As the scope, actors, forms, approaches, trends, and influencing factors of innovation inputs continue to evolve, the research objects, domains, and methodological perspectives of innovation inputs analysis are continuously expanding and becoming more refined. The optimal allocation, efficient management, and strategic decision-making of innovation inputs necessitate a systematic and scientific measurement framework. This study develops a theoretical framework for the innovametrics of innovation inputs, emphasizing their role throughout the innovation process. The framework aims to provide analytical perspectives and methodological tools for addressing key measurement issues related to the level, structure, and influencing factors of innovation inputs. Based on a review of the evolution of research on innovation input measurement, this study categorizes key measurement issues into three dimensions: development, structure, and dynamics. It further proposes the key issues and analytical approaches associated with each dimension. Additionally, considering advancements in innovation input management and practical demands, this study outlines future research directions in innovametrics. The development of this theoretical framework not only advances the theoretical and methodological foundations for optimizing innovation input allocation, management, and decision-making but also provides a systematic framework to guide academia, policymakers, and industry practitioners in understanding and effectively applying relevant measurement theories and methodologies.

This paper preliminarily explores the potential application of the automated market maker (AMM) mechanism in China’s Central Bank Digital Currency (CBDC) for cross-border transactions. The study focuses on two primary approaches to managing exchange rate volatility risks through AMM: one relies on direct regulation via central bank reserves, while the other achieves reserve-free regulation through market incentives. The research indicates that the reserve-free regulation mechanism, by setting target ranges and providing economic incentives, can effectively guide liquidity concentration and reduce exchange rate volatility risks, while simultaneously decreasing reliance on foreign exchange reserves. However, this mechanism may also exert certain influences on market liquidity distribution and price discovery. Future research could further investigate dynamic market behaviors, policy coordination, and international cooperation to refine the exchange rate regulation mechanisms in CBDC cross-border transactions, manage risks associated with the cross-border use of the digital yuan, and provide theoretical support and practical references for the internationalization of the digital yuan.

As an innovation and development of Marxist productive forces theory, new quality productive forces have received widespread attention since its proposal. In accordance with the connotation and characteristics of new quality productive forces, this study constructs an indicator system based on TEI@I methodology. With the comprehensive consideration of industrial development status and government’s catalytic role, we collect basic data from four aspects: Industry, workers, infrastructure, and policy basis, and explore relevant policy texts to measure new quality productive forces of 31 provinces in China. The results show that the development level of China’s new quality productive forces is at the early stage, and has a certain space aggregation and uneven development between the east and the west. The construction of new infrastructure and the cultivation of new workers will be the key points to the development of new quality productive forces in the future.

By characterizing COVID-19 as exogenous shocks to labor supply and tertiary sector production, we use CGE method to build a general equilibrium model to examine the effects on China’s industrial structural adjustments. Using China’s economic data from 2017 to 2023, we conduct numerical simulations through adjusting initial labor endowments and production scale parameters of the tertiary industry. Compared to no-pandemic scenarios, labor shifts toward the tertiary industry and capital flows into the secondary industry. The primary, secondary and tertiary industries experience the largest, smallest, and moderate reductions in output, respectively, leading to an increasing share of the secondary industry and a decreasing share of the tertiary industry. Moreover, supply side shocks transmit to the demand side, resulting in a decline in consumption and fluctuations in foreign trade, and eventually a continuous decline in national income relative to the no-pandemic benchmark. From 2020 to 2022, the epidemic caused annual social welfare losses of 7.83%, 7.24% and 7.01%, respectively. Our findings confirm that the international trade fluctuations do not alter China’s industrial adjustment trajectory. The limited overall impact of international trade on China’s economy suggests that domestic cycle remains the primary driver of growth. These insights offer policy recommendations for navigating complex and multiple exogenous shocks and optimizing China’s industrial structure in the post-pandemic era under the dual cycle development strategy.

China’s mass construction of new road infrastructure, represented by high-speed railways, provides an excellent narrative for the relationship between transport infrastructure and income inequality. Based on the data of Chinese industrial enterprises and the statistical information about prefecture cities from 2000 to 2013, this paper explores the impact of new road of urban high-speed railways on the labor pay gap within enterprises from the perspective of labor market change. Our results show that the new road of urban high-speed railways will accelerate the inter-regional flow of labor factors, cause the adjustment of labor market, and reduce the labor pay gap within enterprises by promoting the integration of labor market and changing the labor and employment structure. The heterogeneity analysis shows this pro-equal effect of new road infrastructure varies among industries, cities and enterprises. Further analysis verifies the existence of spatial spillover effect of the impact of urban high-speed trains on the labor market, indicating that the operation of urban high-speed trains will also reduce the labor market segmentation degree of other cities, which is conducive to the formation of a unified national labor market.

