Well-established doing business works as an important basis for building a modern economic system and promoting a high-quality development. Based on the indicator system of business environment at city level in China, which contains four first-level indicators, 15 second-level indicators and 24 third-level indicators, we quantitatively evaluate the business environment across 296 prefectural-level and above cities in mainland China from 2017 to 2021.

Financial reporting quality is critical for businesses, stakeholders, and government to ensure transparency and accountability. The purpose of this paper is to investigate the relationship between corporate governance, financial reporting quality, and ownership structure as a moderating factor for Chinese stock exchange-listed firms. Quantitative data of 550 listed firms from 2012 to 2022 are collected from the annual reports. For investigating the relationship between variables, panel data analysis with random and fixed effect models is used. Our results show that corporate governance’s different attributes such as Auditor brand name, Existence of an audit committee, independent board, family ownership, and profitability have a significant negative impact on the audit report lag that decreases the lags and increases the financial reporting quality in China listed firms. Auditor opinion, Board diligence Board size, and CEO duality have a significant positive impact on the audit report lag that increases the lags and decreases the financial reporting quality of China-listed firms. Furthermore, our findings show that ownership concentration has no moderating effect between corporate governance, different attributes, and financial reporting quality. Family ownership, on the other hand, has a strong moderating effect between corporate governance characteristics and financial reporting quality. However, due to limitations, this study provides the opportunity for future research on corporate governance mechanisms in different cultures and environments. Moreover, this study has some important implications for investors, policymakers, and government.

This is the replication package for the paper titled "Rural Pensions, Labor Reallocation, and Aggregate Income: An Empirical and Quantitative Analysis of China."

Abstract

This research was conducted in China in 2003. Data from 2400 establishments were analyzed.

The Investment Climate Surveys (ICS) were conducted by the World Bank and its partners across all geographic regions and covered firms of all sizes in many industries. The ICS collected a wide array of qualitative and quantitative information through face-to-face interviews with managers and owners regarding the investment climate in their country and the productivity of their firms. Topics covered in the ICS included the obstacles to doing business, infrastructure, finance, labor, corruption and regulation, contract enforcement, law and order, innovation and technology, and firm productivity. Taken together, the qualitative and quantitative data helped connect a country’s investment climate characteristics with firm productivity and performance.

Firm-level surveys have been conducted since 1998 by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit (FPDEA). Enterprise Surveys, a replacement for Investment Climate Surveys, are now conducted by the Enterprise Analysis Unit.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instrument is available: World Bank Investment Climate Survey

The global quantitative loading system market is experiencing robust growth, driven by increasing demand across diverse sectors like power plants, gravel plants, and mixing stations. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 7% during the forecast period (2025-2033). This growth is fueled by several factors including the rising need for precise material handling in various industries, automation advancements enhancing efficiency and reducing operational costs, and stringent environmental regulations promoting precise material control to minimize waste. The top-loading system segment currently holds a significant market share, but the bottom-loading and world quantitative loading system production segments are expected to witness substantial growth due to their increasing adoption in large-scale industrial applications. Geographically, North America and Europe currently dominate the market, benefiting from well-established industrial infrastructure and high adoption rates. However, rapidly developing economies in Asia Pacific, particularly China and India, present significant growth opportunities due to increased infrastructure development and industrialization. The competitive landscape is characterized by a mix of established international players and regional manufacturers. Key players like Sron, WRIKU, and Joloda International are leveraging their technological expertise and established distribution networks to maintain market leadership. However, several emerging players from regions like China are gaining traction, particularly in the manufacturing of cost-effective quantitative loading systems. Future growth will be influenced by factors such as technological innovation, particularly in areas like sensor integration and data analytics for improved precision, along with the increasing focus on sustainable and environmentally friendly loading solutions. The market is also expected to witness consolidation through mergers and acquisitions as companies strive to expand their market share and enhance their product portfolios. The long-term outlook for the quantitative loading system market remains positive, indicating substantial potential for growth and investment.

The Algorithmic Trading market is experiencing robust growth, projected to reach $16.06 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 14.34% from 2025 to 2033. This expansion is fueled by several key drivers. Increased adoption of sophisticated trading strategies by both institutional and retail investors seeking improved efficiency and profitability is a significant factor. The rising availability of high-frequency data and advanced analytical tools empowers more precise and faster trade execution, further boosting market growth. Technological advancements such as artificial intelligence (AI), machine learning (ML), and cloud computing are streamlining algorithmic trading processes and reducing operational costs. Furthermore, regulatory changes aimed at fostering innovation and transparency in financial markets are also contributing to the sector's expansion. The market is segmented by component (solutions and services) and end-user (institutional investors, retail investors, long-term investors, and short-term investors). Institutional investors currently dominate the market due to their higher capital base and sophisticated trading needs. However, the retail investor segment is witnessing rapid growth, driven by increased accessibility to algorithmic trading platforms and educational resources. Geographic distribution shows strong performance across North America (particularly the US), APAC (led by China and Japan), and Europe (with Germany and the UK as key markets). The competitive landscape is highly dynamic, with a mix of established players like Refinitiv and Thomson Reuters alongside innovative technology companies such as 63 Moons Technologies Ltd. and AlgoBulls Technologies Pvt. Ltd. Key competitive strategies include product innovation, strategic partnerships, and aggressive expansion into new markets. Industry risks include regulatory scrutiny, cybersecurity threats, and the potential for market manipulation. However, the overall outlook remains positive, with continued technological advancement and growing investor adoption likely to sustain the market's high growth trajectory. The significant market size and high CAGR suggest considerable investment potential and opportunities for both established players and new entrants. Successfully navigating regulatory hurdles and effectively mitigating cybersecurity risks will be crucial for sustained success within this sector.

