The report of the 19th National Congress of the Communist Party of China (CPC) calls for improving the regulatory framework underpinned by both monetary policy and macro-prudential policy (hereinafter referred to as the “two-pillar” policy framework, ensuring that no systemic risks occur, and maintaining the overall stability of the economy and financial system. In theory, the traditional monetary policy framework is apt for total volume control. Although it plays a fundamental role in maintaining economic and financial stability, the policy framework anchored by CPI cannot effectively prevent financial imbalances and systemic risks in specific areas. At the same time, macro-prudential policies can make targeted adjustments based on the monetary policy, and can thus better achieve the objectives of financial stability and the prevention of systemic risks.
Although the research on macro-prudential policies is abundant and has been enriched constantly, there are still some controversies on whether the inclusion of macro-prudential policies is conducive to resisting economic and financial shocks and the role of counter-cyclical adjustment tools in macro-prudential policies, which call for further research. In particular, there are few studies on how to form a reasonable and effective coordination between monetary policy and macro-prudential policy under the “two-pillar” framework. In light of the practical situation of China's economy, this paper tries to construct a DSGE model with four departments based on the Tayler & Zilberman (2016) model. This paper complements and extends the existing literature in the following three basic aspects. First, this paper introduces collateral constraint and several key financial variables, such as the probability of default, non-performing loan ratio and the probability of collateral recovery to the traditional DSGE framework, and identifies the specific impacts of different financial shocks and the endogenous transmission mechanism between finance and entity economy. Second, the paper introduces the central bank and describes a policy portfolio that includes both monetary policy rules and macro-prudential policy rules, and provides basic modeling design for the analysis of the “two-pillar” regulatory system. Third, based on the improved model framework, this paper examines a number of alternative pegging variables for the macro-prudential policy, and comprehensively analyzes whether and how the macroprudential policy can help to achieve the goal of financial stability and economic stability when facing different economic or financial shocks.
The purpose of this study is to investigate the effect of the coordination of monetary policy and macro-prudential policy on economic and financial stability, especially in the face of economic and financial shocks. Bringing the “two-pillar” policy framework and the financial department into a DSGE model, this study reached three basic conclusions. First, the coordination of monetary policy and macro-prudential policy exerts a better effect on stabilizing the economic and financial system than pure monetary policy tools. Second, the “two-pillar” policy framework is especially effective in stabilizing the economic and financial system when facing with financial shocks. This result implies that macro-prudential policy plays a complementary role to monetary policy by stabilizing financial system, whereas monetary policy focuses on stabilizing the entity economic system (output and inflation). Third, the “two-pillar” policy framework helps improve the stability of the economic and financial system regardless of whether the monetary policy tool is price-based or quantitative. As a result, the effectiveness of the “two-pillar” policy framework does not vary with different monetary policies, which provide an evidence that the “two-pillar” policy framework has general applicability.

The rapidly increasing leverage ratio of China's household sector has attracted more and more attention in recent years. It is of great practical significance to analyze the current household sector leverage ratio and what causes it to increase. This paper analyzes the leverage ratio and debt paying ability of Chinese households using data from household loans and a survey on urban depositors. It also empirically studies the reasons for the rapidly increasing household leverage ratio using the regional household sector leverage ratio, calculated via regional household loans.
The data demonstrate the continual rapid growth of China's household leverage ratio, which was 60.5% in 2018. The ratio has remained well above the household leverage ratios of other emerging countries. The household leverage ratio in China is lower than that of major European and American countries, but higher than that of Japan and the euro area. The household leverage ratio in China is close to the international warning line of 65%. All previous big increases in China's household leverage ratio were driven by the increase in household housing loans, which is very common during housing market expansion. Furthermore, the household leverage ratio has prominent structural problems. From the debt purpose perspective, household debt mainly consists of housing loans. From the borrower perspective, the amount of one loan contract increases as the distribution of debt becomes centralized. From the perspective of debt paying ability, some low-income households and aged populations are under greater debt pressure. From the assets and liabilities perspective, the average asset liability ratio of urban depositors in China is approximately 10% and relatively stable. However, most of the household sector's assets are concentrated in real estate. Thus, the financial asset liability ratio of China's household sector is high, reaching 37.9% in 2018.
