At present, chat generative pre-trained transformer (ChatGPT) as a representative of the rapid development of large language models, is widely used in stock market investment, algorithmic trading, risk management and other fields. This provides financial investors with new decision-making tools and investment paths. In this paper, we construct an investment trading model based on the bidirectional encoder representation from transformers (BERT) model and chat generative pre-trained transformer (ChatGPT) for the Chinese stock market, and realize the trading signals from financial news text data and traditional financial data. For the text data, the daily financial news is captured and matched with the corresponding stock codes. Secondly, we input the news text data into the trained fine-tuning BERT (FTBERT) model to get the sentiment tendency of each news item, and select the positive financial news as the positive investment trading signals. For the traditional financial data, we use the advanced parsing capability of chat generative pre-trained transformer (ChatGPT) to analyze the historical data of Chinese stock market. By adjusting the prompt to read the data, the key factors for stock investment are constructed, and the daily scores of each stock are output. Finally, the daily investment signals of each stock are obtained based on different data types, which are used as the basis for constructing investment portfolios and building effective investment strategies. The empirical results show that chat generative pre-trained transformer (ChatGPT) effectively determine the sentiment tendency of text. The fine-tuned model can effectively assist quantitative investment and bring investors excessive returns. This study attempts to apply big language modeling to financial investment and shows its potential value in generating stock investment signals. With the continuous development of technology and changes in the market environment, this artificial intelligence-based investment strategy will continue to evolve and create more value for investors.

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.

In the background of the rise of the anti-globalization trend in the postepidemic era, China's economic development is expected to rely more on the increase in the degree of dependence on the pull of domestic demand, so the study on how to promote the level of consumption of the residents and the expansion of domestic demand is also becoming more and more critical. The deep integration of digital economic development and traditional industries can release a sizable "digital dividend", colossal energy, is the expansion of domestic demand, the realization of the "domestic cycle" of the critical potential driving force. Given this, based on the China Family Panel Studies (CFPS) database, this paper constructs the digital economy development level index at the provincial level in China. It empirically examines the theoretical analysis and empirical test on the effect of digital economy development on residents' household consumption. The study finds that: (ⅰ) Digital economy development can promote residents' household and per capita consumption. This conclusion still holds after a series of robustness tests. (ⅱ) The development of the digital economy can positively impact residents' consumption level, mainly through improving the quality of residents' income. However, optimizing the consumption environment is not the main reason for the digital economy to promote consumption.(ⅲ) From the heterogeneity analysis, digital economy development has a more noticeable effect on the consumption enhancement of regions with low unemployment rates and households with low labor costs. (ⅳ) This paper further discusses the impact of digital economic development on the consumption structure and finds that digital economic development makes it diffcult to enhance residents' subsistence consumption but significantly increases the developmental consumption expenditures of income groups at all levels and also substantially promotes the enjoyment expenditures of high-income households. The findings of this paper provide theoretical support and a decision-making basis for further utilizing digital economic development to release consumption potential.

This paper compares and analyzes the differences in stock pricing between news sentiment and social media sentiment in two different time dimensions, daily and monthly, using individual sentiment data from the Thomson Reuters MarketPsych Indices and trading data from the US stock market from 2010 to 2019. The empirical results indicate that social media sentiment performs better at the daily level than news sentiment, and news sentiment has a stronger explanatory power on stock returns at the monthly level than social media sentiment. Specifically, at the daily level, this paper constructs news sentiment factor and social media sentiment factor, and finds that social media sentiment factor still exhibits significant excess returns under the Fama-French five-factor model, while news sentiment factor no longer exhibits excess returns. In addition, social media sentiment factor can explain most market anomalies at the daily level, while news sentiment factor cannot. In order to investigate the reasons, this paper conducts a Granger causality test, indicating that the response speed of social media sentiment factor is 3 to 4 trading days faster than that of news sentiment factor. At the monthly level, this paper finds that news sentiment improves its ability to explain anomalies, while the explanatory power of social media decreases significantly. In addition, for volatility anomalies and idiosyncratic volatility anomalies, the monthly news sentiment factor has a significant explanatory power, while the explanatory power of the monthly social media sentiment factor is not significant.

