For China, preventing systemic economic risks while achieving high-quality economic development has become a general objective. Debt-financed investment by local governments is one popular tool for stabilizing economic growth and supply-side structural reform. Local government debt has been expanding on a massive scale since the 2008 financial crisis. By the end of 2019, local public debt had risen to 21.3 trillion yuan, more than double the amount in 2013. At the same time, macro returns on capital have been falling. According to the calculations of Bai Chong ’en and Zhang Qiong (2014), since 1993, China's return on capital has been on a downward trend, especially since 2008. Compared with the beginning of reform and opening up, return on capital dropped by 11.3 percentage points in 2013. To explain this, we empirically test the effect of local public debt expansion on return on capital and regional heterogeneity by using matching data on local public debt and return on capital at the prefecture-level city level, and examine the three mechanisms of the effect.
In the past decade, when promoting economic development, local governments have increasingly relied on debt-based investment and financing. The resulting huge public debt greatly loosens local governments' budget constraints, but at the same time it occupies a large amount of credit capital, which will inevitably lead to capital misallocation if investment efficiency is low or other more productive types of investment are crowded out. From the perspective of the local public debt operating process (i.e., financing - investment - repayment), in the financing stage, large-scale borrowing by local governments may crowd out the credit resources of banks, thus raising the cost of credit capital of enterprises within the jurisdiction, leading to a crowding out effect. Second, in the investment stage, local government debt funds are mainly used for infrastructure construction. If infrastructure investment has low efficiency, this also indirectly indicates the low efficiency of local government debt fund expenditure. Finally, in the repayment stage, the widespread phenomenon of using land sales to pay off debts and land financing will encourage local governments to push up house prices and develop real estate; however, when capital is excessively concentrated in real estate, this will aggravate the economic structural imbalance and capital mismatch between industries. To put it simply, local public debt has an effect on capital mismatch through the crowding out of micro-enterprise investment and financing, the efficiency of infrastructure investment, and the capital concentration of real estate, and thus affects the return on capital.
This paper makes three contributions to the literature. First, in terms of data, it uses matching data of the new calibre of public debt and return on capital at the prefecture level to conduct regression analysis, and provides more representative basic data for empirical analysis. Second, in terms of content, although the literature discusses various factors that affect China's return on capital, it does not consider the effect of China's expansion of local public debt on the influence of return on capital. This article makes up for this research gap and enriches the literature by identifying the influencing factors of return on capital. Third, in terms of mechanism, this paper takes capital mismatch as a logical starting point and comprehensively analyzes the intermediary mechanism of local public debt affecting the return on capital from three perspectives: the efficiency of infrastructure investment, the proportion of investment in real estate, and the crowding out effect of enterprise investment. It provides new evidence to understand the internal correlation between local public debt and return on capital.
The main conclusions of this paper are as follows. First, the expansion of local public debt significantly reduces the return on capital. Second, the negative impact of local public debt on return on capital is mainly realized through three mechanisms: reducing the efficiency of infrastructure investment, increasing the proportion of real estate investment, and squeezing out corporate investment. In short, the expansion of local public debt leads to a decline in the efficiency of capital allocation. Third, the negative impact of local public debt on return on capital is more obvious in non-urban agglomerations, non-large and non-medium-sized cities, and cities with greater dependence on land financing. These conclusions are of great significance for understanding China's economic growth model, and provide policy reference for deepening the reform of the investment and financing system of local governments. In the future, we should pay greater attention to the performance management and efficiency of debt funds to promote high-quality economic development.