This paper uses provincial household loan data to compute provincial household leverage ratio data and then applies the panel data model to analyze the causes of the rapidly increasing household leverage ratio. The main control variables of the model include financial development factors, population age structure, income level, social security expenditure, urbanization level and income inequality. It uses yearly data, ranging from 2007 to 2017. The results of the model show that rapid increases in housing market price and housing sales have significant positive effects on the household leverage ratio. Financial development level, the tool variable of credit availability, also has a positive effect on the household leverage ratio. Age structure has significant effects, the old-age dependency ratio has a positive effect and the adolescent dependency ratio has a negative effect on the household leverage ratio.
The data analysis and empirical study results show that the rapid increase in China's household leverage ratio is related to the fast growth of household income, the improving availability of consumption credit and the age structure of the Chinese population. They also indicate that the ratio is reasonable to some extent. However, some problems remain and require further attention, such as the uneven distribution of household leverage and the great debt pressure of some low-income households and aged populations. This paper raises four policy suggestions. First, in view of the currently uneven regional distribution of the household sector leverage ratio, it should be stabilized at the city level, not the national level. Second, housing market prices should be stabilized to prevent debt and housing loans from increasing. Third, government investment in social security should be increased to improve the debt-paying ability of low-income households and aged populations. Fourth, the management of personal loan insurance should be strengthened to ensure that borrowers' debt does not exceed their debt-paying ability.
This paper's main contribution lies in its expansion of household sector leverage ratio research through the use of provincial rather than national data. Future research should further investigate the microeconomic mechanism underlying the macro household leverage ratio.

As an important and stable source of fiscal revenue for local governments in mature market economies, the housing property tax has unique benefits, such as supporting local infrastructure construction and public services, regulating income distribution, and promoting social equity. Since the advent of China's housing-stock era and the reduction of land-based fiscal revenue, the urgency of housing property tax reform has increased. However, the imminent introduction of the housing property tax will have a major impact on consumption, investment, output, and financial stability, due to the real estate industry's long economic chain and wide range of connections. As housing property tax reform has an impact on the macro economy by affecting the real estate market, it is highly important to study the macroeconomic effects of levying a real estate tax, and to provide theoretical support for housing property tax reform.
The current domestic and foreign literature on real estate taxes focuses on its impact on housing prices. However, due to the special status of real estate in the national economy, housing property tax will affect investment in real estate development and in the real economy. It will also influence residential consumption, economic growth, and financial stability by affecting housing prices. Based on the above considerations, this paper extends Dong et al. (2019) and analyzes the macroeconomic effects of housing property taxes by examining entrepreneurs' portfolio decisions on real estate purchases and real economy investments and incorporating important economic indicators such as housing prices, investments, and outputs into the mainstream New Keynesian framework. The paper makes two main contributions to the literature. First, it discusses the impact of introducing a housing property tax on both the real estate market and the real economy. Second, it considers the different impacts of levying a housing property tax when banks can or cannot clearly distinguish whether entrepreneurs' loans are invested in the real economy or real estate market.
The research results show that the imposition of a real estate tax has a significant inhibitory effect on real estate investment, house prices, and new housing production. The tax also has a dual effect on real economy investment, due to an increase in the positive crowding-in effect and a decrease in the negative mortgage effect. In the short term, when banks cannot distinguish whether entrepreneurs' loans are for the real economy or for the real estate market, the negative mortgage effect is greater than the positive crowding-in effect, so the total physical capital and output decreases. When banks can clearly distinguish between real economy and real estate loans, the negative mortgage effect becomes less than the positive crowding-in effect, and the total physical capital and output increases.
In general, the levying of a housing property tax has a certain negative impact on the real estate industry. In the short and long terms, it has a major impact on housing prices, new housing production, investment for real estate development, and the rate of return on such investment. From a macroeconomic point of view, the levying of a housing property tax has a long-term negative impact on consumption, real economy investment, and output. The short-term impact is determined by the production enterprises' leverage, the share of final returns of entrepreneurs' investment in real estate projects, and whether it is possible to clearly distinguish if entrepreneurs' loans are invested in the real economy or real estate market.
