subsequent related research. Finally, this paper calculates the asset loss caused by typhoon disasters to enterprises through a wind field model, which can provide a scientific basis for financial institutions to make future lending decisions and help avoid the influence of pro-cyclical thinking.
This study provides new empirical evidence for banks' climate risk governance. The policy implication of this paper is clear: it is important to pay attention to the banking risk and financial stability issues arising from climate change such as typhoon disasters. Considering the possibility of typhoon attacks in the future, on the one hand, banks and other financial institutions should make good preparations for short-term typhoon-induced credit losses, improve ex-ante collateral prevention preparations, control credit quality in a timely manner, and also strengthen co-operation with insurance companies so as to make preparations for double-insurance of collaterals both ex-ante and ex-post. On the other hand, banks and other financial institutions should incorporate climate risk into risk management and improve the identification and quantification of climate risk, regularly implement climate and environmental risk stress tests, and calculate the probability of default and the default loss rate of lending enterprises under different scenarios, so as to ensure that the financial sector will fully consider the climate and environmental risks.

Innovation is an important factor in economic growth and prosperity. Innovation includes both technological and non-technological innovation. Research has focused on the influencing factors of technological innovation. Relatively little literature has examined the factors influencing non-technological innovations, especially the impact of the macroeconomic environment. The financial system plays an important role in the allocation of innovation resources. There may be differences in the impact of different financing modalities, such as banks and stock markets, on innovation. Most previous studies have examined the impact of a single financing modality, such as bank loans. Fewer have provided a comprehensive and systematic comparison of the various financing modalities.
Therefore, we explore the factors driving technological innovation and non-technological innovation based on data from the 2020 National Enterprise Innovation Survey in China at the prefectural and municipal levels. Using a seemingly unrelated regression (SUR) model based on a system of simultaneous equations, we compare the impacts of bank loans, the stock market, the bond market and venture capital investment on technological and non-technological innovation. Our aim is to answer the following questions: whether the support of the banking sector, which is dominant in China's financial system, is stronger or weaker than that of other financing modalities; and how to guide the banking sector to promote the synergistic development of technological and non-technological innovations.
This paper makes the following findings. Each financing modality has a significant role in promoting technological and non-technological innovation. Bank loans play a smaller role in technological innovation than other forms of financing. The role of bank loans in non-technological innovation is smaller than those of the stock market and venture capital, and larger than that of the bond market. When firms are constrained by financing, the expansion of bank loans promotes only non-technological innovation, while other forms of financing still promote both technological and non-technological innovation. Intellectual property protection, financial guarantees, and digital finance guide bank loans to promote synergies between technological and non-technological innovation. Factors affecting technological innovation also include openness to the outside world, market competition, cultural atmosphere, economic base, and entrepreneurial qualifications. Factors affecting non-technological innovation also include openness to the outside world and the age of entrepreneurs. Therefore, we put forward a series of policy recommendations, including encouraging non-technological innovation, improving the multi-level financial market system, upgrading the ability of banks to serve enterprises in innovation, and strengthening the protection of intellectual property rights.
