Large language models (LLMs) have powerful natural language processing capabilities. In this paper, we systematically review the recent literature in this field and highlight the new research opportunities that LLMs bring to text analysis in economics and finance. First, we introduce GPT and BERT, the two most representative LLMs, as well as a number of LLMs developed specifically for economic and financial applications. Additionally, we also elaborate on the fundamental principles behind applying LLMs for text data analysis. Second, we summarize the applications of LLMs in economic and financial text analysis from two perspectives. On the one hand, we highlight the significant advantages of LLMs in traditional text analysis scenarios, such as calculating text similarity, extracting text vectors for prediction, text data identification and classification, building domain-specific dictionaries, topic modeling and analysis, and text sentiment analysis. On the other hand, LLMs have strong human alignment capabilities, thus opening up entirely new application scenarios, i.e., acting as economic agents that simulate humans in generating beliefs or expectations about texts and making economic decisions. Finally, we summarize the limitations and existing research gaps that LLMs face in pioneering new paradigms of economic and financial text analysis research, and discuss potential new research topics that may arise from these issues.

Enhancing the welfare of the people is one of the core goals of high-quality development in China's new era. Digital financial inclusion plays a crucial role in improving the subjective well-being of Chinese residents. Utilizing the data from the China Household Finance Survey from 2013 to 2019, and integrating city tiers with municipal digital financial inclusion indices, this paper empirically investigates the impact of digital financial inclusion development on residents' subjective well-being using the ordered Probit model. The findings indicate that the development of digital financial inclusion significantly enhances the subjective well-being of residents. In terms of dimensions, its breadth of coverage and depth of use have a positive impact on residents' well-being, while the degree of digitalization has a negative effect. Moreover, the impact of digital financial inclusion development on subjective well-being varies significantly across different relative income and educational levels. Mechanism analysis shows that the development of digital financial inclusion enhances subjective well-being through three pathways: Improving residents' financial literacy, improving economic conditions, and enhancing social security levels.

This study investigates the impact of two types of FinTech developed and utilized by banks and non-bank financial institutions on fraudulent behavior in China's A-share listed companies. Based on panel data from 2011 to 2020, the research findings suggest that both types of FinTech can suppress corporate fraud by enhancing internal control levels and external monitoring levels. Heterogeneity analysis indicates that the inhibitory effects of both FinTech types are more pronounced in companies with higher levels of digital transformation and lower levels of information disclosure. Additionally, due to differences in operating conditions, strategies, and objectives of FinTech developers, the inhibitory effect of bank FinTech is significant across all firms, whereas the effect of non-bank FinTech is only significant in high-risk firms. When distinguishing types of corporate fraud, both FinTech types significantly inhibit fraudulent activities related to information disclosure, fund utilization, and other categories. Further analysis reveals a complex interaction between the application effects of bank FinTech and non-bank FinTech. Specifically, the inhibitory effect of bank (non-bank) FinTech is significant when the development of other FinTech is high (low). By simultaneously incorporating both types of FinTech and their interaction terms, significant synergistic inhibitory effects are observed in fund misuse and other types of fraud. Finally, the results indicate that the synergistic development of both types of FinTech may introduce potential risks. In summary, this paper, by identifying the impact of FinTech development on corporate fraudulent behaviors, highlights the common characteristics and individual differences of different types of FinTech, emphasizes potential future cooperation opportunities between bank and non-bank FinTech, and points out potential risks in the development of FinTech.

Utilizing the 2015, 2017, and 2019 China Household Finance Survey (CHFS) data, combined with the income transition matrix analysis method and empirical analysis method, this study systematically investigates the impact of digital finance on income mobility and income inequality among rural households. The income transition matrix analysis reveals that rural households using digital finance have a higher probability of upward income mobility compared to those not using digital finance. Empirical research has found that digital finance significantly promotes upward income mobility and significantly reduces income inequality among rural households. The mechanism of action indicates that digital finance enhances households' income mobility by improving financial accessibility, facilitating the accumulation of development factors, and promoting off-farm employment opportunities. Furthermore, compared to middle and high-income rural households, digital finance has a greater impact on financial accessibility, development factor accumulation, and off-farm employment for low-income rural households. This consequently reduces income inequality, showcasing the inclusive growth characteristic of digital finance. Further analysis reveals that digital finance primarily impacts rural households' property income and wage income through these three pathways, ultimately promoting overall income mobility and reducing income inequality among households. Both digital payments and digital wealth management significantly contribute to upward income mobility and the reduction of income inequality among rural households, while digital lending has a negligible impact. This study provides empirical evidence to support the enhancement of policies aimed at fostering sustained income growth for rural households and optimizing the rural income distribution pattern through digital finance.