In view of the fact that the introduction of a housing property tax has a great impact on China's real estate industry and on the nation's macroeconomics, this paper suggests that the principle of “legislation first, full authorization, and step-by-step advancement” should be adhered to, and the housing property tax should be introduced in a timely and stable manner. In particular, the government should be more cautious about introducing the housing property tax during the current economic downturn resulting from the COVID-19 epidemic. Some auxiliary measures should be taken. For example, exemptions should be given for certain numbers or areas of family housing to reduce the impact on most households. When the housing property tax is levied, taxes imposed during the construction phase and the transaction process should be reduced. The anticipation managements should be strengthened as a means to stabilize land prices and house prices.The housing's purchase and loan restrictions on households should be gradually eliminated.Before the housing property tax levying system is established and rationalized, it should be introduced with a nominally small tax rate, such as an annual 0.1% or 0.2%, and then be gradually increased as the economy grows stronger in the future.

The historical experience of global economic operations demonstrates that housing price and real estate credit are key factors affecting financial stability. With China's real estate market developing rapidly and housing prices increasing continuously in recent years, real estate credit in China has expanded too quickly. This has caused significant risk to the financial system.
This paper describes the mechanism though which systemic financial risk based on the real estate market forms and establishes a phased and cross-sector network model to characterize it. The risk formation process is divided into three stages. The first stage is the risk accumulation stage. The second stage is the risk spillover stage. Financial risk spills over from real estate companies, the residential sector and non-real estate companies and the government sector to the financial sector, respectively. The third stage is the risk contagion stage.
This paper constructs a systemic financial risk network model. In this model, if housing prices decrease significantly, the total loss, which causes systemic risk, consists of the default loss of financial institutions and the contagion loss of the financial system. The default loss is caused by the bankruptcy of real estate companies, foreclosures in the residential sector, the default of other real estate mortgage companies and the local government's default on its debt due to the reduction of income from land. The contagion loss includes the total asset loss of the financial system caused by the bankruptcy of financial institutions, the liquidity run loss among financial institutions and the asset sell-off loss for active deleverage.
Based on the above model, this paper constructs a systemic financial measurement indicator (SR) and two structural indicators (a fragility indicator and a contagion indicator). Using data from 16 listed commercial banks between 2006 and 2017, it also studies the level and structure of systemic financial risk triggered by steep reductions in housing prices. Furthermore, it constructs early-warning indicators for real estate market based systemic financial risk. The relevant data come from the annual reports of the 16 listed banks and the Wind database. The results indicate that the financial system's potential total loss increases exponentially when housing prices decrease by 30%. The average annual growth rate of the potential total loss is 22.70%. In 2017, it reached 6,500.682 billion yuan, accounting for 8% of the GDP. The real estate market based systemic risk increased, peaking at 60.52 in 2008 and then decreasing to 33.28 in 2017. The fragility indicator (FLI) exhibits wave oscillations and changes inversely with real estate loans/equities. The contagion indicator (CTI) decreases continuously from 2012 to 2017. It changes consistently with the financial market pressure index and the proportion of financial institutions' mutually held assets to total assets. Thus, the more relevant financial institutions are or the worse the financial market environment is, the greater the contagion loss suffered by the entire financial system is. The early warning indicator of real estate market based systemic financial risk (SRWI) oscillates and converges on its trend. This shows that systemic financial risk is generally controllable and decreases in a convergent way.
This paper makes the following contributions. First, it explains the formation of real estate market based systemic financial risk. Second, it constructs a phased and cross-sector network model for systemic financial risk in the real estate market. This model provides theoretical support for measuring systemic financial risk, which may be caused by a dramatic decrease in housing prices. Third, this paper measures the total loss in the financial system due to significant decreases in housing prices and describes the change in systemic financial risk based on the real estate market. Fourth, it constructs early warning indicators of real estate market based systemic financial risk and sets several warning intervals to carry out the “early identification, early warning, early detection, early disposal” guidelines.
This paper also makes several suggestions. The fundamental institution of and a long-term development mechanism for the real estate market should be promoted. The monitoring and early warning of real estate market based systemic financial risk should be strengthened. The real estate finance macro-prudential policy system should be improved. The loan-to-value ratio requirement should be adjusted according to cities' actual situation. Finally, regulatory arbitrage should be strictly prohibited.