Relative to existing studies, the main contributions of this paper are as follows. In terms of theory, first, this paper enriches and complements the regional innovation literature. Previous regional innovation studies have mostly focused on technological innovation and neglected non-technological innovation. In this paper, we incorporate both types of innovation into the model to comprehensively examine the current state of innovation at the city level in China and analyze the regional differences in innovation path choices. Second, this paper expands the theory of non-technological innovation. Most previous studies on the influencing factors of non-technological innovation have considered enterprise micro-factors. This paper utilizes prefecture-level city data to explore the impact of the regional economic and social environment, and it reveals the driving factors of non-technological innovation at the city level. Third, this paper compares the impacts of bank loans and other financing modalities on innovation. The degree of matching between the risk appetite characteristics of the investor and the capital demand characteristics of the financier determines the difference in impact on technological innovation. For non-technological innovation, the difference in impact is determined by whether each financing modality can effectively fulfill the function of corporate governance. The magnitude of financial pressure exerted on firms by each financing modality leads to differences in the impact on innovation decisions. In terms of empirical analysis, first, previous studies on non-technological innovation in China are mostly based on small-scale questionnaire surveys and lack basic and global data support. The data source in this paper is the National Enterprise Innovation Survey conducted by the National Bureau of Statistics, which is the largest and most specialized and authoritative innovation survey in China. Our research can provide an effective policy reference basis for promoting the coordinated development of regional innovation. Second, in this paper, technological innovation and non-technological innovation are both used as explanatory variables, and a SUR model is used for parameter estimation, with the aim of introducing a mutually reinforcing relationship between the two innovation types into the model. Third, we use innovation survey data to construct financing constraint indicators to intuitively reflect the subjective feelings of firms regarding innovation financing dilemmas. We also construct an indicator to reflect the financial guarantees for prefecture-level municipalities.

Against the backdrop of a sluggish global economy and pervasive uncertainties, understanding the micro-decision-making mechanism of residents' consumption is critical to unleash consumption potential, promote consumption upgrades, stimulate domestic demand, and foster high-quality economic development. Financial investment serves as a pivotal tool for consumers to smooth consumption throughout their lives. Premised on financial market frictions, investment income from financial assets emerges as a significant source of property income, demonstrating a greater marginal propensity to consume compared with wage and household business incomes, thereby inducing a wealth effect. With the popularization of mobile Internet and the rapid development of digital finance, residents increasingly engage with online wealth management products via Fintech platforms, facilitating their participation in financial markets and accumulation of property income.
Based on the classical wealth effect theory, this study randomly selects the financial behavior and consumption behavior data of about 30,000 active users from a unicorn-level Fintech platform in China—Alipay—and analyzes the impact of online wealth management on residents' consumption expenditure and consumption structure. The main conclusions are as follows. (1) Online wealth management investment income significantly promotes residents' consumption, especially e-commerce consumption. This may be by allowing consumers to quickly switch between wealth management and e-commerce apps through smartphones, and through shortening the time lag of the conversion of online wealth management investment income into consumption. After resolving the potential endogeneity problem using the DML model and by using consumption expenditure growth rate as the explained variable, the empirical results still show that online wealth management investment income significantly promotes household consumption, especially e-commerce consumption. (2) As the sample comes from a Fintech platform and does not represent participants in the wider economy and society, to verify the robustness and reliability of the benchmark results we further use data from the China Household Financial Survey 2019 (CHFS-2019) for cross-validation. The empirical results still verify that online wealth management investment income significantly promotes household consumption expenditure, especially e-commerce consumption, indicating that the wealth effect of online wealth management is robust. Furthermore, the results of intermediary mechanism analysis show that online wealth management investment income promotes residents' consumption by optimizing the income structure. (3) Focusing on the extent of the wealth effect among the sinking population, when the explained variable is consumption expenditure, the coefficient of the interaction between online wealth management investment income and the degree of sinking changes from negative to positive as the degree of sinking increases, but the change is not significant. Therefore, the wealth effect of online wealth management does not have a significant sinking effect. (4) To further promote high-quality development, it is necessary not only to effectively improve residents' consumption expenditure but also to upgrade their consumption structure. Based on this, we further study the impact of online wealth management investment income on the upgrading of residents' consumption structure from a structural perspective. The results show that online wealth management investment income has a significant promoting effect on residents' expenditure on various goods on e-commerce platforms, and it has a particularly significant positive impact on the proportion of development and leisure consumption, which helps to promote the upgrading of residents' consumption structure.