In order to explore how green technology innovation and the development of the digital economy can jointly promote green economic growth, this paper constructs a general equilibrium model that includes green technology innovation and digital transition. Combining with the real-world data at the city level in China, from the two aspects of economic growth and carbon emissions, it analyzes the impact and the mechanism of action of the digital economy collaborating with green technology innovation on green economic growth. It found that: 1) Green technology innovation has a "U-shaped" impact on economic growth and carbon emissions. That is, after exceeding a specific threshold of technological innovation level, with the continuous increase in the level of green technology innovation, the economic growth rate will continuously increase, and carbon emissions will continue to decrease. Moreover, the development of digital economy will strengthen the impact of green technology innovation, resulting in a steeper "U-shaped" relationship. 2) The development of economic digitalization has both mediating and moderating effects. Green technology innovation has a positive "U-shaped" impact on the development of the digital economy. That is, an increase in green technology innovation can promote the development of digital economy. In turn, the development of digital economy further moderates the impact of green technology innovation on economic growth and carbon emission reduction, strengthening the positive effect of green technology innovation on green economic growth. 3) The digital economy's enhancement of the impact of green technology innovation on green total factor productivity is the primary mechanism by which the digital economy, in collaboration with green technology innovation, drives green economic growth. 4) Policies to promote the development of economic digitalization need to be accompanied by higher carbon taxes. Although there are short-term economic costs, there are advantages in terms of long-term economic growth and environmental quality. This research combines the study of the green transition of economic development with that of digital transition, providing crucial theoretical support for the coordinated advancement of the green and digital transition of the economy to ensure stable economic growth.

In the context of the development of financial technology, we start with the complex characteristics of financial big data and elaborate on the importance of transfer learning of using multi-source data information to assist target tasks. We explain the significance of transfer learning technology in dealing with data heterogeneity from the perspective of multi-source data, and summarize the relevant concepts and methods of transfer learning technology, including data-driven and model-based transfer learning methods. In addition, this paper proposes the unified framework of transfer learning method based on generalized moment estimation (GMM), gives the effective algorithm of transfer learning, and applies the proposed method to the application of transfer learning in risk value (VaR) and risk measure based on expected quantile (expectile) under multi-source data. Then, we simulate two scenarios where samples are of insufficient or imbalanced sample sizes, respectively, in the application to personal bank credit evaluation, with tests of the prediction accuracy of three transfer learning methods, and analysis of the importance of filtering resource domain information. Finally, we described more application scenarios and development prospects of transfer learning in the financial field.

Promoting mass entrepreneurship and innovation is of great significance for advancing economic structural adjustment, creating new engines of development, enhancing new driving forces for development, and pursuing a path of innovative-driven development, as well as promoting social upward mobility and fairness and justice. This study utilizes data from three rounds of the China Household Finance Survey (CHFS) conducted from 2013 to 2017 to measure the degree of opportunity inequality in China and empirically investigates its effects on resident entrepreneurship and the underlying mechanisms. The results indicate that the rise in opportunity inequality significantly stimulates the entrepreneurial motivation of resident households. For each standard deviation increase in opportunity inequality, the probability of household entrepreneurship increases by 1.14%. Mechanism analysis shows that opportunity inequality stimulates residents' pursuit of status, thereby promoting entrepreneurship among residents. Furthermore, expanded research findings reveal that digital finance strengthens the positive driving effect of opportunity inequality on entrepreneurship. This enhancement effect is only present in urban areas and is achieved by improving loan accessibility. Additionally, this study finds that livelihood-oriented fiscal expenditures also amplify the promotion effect of opportunity inequality on resident household entrepreneurship. In heterogeneous analysis, households with lower educational levels, lower total assets, and no unemployment insurance show stronger entrepreneurial motivations. Finally, this study finds that opportunity inequality suppresses the entrepreneurial performance of entrepreneurial households, indicating that government policies should focus on strengthening equal opportunities and supporting resident entrepreneurship.

In the process of low-carbon transition, enterprises require substantial financial support for related investments. Therefore, the effectiveness of carbon pricing policies depends on a well-functioning financial market. However, in reality, financial markets face various frictions that hinder the flow of capital, leading to inefficient allocation of resources. These frictions may affect corporate investment behavior, thereby weakening the implementation effects of carbon pricing policies. This paper, focusing on the issue of financing constraints, constructs an environmental-dynamic stochastic general equilibrium (E-DSGE) model incorporating a financing collateral constraint mechanism to analyze the impact of financing constraints on the effectiveness of carbon pricing policies and explores corresponding policy responses. The results show that: 1) From the perspective of environmental benefits, financing constraints weaken the "emission reduction effect" of carbon pricing policies, suppress corporate low-carbon investments, and reduce corporate emission intensity; 2) From the perspective of economic costs, financing constraints amplify the cost impact of carbon pricing on enterprises, restrict output growth, and increase the overall economic cost of the low-carbon transition; 3) Introducing carbon asset-backed loans as a complementary measure to carbon pricing policies can effectively mitigate the negative impact of financing constraints on carbon pricing policies; 4) Numerical simulation shows that financing constraints increase the proportion of carbon pricing-related costs in enterprises' total production costs from an average of 15.31% to 19.47% annually, while reducing the annual average scale of low-carbon investments by approximately 37%. Furthermore, providing more carbon asset-backed loans to high-emission enterprises can significantly enhance policy benefits. The conclusions of this paper are of great significance for improving mechanisms for green and low-carbon development and establishing a systematic climate policy framework.