Technology-seeking and “capital-for-technology” are major features of China's strategy for “going global” with innovation-driven development. In recent years, Chinese enterprises' outward foreign direct investment (OFDI) and stocks have been among the best in the world. At the same time, no matter how much OFDI enablesa company's technological innovation, the company cannot succeed without effective support for capital allocation, and a distortion of the capital markets has a negative impact.Mitigatingthe impact of capital market distortion is an important challenge for corporate decision-makers. In view of this challenge, this paper attempts to build a framework for analyzing the transmission mechanismsby which technological innovation affects Chinese OFDI enterprises.
The data used in this paper come from the “Chinese Industrial Enterprises Database” (1998-2009), the “Abstracts of Chinese Patent Database, 1985-2012” , and the “List of Overseas Investment Enterprises (Institutions).” The main conclusions of the paper are as follows: (1) In recent years, OFDI from Chinese companies has had a positive effect on technological innovation. The main mechanism by which OFDI helps enterprises to promote technological innovation is factor-intensive conversion, which serves to improve an enterprise's efficiency in terms of management and production. (2) At present, distortion of the capital market remains a real problem in China, and such distortion significantly suppresses the effects of OFDI-based technological innovation.Distortion of the capital market operates as an external friction factor that interferes with the effective allocation of resources for innovation.
Unlike previous studies, this paper circumvents the debate about whether “market-led” or “bank-led” financial structures cause capital market distortions. The core issues explored are which factors cause these distortions,and how they affect OFDI-driven technological innovation in Chinese companies. In addition, the paper considers the selection of patented technology as a form of innovative behavior that is legally effective, and can affect the overall competitiveness of an enterprise's or even a country's capacity for independent innovation. This paper makes two marginal contributions. (1) It builds on existing research to construct a model of the relations between the three major factors of “factor-intensive conversion,”“management efficiency,” and “production efficiency.” This paper uses a recursive measurement model and draws on enterprise-related data to conduct a series of tests,and obtains a robust, credible research conclusion. This conclusion provides a reasonable explanation regarding the path by which Chinese companies can effectively use OFDI to obtain advanced foreign technologies. (2) Based on previous research concerning the impact that capital factor market distortions have on technological innovation, this paper incorporates the distorting factors affecting Chinese capital markets into aframework for analyzing the effects of OFDI technology innovation.Empirical tests are conducted to identify the mechanisms and results of capital factor market distortions that affect OFDI-driven technology innovation, thereby providing a new perspective on this issue.
During the process of marketizing capital elements, the effective mechanisms for making an effective link between technology and the economy, and for transforming knowledge into material form (from potential productivity to actual productivity) remain little understood. This article suggests that in the current stage of financial supply-side structural reform, China should build a plan to support the financialization of market-based intellectual property.Making such a plan is a different task from that of capital regulation, as it must include consideration for the equity, securitization, and liquidity of intellectual property. The proposed program is mainly concerned with “inducing” a financial mechanism that effectively combines capital elements and human capital, thereby providing more economic incentives for positive behavior on the part of entities (enterprises and individuals) that are willing to increase their R&D investment. Such an approach can channel more financial and human resources toward participation in corporate technological innovation. At the same time,this approach can improve the efficiency of corporate OFDI innovation and enable the transformation of China's economy.

At present, attracted by the higher returns in the financial industry, China's industrial sector is engaging more shadow banking activities. Material production, employment absorption and technological innovation are drivers of economic growth. Therefore, funds being diverted out of the real economy inevitably reduces investments in research and development, which is not conducive to the stability of China's financial market and the real economy.
The report of the 19th National Congress of the Communist Party of China stressed that “deepen institutional reform in the financial sector, make it better serve the real economy” and “improve the financial regulatory system to forestall systemic financial risks” .Therefore, identifying the macroeconomic aspects of shadow banking by non-financial enterprises is of great theoretical and practical significance for serving the real economy. Understanding these factors will help to prevent funds being diverted out of the real economy and promote the long-term and stable development of the economy.
We analyze the impact of financial mismatch on shadow banking by non-financial enterprises from a theoretical perspective and consider the possibility of heterogeneous effects in different regions and for different companies. Next, we analyze the mechanism behind the effect of financial mismatch on shadow banking. Finally, we construct an empirical model and use data on non-financial listed companies from 2004 to 2015 to test the relationship between financial mismatch and non-financial enterprises' shadow banking behavior.