Compared with previous studies, the possible marginal contributions of this article are as follows. (1) Based on micro-data, namely individuals' monthly online wealth management and consumption behavior on a unicorn-level Fintech platform, the study verifies the wealth effect of online wealth management and further explores the heterogeneity of this effect among sinking groups such as rural residents, people in western regions, and residents in third-tier cities. (2) The study expands the research perspective on the wealth effect of online wealth management from that of consumption expenditure to that of consumption structure, and explores the heterogeneous impact of online wealth management investment income on basic, development, and leisure consumption, providing useful empirical evidence to further standardize the development of online wealth management in its current development stage and promote the upgrading of residents' consumption. (3) The study uses CHFS-2019 data to conduct cross-validation on the wealth effect of online wealth management, and further reveals the mechanism by which online wealth management promotes consumption, i.e., by optimizing residents' income structure.

The demand for long-term rental housing continues to grow as an increasing number of households choose to rent instead of buying their first homes. In major cities such as Beijing, Shanghai, Guangzhou, Shenzhen, and Hangzhou, the proportion of renters among the total population reached 40% by 2022. This paper systematically examines the relationship between housing price growth expectations and long-term rental demand using a proprietary dataset comprising over 400,000 rental contracts from a long-term rental platform and approximately 500,000 second-hand housing transactions in Beijing between 2015 and 2019.
We address the empirical challenge of measuring long-term rental demand by analyzing leases through the innovative business model of a long-term rental platform. Acting as a “second landlord” in the rental market, the platform signs long-term leasing contracts with property owners, acquires properties with fixed rents, and sublets them to tenants after standardizing renovation and furnishing. To minimize vacancies and maximize profits, the rental platform prioritizes tenant renewals. Therefore, tenant renewals with the platform serve as a reliable proxy for long-term rental demand unaffected by landlords.
To investigate the impact of housing price expectations, we exploit the implementation of the differentiated housing purchase restriction (HPR) policy in Beijing in 2017 as an exogenous shock. We find a significant negative (positive) correlation between house price growth in the tenant's neighborhood and the tenant's renewal rate before (after) March 2017. Prior to the HPR policy, during a period of rapid house price growth, a 1% increase in house price growth is, on average, associated with a 2.2% decrease in tenant renewal rate after controlling for tenant, leasing, and rental property characteristics and including neighborhood and year-month fixed effects. In contrast, following the HPR policy, a 1% increase in house price growth is significantly associated with a 1.9% increase in tenant renewal rate. Our pre-trend analysis confirms that the HPR policy is critical to reversing the impact of house price growth on long-term rental demands.
Our results suggest that as the HPR policy stabilizes house price expectations, tenants residing in neighborhoods with faster house price growth become more inclined to choose long-term rentals as their housing solution. Tenants who continue renting from the platform are more likely to switch to new leases with higher rent and to entire rentals, indicating a trend of moving up the housing ladder as they decide to rent instead of buying. We further find that in the post-HPR period, house price growth in multiple past periods is positively related to tenant renewal rates, with stronger effects observed as the renewal time approaches. Moreover, both the volatility and dispersion of house price growth are significantly and positively associated with tenant renewal rates.
Our empirical findings align with the predictions of extrapolative expectations theory and diagnostic expectations theory in the economics and finance literature (Malmendier and Nagel, 2016; Bordalo et al., 2018; Bordalo et al., 2020). During house price booms, tenants extrapolate recent house price information and conclude that prices will continue to rise in the future. However, the introduction of the HPR policy serves as a diagnostic signal that prompts tenants to adjust their expectation formation mechanism and recognize the potential downsides.
Apart from housing price expectations, the HPR policy may impact tenants' demand for long-term rentals by increasing the cost of home purchases. However, the differentiated HPR policy implemented in Beijing aims to deter speculative home purchases while safeguarding reasonable home purchase demand, thus having a relatively minor impact on first-home buyers. Notably, 94% of the tenants in our sample are non-local residents with an average age of 29 years, suggesting that most of them, who are eligible to purchase their first home, are not directly affected by the tightened credit constraints of the HPR. This is supported by a heterogeneity analysis showing that the effects are not related to the age of tenants but are more pronounced among tenants from less developed provinces. These pieces of evidence collectively indicate that direct credit restrictions are not the primary mechanism driving our results.