This paper introduces digital financial capability into the intertemporal decision model, constructs a theoretical analysis framework to explore the impact mechanism of digital financial capability on household wealth accumulation, and conducts an empirical test based on the data of China Household Finance Survey (CHFS). The research shows that digital financial capability can significantly promote household wealth accumulation in China, particularly for rural households and those with low education and low wealth levels. Mechanism analysis shows that increasing financial investment returns and promoting social interaction are two channels through which digital financial capability can improve household wealth accumulation. Further analysis shows that there are structural differences in the impact of digital financial capability on household wealth accumulation, which can improve the allocation of productive assets and financial assets, and reduce the holding of housing assets and other non-financial assets. The above research conclusions provide a new perspective to explain the accumulation of household wealth in China, and also provide a reference for the formulation of relevant policies to promote common prosperity.

With the rapid development of digital finance, digital transformation has become a strategic imperative for commercial banks. This paper employs a more comprehensive data set from China Banking Database (CBD), and examines the relationship between bank digital transformation and systemic vulnerability risks. Empirical results indicate that there is a significant inverted U-shaped relationship between the level of bank digital transformation and its own systemic vulnerability risks, and the results are robust, remaining valid even after addressing endogeneity issues. Mechanism analyses reveal that bank digital transformation indirectly affects its own systemic vulnerability risks by altering income acquisition efficiency and active risk-taking. The empirical findings of this paper have important practical implications for preventing systemic financial risks in the banking industry and understanding the balance between digital innovation and risk management.

Entrepreneurship is one of the key means to ease the pressure on social employment, and it is also a long-term driving force to ensure medium-high economic growth. The in-depth development of digital inclusive finance has stimulated the vitality of entrepreneurship, but whether it can effectively improve the quality of entrepreneurship is still a topic worth exploring. Using data from the China Household Finance Survey (CHFS), we find that digital inclusive finance has a positive impact on improving the performance of household entrepreneurship. Further analysis shows that optimizing the external entrepreneurial environment such as regional credit environment, regional innovation level, and market integration, is an important way for households to improve their entrepreneurial performance. In addition, based on the differences in the characteristics of entrepreneurial subjects and regional characteristics, the paper finds that the impact of digital inclusive finance on entrepreneurial performance is more significant in groups with medium and high financial literacy, long-distance groups, and groups in more developed areas. This paper has certain reference significance for further promoting the development of digital inclusive finance and better improving the quality of entrepreneurial development.

The end-to-end portfolio selection strategy based on deep learning exhibits high decision-making performance, but its black-box nature hinders interpretability of the decision mechanism. In this paper, we propose a comprehensive end-to-end portfolio selection strategy that combines decision-making capability with interpretability using deep learning, reinforcement learning, and knowledge distillation method. Firstly, by leveraging an improved Transformer to alleviate its quadratic complexity issue, a long sequence representations extractor is proposed. Then, through the employment of a cross-assets attention network and reinforcement learning algorithm, a non-linear "black-box" model is constructed to facilitate dynamic allocation in financial assets. Next, by calculating the gradients of model's outputs with respect to the asset features, we compute significance vectors in the feature space to identify key influential features. Finally, a linear regression model is applied to the identified key features, resulting in a straightforward and economically interpretable end-to-end portfolio selection strategy. Empirical results demonstrate that this interpretable end-to-end portfolio selection strategy based on Transformer and key features achieves favorable return and risk performance, with both decision-making power of deep learning and interpretability. This study provides a portfolio selection strategy that combines efficient decision-making capability and interpretability, contributing to the application of deep learning in the financial domain.

Exploring the impact of uncertainty on the resilience of Chinese residents' consumption is of great practical significance for expanding domestic demand, smoothing the domestic cycle and economic recovery. By constructing a stochastic computable general equilibrium model of China's domestic demand market, this paper measures the impact of uncertainty shocks on the resilience of household consumption from the perspectives of aggregate and structure, and examines the guarantee mechanism of different policy combinations to expand the resilience of household consumption from the institutional perspective. The results show that income level is the most important factor affecting the resilience of residents' consumption, and the impact of multi-risk cross-infection on residents' consumption resistance is greater than the simple superposition of single factors, and the recovery shock may have a reverse impact on different economic indicators. The resilience of urban and rural residents' consumption is structurally different, and the recovery shocks on the supply side and the demand side jointly affect the resilience of the domestic demand market, among which excess supply will also cause a decline in economic benefits. To improve the resilience of household consumption and achieve the goal of expanding domestic demand, it is necessary to integrate the roles of the government and the market, and the policy guidance needs to focus on employment issues and realize the free flow of land and capital factors between regions. This paper further puts forward corresponding policy recommendations. This paper uses the method of calculable general equilibrium to explore the resilience of household consumption, which breaks through the limitations of local equilibrium in existing studies and comprehensively and systematically measures the resilience of household consumption. By introducing random numbers, the impact of uncertainty shocks on the resilience of household consumption is more effectively simulated, which provides more specific and detailed theoretical support and policy enlightenment for the expansion of household consumption.

The integrated development of urban and rural areas is a symbol of the modernization of a country and region, and also an inevitable requirement of Chinese path to modernization. How to effectively measure its development process and level has become a hot issue of great concern in the academic community. This article aims to comprehensively review the relevant literature on measuring the level of urban-rural integration development from an economic perspective, clarify and define the concept of urban-rural integration as well as its four dimensions, including economic, social, spatial, and ecological integration. Then summarize the existing measurement indicators, methods, and regional comparative studies of the multidimensional integration level between urban and rural areas, and sum up the focus and characteristics of various studies. On this basis, further research ideas for the construction and measurement of the indicator system for urban-rural integration development are proposed from four dimensions: The connection between objective and subjective factors, the bidirectional flow of factors, the combination of macro and micro data, and the lower level of indicator construction. At the same time, we attempt to provide theoretical research references for policy formulation to improve the coordination and integration of urban and rural development in China in the new era, and to achieve the goal of common prosperity.