We make three contributions to the literature. First, this article is the first to explain the increasing trend of non-financial firms' use of shadow banking and the decline in the entity investment rate from the perspective of financial mismatch. Second, we investigate the impact of financial mismatch on shadow banking and allow for heterogeneity at the regional and enterprise levels. Allowing for heterogeneity has important theoretical significance for deepening and extending our conclusions. Third, we use the intermediary effect model to analyze the mechanism behind the impact of financial mismatch on the extent of a firm's use of shadow banking, supplementing theoretical understanding of the macroeconomic impact of shadow banking activities.
Our conclusions show that increasing the level of financial mismatch generally promotes the scale of shadow banking by non-financial enterprises, but that this effect is only significant in regions with greater financial deepening and fewer market-driven financial activities. Zombie companies and less profitable companies are affected by the “profit chase” and “investment substitution” mechanisms. The increase in the level of financial mismatch plays a stronger role in promoting enterprises' use of shadow banking. A second conclusion is that an increase in financial mismatch depresses productive investments; this effect is more significant for enterprises with higher capital specificity. Third, the results of the intermediary effect model show that financial mismatch mainly affects corporate investment behavior through financing constraints rather than through the channel of the return on capital. Financial mismatch also increases the scale of a firm's shadow banking by reducing the scale of its corporate entity investment.
We show that financial mismatch is the root cause of the shadow banking of non-financial enterprises, industry hollowing and systematic risk accumulation. Financial mismatch reduces real investments and drives funds being diverted out of the real economy, which is not conducive to the long-term and sustainable development of the economy. To suppress the over-development of the shadow credit market and make financial sector better serve the real economy, it is important to improve the allocation efficiency of financial resources between different economic entities, alleviate credit discrimination by financial intermediaries and accelerate the development of capital markets.

Insurers and insureds having different information advantages, the insurance market is not only an essential source of adverse selection theory, but also an important place to verify the theory. Adverse selection in the market affects the profit of the life insurance company as well as the existence of the market, it is, therefore, of great practical significance to judge whether there is such selection in the life insurance market, which directly determines the behavior of the regulator, the insurer and the insured.
If consumers are free to choose the security, the higher the security, the higher the price of insurance products, then the risk level of consumers is positively related to the security they choose. This is the positive correlation theory to verify the existence of adverse selection. Robust as it is, it does not rely on the setting of functional forms, nor does it require special assumptions about preferences, technology, and equilibrium.
In this paper, CHARLS data and positive correlation theory are used to test the adverse selection in China's term and whole life insurance market. The risk of death is measured in accordance with the respondents' probability of living to a certain age and their self-rated health, then whether there is a correlation between the risk of death and the security is obtained. The combination of long-term and short-term indicators overcomes the above-mentioned shortcomings of using only mortality indicators. Of course, this correlation may also be caused by the omission of some unobservable variables. To eliminate the impact of missing variables, this paper, in reference to the existing literature, controls the consumer's personal and income characteristics, altruistic motivation, risk attitude and family financial indicators. Not only does these data test the correlation between risk and security, but the robustness of this correlation. The final empirical results indicate there is no adverse selection in China's life insurance market due to altruistic motives.
This article for the first time uses CHARLS data to study the problem of adverse selection in China's life insurance market. There are three innovations in testing methods and research ideas: First, unlike the existing literature that uses only mortality indicators, we use both the long and the short-term indicators to measure the risk of death. Therefore, the conclusions obtained are more robust. Second, having controlled altruistic motivations, risk attitudes, and household financial indicators, we analyze the adverse selection of the life insurance market from the perspective of generalized and intensive margins, divide the sample into term life insurance and whole life insurance for regression, making the model conclusion more credible. Besides, though the existing literature believes that altruistic motivation and risk attitude are the two reasons leading to the absence of adverse selection in the life insurance market, empirical evidence cannot be given yet. This paper, with the bivariate Probit model, finds out that for consumers of China's term life insurance and whole life insurance altruistic motivation affects the correlation between death risk and security.
The core conclusion of this paper is of great significance to the healthy development of China's life insurance industry. In the first place, product innovation is one of the important ways to ensure no adverse selection in China's life insurance market. Moreover, Chinese consumers purchase term life insurance or whole life insurance products out of altruistic motives. In order to attract these people, insurers should appropriately reduce the price of life insurance products.