This paper makes several contributions to the literature. First, we address the empirical challenge of measuring tenants' willingness to rent for an extended period by utilizing a unique dataset from a long-term rental platform, which ensures that the renewal information reflects tenants' decision-making rather than supply-side factors. Second, we verify the spillover effects of the housing transaction market on tenants' demand for long-term rentals, thereby enriching the research on the relationship between the housing sales market and the rental market. Third, we provide empirical evidence of the application of extrapolative expectations theory and diagnostic expectations theory in real decision-making processes regarding tenants' rental renewal choices. Our empirical findings demonstrate the crucial role of future house price growth expectations in influencing residents' demand for long-term rentals.

In the face of environmental issues and limited resources, how to coordinate the relationship between development and emission reduction, while alleviating business cycle fluctuations and encouraging enterprises to take the initiative in emission reduction, is of great significance for achieving a “win-win” situation between the economy and environment. To answer this question, it is necessary to first clarify the relationship between business cycles and corporate emission reduction behavior and environmental performance. Thus, this paper focuses on pollution emission intensity, aiming to explore the dynamic processes, internal mechanisms, and policy responses of different corporate reduction behaviors in different business cycles in China, to facilitate green and high-quality enterprise development.
Based on the pollution data of Chinese industrial enterprises from 1998 to 2013, we observe the countercyclical characteristics of pollution emission intensity. To explain this finding, this paper constructs a two-sector DSGE model with environmental constraints to analyze the relationship between business cycles and pollution emission intensity. In the model, both high-emission and low-emission projects are established. Low-emission projects introduce cleaner production equipment into the production function, so their pollution emission intensity is lower than in high-emission projects, resulting in lower environmental negative externalities. The model also includes enterprise heterogeneity with respect to management ability for cleaner production equipment, such that enterprises' choice of optimal project depends on their own ability to manage low-emission projects. Specifically, enterprises with high management skills are more likely to choose low-emission projects. To describe the externality of pollution emissions, this paper introduces pollutant-adjusted labor into the household utility function. This setting helps to elucidate the general equilibrium effects of pollution emissions on the macro-economy. Subsequently, through impulse response functions, this paper analyzes the key mechanism underlying the countercyclical characteristics of pollution emission intensity from both the enterprise and household sectors as well as policy response methods.
The results show that when the economy is in a downturn, the return on investment falls more sharply for enterprises that invest in low-emission projects than for those that invest in high-emission projects, leading to a decrease in the number of enterprises investing in low-emission projects and a rise in pollution emission intensity. At the same time, as pollution emission intensity rises, it increases the negative externality of pollution emissions on labor, which lowers enterprise profits through decreased labor supply and increased labor costs, further hindering sustainable economic development. Policy analysis shows that when the economy is in a downturn, policies that target pollution emission intensity, such as emissions taxes, price subsidies for cleaner equipment, and credit restrictions on polluting enterprises, can effectively curb the rise in pollution emission intensity. Specifically, emissions tax policies and subsidy policies have positive effects on the environment through structural effects in the enterprise sector, helping to promote corporate low-emission transformation.
The marginal contribution of this paper mainly lies in discovering a negative relationship between US manufacturing output and industry-specific pollution emission intensity documented in the existing literature. However, the relationship between China's business cycle and pollution emission intensity remains unknown. Based on empirical analysis, we find that pollution emission intensity is countercyclical. Moreover, we construct a model to attempt to explain this feature, providing preliminary empirical evidence and theoretical support for the relationship between business cycles and pollution emissions. This paper focuses on analyzing the short-term impacts of taxation, subsidies, and other environmental policies. Future research directions include studying long-term policy effects based on growth models from cost-benefit perspectives.