For the central bank to maintain financial stability and carry out macro-prudential management, it is essential to have timely and efficient monitoring of financial market conditions. The stability of financial institutions depends on the conditions of the financial market, and the effects of monetary policy and macro-prudential policy are transmitted through the financial market; the policies themselves are also responses to financial market conditions. In addition, financial market data contain highly forward-looking information. Major changes in the financial and economic system, such as policy adjustments and stress events, will be reflected in the financial market data. The central bank also needs to closely monitor financial market conditions to select the policy implementation window in advance, make adjustments during policy implementation, and evaluate the policy effect. A good method of monitoring the overall risk level of the financial market is to construct a financial market stress index with selected indicators of the financial market. Overseas researchers and institutions, and more recently domestic researchers, have extensively explored the construction of financial market stress indexes. Most financial market stress indexes constructed by domestic researchers can identify financial market stress events, but the index construction and stress state identification still show deficiencies. The frequencies of financial market stress indexes in the literature are relatively low, as they are limited by data availability and construction methods. Some studies use indicators such as the non-performing loan ratio of the banking sector, but the data have some lag and can be manipulated. We believe that constructing a financial market stress index with pure financial market data can address the deficiencies of the literature. Furthermore, as interest rate liberalization continues, the representativeness and effectiveness of a financial market stress index that measures systemic financial risk using financial market data will be further improved. In this paper, the construction of the financial market stress index involves two steps. The first is the construction of each sub-market stress index, and the second involves compiling the full financial market stress index based on these sub-market stress indexes. This paper selects 17 indicators, calculated with transaction data from China's bond market, stock market, money market, and foreign exchange market, to construct sub-market stress indexes using the empirical cumulative distribution function method. It then constructs the financial market stress index with the sub-market stress indexes, using the time-varying correlation between them to depict the cross-market contagion characteristics of systemic financial risk. The purpose of constructing the financial market stress index is to monitor and evaluate the stress level of the financial market, especially high stress states. Some studies define a high stress state as occurring when the current value of the financial market stress index exceeds the mean of its historical values by a specified number of standard deviations. Other studies determine stress states by comparing the current value of the financial stress index with its values during the financial crisis. None of these methods make sufficient use of the information contained in the financial market stress index. The Markov regime switching model proposed by Hamilton is a more proper method for identifying financial market stress states. This paper assumes that there are two stress states in the financial market—high and medium-to-low, which is preliminarily supported by the analysis of the historical distribution of the financial market stress index. It then establishes the Markov regime switching model to identify stress states. Through back testing, our financial market stress index is found to accurately reflect historical stress events; for example, the large number of securities firms on the verge of bankruptcy in 2003, the global financial crisis of 2007-2008, the European sovereign debt crisis, interbank liquidity strains in June 2013, abnormal stock market fluctuations in 2015, and the COVID-19 outbreak. Our financial market stress index, which has the advantages of robustness and high frequency, is a powerful tool to monitor and evaluate systemic financial risk, select a policy implementation window, and evaluate the policy effect.

Automation and artificial intelligence (AI) are major trends in the workplace that have significantly improved production efficiency. Through the Internet+, big data, and cloud computing, AI has sparked a global technological revolution that has changed the traditional social order. While automation and AI have a positive impact on economic growth, they also negatively affect many traditional occupations. Many jobs may be replaced by automation. Studies show that the decline in the employment and wages of low-and medium-skilled workers can be attributed to the application of automation and AI. The rapid development of the AI industry and the aging of the population have drawn attention to how automation and AI affect China's labor market. Unlike the previous three technological revolutions in which machines and equipment replaced manual labor, automation and AI are being integrated into the production process. This not only requires machines to approximate human dexterity but, more importantly, it means that machines are gradually developing cognitive abilities similar to those of a human. This transformation will have a significant impact on the labor market. In China, the impact of automation and AI will be more pronounced, partly because China is at the forefront of AI development, and partly because of its large population and labor-intensive industrial structure. However, little research (especially quantitative research) examines how China's labor market will be affected by automation and AI. This study estimates the effect of automation and AI on China's labor market and suggests relevant countermeasures.
Based on Frey and Osborne's (2017) estimation of the probability of computerization for 702 detailed occupations, this paper estimates the probability that each occupation in China will be replaced by automation. Based on these estimates, we use data from latest Census and China Family Panel Studies to estimate the automation-substitution probability by city, and gauge the macro impact of automation on China's labor market. Finally, we empirically examine the effect of automation on labor market outcomes, such as employment at the city level and individual working hours. The results show that automation has a significant and negative impact on employment but a positive impact on working hours. The effect is larger among vulnerable groups in the labor market, such as women, those with a low level of education, the elderly, and migrants.
The contributions of this paper are as follows. First, it shows how the labor market in developing countries is affected by automation. Second, it estimates the replacement probability for each occupation in China, and comprehensively assesses the possible substitution effect of automation. Third, it estimates the impact of automation on the quantity and intensity of employment and conducts a heterogeneity analysis using labor force characteristics, to provide a comprehensive assessment of the impact of automation. Finally, the paper uses data on EU robots as a proxy for AI in China to examine the impact of AI on the labor market from different perspectives.