The formulation of science and technology financial policies directly influences the direction of national economic development. Quantitative evaluation of these policies is an important method to reflect the consistency and strengths and weaknesses of policy interrelations. This paper analyzes 16 science and technology financial policy documents issued by the Chinese central government from 2016 to 2022, using text analysis and content analysis to extract keyword frequencies, and constructs 9 primary variables and 34 secondary variables. For the first time, a PMC-AE index model for science and technology financial policies is established, and a quantitative evaluation is conducted on 5 significant policy documents out of the 16. The results show that, from an overall analysis, Policy 1 and Policy 4 are at a good level, while the other three policies are at an excellent level. From the analysis of individual policy PMC-AE indexes, the rankings in descending order are: P2 > P5 > P3 > P4 > P1. Overall, the policies effectively meet the needs of China’s science and technology financial development, with P2, P3, and P5 being at an excellent level, P4 at a good level, and P1 at an acceptable level, mainly reflecting the need for improvement in aspects such as policy synchronization with the current stage, targeted entities, guiding fields, and policy content. It is recommended that Chinese government departments should focus on five aspects in policy formulation: building a talent system for science and technology finance, improving the quality of financial services, coordinating central and local financial policies, protecting intellectual property rights in science and technology finance, and strengthening financial supervision. This will be conducive to the effective implementation of science and technology financial policies.

The formulation of science and technology financial policies directly influences the direction of national economic development. Quantitative evaluation of these policies is an important method to reflect the consistency and strengths and weaknesses of policy interrelations. This paper analyzes 16 science and technology financial policy documents issued by the Chinese central government from 2016 to 2022, using text analysis and content analysis to extract keyword frequencies, and constructs 9 primary variables and 34 secondary variables. For the first time, a PMC-AE index model for science and technology financial policies is established, and a quantitative evaluation is conducted on 5 significant policy documents out of the 16. The results show that, from an overall analysis, Policy 1 and Policy 4 are at a good level, while the other three policies are at an excellent level. From the analysis of individual policy PMC-AE indexes, the rankings in descending order are: P2 > P5 > P3 > P4 > P1. Overall, the policies effectively meet the needs of China’s science and technology financial development, with P2, P3, and P5 being at an excellent level, P4 at a good level, and P1 at an acceptable level, mainly reflecting the need for improvement in aspects such as policy synchronization with the current stage, targeted entities, guiding fields, and policy content. It is recommended that Chinese government departments should focus on five aspects in policy formulation: building a talent system for science and technology finance, improving the quality of financial services, coordinating central and local financial policies, protecting intellectual property rights in science and technology finance, and strengthening financial supervision. This will be conducive to the effective implementation of science and technology financial policies.

This study aims to enhance stock timing predictions by leveraging large language models (LLMs), specifically GPT-4, to filter and analyze online investor comment data. Recognizing challenges such as variable comment quality, redundancy, and authenticity issues, we propose a multimodal architecture that integrates filtered comment data with stock price dynamics and technical indicators. Using data from nine Chinese banks, we compare four filtering models and demonstrate that employing GPT-4 significantly improves financial metrics like profit-loss ratio, win rate, and excess return rate. The multimodal architecture outperforms baseline models by effectively preprocessing comment data and combining it with quantitative financial data. While focused on Chinese banks, the approach can be adapted to broader markets by modifying the prompts of large language models. Our findings highlight the potential of LLMs in financial forecasting and provide more reliable decision support for investors.

PMC matrix of the five science and technology finance policies.

Average Metric Values for Random Forest Model Comparison.

In recent years, with the development of science and technology in China, the financial industry has also undergone significant changes. In the diversified financial field, the diversified financial system headed by financial technology gradually occupies a dominant position. The cash of financial technology has played a very important role in improving the efficiency of financial services. How ever, fintech goes hand in hand with fintech risks. This paper uses AHP + fuzzy comprehensive evaluation model, seeks 50 financial experts to comprehensively quantify the risk of financial industry, and explores the leading factors of China's financial industry risks at present, so as to make predictable intervention. It is found that technical risk, moral risk and legal risk, with a weight of 76% and a fuzzy evaluation index of "high", are the main factors affecting financial technology risks, while traditional financial risk account for the majority but only account for 24%. Although the weight ratio is not large, it still cannot be ignored. The purpose of this paper is to quantify the vague financial industry risk, explore the dominance of financial technology risk and traditional financial risk in the current financial industry, and conclude that in the face of the future development of China's financial industry, it is necessary to pay more attention to intervening in the risk brought by financial technology, so as to optimize resource allocation, but traditional financial risk cannot be ignored.