This study investigates the relationship between climate change and systemic risk in China's financial system. First, it examines the responsiveness of systemic risk in the banking, securities, and insurance sectors to extreme climate events, assessing how different financial industries withstand such disasters. The findings confirm that certain extreme climate events can exacerbate systemic financial risk. Second, by constructing a nonlinear autoregressive distributed lag (NARDL) model, this study analyzes the impact of the performance of green and brown market stock indices on the systemic risk of financial sub-sectors. The results indicate that in the short term, an increase in the risk of brown assets and a decrease in their indices significantly amplify systemic risk in the financial industry. However, in the long term, an increase in the brown asset index raises systemic risk in the banking sector, while an increase in the green asset index reduces systemic risk in the securities sector. Furthermore, a reduction in green asset risk significantly lowers systemic risk in the banking sector. In addition, this study underscores the importance of policies addressing the increasing frequency and severity of climate-related disasters. It recommends differentiated financial prudential regulations for green and brown sectors to minimize transition risks associated with climate policy implementation while mitigating physical risks. This approach is crucial to improve risk management frameworks in the financial industry, thereby reducing the impact of both physical and transition risks on systemic risk.

With the rising labor costs and increasing resource and environmental constraints in China, coupled with geopolitical conflicts, related industries or production processes are shifting to emerging economies such as Southeast Asia, South Asia, and Mexico. Among these, India's development potential has garnered significant attention, and the "China-to-India industrial relocation model" in the global industrial chain poses a greater impact and threat to China. This paper constructs a pre-quantitative model to measure the impact of industrial relocation on the home country. It designs three scenarios—Ultra-long-term, medium-to-long-term, and short-to-medium-term—And uses counterfactual analysis to assess the impact of India's absorption of China's industrial relocation on China's GDP and employment under different scenarios. The research results indicate that the relocation of industries from China to India will generate significant socio-economic shocks. In the ultra-long-term, this industrial transfer could lead to a 15.6% reduction in China's GDP, a 16.8% decrease in the overall income of the workforce, and a reduction in the number of employed people by 110 million. The impacts are also substantial in the medium-to-long-term and short-to-medium-term scenarios. By sectors, the relocation of low and medium-low R&D intensity manufacturing sectors has a significant impact on the Chinese economy in both the short-to-medium and medium-to-long term perspectives. The relocation of high R&D intensity manufacturing sectors, represented by the computer industry, also causes considerable negative effects on the Chinese economy in the ultra-long-term perspective. This quantitative analysis helps anticipate the economic impact of future changes in industrial layout on China's economy and facilitates the development of preemptive strategies. Based on the medium-to-long-term international economic outlook and the characteristics of domestic regional and industrial economic development, we propose three policy recommendations to provide scientific reference for decision-making by relevant government departments.

In the digital economy, the emergence of digital currencies has attracted considerable attention from both investors and researchers. However, their high volatility characteristics present new challenges in investment decision-making and risk assessment. To capture the characteristics comprehensively, this paper proposes a novel approach for constructing confidence regions for interval-valued variables based on the exponentially decay weighted bootstrap. The coverage area of the confidence regions and tail quantiles provide new indicators for assessing the volatility and tail risks in the market. Empirical results using Bitcoin as a case study demonstrate the proposed approach outperforms other traditional point-based methods such as exponential weighted moving average in measuring the uncertainty and intraday price volatility. Furthermore, the derived tail quantiles exhibit superior predictive performance for tail risk compared to Value-at-risk methods and the exponential weighted moving average, as evidenced by various tests. The proposed methodology not only contributes a new statistical tool for analyzing digital currency volatility but also provides novel perspectives for extreme risk management in financial markets.

Financial Technology (FinTech) combines financial, inclusive and technological aspects. Under the new development pattern, promoting China's common prosperity cannot be separated from the support of FinTech. Based on the provincial panel data of China from 2011 to 2020, this paper first constructs the common prosperity index from the three dimensions of development, sharing and sustainability, and then examines the impact and function mechanisms of FinTech development on China's common prosperity. The results show that FinTech development can significantly promote China's common prosperity. Further analysis reveals that the coverage of FinTech has a more significant promoting effect on China's common prosperity, and the promotional effect of FinTech development is more obvious on the sustainability of common prosperity, followed by development and the weakest sharing. The results of mechanism analysis show that FinTech development can promote human capital accumulation, enhance marketization, promote the development of the circulation industry, boost residents' consumption, and thus contribute to China's common prosperity by smoothing the domestic circulation. Heterogeneity testing indicates that there exists a regional Matthew effect in FinTech development, but this effect can be mitigated by increasing innovation activities. Therefore, this paper proposes to continuously improve the quality and efficiency of FinTech development, smooth the domestic circulation, strengthen the tilt of digital basic resources, and enhance regional innovation vitality, so as to make FinTech more effective in adding impetus to the realization of China's common prosperity.