In order-driven markets, limit order book serves as a crucial information carrier that centrally reflects traders' intentions and market liquidity. Quantitatively assessing the information content of the micro-characteristics of the limit order book lays the foundation for in-depth exploration of market dynamics and is significant for precise stock price prediction and effective evaluation of market efficiency. This paper rebuilds limit order book using tick-by-tick order flow data. Based on deep learning model, it incorporates incremental information of order flow measured from the horizontal time dimension and stock information of order flow measured from the vertical space dimension, in addition to multi-level price and transaction information. The information content of limit order book characteristics is comprehensively explored through predicting multiple microstructure variables. Empirical results indicate that compared to transaction data, unfilled order flow data contains richer and more effective information, demonstrating significant advantages in predicting micro indicators. As the prediction window extends, this information advantage becomes more prominent, highlighting the important role of order flow data in market analysis and prediction. However, transaction data has higher information transmission efficiency and can provide complementary information for predicting market indicators. The combined use of these two features can effectively improve prediction performance. This conclusion remains robust in the analysis of heterogeneity regarding stocks' own characteristics. Additionally, price levels and cross-asset effects significantly impact the information content of characteristics. Therefore, careful selection of market depth and thorough consideration of market environmental factors are necessary during the process of order flow data mining.

The accuracy of stock return prediction has a critical impact on investment decisions. The advent of deep learning models has markedly improved the accuracy of return forecasts. However, stock market sequences are often observed with anomalies that can distort key statistical measures, obscure the true trends of the data, and diminish the predictive capabilities of deep learning models. In extreme cases, these anomalies can result in erroneous investment decisions. Based on the presence of anomalies and the learning dynamics of gradient descent algorithms, this paper introduces a novel loss function, the threshold distance weighted loss (TDW), which is designed to mitigate the susceptibility of the model to outliers by assigning variable weights to data samples. The TDW loss function has been tested through simulation studies and empirical analysis. These evaluations have confirmed the improved predictive accuracy and robustness of the method, highlighting its potential to deliver consistent positive returns to investment portfolios and to bolster informed financial investment decisions.

The China-U.S. trade dispute has been a significant event in the field of international trade in recent years, with profound impacts on bilateral trade relations. This study examines the effects of the 2018 and 2019 tariff increases imposed by the Trump administration on Chinese goods using the synthetic control method from two perspectives. First, we analyze the dynamic impacts of tariff policies on China's total exports to the U.S., identifying changes before and after policy implementation. Second, we investigate the heterogeneous impacts across 21 HS product categories, uncovering variations in sensitivity to tariff policies. Furthermore, based on the historical policy effects, this study predicts the potential impacts of similar tariff policies under Trump's new administration on China-U.S. trade. The results suggest that if similar policies are implemented, China's total exports to the U.S. will experience further declines. In the early stage of the policy, market behaviors such as stockpiling and anticipatory exports may temporarily drive up export values, but the decline in exports will gradually increase thereafter. Products with high price elasticity are likely to face greater negative impacts compared to high-value-added products, but high-tech products will also face significant pressure due to U.S. technological restrictions. This study provides valuable insights into the economic impacts of the China-U.S. trade dispute, evaluates the consequences of tariff policies, and offers guidance for policymaking.

Improving residents' happiness is the fundamental purpose of development and an important measure of development effectiveness. Leveraging big data and large language model technologies represented by ChatGPT, this study explores the possibility of accurately measuring residents' subjective happiness on a national scale. We propose a method for constructing a residents' subjective happiness index based on Weibo data and develop a large language model for sentiment analysis, SentiGLM, built on ChatGLM3, to enhance the accuracy and effectiveness of sentiment classification in Weibo texts. The SentiGLM model significantly improves the performance of sentiment analysis tasks on Weibo texts through low-rank adaptation fine-tuning on a multi-task instruction dataset. Based on approximately 60 million Weibo text data points, this study calculates the subjective happiness index at regional and national levels in China for the first time across four temporal granularities: yearly, monthly, weekly, and daily. The study finds that SentiGLM significantly outperforms traditional machine learning models (such as BERT, LSTM, and SnowNLP) in sentiment analysis of Weibo texts. Moreover, compared to traditional survey methods, the measurement approach based on large language models demonstrates superior cost-effectiveness, timeliness, and robustness, while also providing finer granularity in both temporal and spatial dimensions.

This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple large language models (LLMs) as heterogeneous artificial economic agents. We first evaluate five LLMs' economic decision-making capabilities in solving two-period consumption allocation problems under two distinct scenarios: With explicit utility functions and based on intuitive reasoning. While previous research has often simulated heterogeneity by solely varying prompts, our approach harnesses the inherent variations in analytical capabilities across different LLMs to model agents with diverse cognitive traits. Building on these findings, we construct a multi-LLM-agent-based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets. Using interest income taxation as a case study, we demonstrate how the MLAB framework can simulate policy impacts across heterogeneous agents, offering a promising new direction for economic and public policy analysis by leveraging LLMs' human-like reasoning capabilities and computational power.