Although this study focuses on showing correlations rather than inferring causality, it is nonetheless informative about the impact of automation and AI on China's labor market, and has important policy implications. While China promotes the development of new technologies such as AI, it needs to address their potential negative impact on the labor market. First, this impact needs to be comprehensively assessed, because its effect will differ across industries and workers. Second, more attention needs to be paid to vulnerable groups in the labor market (e.g., women, low-educated workers, older workers, and migrants). Efforts to improve their labor skills and human capital through vocational training are needed to alleviate the negative impact of automation and AI on them. Finally, attention needs to be paid to the impact of automation and AI on the welfare of workers, particularly the polarization of income and social class. Technological progress needs to be harnessed to promote economic development while improving the welfare of workers and maintaining social equity.

During the 14th Five-Year Plan period, China has set forth new requirements for the development of strategic emerging industries and new goals of “characteristic development, complementary advantages and reasonable structure.” Industrial development should focus on seizing opportunities for future industrial development and cultivating leading and pillar industries. However, it remains unclear how to develop strategic emerging industries and transform industrial policy support from quantity-oriented to quality-and efficiency-oriented.
This paper studies the mechanism and effective support space of industrial policy on firms' markups. The results show that first, the strategic emerging industry policy has significant positive effect ,because firm's productivity and R&D are relatively high in this industry.Second, in terms of the mechanism, strategic emerging industry policies affect firms' markups through the cost and price channels. Currently(1998-2013), the negative cost effect is greater than the positive price effect, resulting in an overall negative effect. Third, in terms of heterogeneity, the industrial technology gap has a significant negative moderating effect on firms' markups, and a positive trend is observed between this effect and the technological convergence level. Fourth, further analysis shows that the innovation trap of focusing on quantity but neglecting quality, a consequence of support, is the main cause of decreasing overall markups.
This paper presents innovations in several areas. Theoretically, it explores the mechanism by which industrial policy affects firms' markups in the context of China's efforts to catch up with more developed economies. It amends industrial policy support theory and considers the technology gap in its conceptualization of the support space. Empirically, this paper reveals the effect of the strategic emerging industries policy on firm markups. Previous studies have obvious sample selection bias and fail to observe dynamic changes in markups (Lu et al., 2014; Liu, 2016). This paper uses industrial firm database records from 1998 to 2013 to study the effect of the strategic emerging industries policy, and it uses patent data with complete citation information to estimate patent quality. The innovation incentive of emphasizing quantity but neglecting quality, a consequence of the strategic emerging industry policy, is identified as the main cause of the negative markup effect. In terms of policy, this paper provides empirical evidence for the optimal implementation space of industrial policy. From the perspective of the technology gap, this paper provides an effective approach to optimizing the policy implementation space and to promoting the dynamic evolution of government support from pioneering to enabling. This transition can enhance the effectiveness of industrial policy and promote the development of high-quality innovations.
This paper also has several policy implications. First, the levels of industrial development, quality and efficiency should be considered crucial targets of industrial policy support. Policymakers should consider relaxing the assessment of the total number of patents and number of patents per capita, not setting binding indicators, reducing selective subsidies, strengthening the dynamic tracking of patent implementation and rewarding patent achievements with a good industrialization effect and high degree of marketization at a later time. Also, the combination of industrial policy implementation and the industrial technology gap should be maintained. Second, optimization of the market environment and government functions is a crucial strategy to improve the quality of strategic emerging industries. In the early stage, industrial development mainly depends on a high-quality business environment and competitive neutral market environment, and the government effectively handles market competition as a regulator. When the technology gap is relatively small, the government should effectively manage market competition and drive innovation as an enabler. Third, the deep integration of strategic emerging industries and various industries should be targeted for the optimization of industrial policy. A reliance on high-quality innovations by strategic emerging industries should expand the number of important products and key core technologies in the industrial supply chain, focus on upstream core links and enhance the ability of the chain owner to lead and drive the industrial supply chain. Key departments should be created to effectively link the innovation and industrial chains and enhance modernization of the industrial and supply chains.

Social mobility affects national stability and long-term growth. Equality of opportunity is crucial to the stable development of society. Since the reform and opening up began, China's economy has experienced rapid growth, leading to significant improvement in people's living standards. This has led to the realization of the concept stated as “some people get better off earlier” and an increasingly insurmountable gap in social class. Additionally, the correlation coefficient of intergenerational education and income in China has continued to rise over the past 30 years, indicating a decline in intergenerational mobility.