This paper tests the profitability of the factor momentum strategy in the Chinese market, and gives a reasonable explanation for the source of excess returns of the factor momentum strategy. It is found that the factor momentum strategy can obtain significant excess returns in the A-share market, and the bull side contributes most of the returns of the strategy. After considering the control of multiple cross-sectional indicators, different economic states, and the use of different factor numbers as a factor sample, the return of factor momentum strategy is still significant. From the perspective of behavioral finance, this paper further finds that the lower investor sentiment, the higher the return of factor momentum strategy. In extreme market conditions, the return of factor momentum strategy is higher than that of stable market. This paper provides strong evidence for the feasibility of Chinese institutional investors' market timing based on factor momentum, and has some inspiration to enrich the value investment strategies of institutional investors.

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.

This article is based on the 2017 and 2019 China Household Finance Survey (CHFS) data. It conducts a systematic study on the impact and mechanism of commuting on residents' subjective well-being in China from urban-rural heterogeneity and demographic heterogeneity perspectives. Research has found that, firstly, the three aspects of commuting: i.e., commuting time, commuting distance, and commuting method have a significant impact on residents' subjective well-being. Commuting time has a significant negative effect on residents' subjective well-being. In contrast, longer commuting distance compensates for the negative impact of long-distance commuting on residents' subjective well-being by enhancing the utility of other aspects. Among commuting methods, at present, public transportation has the most significant inhibitory effect on residents' subjective well-being. Secondly, the analysis of the mechanism of action shows that the impact of commuting time, commuting distance, and commuting method on residents' subjective well-being shows substantial heterogeneity due to differences in regional location and individual characteristics. The impact is more significant in urban areas, eastern regions, high-priced housing areas, male residents, married residents, and residents with children. Thirdly, further exploring the external conditions that enhance the subjective well-being of residents through commuting, excessive construction of bridges, overpasses, etc., is not conducive to improving the quality of commuting but may damage the subjective well-being of residents.

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.

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.

With the rise of social media, its impact on the financial transparency of publicly listed companies has received increasing attention. This study investigates how social media, particularly posting activity on East Money's stock message boards, affects the financial fraud behavior of listed companies. Utilizing data from East Money's stock message boards and a bivariate probit regression model, the study finds that the number of posts on the message boards is inversely related to the probability of fraud occurrence and positively related to the probability of fraud detection. This finding indicates that social media may play a dual role in both deterring financial fraud and uncovering it. To address endogeneity issues, the study employs an instrumental variable approach. Additionally, based on the "fraud triangle" theory, the paper proposes and validates two mechanisms through which message board posting activity reduces the likelihood of financial fraud: By decreasing potential opportunities for fraud and increasing the difficulty of rationalizing fraud. Heterogeneity analysis reveals that negative posts and posts by senior users are more effective in curbing financial fraud. This research not only enhances the understanding of how social media can function in corporate governance but also provides insights for regulatory authorities on leveraging social media for financial supervision.

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.

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.

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.

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.

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.

With the development of the economy, the issue of government taxes and debt has also become the focus of attention. Especially during the economic downturn, how to achieve stable economic development through reasonable regulation and control of tax and debt levels has also become the focus of government discussions. Considering that real life will face the impact of inflation, this paper will construct an optimal government tax and debt model in the context of inflation, and try to analyze the impact of inflation on China's optimal tax and debt. Firstly, the stochastic analysis theories are used to derive the dynamic equation of the real government debt value in the inflationary uncertainty environment, and then the Hamilton-Jacobian-Bellman (HJB) equation of the optimal real government value in the inflationary uncertainty environment is derived by using the dynamic programming principle. Finally, the numerical simulation of the optimal tax and debt of the government is carried out to analyze the impact of inflation on the optimal tax and debt of the government, as well as the impact on the time to reach the debt capacity, and predict the time when China will reach the debt capacity. The results suggest that under optimal taxation and debt, higher expected inflation and lower inflation volatility reduce the marginal cost of tax and debt, and shorten the time to reach debt capacity.

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.