Based on the traditional financial indicators, this paper applies text analysis and natural semantic processing methods to reconstruct the enterprise risk identification index system based on past and future perspectives. Then, it introduces machine learning methods to construct an enterprise risk identification model based on the financial data of listed companies and the textual information of management discussion and analysis as the data source for enterprise risk identification and prediction. The conclusions of the study are as follows: 1) By providing additional information, the risk measurement scale can be improved, and a three-dimensional risk identification system that combines temporal sensitivity and emotional insight can more comprehensively and accurately measure and identify business risks. 2) Introduces machine learning algorithms to compare the predictive accuracy of the AdaBoost model, Hist Gradient Boosting model, Random Forest model and Bagging model, and finds that the AdaBoost model is optimal, has the best robustness, and can be used for enterprise risk identification and prediction. 3) By applying machine learning and SHAP methods to rank the importance of enterprise risk characteristics and analyze the mechanism of enterprise risk identification, the key influencing factors of enterprise risk can be identified, and the impact mechanism of various risk characteristics on the enterprise risk identification model can be observed. This study can provide empirical evidence and decision support for the design of enterprise risk identification index system and optimization of risk identification model, as well as promote the high-quality development of enterprises and supply chain security and stability.

This paper constructs a credit risk prediction model based on deep learning (CDL), and explores the economic mechanism behind it. Empirical result shows that CDL model can predict corporate bond credit risk more accurately compared with classical machine learning model and ordinary neural network model. Mechanism analysis shows that CDL model has stronger nonlinear prediction ability for bonds with higher relative risk. In terms of enterprise characteristics, valuation and growth indicators and intangible asset indicators are more important in the model prediction. In addition, CDL model identifies bonds with high risk by effectively identifying economic characteristics such as small trading volume, high financing constraints, and low internal control quality. This paper provides a new way to predict bond credit risk, which is helpful to maintain financial market stability and promote high-quality economic development.

Algorithmic trading has emerged in the A-share market in recent years, and its impact on capital market pricing efficiency has received wide attention across industry and academia. This paper focus on companies listed on the SZE Growth Enterprises Market and SSE STAR Market, aiming to explore the influence of algorithmic trading on the information content of stock prices before the release of quarterly earnings announcements. Firstly, by considering the trading system features and investor structure characteristics of the A-share market, this paper constructs algorithmic trading indicators tailored to the A-share market. Based on these indicators, empirical tests reveal that algorithmic trading reduces the information content of stock prices before earnings announcements, indicating that the liquidity demand strategy of algorithmic trading plays a dominant role. Further mechanism analysis shows that the negative impact of algorithmic trading on stock price information content stems from increased transaction costs for slower investors and reduced large orders from informed traders. Lastly, the research finds that while algorithmic trading exhibits a crowding-out effect on informed traders, it also mitigates abnormal stock price fluctuations caused by noise trading. This study deepens our understanding of the economic consequences of algorithmic trading at the market level and provides insights for regulators to improve related policies concerning algorithmic trading.

Since the proposal of the capital asset pricing model (CAPM), the risk-return relationship has always been a key issue in academic research. However, the current debate on the effectiveness of the CAPM model is still ongoing, and the conclusions are not unified. Based on existing studies, this paper comprehensively considers the differences in trading mechanisms, trading characteristics, and investor types between the intraday and overnight periods, innovatively introduces a time heterogeneity perspective to decompose the daily trading period into two parts, and further studies the risk-return relationship in China. Specifically, we use the portfolio-sort way and Fama-MacBeth regressions to test the relationship between systematic risk (proxied by the beta coefficient) and stock returns and find positive correlations from the daily and intraday betas. This positive relationship does not exist in overnight periods and may even display a high-risk but low-return phenomenon. The previous studies based on the daily data mainly display findings from the intraday trading period but fail to reveal the role of overnight trading, and this paper tries to supply them. In addition, the positive relationship is stronger in stocks with small market capitalization, poor liquidity, and high idiosyncratic risk, and investor sentiment and arbitrage limitation also play an essential role. Our results are robust under the adjustment of different factor models, alternative beta measurements, and various subsamples. This paper is of great importance for investors to further understand the CAPM model, understand the heterogeneous performances at three periods, and improve the risk-return evaluation framework. Our paper also helps regulators revise the related policies and pricing mechanisms and achieve effective measurement of systemic risk in China's A stock market.

How can digital government construction empower real economy high-quality development through a sound data base system? This paper takes the local government SME financing service platform as an exogenous shock of digital government construction to study the impact of such digital government construction on SME investment. This paper finds that the establishment of the financing service platform significantly increases the investment level of SMEs. Heterogeneity analysis shows that the impact of the financing service platforms on corporate investment is more significant among firms with lower collateral value, underinvestment and among firms in areas with higher legal protection. Mechanism analysis further indicates that financing service platforms mainly improve the investment level of small and medium-sized enterprises by alleviating financing constraints and strengthening supervision and governance. This study expands the relevant literature on digital government construction and enterprise investment, and has enlightening significance for the government to guide financial institutions to provide high-quality financial services for enterprises, especially small and medium-sized enterprises.