Investor protection is an essential requirement for the sustained and healthy development of capital markets. The construction of legal systems is crucial for investor rights, decision-making power, and oversight, forming the foundation for safeguarding investor interests. When legal frameworks are incomplete, companies may neglect precautions against misconduct, potentially engaging in speculative behavior and evading responsibility. Simultaneously, regulatory authorities face challenges in enforcement, and corruption may thrive. In the absence of strong legal constraints on corporate behavior, investors are susceptible to information concealment or manipulation, leading to erroneous decision-making and a lack of protection for investor interests.
Abnormal stock price fluctuations represent a significant avenue through which investors' interests are compromised in capital markets. Research has indicated that companies employ tactics such as earnings management and selective information disclosure to conceal adverse news, releasing accumulated hidden information to the market, resulting in stock price crashes. Companies with poor transparency face more severe risks of stock price crashes. The literature has predominantly analyzed ways to reduce abnormal stock price fluctuations and crash risks from a micro-perspective. However, the integrity of macro-legal systems also plays a crucial role in influencing stock price volatility and investor protection. Inadequate legislation provides opportunities for corporate concealment. When legal systems are imperfect, companies may sacrifice long-term interests for short-term gains, laying the groundwork for subsequent operational and sudden stock price risks.
Using a sample of Chinese A-share listed firms, this paper takes the implementation of China's Environmental Protection Law in 2015 and the enactment or revision of local environmental protection laws and regulations from 2005 to 2016 as natural experiments and examines the impact of environmental legislation on the protection of investors' interests and the corresponding mechanisms from the perspective of abnormal fluctuations in the capital market. We find that environmental legislation significantly increases the behavioral constraints of heavily polluting firms, compared with non-heavily polluting firms, and reduces the risk of a stock price crash. The channels of this effect involve environmental legislation inducing heavily polluting firms to increase investment in environmental protection and improve the quality of their environmental information disclosure. Moreover, after the introduction of environmental legislation, the risk of a stock price crash is more pronounced for heavy polluting enterprises with more environmental protection investment and higher-quality environmental information disclosure. In addition, we find that polluting companies audited by high-quality auditors or tracked by analysts show a more significant reduction in stock price crash risks after the implementation of environmental legislation, indicating a complementary relationship between external supervision and environmental legislation.
The contributions of this paper are threefold. First, utilizing a natural experiment of environmental legislation, the study reveals the impact of a robust legal system on investor protection in capital markets. Previous research on investor protection has mainly focused on the role of corporate governance, with conclusions about the impact of legal regulations often derived from cross-national and cross-regional comparisons. This study finds that the strengthening of environmental legislation increases constraints on the behavior of polluting companies, reduces their operational risks, lowers the risk of abnormal stock price fluctuations, and safeguards the interests of stock market investors. Second, by using abnormal capital market fluctuations as a starting point, this paper further elucidates the economic consequences of strengthened environmental regulations. The results suggest that the impact of environmental legislation on company value does not simply correspond to changes in stock prices after the introduction of these laws but is primarily reflected in a reduced likelihood of abnormal stock price fluctuations. Third, the paper provides a new perspective on crash risks in the Chinese capital market. Previous research on crash risks has largely focused on micro-level triggering mechanisms, such as information transparency, corporate governance mechanisms, and insider trading. This paper reveals the influence of macro-factors, such as legal systems, on abnormal capital market fluctuations.

At the Central Financial Work Conference in October 2023, President Xi Jinping emphasized the need to uphold risk prevention and control as the perennial focus of the financial profession and ensure that risks are identified, revealed, and resolved as early as possible. It remains a key future task for the financial profession to improve the risk monitoring and early-warning system and the ability to reduce and control financial risk. Considering the great attention aroused by tail-risk events, such as the simultaneous crashes of both the stock and bond markets, a major priority is to identify the co-movements of risk across different markets and enhance the risk early-warning system for both the stock and bond markets. In the context of stock-bond risk co-movement, it is of both academic and practical significance to forecast stock market and bond market risk accurately and effectively using state-of-the-art machine learning techniques. This will help to overcome the limitations of traditional risk prediction methods and improve the risk monitoring and early-warning system for both the stock market and bond market. It will also contribute to identifying risk co-movements across different markets, thus preventing extreme risk events like simultaneous crashes of the stock and bond markets.
