Bank Of China Research Articles

The performance and stability of financial institutions after digital transformation: The importance of regional policies

Competition among financial institutions is extremely fierce. How digital transformation can enhance banks’ competitiveness is a crucial question. In this study, we investigate the relationship between digital transformation and financial institution performance. Based on a sample of all commercial banks in China between 2010 and 2022, this study finds strong evidence that digital transformation improves bank performance. Furthermore, we find that this relationship is more pronounced when a region implements policies that encourage digitalization. Overall, this study contributes to the rapidly growing literature on enterprise digital transformation.

Stock Price Analysis and Forecasting of Bank of China Based on ARIMA Model

The stock market is of great importance to the development of the national economy. The price changes of bank stocks have an important impact on the whole stock market. Therefore, it is of practical value to model the changes in bank stock prices and forecast the future trends. Currently, there is a limited number of studies examining the utilization of the ARIMA model in forecasting stock prices. It is crucial to investigate its level of accuracy. This study employs the ARIMA model to conduct an empirical analysis on the time series data of Bank of China's opening price from January 1st, 2024, to January 31st, 2024. The objective is to predict the stock price for the subsequent 28 days and compare it with actual values. The findings reveal that short-term stock price predictions based on the ARIMA model exhibit a high degree of precision. In conclusion, the ARIMA model can offer valuable insights and serve as a theoretical foundation for investors and decision-makers to some extent. This paper will provide reference for the subsequent research on stock price forecasting and ARIMA model, and fill the existing research gaps.

A hybrid pseudo‐Malmquist and Grey–TOPSIS model for group efficiency comparison: Bank credit allocation efficiency comparative analysis in China

AbstractThis study examines the efficiency of credit allocation among various types of banks in China. Seven different varieties of Chinese banks' performance are examined from 2011 to 2021. We introduce a novel hybrid pseudo‐Malmquist–Grey–TOPSIS model that effectively addresses the need for integrating uncertainty and heuristics in relocation decision‐making. Our analysis reveals that the Joint‐Stock Commercial Bank demonstrates superior performance in terms of credit allocation efficiency. In contrast, rural commercial banks exhibit suboptimal performance when it comes to credit allocation convenience, indicating the importance of prudent credit risk management for this category of banks. It is important to note that the loan allocation performance of each bank category is primarily influenced by the distinct characteristics associated with that specific bank type. However, it is crucial to understand that the success of each bank category's frontier, regardless of its quality, should not be considered a causative determinant of the overall performance of such types of banks.

Systemic Risk Arising from Shadow Banking and Sustainable Development: A Study of Wealth Management Products in China

Shadow banking is a main way for the financial market to serve the real economy today, and this process is closely related to systemic risk. This study examines the impact of shadow banking associated with sustainable development in China’s banking on systemic risk. We analyze the data obtained from a rich sample of 31 listed commercial banks in China and shadow banking represented by wealth management products (WMPs) by constructing a dynamic complex interbank network model. The results show that the risks and vulnerabilities generated by shadow banking spread out through the interbank network and cause systemic risk to increase. The effect operates through increasing the number of default banks, reducing banks’ survival rate and profit, and forcing central bank bailout funds expansion. However, it has a positive impact in terms of augmenting liquidity and enhancing investment opportunities. Furthermore, the variability in the influence of different categories of shadow banking is assessed, emphasizing that short-term shadow banking exerts a more pronounced impact on systemic risk. In addition, the heterogeneity of the shadow banking effect on different types of commercial banks is explored, revealing that local and rural commercial banks experience a more conspicuous effect compared to state-owned and joint-stock banks. Our findings highlight that improving external supervision, promoting financial internal governance, and constraining credit linkages are vital for alleviating the increase in risks in shadow banking and maintaining the sustainable development of banking.

Air pollution and bank risk taking: Evidence from China

In the context of frequent environmental events that attract widespread attention, this paper investigates the impact of air pollution on bank risk-taking. Using the data of 105 city commercial banks in China from 2014 to 2021, our findings suggest that air pollution leads banks to reduce risk-taking, which is primarily driven by the frequency of extreme pollution days. Banks in cities with higher government environmental concern and those with smaller asset scale and lower risk resistance are relatively more sensitive to air pollution. The transparent environmental supervision weakens the effect of air pollution on bank risk-taking.

Impact of fintech on the loan structure of urban commercial banks in China

The emergence of financial technology has created both opportunities and disruption in commercial banking. Commercial banks loans remain the primary source of finance for Chinese enterprises. Therefore, it is imperative to investigate the impact of financial technology on Commercial banks loan structures. This study uses relevant data from Chinese urban commercial banks from 2011 to 2019 and adopts empirical research methods to analyze the mechanism underlying the impact of fintech on the loan structure of urban commercial banks. The results indicate that the fintech is associated with improving credit and guaranteed loans, reduces mortgages and pledged loans, and promotes the optimization of loan structure of urban commercial banks. Fintech has a significant impact on the credit loan structures of urban commercial banks in the Eastern region, whereas its impact on urban commercial banks in the Central and Western regions is relatively small.

From finance to legacy: Exploring Bank of China’s modern buildings as serial cultural heritage

In the early 20th century, China underwent profound societal transformations across cultural, political, economic, and military domains, culminating in the birth of its first national bank, the Bank of China. Enduring for over a century, the Bank of China stands as a testament to the emergence and evolution of a modern nation-state. The edifices housing this paramount institution, as well as their geographical locations and architectural designs, serve as prime examples for studying modern Chinese architecture and social history. This article, rooted in field surveys, traces the development history and current conservation status of the 26 existing Bank of China buildings. It analyzes their locations, construction backgrounds, architectural features, and functional uses. Through an assessment of the historical, artistic, scientific, and socio-cultural values of these bank buildings, this study employs the perspective of “serial cultural heritage” to propose a value-based thematic interpretation aimed at revitalization. In particular, it accentuates that analyses and interpretation of values are fundamental prerequisites for rebuilding and displaying the cultural identity of these buildings.