In recent years, Southeast Asian and South Asian countries have significantly increased their share in the global supply chain, showcasing notable economic resilience and growth potential. During this phase of accelerated economic development, rapid industrialization and urbanization have led to a continuous rise in carbon dioxide (CO₂) emissions, exacerbating the challenges of climate change. In this context, emerging economies in Southeast and South Asia must develop tailored emission reduction targets and pathways to mitigate the adverse impacts of climate change effectively. A comprehensive, detailed, and unified CO₂ emission inventory serves as a critical foundation for assessing emission trends and formulating strategic planning pathways. To this end, this study integrates multi-scale energy, population, and economic data to construct CO₂ emission inventories for 11 countries in Southeast and South Asia from 2010 to 2020. The study highlights the carbon emission heterogeneity across countries at various spatial scales, energy types (including biomass), and disaggregated economic sectors. Furthermore, it provides scientific insights to support developing economies in crafting both short- and long-term energy transition strategies, as well as designing context-specific CO₂ emission reduction policies at the national and regional levels.

With the ongoing emergence of global infectious disease virus variants and the increasingly severe biological security situation, the accuracy and efficiency of infectious disease detection have become crucial components in monitoring virus spread and protecting public health. However, the reliability of detection is often challenged by factors such as technical limitations and operational standardization. This paper examines the accuracy of large-scale detection of infectious diseases. Taking the COVID-19 epidemic as an example, we calculated the false-negative predictive values of "single test" and "pooled test" based on Bayesian statistical modeling, and proposed optimal testing strategies for different areas and sensitivities. It includes the selection of "single testing" and "pooled testing" strategies, the optimization of testing population and frequency, and adjustments to the intervals of regular testing. Furthermore, we computed the corresponding testing accuracy and optimal interval times for gender-separated testing schemes in low-risk areas. Finally, combined with the calculation results of this study, we formulated policy recommendations concerning infectious disease detection, aimed at providing a scientific foundation and policy support for effective disease prevention and control.

This paper will focus on the of gravity models, which are important theoretical spatial models. The gravity models are generalization of the natural gravity model in physics but they need to be generalized to have interactions with n regions instead of just two regions. A gravity model in economics concerns flow variables. For estimation of those spatial flow models, we will consider the classical estimation approach.

From the perspective of the micro level, this paper constructs a variable to measure the low-carbon transition of Chinese A-share listed companies. From 2007 to 2021, the degree of low-carbon transformation of China's A-share listed companies has gradually deepened, and the number of companies participating in low-carbon transformation has gradually increased. Firms with larger sizes and higher profitability, and firms with significant carbon emissions are more likely to adopt low-carbon practices. Our empirical analysis shows that the low-carbon transition has a significantly and positive effect on firm value. This effect is stronger for firms that face stricter environmental regulations. The increasing of market attention, improving of financing constraints, easing financing constraints and the reducing of financing costs are the plausible channels that low-carbon transition affects firm value. These findings offer valuable insights for promoting green and low-carbon development and provide empirical evidence supporting the transition of firms towards a low-carbon economy.

This paper employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) method to study the total volatility spillover effects across nine critical financial markets impacted by the COVID-19 pandemic. These markets include crude oil, natural gas, emerging and developed country stocks, industrial metals, precious metals, cryptocurrencies, agricultural products, and foreign exchange markets. The study examines the roles and pairwise relationships of these markets during and after the pandemic. The findings indicate that the system's overall shock reached its peak at the onset of the pandemic and decreased over time. Notably, the pairwise volatility spillover analysis shows significant changes in risk contagion among the markets compared to the period before the pandemic. Specifically, the crude oil market shifted from a net spillover to a net receiver of volatility; the cryptocurrency market transitioned from a less significant role to a major net spreader of risk, whereas the agricultural products market was minimally affected by the pandemic.

Since the global financial crisis in 2008, the United States has actively promoted the reshoring of manufacturing, creating development opportunities for domestic manufacturing while profoundly impacting the global industrial structure and layout, particularly posing significant challenges to the development of China's manufacturing industry. Based on the multi-regional input-output data in constant prices from 2009 to 2022 released by the Asian Development Bank, this paper measures the dynamic evolution of the manufacturing international competitiveness of China and the United States under the background of the U.S. manufacturing reshoring policies. It also employs a structural decomposition model to explore the driving factors and their contributions to the changes in the manufacturing international competitiveness with different technological levels at various stages. The study finds that: 1) The U.S. manufacturing reshoring policy has achieved certain effects in dealing with the issue of industrial hollowing, increasing employment, reducing supply chain risks, and improving economic conditions; 2) From 2009 to the present, on a global scale, the international competitiveness of U.S. manufacturing has not seen a significant improvement, while China's manufacturing international competitiveness has continued to strengthen, with the trend of industrial transformation and upgrading continues; 3) In recent years, the competition between China and the U.S. in the fields of medium and high-tech manufacturing has intensified; 4) The decomposition of international competitiveness indicates that the resilience of supply chains, the evolution of traditional trade patterns, and the reconstruction of global value chains are beneficial for enhancing China's manufacturing international competitiveness; whereas domestic drivers that could consolidate and strengthen the comparative advantage of high-tech manufacturing in the U.S. have not been formed. The empirical results of this paper provide implications for how China can further enhance the international competitiveness of its manufacturing industry.