Multiple input-output tables for the five science and technology finance innovation policies.

An increase in a currency internationalization levels can positively impact its credibility in international economic activities, and expand the effective demand and optimize the supply structure for the country’s financial service trade. In this way, a state can improve its financial service trade competitiveness in the international market. This study builds a vector autoregressive model based on time-series data of China-US financial services trade from 2010 to 2021, analyzes the impact of different quantitative indicators of RMB internationalization on this trade from the impulse response results, and validates the conclusions using various inspection methods. The results show that the increase in RMB internationalization helps to narrow the China-US financial services trade balance, but with a significant lag. And this effect is heterogeneous in different dimensions, demonstrated by the fact that the development of overseas RMB securities business is more important for the level of RMB internationalization to narrow the China-US financial services trade balance. Finally, among the specific measures to improve its financial services trade, China should focus on developing the international competitiveness of the traditional RMB deposit and loan financial sector, while the competition in the overseas market for high value-added financial businesses must also not be neglected. Furthermore, China needs to implement more targeted RMB internationalization development policies at different levels in the future to provide high-quality financial services to the rest of the world and aid in the economic recovery of the world in the "post-pandemic" era.

Variable settings for the PMC-AE index model of science and technology financial policy.

An increase in a currency internationalization levels can positively impact its credibility in international economic activities, and expand the effective demand and optimize the supply structure for the country’s financial service trade. In this way, a state can improve its financial service trade competitiveness in the international market. This study builds a vector autoregressive model based on time-series data of China-US financial services trade from 2010 to 2021, analyzes the impact of different quantitative indicators of RMB internationalization on this trade from the impulse response results, and validates the conclusions using various inspection methods. The results show that the increase in RMB internationalization helps to narrow the China-US financial services trade balance, but with a significant lag. And this effect is heterogeneous in different dimensions, demonstrated by the fact that the development of overseas RMB securities business is more important for the level of RMB internationalization to narrow the China-US financial services trade balance. Finally, among the specific measures to improve its financial services trade, China should focus on developing the international competitiveness of the traditional RMB deposit and loan financial sector, while the competition in the overseas market for high value-added financial businesses must also not be neglected. Furthermore, China needs to implement more targeted RMB internationalization development policies at different levels in the future to provide high-quality financial services to the rest of the world and aid in the economic recovery of the world in the "post-pandemic" era.

Science and technology financial policies issued by the central government from 2016 to 2022.

This paper applies quantitative and narrative approaches to fiscal and financial policies of Chinese small and medium enterprises (SMEs) in order to study the support effect of macro policies on micro enterprises. As the first researchers to focus on the firm heterogeneity of SMEs’ policy effects, we show that the “flood irrigation” supportive policies for SMEs have not achieved the expected “help the weaker” effect. Non-state-owned SMEs and small(micro) enterprises have a low sense of policy gain, which is contrary to some “positive” research conclusions from China. The mechanism study found that “ownership” and “scale” discrimination suffered by non-state-owned and small(micro) enterprises in the financing process are key. We suggest the supportive policies for SMEs should shift from “flood” to “precise drip” irrigation. The policy benefits of non-state-owned, small and micro enterprises need to be emphasized. More targeted policies need to be studied and provided. Our findings shed new light on the formulation of supportive policies for SMEs.

An increase in a currency internationalization levels can positively impact its credibility in international economic activities, and expand the effective demand and optimize the supply structure for the country’s financial service trade. In this way, a state can improve its financial service trade competitiveness in the international market. This study builds a vector autoregressive model based on time-series data of China-US financial services trade from 2010 to 2021, analyzes the impact of different quantitative indicators of RMB internationalization on this trade from the impulse response results, and validates the conclusions using various inspection methods. The results show that the increase in RMB internationalization helps to narrow the China-US financial services trade balance, but with a significant lag. And this effect is heterogeneous in different dimensions, demonstrated by the fact that the development of overseas RMB securities business is more important for the level of RMB internationalization to narrow the China-US financial services trade balance. Finally, among the specific measures to improve its financial services trade, China should focus on developing the international competitiveness of the traditional RMB deposit and loan financial sector, while the competition in the overseas market for high value-added financial businesses must also not be neglected. Furthermore, China needs to implement more targeted RMB internationalization development policies at different levels in the future to provide high-quality financial services to the rest of the world and aid in the economic recovery of the world in the "post-pandemic" era.

The final weight of each sub-indicator of RMB internationalization.