Although research focuses extensively on measuring and describing intergenerational mobility, relatively few studies examine the ability of public policy to increase social mobility. Drawing on data from the Chinese Household Income Project Survey 2013 and National Population Survey 2005, this paper uses temporal and geographical variations in the implementation of the Compulsory Education Law (CEL) as an exogenous shock to identify how education policy impacts intergenerational mobility. We find that the CEL has a significant beneficial effect on intergenerational education mobility, as the greatest benefits are incurred by children whose parents have low levels of education and employment and earn fewer benefits.
This paper makes four major novel contributions to the literature. First, this study of intergenerational mobility is focused on the factors that influence educational mobility; therefore, this paper fills a gap in the literature, which is focused mainly on income or occupational mobility. Second, this paper examines the mechanism underlying the effect of compulsory education on intergenerational mobility, whereas the literature focuses on the measurement and definition of intergenerational mobility but fails to explore what influences it. Third, this paper identifies the impact of public policies on social mobility, using the CEL as an exogenous shock; thus, it addresses the identification of cause and effect, which is the main challenge encountered in studying the factors influencing mobility. Fourth, the literature contains many discussions on the effects of compulsory education policy on the rates of return and health. This paper extends these discussions from the influence of compulsory education policy in China to intergenerational mobility. It thus complements the literature and provides a reference for adjusting compulsory education policy.
The findings also have important policy implications. For example, a policy of compulsory nine-year education is shown to have a positive impact on the promotion of educational mobility because it significantly promotes the education of children of lower socioeconomic status by mitigating family constraints. Therefore, this policy effectively promoted the accumulation of human capital in the early stages of the reform and opening up and laid a good foundation for subsequent economic and social development. However, some recent changes emphasize the need to extend the duration of compulsory education. On one hand, rapid economic and social development have increased the demand for high-quality labor; on the other hand, the trend of overall social mobility in China remains negative. Extending the number of years of compulsory education can simultaneously alleviate both of these socioeconomic issues, thus enabling China to achieve educational equity and, importantly, to improve the overall national quality. The ability of public policy to improve intergenerational mobility should be further exploited to promote social equity and justice by equalizing educational opportunities.

Since the 1990s, central bank communication has become a hot issue in macroeconomics and finance. Many scholars have conducted meaningful research on the issues such as the measurement of central bank communication, central bank communication and inflation expectations, and central bank communication and financial markets. Among them, the influence of central bank communication on financial markets and asset prices has received wide attention. A large number of empirical studies have shown that central bank communication has a significant impact on the stock market, the bond market and the foreign exchange market. As the People's Bank of China (PBOC) has paid increasing attention to policy communication in recent years, many Chinese scholars have conducted research on the impact of PBOC’s communication on China's financial markets. However, there are two major shortcomings in the existing studies. First, they only focus on monetary policy tendency of PBOC’s communication and ignore other information contained in the communication. Second, most scholars construct quantitative indicators by manual reading and scoring, making the results highly subjective.
This paper uses text analysis techniques to analyze 71 Monetary Policy Implementation Reports (hereinafter referred to as “the reports”) of PBOC, calculates the text sentiment (tone), the similarity and readability and other text indicators of the reports, and explores the relationship between these text indicators and the macro economy and the stock market. Based on the Chinese financial sentiment dictionary developed by Jiang et al. (2020), this paper uses the sentiment unit method to calculate the tone of the reports. In addition, this paper uses TF-IDF weighted cosine similarity to characterize the similarity of the reports, and uses average sentence length to characterize the readability of the reports. The paper then uses correlation analysis to examine the relationship between the tone of the reports and macroeconomic indicators such as economic growth, inflation, and interest rates. With reference to Ehrmann and Fratzscher (2009), Zhang and Hu (2014), this paper adds tone, similarity and readability to the EGARCH model to explore whether textual indicators of the reports affect stock market returns and the volatility on the trading day after the release. Furthermore, this paper decomposes the content of the reports into two parts: economic and financial fundamentals and central bank policy guidelines, calculates the tone of the two parts and examines their impacts on the stock market respectively.
The empirical results show that the tone of the reports is significantly correlated with macroeconomic indicators such as economic growth, inflation, and employment levels, and higher value of tone indicates better economic situation. After controlling for variables such as economic growth and monetary policy, the tone of the report has a significant positive impact on stock market return after the report is released. The similarity of the report has a significantly negative impact on stock market volatility, whereas the readability of the report does not have a significant impact on stock market volatility. Further research shows that it is the part reflecting the central bank's policy guidelines rather than the part reflecting macroeconomic and financial fundamentals that has a significant impact on stock market returns.