Gold and silver, due to their unique financial properties, have become preferred choices for investment and asset preservation. Accurately quantifying and predicting their price fluctuations is crucial for investors' risk management decisions. This paper introduces a rich set of feature variables and employs a forward rolling algorithm to forecast the realized volatility (RV) of gold and silver futures in Shanghai. We compare the performance of various machine learning models under different loss functions and evaluation methods. The results indicate that the gradient boosting decision tree (GBDT) models demonstrate superior performance in forecasting the futures market for precious metals. Furthermore, this study integrates the XGBoost model with interpretability tools to analyze the dynamic contributions of feature variables to the predicted values in the precious metals futures market. It also assesses the heterogeneous impact of significant variables on predictive performance. Our findings reveal the critical role of market sentiment variables, as well as the relative contributions of macroeconomic variables and volatility decomposition variables under different market conditions. The research provides clear evidence for the selection of factors and models in forecasting precious metal futures market volatility, offering credible investment and management recommendations for investors and regulators in this market.

The open sharing of data resources is key to unlocking the value of data elements. Assessing the impact of government data openness on corporate sustainable development is of significant importance for promoting high-quality economic and social development. This paper uses the openness of government data as a quasi-natural experiment, taking Chinese listed companies from 2009 to 2022 as the research sample, and explores the impact of government data openness on corporate economic performance and environmental performance through the difference-in-differences model. The study demonstrates that government data openness has brought dual benefits to corporate economic and environmental performance, that is, government data openness has promoted corporate sustainable development. The reason is that government data openness can promote corporate technological innovation and improve the efficiency of corporate operation and management. Further research finds that the role of government data openness in promoting economic performance is more significant in state-owned enterprises, regions with a better business environment, and areas with better digital infrastructure conditions, while the enhancement of environmental performance is more fully demonstrated in state-owned enterprises, non-heavy polluting enterprises, and regions with higher environmental regulation intensity. This study reveals the role of government data openness in improving corporate economic and environmental performance, providing important empirical insights for promoting sustainable economic and social development and enhancing the scientific formulation of data openness policies in the context of "Dual Carbon" goals.

Stock index forecasting is crucial for financial market regulation and investment decision-making. From a fresh perspective of industry risk connectedness, this study proposes a novel multivariate time series graph neural network (MTGNN) model based on risk spillover network. The model uses risk spillover network as the spatial dependency and multiple node features, including sentiment indicator and net risk spillover index as the temporal dependency to predict industry indices. A comprehensive comparison is conducted among the proposed model and five alternatives. Additionally, this study integrates point and interval forecasting results to propose a trading strategy with interval constraints. The study shows that: 1) The MTGNN model based on risk spillover network outperforms traditional machine learning and deep learning methods in forecasting industry stock indices; 2) the investor sentiment indicator and net risk spillover index significantly enhance the prediction performance of the MTGNN model; 3) the interval-constrained trading strategy ensures high returns and greater stability during backtesting. This study offers investors a practical tool for forecasting stock indices and provides decision support for macroprudential regulation.

The structure of the financial system is constantly changing under the impact of the macro environment, and risk spillover is the key to analyze systemic risk. In order to break through the dimension limitation and model specification of traditional parametric models, this paper proposes a semiparametric method, Dynamic Bayesian-Local Gaussian Correlation Network (DBN-LGCNET) to measure the time-varying nonlinear correlation between the general and tail risks. The model is applied to the data of 65 listed financial institutions in China's A-share market, and the results show that: 1) There are obvious tail risk spillovers in the financial system. 2) Risk spillover in the financial industry display heterogeneity, with the source of general risk propagation mainly in the banking sector and the source of tail risk propagation mainly in the securities sector. 3) Risks propagate dynamically among financial institutions, state-owned banks demonstrate a consistent capacity to absorb risk spillovers, whereas small and medium-sized banks show a lesser ability to cope with extreme events. 4) After an extreme event, the impact of the banking industry in the general correlation network is enhanced and the impact of the securities industry is weakened. Links between financial institutions in the tail correlation network are strengthened, especially insurance institutions.

In the context of China's emphasis on "maintaining the bottom line of no systemic financial risks", this paper examines the impact of industry bubbles on systemic risks contribution from an industry perspective. Furthermore, we construct inter-industry risk networks to identify the risk sources under different levels of systemic shocks. This provides effective policy references for deepening the reform of the new development pattern in which the domestic grand cycle plays a leading role. The empirical results indicate that, apart from the promoting effect of asset price bubbles in the Agriculture, Water, Environment, and Utilities Management, and Culture, Sports, and Entertainment industries, the impact of most industry bubbles on the systemic risk contributions of firms within those industries is either insignificant or inhibitory. However, when investor sentiment is high, the systemic risk of companies within the industry increases during a bubble period. In the cumulative effects of the local projection model, the promoting effect of bubbles in most industries shows a sustained upward trend, with the cumulative effect of promotion being heterogeneous. In the quantile risk network, industry connections tighten during extreme shocks compared to normal periods. Additionally, the risk defense capabilities of each industry vary depending on the intensity of systemic shock. Besides, the risk sources in industry bubble networks are predominantly in the Agriculture, Manufacturing, Transportation, Catering, and Leasing industries. This implies that risk shocks propagate outward, revolving around production, distribution, circulation, and consumption as the core, indicating that China's economy is transitioning towards a new development pattern centered on the domestic grand cycle.