Crude oil price forecasting is critical for global economic stability and the development of the energy industry. However, existing single neural network methods face significant limitations: Increasing the depth and width of the network provides only marginal performance improvements while exacerbating overfitting risks and reducing generalization capabilities. To address these challenges, this paper proposes a method based on model merging with multiple structural neural networks. By constructing shallow MultiLayer Perceptron variant networks with sparse connections and skip connections, and assigning appropriate weights to the predictions of these neural networks, the proposed method significantly enhances prediction performance. Experimental results demonstrate that, for crude oil price time series forecasting, the method achieves superior accuracy compared to deep neural networks, without relying on complex and deep architectures of neural network. Instead, it leverages the merging of multiple structurally simple small-scale neural networks to deliver robust and precise predictions.

The construction of digital government enhances governance capacity through government information disclosure, digital governance, online services, and external supervision. It empowers enterprises by improving their information access, technological innovation, and cost reduction, thereby promoting ESG performance. This paper examines the impact of digital government on corporate ESG performance using A-share listed companies from 2012 to 2023 and a quasi-natural experiment of “big data management reform” with the DID method. The results show that digital government significantly improves corporate ESG performance. Robustness tests, including controlling for concurrent policies, digital economic levels, and market expectations, confirm the stability of the findings. Heterogeneity analysis reveals that the impact is more pronounced for firms with low digital transformation, high pollution levels, and non-state ownership. Mechanism analysis indicates that digital government affects ESG performance by reducing financing constraints, promoting digital innovation, and lowering transaction and agency costs. This study provides empirical evidence for the positive effects of digital government and offers insights for policy-making.

In this paper, we integrate the selective state space model (SSSM) with the long short term memory (LSTM) neural network to construct a selective long short term memory (SLSTM) neural network model. We propose a novel dynamic covariance matrix prediction method and apply it to improve the hierarchical risk parity (HRP) asset allocation strategy. Firstly, we propose an SLSTM model with long-term memory function and dynamic adjustment mechanism and design a composite loss function for dynamic covariance matrix prediction. Secondly, we apply the prediction results to the HRP to optimize financial asset allocation strategies. Finally, we select data from mainstream fund products in the financial market and conduct empirical and sensitivity analyses to verify the effectiveness and rationality of the model. The results show that: 1) The SLSTM model significantly improves the effectiveness of dynamically predicted covariance matrix compared to the LSTM model. 2) The composite loss function enhances the ability of model to learn the key attributes of the covariance matrix, thereby effectively improving the accuracy of covariance matrix prediction. 3) Compared with other asset allocation strategies, the HRP asset allocation strategy based on covariance matrix prediction by using the SLSTM model can improve risk-adjusted returns.

Air quality across different regions in China varies due to factors such as population density, geographical location, and climate. To address air pollution, the Chinese government has implemented a series of stringent energy-saving and emission-reduction measures to mitigate environmental damage. To gain a deeper understanding of the impacts of these policies on air quality, this paper proposes a policy evaluation framework based on a class of spatio-temporal model incorporating exogenous variables. By integrating the generalized Yule-Walker estimation method, it innovatively addresses the challenges of estimating exogenous variables within the model. Through theoretical derivations, simulation experiments, and empirical analysis, the reliability and applicability of the proposed model are validated. Notably, in evaluating air quality management policies during the 2014 APEC summit, this study provides more detailed quantitative results, revealing the heterogeneous impacts of policy implementation across different times and regions.

At the micro level of the impact of digital transformation, there is a lack of research on the impact of digital transformation on management self-interest behavior. This paper constructs a fixed-effects model and analyzes the effect and transmission path of digital transformation on management self-interest based on the data of A-share listed companies. The study shows that the digital transformation of enterprises restrains the self-interested behavior of management by reducing information asymmetry, improving the efficiency of corporate governance, and reducing the flexibility of corporate finance and that the digital transformation of enterprises has heterogeneous effects on the self-interested behavior of management in different regions, levels of competition, and industries. The policy recommendations are to increase the efforts of digital transformation, reduce information asymmetry, improve the efficiency of corporate governance, reduce corporate financial flexibility, and differentially regulate the self-interested behavior of management. This paper responds positively to the spirit of promoting the deep integration of the real economy and the digital economy and takes the self-interested behavior of the management as a breakthrough to provide empirical explanations and decision-making references for the digital transformation of enterprises to promote corporate governance and improve operational efficiency.