In this article, the adverse selection of the life insurance market is tested based on CHARLS data and the conclusion that there is no adverse selection in the life insurance market is obtained. Yet, there are still many follow-up issues to be further explored. For example, this paper has listed four evidences to prove no adverse selection in life insurance markets, but due to data limitations, only two empirical evidences are given. Therefore, using macro and micro data of China to provide empirical evidence for other reasons is our next research direction.

How geographic distance affects economic activities is an important issue. Studies have provided evidence that geographic proximity plays an important role in bank lending, venture capital investing, analyst forecasts and the stock returns of fund managers, indicating that it facilities monitoring and access to information. However, all of these findings are based on arm's-length transactions. Due to the data limitations of internal capital markets, determining the extent and mechanism of the effect of geographic distance on the loan contracts of intra-group loans is difficult. Little, if anything, is known about the prevention of loan risk in internal capital markets. In particular, China is a country with a vast territory, so firms affiliated with business groups are widely geographically distributed. In this study, we attempt to determine how geographic distance affects the loan contracts of intra-group loans. This offers insights into the behavior of lenders and borrowers in dealing with loan risk in internal capital markets, thereby developing an understanding of the micro-foundation of capital allocation in internal capital markets.
The China Securities Regulation Commission requires listed firms to disclose all of their entrusted loans in official documents. Many of these entrusted loans are loans between affiliated companies within business groups, which is a typical means of capital allocation in internal capital markets. Therefore, detailed information on loan contracts and the locations of lenders and borrowers is available. We manually collect detailed information on entrusted loans, including loan terms, such as interest rate, maturity, amount, collateral and loan purpose, and defaults. Using Google Earth to identify the latitudes and longitudes of firms' addresses, we calculate the aerial distances between the geographic coordinates of affiliated companies within business groups.
Using a hand-collected dataset of entrusted loans within business groups in China, we examine the effects of geographic distance on loan contracts and loan risk, focusing on the role of information. We find that contracts tend to be more restrictive when firms seek loans from remote lenders: lenders not only demand collateral, but also restrict loan purposes. Consistent with the notion that an increase in distance makes it harder for lenders to monitor borrowers and gather soft information, our results strengthen when the information friction between borrowers and lenders is greater. Furthermore, the results based on borrower default data indicate that dynamically adjusting the severity of contracts to cope with information asymmetry effectively reduces the risk of default.
We contribute to both the literature on the role of geographic distance in economic activities and the literature on firms' capital allocation in internal capital markets. First, we provide direct evidence that geographic distance plays a significant role in loan contracts in internal capital markets. This means that the information collecting and monitoring associated with distance are important determinants of capital allocation in business groups. Thus, we enhance understanding of the influence of geographical distance on internal capital allocation. Second, we are the first to investigate the effect of geographic distance on intra-group loan contracts using hand-collected data on entrusted loans in internal capital markets in China. We find that loan contracts become more restrictive as the distance between borrowers and lenders increases. Furthermore, strict contractual arrangements effectively reduce loan risk. These findings shed new light on how to prevent loan risk through loan contracts. They also deepen understanding of the internal loan contract design, which is of great significance to effectively controlling operation risk in internal capital markets.

Information disclosure significantly affects listed companies' market value and investors' decisions. After years of practice, regulators have recognized that the public listing system should focus on information disclosure, rather than evaluating issuance quality, which should be left for the market to assess. Furthermore, the deferred income tax data associated with corporate profit and management flexibility has become an arena for gaming between regulators and listed companies.
This paper highlights the important role of deferred income tax in the early alerting of fraudulent activities. It reveals what motivates companies to abuse their deferred income tax data, both theoretically and empirically. A sample of A-share companies from 2000 to 2017 is analyzed, controlling for partial observability and endogeneity problems. The deferred income tax indicator is significantly positively correlated with the tendency of listed companies to commit fraud and significantly negatively correlated with the probability of fraud detection. This result implies that regulators focus on monitoring the financial indicators of A-share companies, but fail to recognize the relationship between deferred income tax data and corporate disclosure fraud. Such oversight may incentivize listed companies to manipulate deferred income tax data to boost their financial performance. According to Spence's signaling theory, listed companies transmit information about their corporate value to the market through signals, such as financial performance. Thus far, deferred income tax adjustment has served as a convenient tool for listed companies in managing earnings at low costs. It constitutes a “soft signal”, which bears a low threshold to mimic but conveys less reliable information on companies' fundamental value.