The commonly used Taylor rule generally stares at the output gap and the inflation gap, but under major exogenous shocks, the Taylor rule may need to adjust the target of staring at the right time in order to improve the adjustment effect of monetary policy. This paper takes the new crown epidemic as a representative of exogenous shocks, and constructs the PVAR model in stages before and after the exogenous shocks, and comparatively studies the effectiveness of the Taylor rule after adjusting the target of focus. The study shows that the Taylor rule is effective for developed countries and floating-exchange-rate countries; moreover, the Taylor rule with the addition of exchange rate and financial stability can better cope with exogenous shocks. The policy recommendations are: To improve the international monetary policy coordination mechanism, to establish a monetary policy control framework to cope with exogenous shocks in sudden crisis events, and to implement a stable exchange rate policy and a macro-prudential control policy for cross-border capital. China should adjust its monetary policy in line with its own development characteristics, so as to realize the optimal control of the macroeconomy and resist the adverse effects of exogenous shocks.

This study proposes a new risk perception factor (FRP) and adds it to Liu et al.'s (2019) three-factor model to form a four-factor model (henceforth RPM4 model) for the Chinese stock market. The results show that the FRP's volatility, Sharpe ratio, and maximum drawdown have significant advantages over the factors of popular asset pricing models. Further testing reveals that the RPM4 model contains more information and has a clear advantage in explaining stock portfolio returns, and performs well in the R² comparison tests for cross-sectional regressions. The RPM4 model is a useful complement to the Fama and French (1993) three-factor model, as well as Liu et al.'s (2019) three- and four-factor models.

To investigate the impact of the pilot policy of China's carbon emissions trading system (ETS) on the transition risk of provincial financial institutions and its mechanism is an important research topic with practical significance and theoretical value. In this paper, we constructed transition risk indicators for provincial financial institutions, and analyzed the effects of the ETS pilot policy on the transition risk of financial institutions in the pilot areas and its mechanism by using the staggered double difference method (staggered DID), the two-step method of mediation effect analysis, and the generalized method of moments panel vector autoregression model (GMM-PVAR). The study finds that: firstly, the ETS pilot policy increases the transition risk of financial institutions in the pilot region, and the heterogeneity of environmental regulatory policies on the effects of the pilot policy is more significant than that of geographical factors; secondly, the ETS pilot policy mainly works through the channels of direct costs of green transition, business returns, green technological advances, and green credits, among which there is heterogeneity in the mechanism of green technological advances; lastly, the green technological advances and the green credits of enterprises are related to the direct costs of green transition, and there is heterogeneity in the mechanism of green technological advances. Finally, there is a dynamic lag effect between the green technology progress and the enterprise business income mechanism, which makes the ETS pilot policy have a lagging negative impact trend. This paper is the first to explore the inherent dynamic connection between the ETS pilot policy and the transition risk of provincial financial institutions from the level of quantitative analysis, and puts forward relevant suggestions for balancing the implementation of the policy and the risk management of the transition, so as to provide a reference basis for the steady promotion of the "dual-carbon" process and the safeguarding of financial stability in the low-carbon economy.

The proportion of rent to household income is an important indicator for measuring the rental burden of urban households. Due to limitations in data availability, the ratio of annual average rent to average household income in a city is often used to study the rental burden. However, this method often leads to a general logical conclusion that low- to middle-income households bear a heavier burden, without providing quantitative results for households at different income levels. This paper utilizes grouped data on rental households from the Seventh Population Census and data on per capita disposable income of urban households. By introducing two continuous distribution functions for rent and income, it overcomes the issue of inconsistent statistical calibers and constructs a model for matching rent with household income efficiency, simulating and calculating for typical cities. This model can not only accurately calculate the overall average ratio of rent to household income in various cities but also detail the actual rental burden for households at different income levels. While revealing the differences in rental burdens among different cities and income groups, this model is also operational and universally applicable, aiding in the assessment of the fairness and effectiveness of the rental market.

It is of great value to understand the mechanism of stock return, but China's stock market is affected by many factors at home and abroad, and there are regular behaviors, which makes it diffcult to carry out research. In this paper, a functional varying coeffcient single index model with subgroup structure is proposed and applied to analyze the influencing factors of stock return. The influencing factor system includes three dimensions: macro, mesoscopic and microscopic. At the same time, this paper uses functional data structure to depict calendar effect, and uses subgroup structure to depict industry effect, making the model as comprehensive as possible and suitable for our national conditions. Finally, it is found that the influence direction of macroeconomic factors on stock return varies throughout the year, and the influence of international crude oil price change rate on stock return has industry heterogeneity. At the same time, profitability, value-creating ability and valuation level have great impact on enterprise evaluation and stock return rate. Furthermore, based on some regularity conditions, this paper establishes the asymptotic theory for the estimators of the index parameters, link function, and coeffcient function in the proposed model.