Accurate measurement is a prerequisite for understanding the digital economy and conducting follow-up research. This article follows the practices of BEA and OECD and calculates the added value of China’s provincial digital economy. Based on the calculation, we examine how development of the digital economy affects income gap between urban and rural areas. The results show that China’s digital economy is developing rapidly with huge gaps between provinces, and the digital infrastructure is the foundation of digital economy development. The development of the digital economy has a U-shape effect on the income gap between urban and rural areas. Further mechanistic analysis showed that the development of the digital economy promotes the transfer of rural labor and delay the U-shape turning point. It also promotes the development of agricultural modernization, increases the income of farmers and narrows the income gap between urban and rural areas. Moreover, it can narrow the urban-rural income gap in adjacent areas through spatial spillover effects. This paper provides policy recommendations for promoting inclusive development of the digital economy.

How to measure the climate transition risk faced by the high-carbon industry, as well as how to effectively identify and mitigate the systemic risk spillover and contagious effects of the high-carbon industry, is an important issue facing policymakers and the academic community. This paper constructs a high-dimensional time-varying vector autoregression index model is used to measure the spillover effects within and between high-carbon industries. The research finds that the high-carbon industry faces the highest climate transition risk, while the financial industry has the lowest. The greater the climate transition risk, the higher the systemic risk faced by listed companies, and the stranded assets of the high-carbon industry are an important transmission path. Under different policy backgrounds, the risk spillover effects of the high-carbon industry to related industries show differentiated inclinations, and the main risk spillover targets of the high-carbon industry are all associated with their own production or financial networks. The banking industry always performs as a risk absorption role at the center of the risk network and is highly associated with the high-carbon industry. This paper provides a basis for governments and regulatory authorities to understand the impact of transition risk on the high-carbon industry and the industry correlation, and provides certain reference value for resolving the cross-industry transmission of systemic risks in the high-carbon industry.

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.

Since reform and opening up in 1978, China has achieved a remarkable economic growth miracle. For a socialist market economy with Chinese characteristics, the task of accurately summarizing its developmental experiences and elevating them into a broadly applicable theory of economic growth necessitates the measurement of both publicly owned and non-publicly owned segments of the economy. The Third Plenary Session of the 20th Central Committee of the Communist Party of China has explicitly called for “the calculation of value added in the state-owned economy”. Drawing on the officially published data of the National Bureau of Statistics, this paper measures the value added of the public-ownership economy, primarily composed of state-owned entities, and the value added of the non-public economy, chiefly represented by private enterprises, by examining various sectors of the national economy. In doing so, it provides preliminary supplementary historical data for both publicly owned and non-publicly owned economies dating back to 1978. The results of this study shed light on the shifts in the overall ownership structure of China’s economy, as well as the changes within industry-specific ownership structures since the beginning of reform and opening-up, thus offering a critical foundation for deeper investigations into China’s economic growth miracle.

This paper proposes the “Chapter One Paradox in Macroeconomics”, and organizing and revealing the explicit and implicit theoretical principles of economic statistical within the gross domestic product (GDP)accounting by conducting a systematic deconstruction of eleven embedded measurement paradoxes. The study seeks to dispel two prevalent misconceptions regarding GDP accounting: First, “political arithmetic” is not simplistic merely due to its computational nature, when in reality, achieving “additivity” and “comparability” with socioeconomic significance proves extraordinarily challenging; second, the international statistical standards are often based on the provisional epistemological frameworks of the discipline, and “operation with inherent flaws” is unavoidable, thus requiring thorough examination. Economics statistics is a fundamental discipline that requires continuous in-depth study, otherwise, our judgments on “national significant issues” may be prone to bias, and the development of economic theory itself could face disruptive risks.

This paper utilizes macroeconomic indicators from 18 Belt and Road Initiative (BRI) countries from 2012 to 2023 to construct a quantile factor-augmented vector autoregression (QFAVAR) model. This model captures the median and tail quantiles of various macroeconomic indicators to analyze the impact of shocks from China and other global factors on the macroeconomic variables of BRI countries. The findings reveal that: 1) A decline in China’s economic policy uncertainty and the volatility of the RMB exchange rate helps reduce uncertainty in prices and output in BRI countries; 2) following the COVID-19 pandemic, the inclusion of China’s macroeconomic variables enhances the ability to predict the short-term left-tail risks of interest rates, CPI, industrial output, and exchange rates in BRI countries; 3) after joining the BRI, most participating countries experience improved predictability of both left-tail and right-tail risks, leading to reduced uncertainty. This study provides valuable insights for a deeper understanding of the macroeconomic effects of the BRI and contributes to fostering higher-level cooperation between China and BRI countries.