This paper conducts an analysis based on a sample of Chinese listed companies in the period from January 2015 to September 2022. First, we employ the MVMQ-CAViaR model and quantile regression method to investigate the tail-risk co-movements between the stock market and bond market. Additionally, we distinguish between the heterogeneous risk spillover relationships of companies with and without default records. The results show that stock-bond risk co-movement is more significant for companies that have defaulted.
Next, this paper utilizes state-of-the-art machine learning techniques, namely the quantile regression forest, quantile gradient boosting model, and quantile regression neural network, to construct prediction models for tail risk based on the emerging perspective of stock-bond risk co-movement. On this basis, we further evaluate the bi-directional predictive power between stock market risk and bond market risk using the quantile loss function, quantile goodness of fit, and Diebold-Mariano test. For most prediction models for the “bond market risk → stock market risk” direction, the results indicate that considering the bond market risk increases the robustness and accuracy of the forecasting of stock market risk by improving the goodness of fit and reducing fitting errors. The application of a machine learning framework including the quantile gradient boosting model, quantile regression forest, and quantile regression neural network significantly strengthens the prediction of stock market risk. In contrast, in the prediction model for “stock market risk → bond market risk”, the quantile gradient boosting model outperforms the other models in predicting bond market risk. This is attributable to the gradient boosting method, which corrects fitting errors through iterative learning, thereby better capturing the stock market risk information.
Furthermore, we divide the sample according to industry attributes to assess the heterogeneous predictive power of different models. We find evidence of asymmetric predictive power between stock market risk and bond market risk, in which a unidirectional predictive power for “bond market risk → stock market risk” is documented in most industries while a bi-directional predictive power for “stock market risk → bond market risk” is only evident in the materials, daily consumption, finance, and real estate industries. Meanwhile, under the “stock market risk → bond market risk” prediction framework, the finance industry has the most forecastable bond market risk, as it can be identified by all three machine learning models.
Finally, this paper yields policy implications for strengthening the risk prediction system for key fields in China. First, policy makers should improve the tail-risk early-warning framework for China's capital market using the new perspective of stock-bond risk co-movement. Second, state-of-the-art machine learning techniques should be promoted in the financial regulation field to enhance the financial stability guarantee system. Third, regulators should improve the industry-level financial risk forecasting system and tail-risk early warning for key fields including the finance industry.

As important information intermediaries, analysts play an indispensable role in ensuring the quality of information disclosure in the capital market by thoroughly mining valuable information and providing it to investors, thus enhancing market transparency and investment efficiency. However, the literature has not yet reached a consensus on whether analyst coverage has a positive impact on companies. Analysts' optimism bias is considered the main reason why their coverage does not always exert a positive effect. This bias not only interferes with analysts' due diligence but also triggers widespread questioning of the professional ethics and competence of securities researchers, posing a potential threat to the quality of information and operational efficiency of the capital market. In response to this issue, scholars have conducted extensive studies of analysts' capabilities and intentions. However, optimism bias remains prevalent even among analysts with high levels of skill or weak private motives. Therefore, effectively stripping away the interference of optimism bias is an important prerequisite for accurately assessing the value of analysts as information intermediaries. Considering that domestic analysts exhibit a significantly higher optimism bias than their foreign counterparts, exploratory studies are particularly important, and the focus on analysts' negative coverage provides a suitable research perspective.