This paper fills a number of gaps in the field of central bank communication and text analysis. First, this paper is the first to use cutting-edge text analysis techniques to conduct a comprehensive analysis of the monetary policy reports of PBOC. Second, this paper fills the gap in quantitative analysis of the sentiment of central bank communication in China's academia. Third, this paper conducts an empirical study on the mechanism of PBOC’s communication affecting the stock market, and proves that the report's tone affects the stock market only through the policy guidance channel.
The findings of this paper are of great significance to strengthening financial supervision and promoting macro-prudential management in China. The results show that PBOC communication can significantly affect the stock market, which fully affirms the effectiveness of PBOC’s communication. Through adequate communication with the market, PBOC can influence asset prices, thereby achieving the purpose of monetary policy regulation and maintaining financial stability. In addition, this paper points out that the part of PBOC’s reports that affects the market is the part that reflects central bank's predictions of the future economic situation and its policy guidance. Therefore, PBOC should use its authority and influence to manage market expectations more effectively through timely announcements and clear explanations of economic and financial situation predictions and monetary policy guidance.

Credit rating agencies (CRAs) play an important role in the capital market. In theory, they provide reliable decision-making information for investors and mitigate information asymmetry in the capital market. However, the increasing number of debt defaults has led some investors to have serious doubts about CRAs. They suspect that CRAs do not play the role of gatekeeper in the capital market and may even collude with issuers to transfer risk to innocent investors. This paper studies whether Chinese CRAs have lost their market reputation. Measuring their market reputation is difficult because it involves a paradox: on the one hand, to build their market reputation, CRAs must provide reliable information to investors; on the other hand, CRAs with a good market reputation can milk investors by providing misleading information. Indeed, if a CRA manipulates a credit rating but has no significant impact on investors or on the debt issuance cost, the rating will be a “rubber stamp”; that is, the CRA actually has no market reputation.
This paper studies the market reputation of Chinese CRAs in two steps: (1) measuring the rating distortion of CRAs; and (2) examining whether and how rating distortion and CRA characteristics affect the debt issuance cost.
To measure rating distortion, this paper makes two salient contributions to the literature. (1) We separate rating distortion from CRAs' private information. In the literature, it is common practice to regress a credit rating with respect to public information and use the residual to measure rating distortion. However, considering that the de facto observable ratings are only given by those CRAs that ultimately win the rating competition, this approach may pool the rating distortion and favorable private information observed by the winning CRA. To extract real rating distortion from the residual, we follow Tian (2011) and introduce the distances between CRAs and issuers to control private information. However, to address selection bias, we not only introduce the average distance between the issuer and all CRAs to capture the monitoring effect but also the variance of the distance to capture the effect of private information on rating competition. Intuitively, credit ratings should decrease in average distance because monitoring becomes more difficult over a greater distance; however, the “observed” credit rating should increase in distance variance, because with a mean-preserving transformation, the debt issuer will choose a CRA’s rating only when the CRA moves closer to the issuer, finds favorable information, and gives a higher rating.
(2) We use the propensity score matching (PSM) method to mitigate possible endogeneity. Because we can only observe the actual rating for each bond, the regression prediction value based on the full sample will result in serious measurement errors if there is great heterogeneity among different bonds. Therefore, correctly measuring rating distortion entails constructing a reasonable benchmark rating for each bond. Our solution is as follows: for each bond, we use PSM based on our public information and distance variables to find similar bonds as a control group. The bonds in the control group should be rated by other CRAs; thus the treatment effect between two groups is a more accurate measurement of rating distortion.
The main data, taken from the Wind database, cover information on corporate bonds and enterprise bonds from January 2009 to October 2017. After excluding bonds without ratings, there are 6,073 observations. Our findings are as follows. First, credit ratings decrease in average distance between debt issuers and the CRAs, in accordance with the monitoring mechanism. Second, credit ratings increase in distance variance, in accordance with the private information channel. Third, on average, CRAs in China still have a good market reputation because upward rating distortion significantly reduces the cost of issuing bonds. Fourth, there is significant heterogeneity among CRAs. Fifth, we perform a DID analysis using high-speed railway openings as a quasi-natural experiment to tackle possible endogeneity problems due to the agglomeration effect in the locations of CRAs and issuers. We find that all of the main results are robust.