Financialization of physical enterprises will lead to the weakening of their main business, reduced motivation for innovation, and even leading to the hollowing out of industries and turbulence in the financial market, which is one of the most important problems that require addressing. In this digital era, the public, as the important supervisor of enterprise operation and the ultimate purchaser of enterprise products or services, may have a governance role in the excessive financialization of physical enterprises. This paper empirically examines the impact of public attention on the financialization of physical enterprises using the data of A-share listed companies in Shanghai and Shenzhen from 2011 to 2022. We note that public attention inhibits financialization of physical enterprises through supervisory pressure and business expansion, and this inhibitory effect is more pronounced in firms that are non-state-owned and have high proportions of institutional shareholding, with better financial information quality, and low financing constraints.

Stock price fluctuations are crucial for the healthy and stable development of the stock market. However, the econometrics methods, which have traditionally demonstrated strong performance in addressing nearly all economic issues, exhibit limitations specifically in stock-related analyses — A gap that has only begun to be addressed with the emergence of artificial intelligence methods as cutting-edge applications in this field. Building on this foundation, this study integrates theoretical economic frameworks with empirical methodologies across disciplines to undertake a comparative investigation into the impact of monetary policy on stock prices and its associated asymmetric effects in China. The findings reveal that econometric models (VAR and MS-VAR) yield only partially satisfactory results in analyzing this issue, whereas the artificial intelligence-driven LSTM model demonstrates good explanatory power. Monetary policy exerts asymmetric effects on stock prices: Expansionary policies during bear markets show greater efficacy than contractionary measures during bull markets. Additionally, the transmission effect of rising prices on stock price is stronger, and the drag of economic recession on stock price is more pronounced. This study provides insights for the monetary policy interventions and offers practical guidance for the application of interdisciplinary methodological approaches in addressing complex economic issues.

The advent of large language models (LLMs) represents a paradigm shift that has profoundly affected market risk spillover mechanisms. Technological breakthroughs, such as the release of ChatGPT-3.5, trigger changes within the technology industries, but also indirectly impact energy markets by increasing the demand for computational resources. We develop an interval-valued vector autoregressive model and propose an interval-based risk spillover matrix and total spillover index (TSI). Utilizing daily stock price data from 2022 to 2025, we quantify the dynamic impact of LLM development on risk spillovers between the technology and energy industry sectors. Our findings indicate that the release of ChatGPT-3.5 amplified risk spillovers among leading technology companies, such as NVIDIA. As various technology giants announced the construction of new data centers, risk transmission emerged between the energy and technology industries. Moreover, Trump’s election victory exacerbated risk spillovers both within and across these industries. Interestingly, our proposed TSI index exhibits a rapid response to significant events and circumvents the discontinuity issues inherent in traditional Diebold-Yilmaz spillover indices based on point-valued volatility estimates, thereby demonstrating enhanced robustness.

The current external environment of China is undergoing profound and complex changes. Under the dual pressures of “low-end diversion” and “high-end reshoring,” the over-reliance on imported intermediate products has increasingly become a barrier to the development of China’s manufacturing industry. This study uses industry-level data from the 2016 World Input-Output Database (WIOD) to calculate the import dependency of intermediate products in China’s manufacturing sector as a core indicator. It systematically examines the impact mechanism of robot application on the domestic value-added rate of exported products. Empirical results show that excessive reliance on imported intermediate products has a significant inhibitory effect on the domestic value-added rate of products. However, the implementation of industrial automation, especially the application of robots, can effectively weaken this negative relationship, and this conclusion remains consistent across multiple robustness tests. Mechanism analysis further reveals that automation achieves this through three pathways: First, by standardizing production processes to improve product quality; second, by promoting production technology innovation to generate spillover effects; and third, by optimizing the allocation of production factors to create a labor substitution effect. This study not only expands the research boundaries of the economic effects of robot technology application but also deepens the understanding of the impact mechanisms of industrial intelligence on the quality of participation in the global value chain from a micro perspective. Against the backdrop of the “Made in China 2025” strategy, exploring the impact of intermediate product import dependency on Chinese export enterprises holds significant practical importance. This research provides practical reference value for the transformation and upgrading of China’s manufacturing industry under the current dual pressures.