Shocked by a series of major international events, global commodity prices have exhibited significant volatility, posing a considerable risk of imported inflation for China. Developing industrial clusters of commodities based on regional resource endowments may be a feasible strategy to address this problem, but its empirical impact has not yet been demonstrated. This study develops an inter-regional input-output price model, integrating econometric models and the hypothetical extraction method, to construct a comprehensive framework for assessing imported inflation. Using this framework, the mitigating effects of Xinjiang's electrolytic aluminum industry clustering on imported inflation is explored. The empirical results indicate that, on one hand, the expansion of industrial scale strengthens the overall price level's linkage with international aluminum prices, leading to an increased transmission effect on the producer price index (PPI). On the other hand, the industrial clustering enhances self-suffciency and pricing power, thereby reducing the domestic price system's sensitivity to international market fluctuations. Although the results up to 2017 show that the former mechanism is stronger than the latter, in the long term, with respect for regional resource endowments and with proper macroeconomic regulation and emergency management policies, the latter impact will be further strengthened, and ultimately mitigating imported inflation.

A comprehensive examination on the structure and resilience of the industrial system, and their changes in the context of major events, contributes to a deep understanding about the relationships of production cooperation among industries and maintains the stability of the system; moreover, this is helpful for scientifically allocating production resources and promoting the transformation and upgrading of industries. This paper takes input-output tables as the data foundation, and combines complex network theory with integer programming method to construct an "input-output-integer programming" complex industrial linkage network; furthermore, it explores the variations in the structure and resilience of China's industrial system. The empirical results show that: ⅰ) The structure of the industrial system significantly impacts the GDP growth rate, and the denser the former is, the faster the latter is, and vice versa. Over the past decade since 2011, the structure has become increasingly loose; ⅱ) major events, such as stimulative economic policies implementation and supply-side structural reform, shorten the distance for resource transfer between industries and improve the structure of the industrial system, but their impacts on the system are localized; however, the events like european debt crisis escalation, China-US trade friction intensification, and COVID-19 outbreak extend the distances, resulting in negative shocks and widespread impacts; ⅲ) compared to in periods of high-speed economic growth, the structure of the industrial system loosens in times of medium to low growth, leading certain industries to play an "intermediary" role cross a large range to coordinate resource allocations and satisfy production demands. This phenomenon is particularly evident during China-US trade frictions and COVID-19; ⅳ) from 2011 to 2020, the resilience of the industrial system consistently remains at a high level but exhibits a weakening trend. Driven by relevant economic policies and market reforms, the system significantly improves its defense, adaptation, and recovery capabilities in the face of impacts from major events.

Due to the outbreak of the global COVID-19 epidemic, the economies and financial markets of various countries have been severely impacted when the nonferrous metals futures market of China is also influenced. Therefore, the study of how the new coronavirus epidemic affects the nonferrous metals futures market of China has an important theoretical and practical value in the development of our country's futures market. This article uses MF-DFA and multifractal spectrum analysis methods to analyze the price datas of four non-ferrous metal futures: Shanghai copper, Shanghai aluminum, Shanghai zinc, and Shanghai lead, and explores the impact of the COVID-19 epidemic on the effciency of these markets, and further analyzes changes in market risk. Empirical results show that except Shanghai copper futures, the market effciency of Shanghai aluminum, Shanghai zinc, and Shanghai lead non-ferrous metal futures had declined and market risks had increased after being affected by the COVID-19 epidemic. Specifically, the effciency of the Shanghai aluminum futures market had dropped most significantly, while the price of Shanghai lead futures had been the most volatile, and market risks had increased the most. In addition, this article rearranges and substitutes the original time series before and after the COVID-19 epidemic and finds that the long-term correlation of the time series contributed more to the ineffciency of the four metal futures markets before the epidemic. While after the epidemic, time series the thick-tailed distribution of the series plays a greater role in the ineffciency of the Shanghai copper futures market. For the other three nonferrous metal futures, the long-term correlation of time series is still a more crucial reason for market ineffciency. The above conclusion illustrates the characteristics of the impact of major impact factors on the market. They also may be instrumental for the nonferrous metals futures market of our country turning to more mature and stable.

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.