In China, accelerating population aging is increasing the national eldercare burden. To address this problem, China has implemented a proactive national strategy that includes a multi-pillar pension system. In addition to the state-run first pillar and employment-based second pillar, the third pillar comprises personal retirement products. However, the rate of participation in commercial pension plans is far lower in China than in developed economies. The studies on this limited participation phenomenon focus on various factors, such as institutional setups and policies, financial market development and individual and household characteristics. However, these studies are based on the “economic man”assumption while largely ignoring crucial cultural factors.
Culture has important effects on individual financial behaviors. It not only affects the formal and informal institutional arrangements in which personal financial activities are embedded but also influences individuals' preferences for risk, liquidity and consumption. All of which shape individuals' financial decision-making. Chinese traditional values are centered on family and strongly emphasize raising children for old age and filial piety, which profoundly influence individual participation in commercial pension plans. Individual trust in financial institutions and individual financial literacy are other important cultural factors that affect the likelihood of participation in commercial pension plans.
Drawing on the CHFS2015 dataset, this study examines how traditional Chinese family values shape financial trust and financial literacy, which in turn influence individual participation in commercial pension plans. The CHFS, a biennial national survey, was first conducted in 2011. It covers a large sample of households from 29 of the 34 Chinese provinces and records detailed information on many variables, such as household income, consumption, financial assets, housing, debt and demographic characteristics. This study's sample consists of 15,659 urban residents who participated in the CHFS2015 and provided complete information on the variables of interest.
Methodologically, we first construct an index of traditional family values using factor analysis. We then use ordinary least squares and logistic regressions to examine the effects of traditional family values, financial literacy and financial trust on participation in commercial pension plans. The Sobel, KHB and bootstrap tests are used to verify the mediating effects of financial trust and financial literacy on the relationship between traditional family values and participation in commercial pension plans. Instrumental variable estimation is also used to address endogeneity concerns. Additionally, we assess robustness by changing the samples and the measurements of the variables. Finally, we examine the effects on subgroups divided by region, educational level and occupation.
The empirical results show that traditional family values inhibit Chinese urban residents from purchasing commercial pensions. The finding remains valid after accounting for endogeneity. Further analysis indicates that financial trust and financial literacy mediate the relationship between traditional family values and participation in commercial pension plans. Specifically, traditional family values reduce individual trust in financial institutions and divert attention from financial information, thus inhibiting participation in commercial pension plans. Meanwhile, traditional family values are also associated with a lower level of financial literacy, which tends to decrease the likelihood of purchasing a commercial pension. Our analysis also reveals that the negative effects of traditional family values are more prominent in underdeveloped regions and among less educated people. In contrast to the rational economic perspective, this study offers a cultural perspective and thus sheds new light on individuals' financial behaviors and financial market development. It not only enriches our understanding of these topics but also has important practical implications. First, Chinese elderly people, who are deeply influenced by traditional family values, prefer to rely on their children and families and thus require innovative family-based elderly care policies and solutions to meet their needs. Second, China should provide more information channels to improve individual financial literacy and financial market regulation and boost public trust in financial institutions. Last but not least, people living in inland provinces and those without a college education would be better served by a more inclusive financial system.
In conclusion, China's elderly care pension market has great potential. All market participants should take the available opportunities and meet its challenges to develop the market in an equitable, balanced and high-quality manner.

Both the non-synchronization of information transmission and investors' sentiments are typical characteristics of financial markets. The former creates information asymmetry, and the latter can lead to the overconfidence of investors. However, few studies consider how information asymmetry can arise from the process of information transmission or how the processing and updating of new information may trigger overconfidence of investors. Both these processes can affect stock prices and the explanatory power of existing models. Incorporating these sources of information asymmetry and investors' overconfidence into the traditional financial analytical framework has important theoretical and practical implications and deepens our understanding of the internal logic of stock price changes.
This study incorporates information asymmetry and investors' overconfidence into the traditional analytical framework and examines how the process of information transmission in real markets affects stock prices. Following Easley and Hara (2004), we model the transmission process of information as a gradual flow of information, which endogenously generates information asymmetry between investors. We then introduce one of the typical psychological characteristics of investors—overconfidence—and establish a two-stage dynamic sequential pricing model to explore whether stock prices are driven by the dual factors of information asymmetry and investors' overconfidence.