The rise of large language models has injected fresh vigor into the development of Robo-advisors in China and promoted the innovation of financial technology. In this context, this paper constructs an AI agent based on the domestic generative language model framework, from sentiment analysis, market prediction, factor indicators and other dimensions, in-depth mining of alternative data and traditional financial data in stock trading signals, and then constructs the Chinese stock market investment and trading strategies. Empirical studies show that AI agent based on the domestic large models have the potential to perform quantitative analysis, and that the investment returns under the combined multiple dimensions will be significantly better than the results of a single dimension. This suggests that by utilizing powerful natural language processing capabilities and data analysis capabilities, the application of domestic large models in Robo-advisors is promising, providing new ideas and methods for the continuous development of quantitative investment. With the evolution of technology, the future AI agent will be able to understand market dynamics and investor needs more deeply, thus providing more targeted support for investment decisions, enhancing overall investment returns and creating more value.

Educational equity is a fundamental guarantee to accelerate the modernization of education and build a strong education country. How to alleviate and eliminate the negative impact of the intense education competition caused by superior and limited educational resources is an issue with little research in current theory and practice. This paper applies data from the China Family Panel Studies (CFPS) in 2018 and 2020 to investigate the spatial spillover effects and threshold social interactions of education expenditures among the Chinese households by the spatial autoregressive model and the social threshold model. We first provide a model specification test for the social threshold regression and show by simulation that the test statistic performs well in finite samples. The empirical study shows that within the same province (county), there is a significant spatial positive correlation in household education expenditure. For every 1% increase in other similar household education expenditure in the same province (county/district), household education expenditures increase by 0.32%(0.152%), showing that there is a spatial spillover effect among family education expenditures. The estimation results based on the social threshold regression show that the endogenous interaction effect of family education expenditure is significantly positive among families with high children’s education expectation, families with low income, and families with high information asymmetry, who are more likely to fall into educational entanglement. This study is the first to empirically test the education spatial competition, which essentially reflects issues such as educational equity and competition for educational resources in China. The study provides practical guidance and management basis for educational policy making and educational resource planning.

Exploring the risk spillover effects between energy and metal futures has important practical significance for improving the quality and efficiency of systemic risk supervision and ensuring the smooth operation of commodity futures markets. However, most existing studies are limited to the low-order moment level and fail to fully reveal the cross-market risk transmission mechanism. Given this, this paper integrates the autoregressive conditional density model and the time-varying parameter vector autoregressive extended joint connectedness approach to explore the higher-order moment and cross-moment risk spillover effects between China’s energy and metal futures markets; secondly, the nonparametric causality-in-quantile test is used to study the Granger causality relationship between geopolitical risks and total spillovers. Empirical results show that risk spillovers between energy and metal markets show significant differences under different moments, and the total spillover of high-order moments is lower than the total volatility spillover. Copper dominates the volatility and skewness spillover, while fuel oil dominates the kurtosis spillover. Copper skewness, zinc kurtosis, and fuel oil skewness dominate the three cross-moment spillover effects, respectively. The dynamic total spillover and net spillover indexes show significant time-varying characteristics and have risen sharply after the outbreak of major crises such as the COVID-19 epidemic and the Russia-Ukraine war. Geopolitical risk is a key driver of energy-metal spillovers, and its predictive power on cross-moment total spillovers is significantly higher than conditional volatility and higher-order moment total spillovers.

Integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models often use Poisson distribution as the conditional distribution, but Poisson distribution cannot describe overdispersion. In order to handle over-dispersed and under-dispersed data at the same framework, this paper studies the INGARCH transfer function model based on the mean parameterized Conway-Maxwell Poisson (CMP) distribution, i.e., the CMP INGARCH $(1,1)$ transfer function model. Afterwards, the softplus function is used as the link function, and the softplus CMP INGARCH $(1,1)$ transfer function model is proposed, which avoids the shortcoming of the rectified linear unit (ReLU) function being non-differentiable at the zero point. This paper adopts the adaptive MCMC algorithm, and conducts MCMC iterative sampling of the parameter group, and provides numerical simulations for four intervention types of the two models. All results have been effectively detected. The example uses sexual crime data from Albury, New South Wales, Australia, from February 1995 to September 2023 for intervention analysis. The final results show that the new model is superior.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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 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.

Commodity is an important part of industrial production and financial investment, and accurate commodity price forecasting is of great significance to safeguard industrial production and help investors avoid risks. However, most of the existing commodity price forecasting models are point-value models based on closing prices, which ignores the volatility information. Therefore we propose a heteroskedasticity threshold autoregressive interval model with exogenous variables (HTARIX) and apply it to the commodity markets. We also construct a test statistic based on interval-valued data to test whether there is conditional heteroskedasticity in the model, and propose a generalized minimum $D_K$ distance estimation. The advantage of our model is that it can capture the conditional heteroskedasticity and nonlinear features of interval-valued time series models. Compared with the point-valued models, our method contains more information of the data. The empirical results imply that HTARIX model performs better than other comparative models in interval-valued commodity price forecasting.