When addressing composite hypothesis testing, empirical process-based statistical tests often lack distribution-free. The Khmaladze transformation, including both the martingale and unitary transformations, provides an effective solution to this challenge. Initially, in the early 1980s, Khmaladze introduced the martingale transformation, specifically designed for testing problems involving continuous distribution functions. Over time, the theoretical foundation of the martingale transformation has been continuously deepened and refined; and its application scope has broadened, covering a wide range of testing problems, including distribution functions and regression models. Entering the 21st century, Khmaladze further proposed the unitary transformation in 2013 and 2016, which is applicable not only to discrete distributions but also to continuous distributions, bringing new perspectives and tools to the field of statistics. However, despite a certain research foundation for the Khmaladze transformation internationally, these two transformation methods have not yet been fully recognized in China. This article aims to sort out the origin, theoretical principles, development process, and current application status of the Khmaladze transformation and to propose some thoughts on its further development and application prospects.

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.

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.

A solid financial condition serves as the foundation for the sustainable development of enterprises. However, due to imperfect systems, structural deficiencies in the market, and relatively low managerial proficiency, financial fraud remains rampant among listed companies in China. This paper aims to identify fraudulent behavior in listed companies' financial statements using textual data from news media disclosure. Additionally, it innovatively introduces fraud indicators incluidng litigation involvement, regulatory inquiries, internal controls, and risk management to explore the issue of identifying financial fraud in China. Research indicates a positive correlation between the frequency of litigation involvement and regulatory inquiries with financial fraud. Furthermore, when companies have effective internal controls and adopt robust risk management measures, instances of financial fraud decrease. This study innovatively analyzes the threshold value of annual net profit indicators in fraud analysis, suggesting that when annual net profit is less than 15 million RMB, the likelihood of fraud significantly increases. Finally, this paper employs resampling techniques to develop imbalanced machine learning models to further test these new-introduced fraud indicators. The empirical results find that random forest model integrated with Cluster Centroid sampling technique can accurately identify the highest 98% of financial fraud samples. By integrating econometric analysis and machine learning models, this paper provides abundant new empirical evidence for identifying financial fraud in listed companies, expands the indicator system, and innovates research perspectives.

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.

Currently, high-quality development is the primary task of building a modern socialist country, and the relationship between economic growth target and environmental protection target is the proper meaning of high-quality development. Based on the panel data set of 223 cities at or above the prefecture level, this paper explores the problem of haze pollution from the perspective of economic growth target mobilization. The research results show that from the national level, the mobilization of economic growth target significantly increases the haze pollution; the strategy imitation of governments at the same level significantly increases the local haze pollution, and the target assessment of higher governments can effectively correct the governance preference of lower governments. The results of the heterogeneity analysis showed that the haze pollution effect mobilized by the economic growth target did not differ significantly between the prefecture-level cities with different administrative levels, but the effect was greater in the prefecture-level cities located in the eastern region and the prefecture-level cities with larger population size. Further analysis shows that the constraint intensity of target mobilization exacerbates the haze pollution effect of economic growth target mobilization, which is the result of the relaxation of environmental regulation intensity in the development model of local governments based on investment-driven growth. The specific reason is insufficient investment in pollution control. This study has positive policy implications to solve the "dilemma balance" between local government economic growth and environmental protection and to accelerate the transformation of economic development model.

Setting economic growth targets is an important means of macroeconomic management for local governments. This paper focuses on the impact of the change of economic growth target setting concept on the optimization of manufacturing industry structure, puts forward the hypothesis of the influence mechanism of regional economic growth target on manufacturing industry structure optimization through logical reasoning and literature induction, and conducts an empirical test based on the provincial panel data of 31 provinces and autonomous regions in China from 2001 to 2020 (not including Hong Kong, Macau, and Taiwan). The analysis of moderating effect shows that the regional economic growth target has a significant promoting effect on the rationalization of the manufacturing industry structure after the change of the concept of regional economic growth target setting, and the imposition of environmental constraints can effectively weaken the inhibitory effect of the regional economic growth target on the upgrading of the manufacturing industry structure. The weakening of the increase in the regional economic growth target will help weaken the inhibiting effect of the regional economic growth target on the optimization of the manufacturing industry structure. The results of the intermediary mechanism test show that the regional economic growth target under the government performance appraisal can inhibit the height and rationalization of the manufacturing industry structure through the distortion effect of investment structure, the crowding effect of technological innovation and the stunting effect of factor market development, which provides a feasible path for reducing the negative impact of economic growth target setting and promoting the optimal development of the manufacturing industry.