Based on the aforementioned ideas, this paper focuses on corporate investment efficiency and comprehensively examines the compound impact and mechanism of action of analysts' negative coverage based on a sample of 485,366 sell-side analyst reports on A-share listed companies in China from 2009 to 2020. The main findings are as follows. First, analysts' negative coverage can simultaneously suppress overinvestment and alleviate underinvestment, significantly enhancing corporate investment efficiency. Second, the positive impact of analysts' negative coverage on corporate investment efficiency is more pronounced when companies face greater market or internal pressures, and negative recommendations have a stronger inhibitory effect on inefficient investments when they are issued by more capable and diligent analysts. Third, after correcting for potential estimation biases in the analysis of mediating effects using doubly debiased lasso (DDL) regression, it is found that the impact of analysts' negative coverage on investment efficiency encompasses both the “stick effect” of exerting short-selling pressure and the “carrot effect” of providing enhanced information. Finally, multi-mediation causal path analysis (MCPA) shows that the carrot effect is stronger than the stick effect, and the direct efficacy of institutional investors is stronger than the indirect efficacy of the capital market.
Our paper makes the following three contributions. First, by extracting negative recommendations from analyst coverage, the paper provides a scientific perspective free from the interference of optimism bias, refining the accurate interpretation of analysts' information intermediary function. In recent years, the frequent occurrence of listed company violations involving analysts in China's capital market has raised widespread concern and deep anxiety in both academic and business circles about analysts' failure to fulfill their “gatekeeper” duties. Against this backdrop, this study will help analysts return to their professional function as objective information intermediaries. Second, given the reality of severe optimism bias in Chinese analyst reports, this paper employs methods such as subsample regression, propensity score matching, CEM, and quantile regression to correct for the sparsity and asymmetry of the negative coverage sample from multiple angles. The results provide robust evidence for the conclusions of this study and offer a versatile scientific reference for solving the problem of scarce samples in empirical research. Third, leveraging the latest research findings on mediation analysis methodologies such as DDL regression and MCPA, this paper provides empirical evidence of how analyst attention affects corporate investment efficiency and offers feasible and reliable solutions for correcting biased estimates caused by omitted variables, testing the interconnectivity among multiple mechanisms, and assessing their relative importance. This enriches the cutting-edge technical methods for testing mechanisms. At a crucial time for the stimulation of corporate vitality and steady advancement of the reform of the registration system, this research will help to clarify policy directions and enhance governance efficiency.

The options market, as an important part of the capital market, promotes price discovery and risk sharing, reduces transaction costs and improves resource allocation efficiency. Since the launch of the first on-exchange option, the SSE 50 ETF option, in 2015, the trading volumes of on-exchange options have continued to grow. Meanwhile, the information transmission efficiency of financial markets is a core issue in financial economics. Well-informed investors predominantly trade in the options market due to this market's high leverage and low short-selling costs, which may lead to delays in the transmission of information between different markets. With the rapid development of China's financial options market, accurately assessing its price discovery role in the overall capital market is crucial to improving market functions and understanding market operating mechanisms.
To examine the price discovery role of China's options market, this paper constructs indicators to extract the implicit information in option transactions and tests the ability of the option-implied information to predict future returns on the stock market. First, we provide evidence that the volatility index, jump factor, and option parity formula deviation can significantly predict negative excess returns on the stock market in the next month, while those excess returns are positively and significantly predicted by the variance risk premium, volatility skewness, and difference in implied volatility changes between call and put options, among other indicators. Second, in an out-of-sample test, all the above indicators retain their predictive ability to a certain extent, and all the option-implied information indicators show significant predictive power in a linear regression model and principal component regression model. Finally, we test two hypotheses regarding the economic mechanism of price discovery in the options market: the information hypothesis and the short-selling restriction hypothesis. According to the information hypothesis, option-implied information can significantly predict the future trends of macroeconomic variables; at the same time, some option-implied indicators can also significantly predict future firm earnings information in the stock market. According to the short-selling restriction hypothesis, when the stock market faces short-selling restrictions or the cost of short-selling increases, investors with private information will be more inclined to trade in the options market, which will increase the speculative effect of this market, in turn affecting the predictive power of the option-implied information.