Three factors motivate financial fraud: financial distress, avoidance of negative performance reporting and excessive valuation upon corporate growth. The first two reflect competitive pressures and the third reflects the rewarding effect of the capital market. Separating accounting and tax standards yields differences between pre-tax accounting profits and taxable income. Factors such as the confirmation time and amount of deferred income tax assets fall within the scope of management discretion. This accounting-tax discrepancy provides an opportunity for companies to abuse their deferred income tax data for earnings management.
This paper makes three contributions to the literature. First, it reveals the anomaly of extensive occurrences of disclosure fraud among A-share companies and the lagging regulatory inspections upon fraud. Furthermore, it offers an effective solution based on a solid analysis of the mechanisms underlying fraud. The literature on disclosure fraud detection is limited. Such academic neglect may explain the regulatory insufficiency regarding corporate fraud and the lack of public monitoring of this regulatory insufficiency. Second, this paper demonstrates the informational value of income tax data in detecting disclosure fraud and its “hard signal” value in revealing the true financial status of a listed company. Third, it showcases the role of machine learning in improving capital market governance.
The Guotaian CSMAR database conveys information on public announcements of regulatory measures on corporate disclosure fraud from authorities. The financial data of A-share listed companies is from the Wind database. A binary probit model is constructed, controlling for endogeneity and partial observability, to analyze the relationship between the deferred income tax indicator and companies' tendency to commit fraud. In the interest of accurately identifying companies with instances of fraudulent activity, a decision tree model is built to implement out-of-sample prediction and form investment portfolios based on the prediction results. Among the out-of-sample firms, the decision tree model identifies 39% of the fraud-free firms with 95% accuracy and identifies 2% of the fraud-committing firms with 100% accuracy. The annual return on assets of the fraud-free firms is 2.80% higher than that of the fraud-committing firms, and the average annual stock return of the fraud-free firms is 3.69% higher than that of the fraud-committing firms.
This paper analyzes the mechanism underlying financial manipulation, shedding light on the predictive power of the deferred income tax indicator in detecting disclosure fraud. For regulators, the timely identification of disclosure fraud is of great significance for combating violations and maintaining healthy stock market development. For investors, risk management is a primary concern. Avoiding companies with major alleged fraud (e.g., LeTV) is a primitive demand for risk control. The decision tree model in this paper provides an analytic framework for assessing the likelihood of listed companies to commit fraud, with parameter settings that address the accuracy and coverage of such assessment.

In the context of the stock market, margin trading is the phenomenon whereby investors pay a certain amount of margin to security companies, borrow a certain amount of funds to buy stocks, and return the funds with interest after a certain period. Accordingly, short selling refers to investors' borrowing and selling securities from securities companies and afterwards returning the securities with interest.
Margin trading and short selling have existed in mature stock markets in Europe, the United States, and Japan for many years. Through margin trading and short selling, informed traders can make better use of private information to increase the information content of stock prices and push stock prices to move closer to their intrinsic value. Arbitrageurs can also rely on margin trading and short selling for risk-free arbitrage, reducing stock mispricing (Miller, 1977) and improving overall pricing efficiency in stock markets.
In the literature, the ratio of margin trading and short selling is generally used to measure the market depth of margin trading and short selling. The relationship between stock returns and both margin trading and short selling is studied. A typical indicator is defined as the ratio of balance on margin trading/short selling and outstanding shares. However, it is possible that this ratio does not fully consider the transaction cost information. “Transaction cost” here refers to the time cost required to cover the margin trading and short selling amount. In contrast, the number of days-to-cover takes the impact of the turnover rate on the transaction cost of margin trading and short lending into account, which helps to determine the level of stocks' mispricing.
China's margin trading and short selling businesses are becoming increasingly mature. In March 2010, the China Securities Regulatory Commission launched an A-share margin trading and short selling business pilot program. To date, the program has been enlarged six times. With this expansion in scale, the balance of margin trading and short selling has also increased, rising from less than 13 billion RMB in 2010 to 1.02 trillion RMB by the end of December 2019. However, at the same time, the phenomenon of asymmetric transactions has been prominent. Typically, 98.85% of the trading balance is due to margin trading. This proportion has remained at almost 99% since 2014. Unlike margin trading transactions, short selling is still subject to many restrictions in China's A-share markets.