Stock index data is influenced by multiple factors, exhibiting nonlinear, non-stationary, high complexity, and high volatility characteristics. Therefore, it is difficult for a single model to fully capture its data features. This paper proposes a hybrid forecasting model for stock index returns based on a decomposition-reconstruction-integration framework. Utilizing variational mode decomposition (VMD), the original high-complexity stock index time series is decomposed. The composite multiscale entropy (CMSE) is employed as a reconstruction indicator to reorganize the stock index data components into long-term trend terms, medium-term impact terms, and short-term disturbance terms. ARIMA, BPNN, and LSTM models are adopted for forecasting based on their respective data characteristics. Finally, the predictions of various frequency components are integrated to obtain the ultimate forecasting results. The proposed method is applied to forecasting eight significant industry stock indices and is compared with models utilizing fine to coarse (FTC), sample entropy (SE), fuzzy entropy (FE), and multiscale permutation entropy (MSPE) as reconstruction methods. Furthermore, two industry rotation strategies, equal-weight investment and dynamic-weight investment, are proposed to validate the performance of the proposed model in practical trading from both conservative and aggressive perspectives. The empirical results demonstrate that CMSE outperforms other reconstruction indicators in stock index forecasting. Compared to benchmark models, the hybrid model presented in this paper achieves lower forecasting errors and higher directional accuracy, and the proposed industry rotation strategies exhibit excellent performance in terms of risk and return.

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.

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.

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.

Under the new situation of promoting orderly carbon peak in all regions of the country, it is significant to evaluate the spatial carbon reduction effect of green credit to play its role in carbon reduction. Based on the panel data of 30 provinces in China from 2010 to 2019, Moran, Geary indices are used to test the spatial correlation of green credit and regional carbon emissions as well as their spatial-temporal evolution trend. The spatial Durbin Model (SDM) is adopted to analyze the local and spatial spillover effect of green credit on regional carbon emissions reduction, and Bootstrap method is used to reveal its mechanism. The results show that: 1) In terms of spatio-temporal evolution, green credit development shows the trend of "first the east, then the inland" among 30 provinces across the country, with the eastern region having the largest scale and the western region growing the fastest. 2) Green credit promotes carbon emissions reduction in local and neighboring regions, the spatial spillover effect is significant. 3) There is significant spatial heterogeneity in the carbon reduction effect of green credit on local and neighboring areas. In the western region, green credit has driven the carbon emission reduction of neighboring regions through resource and technology exchanges, while in the eastern region, the pollution transfer and the loss of technology R&D resources caused by strict green credit regulations has exacerbated the carbon emissions of neighboring regions. 4) Green technology innovation plays an important mediating role in the impact of green credit on carbon emissions reduction. The conclusions can provide an empirical basis for regional synergy and cooperation of green credit, giving full play to the spillover effect of carbon reduction, and promoting the formulation of policies such as carbon reduction according to local conditions.

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.

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.

Enhancing the value investment guidance of institutional investors can help the capital market give full play to its functions of value discovery and resource allocation. We identify mutual fund cliques from the position network of actively managed mutual funds, and explore the impact of mutual fund cliques on the incorporation of stock price information. The results indicate that mutual fund cliques hinder the integration of stock price information, and the conclusion remains valid after a series of robustness tests. In this study, we find through mechanism examination that, at the level of investor trading behavior, mutual fund clique clustering on one hand causes stock liquidity deterioration by reducing competitive trading among clique members and increasing information asymmetry between the mutual fund clique and other external investors. On the other hand, it attracts collective trading from a large number of institutional and retail investors, thereby hindering the incorporation of stock price information. At the level of corporate governance participation, we find that mutual fund clique clustering weakens the market-based supervisory role of the "exit threat'' mechanism on company management, resulting in a decline in the quality of corporate information disclosure and thus impairing the pricing efficiency of the stock market. Furthermore, we find that the delay in stock price information incorporation caused by mutual fund clique clustering is significantly exacerbated during economic downturns, when mutual funds face greater performance pressure, and when individual stocks receive less attention. Moreover, mutual fund clique clustering can induce stock price valuation bubbles and crash risks, laying hidden dangers for the stable operation of the capital market. Starting from the interest binding and shared progress and retreat characteristics within the mutual fund clique, we reveal how this strong network relationship limitation leads to delays in stock price information incorporation by influencing investor trading behavior and corporate governance participation, deepening the understanding of the complexity of institutional investor network structure and its market consequences, and providing important insights for promoting the high-quality development of the mutual fund industry and capital market.