We contribute to the literature in three respects. First, focusing systematically on option-implied information, we assemble a series of option indicators whose ability to obtain the information contained in option trading is widely discussed in the literature. Based on the development status of China's options market, this study scientifically and rigorously explores the price discovery role of China's options market, thus filling gaps in the research on this market. Second, this paper confirms that information transmission affects price discovery in the Chinese stock market. New information tends to first enter the options market before entering the stock market, causing a time delay in information transmission between the two markets, allowing the options market to effectively predict the future price of the stock market. In terms of information content, this study also confirms that the options market contains macroeconomic and firm earnings information in advance of the stock market acquiring this information. Finally, the study enriches research on the spillover effects of capital market regulatory policies. A series of short-selling restrictions, such as stock index futures trading policies and stock market futures discount rates, can also be expected to affect the predictive power of option-implied information.

To actively respond to the complex and ever-changing external situation, and to rationally promote domestic supply-side reforms, China has consistently emphasized increasing domestic openness, as well as adhering to a high level of openness to the outside world. In April 2022, the “Opinions of the Central Committee of the Communist Party of China and the State Council on Accelerating the Construction of a Nationally Unified Large Market” was officially published. This document described the government's aim to reduce and even eliminate institutional and market barriers among regions. In this context, an important requirement for China to achieve steady progress in the economy is to utilize a unified large market to allocate resources, optimize division of labor, and promote growth. Based on the perspective of inter-regional supply-side cooperation among enterprises, this paper examines the impact of the construction of a unified large market on firms' procurement behavior.
From a theoretical perspective, this study innovatively constructs models of the impact of market integration on cross-regional procurement choices, considering both macro-industry connections and micro-enterprise behavior. It argues that if a unified large market is constructed, enterprise procurement choices will lean toward the locations of suppliers with a higher degree of market integration. From an empirical perspective, this study performs numerical simulation to demonstrate, at the macro-industry level, that an increased level of integration in a specific province will induce other regions to procure more intermediate goods from that province's integrated market. Additionally, based on data from the China Statistical Yearbook and CSMAR National Database for the years 2010 to 2019, along with data on the external policy impact of the Three-Year Action Plan for the Integrated Development of the Yangtze River Delta, the study identifies the impact of market integration on the cross-regional procurement choices of micro-enterprises.
The findings are as follows. First, from the perspective of macro-industry linkages, the construction of a unified large market will promote inter-regional trade in intermediate goods. At the micro level, this will be primarily manifested in enterprises choosing to procure more from regions with a higher degree of market integration. Second, the empirical results reveal that an increase in the degree of market integration will alter the location choices of enterprise procurement. Specifically, enterprises will be more inclined to choose regions with a higher degree of market integration for their procurement. This effect will be strengthened by the Three-Year Action Plan for the Integrated Development of the Yangtze River Delta. Third, for enterprises that are more cost-sensitive, such as those with lower productivity, non-state-owned enterprises, those located in the central and western regions, and service-oriented enterprises, the impact of adjusting their procurement location choices due to changes in market integration is more pronounced.
The study's contributions are as follows. First, from a research perspective, it is the first study to investigate the impact of the construction of a domestically unified large market on the regional cooperation relationships between upstream and downstream enterprises. Second, it establishes theoretical models related to market integration and the domestic circular economy from both macro-and micro-perspectives. Third, the study validates the relevant conclusions of the theoretical model through numerical simulation and regression analysis. Finally, building on the identification of the relationship between market integration and enterprise procurement location choices, the study also explores, from a heterogeneity perspective, the “relief and resolution” effects that the integration of supplier locations may have on cost-sensitive enterprises.