Based on the huge difference between margin trading and short selling in China's A-share markets, this paper extends the theoretical model of Hong et al. (2016) to identify factors that affect stock returns from margin trading and short selling, which are the short selling ratio and days-to-cover on margin trading. Furthermore, using the portfolio construction and Fama-MacBeth cross-sectional regression methods, this paper empirically tests these factors' predictive power. The sample includes 1,126 stocks selected from the margin trading and short selling pool from January 2012 to December 2018. The data are obtained from the CSMAR database and cross-validated using the WIND database.
The empirical results show that days-to-cover on the margin trading side has significant ability to predict the stock return, while the financing ratio (LR) does not have significant predictive power. This indicates that the days-to-cover is a better criterion than the LR, and can much more precisely represent the view of undervaluation, which is consistent with the theoretical model without financing restrictions. On the short selling side, however, the days-to-cover has no significant ability to predict stock returns. The short selling ratio (SR) has a significant ability to predict stock returns, which indicates that the SR represents the arbitrageur's view of overvaluation better than the days-to-cover. This is also consistent with our model under short selling restrictions. The results remain robust when they are tested with sub-samples in different periods, after controlling for the institutional investor shareholding ratio and other indicators of margin trading and short selling.

Fund research has usually focused on whether fund managers have the skill to obtain excess returns. In recent years, the application of social network theory in capital market research has provided us with a new perspective for examining fund managers' skill. Fund managers may frequently benefit from their social network by sharing information with each other. A consequence of this practice is the convergence of fund transactions within the given network if managers use shared information to guide their investment. Nevertheless, some funds may deviate from the common trades in their network. We conjecture that managers of these funds rely less on shared information and more on valuable private information that they possess.
Specifically, when a fund manager receives shared information in his network, he will weigh the value of both his private information and shared information and then choose the information that generates the most profitable trading opportunity. That said, if the fund manager chooses to use shared information, it implies that his own private information generates less profit than the shared information. It may also reflect the fund manager's poor ability to obtain private information. Conversely, if the fund manager chooses not to use shared information, this indicates that his own private information generates more profits than the shared information. This may also reflect the fund manager's ability to obtain private information. Overall, we conjecture that the less information a fund manager uses from his network, the stronger his ability to manage the fund.
In this paper, we first create a fund-level network, denoted as the fund's information network, using the semi-annual large equity holdings of China's open-end mutual funds during the period from 2005 to 2017. We then calculate the deviation of the fund's transactions from the common trades of other funds in the network. The deviation thus measures the extent to which the fund manager uses shared information in the network. We find that funds with higher DFN(Deviation from Network) generate significantly higher risk-adjusted returns and also attract larger fund flows. Moreover, the better performance of funds with high-DFN is not driven by managers' shifting into more risky portfolios. On the contrary, it stems from fund managers' superior ability to select stocks with higher idiosyncratic risk.
We also find that the extent to which a fund uses shared information is a natural response to its own comparative advantage. Specifically, large funds and old funds have a wider network and their managers communicate more frequently with each other. Hence they use more information shared in the network. Managers of small funds and new funds communicate less frequently. However, these fund managers have new knowledge structures and more open minds. Thus, they are more likely to adopt an active management strategy. We also find that both fund characteristics and fund manager characteristics have a significant impact on the extent of using shared information. Further, by examining changes in the use of shared information before and after fund managers' turnover, we show that the use of shared information is more related to fund manager characteristics than to fund characteristics.
We further investigate heterogeneity in the relationship between the degree of using shared information and fund future performance under different economic conditions. We find that the relationship is stronger during the period when equity returns are more dispersed. This is also the time period when fund managers' ability is more easily recognized and rewarded. Lastly, we find that the degree of using shared information is positively related to a fund's private information. This finding provides further evidence that the degree to which fund managers use shared information is closely related to their ability.
This study expands identification of fund managers' ability, enhancing our understanding of the role played by fund managers in asset management. The results also suggest that improving fund managers' active management skills is essential for the healthy development of China's capital market.