The main results show that first when investors are presented with new information, adjustments in their expectations of stock returns are positively correlated with the equilibrium price of stocks, that is, increasing investors' expectations of stock returns increases the equilibrium price of stocks, and vice versa. Second, when presented with good news, the proportion of investors who are overconfident tends to increase, the equilibrium prices of stocks increase, and stock returns decline. When presented with bad news, the proportion of investors who are overconfident tends to increase, and both the equilibrium price of stocks and investment loss decline.Third, as the proportion of overconfident investors and the degree of overconfidence increase, the market risk premium decreases. Fourth, investor groups diverge during the process of information transmission, forming heterogeneous beliefs; specifically, investors who have not obtained information and who have not become overconfident think that the stock price is overvalued, whereas investors who have obtained information and who are overconfident think that the price is undervalued, which triggers changes in market volume and stock prices. Fifth, both the proportion of overconfident investors and the increase in overconfidence have a positive impact on market efficiency but a negative effect on market depth. Finally, we use the theoretical results of this study to explain typical volatility characteristics such as asymmetric effects and volatility persistence in real markets.
This study extends the literature in three ways. First, it examines how the gradual flow of information into the stock markets creates potential information asymmetry among investors, which affects the formation of equilibrium stock prices. This is a useful supplement to research on information transmission in the stock markets. Second, based on the self-attribution bias theory, we incorporate a typical behavioral bias of investors￣￣—overconfidence—into our model of the information transmission process, which allows us to consider more realistic market environments. We then explore how stock price formation and changes are driven by the dual factors of information asymmetry and investors' overconfidence, expanding the research on stock pricing and price changes. Finally, we use our theoretical results to analyze market price fluctuations, and find that we can explain typical features such as asymmetry and persistence. This enhances the understanding of the logic of stock price changes in real markets.

Institutional investors managing public funds are rapidly becoming a crucial part of financial markets. Accordingly, there is widespread interest in how these fund managers make investment decisions. Recent studies show that due to the complementarity of information structures, social networks play an important role in fund managers' asset allocation and diversification decisions. We focus on how an important social network-enabled interaction between funds—the fund holdings network—affects the investment performance of funds.
There are different theoretical perspectives on how a fund's co-ownership network affects investment performance. On the one hand, such networks produce information diffusion effects, and fund managers at the center of a fund network will receive signals earlier and obtain more valuable information than fund managers at the periphery of the network. Furthermore, the information held by fund managers at the center of the network will be more accurate, which will have a positive impact on the investment performance of their funds. On the other hand, a fund network can induce the “free riding on friends” effect. Compared with fund managers at the periphery of the network, fund managers at the center of the network support more “free riders”. Under this condition, the fund network may have a negative impact on their fund performance.
We use data on China's capital market stock funds from January 2005 to September 2018 to examine the impact of fund networks on investment performance. The results show that the centrality of a fund's position in a fund network has a significant positive impact on its investment performance. The results have obvious economic significance: we find that an increase of one standard deviation in the degree, closeness, or eigenvector measures of a fund's position in the network increases investment performance by an average of 54.43%, 51.15%, and 50.93% respectively.
We also examine the channels through which network position affects performance. Specially, we examine fund stock selection skills, asset allocation skills, and fund management skills. First, institutional investors located at the center of a network receive rich, accurate, and timely information about stock pricing. This information can promote fund managers' analyses and mastery of stock fundamentals, thereby improving their stock selection skills.
Second, the transmission of information between funds is an important factor that affects managers' asset allocation strategy. Fund managers located at the center of a network have more accurate information and can use their information to allocate assets independently and effectively, that is, they have improved asset allocation skills. Third, social networks can promote the dissemination of information, thereby improving managers' fund management skills.
Finally, we examine the influence of fund networks on fund shares, which we define as a fund's market share. A fund network provides channels for the dissemination and exchange of information. Fund managers at the center of a network receive faster and more valuable information, which allows them to implement high-quality portfolio management and product differentiation strategies. High-quality portfolio management and product differentiation are effective strategies for gaining market share. This study shows that fund networks have a significant positive impact on funds' market share.
This research makes two major contributions. First, we contribute to the literature on the interactions between institutional investors' shareholdings. Most studies have assumed that institutional investors are homogeneous, and there is a lack of research on the heterogeneity of institutional investors and their interactions. We expand this field by constructing a model of a dynamic co-ownership fund network. The fund network allows for the exchange of information between co-ownership funds, reflects the interactions between institutional investors, and distinguishes institutional investors' holdings on the basis of their network location. Using a series of centrality measurement methods, we examine the effects of network location on a fund's ability to obtain information and take actions. Second, we contribute to the research on the impact of social relationships on investment performance by examining the impact of social networks on performance from the perspective of dynamic institutional investor interactions. We build a model of fund networks to illustrate dynamic institutional investor shareholding interaction, and examine its impact on fund investment performance. We find that in a fund network based on co-ownership, positive information diffusion effects dominate, which leads to better investment performance for funds with more relationships. In addition, the social interaction between funds in a network improve the managers' stock selection skills, asset allocation skills, and management skills, which ultimately improve the funds' investment performance.

Investors suffering from the nominal price illusion tend to believe that low (high)-price stocks have more (less) upside potential (Birru and Wang 2016). A number of studies have examined the relationship between the nominal price illusion and corporate financial policies and asset prices in China's A-share stock market (He and Chen, 2003; Li et al., 2014; Yu et al., 2014; Xie et al., 2016; Luo et al., 2017). For example, Li et al. (2014) and Yu et al. (2014) find that unsophisticated retail investors are the net buyers after announcements of stock splits and mutual fund shares splits, indicating that retail investors prefer low-priced assets.
However, in the above studies, whether investors have biased beliefs about nominal share prices is unobservable, and thus the findings are likely to be contaminated by alternative rational explanations. In China, stocks are traded in lots of 100 shares. Therefore, retail investors with binding budget constraints cannot afford stocks with extremely high prices. Furthermore, retail investors who want highly diversified portfolios will also trade stocks with relatively low nominal prices, because such stocks give them more capital allocation flexibility. In other words, retail investors' revealed preference for low nominal price stocks is very likely to be the result of rational considerations, and not the result of the nominal price illusion.
This study uses analysts' price targets to directly test the nominal price illusion hypothesis. It looks at the associations between stock return expectations and nominal share prices. An advantage of our research design is that our setting is unlikely to be affected by the budget constraint. Although budget constraints inevitably impose trading restrictions in investors' portfolio formation, they should not have any real impact on investors' expectations of individual stocks.
We find that analysts' future return forecasts for low nominal price stocks are significantly higher than their forecasts for high nominal price stocks, even after controlling fundamental information, beta, and other characteristics of stock returns. Moreover, the above finding is stronger for hard-to-value stocks, as represented by small size, short listing years, high return volatility, low financial reporting transparency, and more intangible assets. We also use stock split events as exogenous shocks to conduct a difference-in-differences (DID) test, and document that analysts' post-split return forecasts become more favorable after a mechanical drop in a nominal share price, which strongly supports our hypothesis.
In addition, we conduct several further analysis to confirm that analysts' optimism about low nominal price stocks is the outcome of biased belief, rather than two alternative explanations: (1) that low nominal price stocks could earn higher ex-post future returns than high nominal price stocks; (2) analysts with self-serving motivations strategically release favorable target prices for low nominal price stocks to cater to the preferences of investors.
Our paper makes two contributions to the literature. First, by using a large sample of analysts' price target forecasts, we directly identify the impact of the nominal price illusion. Our study documents how and why the nominal price illusion affects investor trading behaviors, corporate financial policies, and market anomalies. Therefore, our study not only confirms previous findings on the nominal price illusion but also provides micro-foundations for the literature (He and Chen, 2003; Li et al., 2014; Yu et al., 2014; Xie et al., 2016; Luo et al., 2017).
Second, our study adds to the analyst literature. Previous studies focus on earnings forecasts and stock recommendations, whereas our study examines whether the nominal price illusion biases analysts' price targets. The findings enrich our understandings of how financial analysts are affected by behavioral biases (Hilary and Menzly, 2006; Hribar and McInnis, 2012; Cen et al., 2013; Pouget et al., 2017; Hirshleifer et al., 2019).
Our study also has policy implications. As professionals such as financial analysts are still vulnerable to the nominal price illusion, retail investors with limited knowledge and skills should be more aware of this illusion when trading stocks. As retail investors are the main participants in China's A-share market, we also suggest that regulators pay attention to self-dealing corporate behaviors that take advantage of unsophisticated retail investors by means such as initiating stock splits to boost the stock price.