THE IMPACT MODEL OF INTEREST RATE LIBERALIZATION REFORM ON THE PROFITABILITY OF STATE-OWNED COMMERCIAL BANKS: A CASE STUDY OF BANK C OF CHINA

This paper explores the definition and development of interest rate marketization in China and its impact on the profitability of state-owned commercial banks. The reform, characterized by the People's Bank of China's gradual easing of interest rate controls and expansion of the benchmark interest rate's floating range, enhanced financial market flexibility and efficiency but challenged traditional banking profit models based on interest margins. Through qualitative case studies and thematic analysis of the Bank C (China), the study finds that Bank C effectively mitigated these challenges by optimizing asset-liability management, adjusting asset and liability structures, and employing proactive risk management. These measures improved its investment returns and profitability. Additionally, Bank C’s adoption of countercyclical strategies and advancements in digital transformation and process automation helped sustain its profit model. The research concludes that Bank C's strategic adjustments enhanced its profitability and competitive edge, offering insights for other banks in similar market conditions and aiding predictions of financial market trends.

Fintech's influence on green credit provision: Empirical evidence from China’s listed banking sector

We explore the impact of financial technology (fintech) advancements on green credit provision, investigating publicly traded banks in China from 2007 to 2022. We particularly focus on credit modelling innovation, examining the non-linear dynamics between fintech evolution and green credit distribution. Results reveal a positive U-shaped correlation. Initial stages of fintech are associated with increased green credit risk, negatively affecting the volume of green credit. However, more established fintech infrastructures significantly enhance green credit volumes by improving resource allocation and credit risk assessment. Utilizing a multiple linear regression approach, we highlight the transformative nature of fintech in advancing sustainable banking practices, particularly through innovations in credit modeling that enhance green credit risk management and resource allocation efficiency.

DIGITALIZATION AND THE “TOO BIG TO FAIL” DILEMMA: MECHANISMS AND ASYMMETRIC EFFECTS OF BANKS’ FINTECH INNOVATION ON TOTAL FACTOR PRODUCTIVITY

Fintech is driving the revolution of finance and profoundly affecting the development of the financial sector. However, few studies examined how commercial banks’ fintech innovation affects total factor productivity (TFP). To build up the fintech index of commercial banks, we use web crawler technology to accumulate news related to the fintech innovation of commercial banks in Baidu news. We use the panel data of 72 banks in China from 2010 to 2020 to explore the impacts and mechanisms of fintech on commercial banks’ TFP. The results show that fintech innovation effectively improves TFP after a series of robustness tests. Further, we find that fintech innovation can improve commercial banks’ TFP by promoting innovations of financial products, increasing risk control capability, reducing cost, and improving profit. Also, the utility of fintech is more significant in banks with more assets, facilities, and human capital, which means that fintech innovation creates a “bigger is better” mindset. Meanwhile, the result of quantile regression shows that the higher the fintech innovation, the more significant the increase in TFP, which further reveals that there is ‘too big to fail’ among commercial banks under digitalization.

Green credit, financing constraints, and corporate investment: From the perspectives of scale and efficiency

Green credit plays a crucial role in resource allocation as it serves as a link between financial systems and environmental protection. This study utilizes the “Green Credit Guidelines” issued by the China Banking Regulatory Commission in 2012 as an exogenous shock, employing the Difference-in-Differences (DID) approach to examine the impact of green credit policies on corporate investment levels. The findings reveal that green credit enhances investment efficiency by reducing the investment scale of high-pollution enterprises. Furthermore, considering the temporal nature of policy impact and strategic responses from enterprises, these effects become evident and increasingly strengthened two years after policy implementation. The financing constraint intermediary mechanism suggests that financing constraints only serve as an intermediary factor between green credit policies and investment efficiency, exerting relatively weak control over short-term investment scales. Additionally, internal and external regulatory mechanisms along with enterprise heterogeneity indicate asymmetric effects resulting from green credit policies. Policy implications derived from this research emphasize the necessity for a rational combination of command-and-control environmental regulations with green credit policies while also improving upon existing green credit measures to prevent strategic corporate behavior.

Measuring the productivity of urban commercial banks in China

This paper contributes to the productivity analysis literature by measuring the productivity of 76 urban commercial banks operating in China's 27 regions from 2013 to 2018. A spatial total factor productivity index is constructed using a spatial autoregressive production function. The index is decomposed into the efficiency change and the returns to scale change, and it identifies both spatial direct (feedback) and spatial indirect (spillovers) components. The result confirms positive spatial dependence in the production of urban commercial banks. Additionally, productivity of China's urban commercial banks increases, which is mainly contributed to by the returns to scale change.

Analysis of the Impact of Enterprise Environmental, Social and Governance Performance on High-quality Development: Taking Industrial and Commercial Bank of China as an Example

ESG (Environmental, Social and Governance) advocates the transformation of enterprises from the pursuit of their own interests to the pursuit of social value maximization, which is highly consistent with the high-quality development goals, and is an effective starting point for enterprises to practice the new development concept and promote high-quality development. Under the “Dual Carbon” target, China has successively issued a series of measures to support the development of ESG. However, due to the short development time, the current domestic ESG standards are not unified, and the degree of improvement also needs to be improved. The mechanism of how to use ESG performance to promote high-quality development is not clear. This paper discusses the mechanism of the ESG performance on high-quality development in the three dimensions of environment, society and governance. Research shows that in the financial industry, for large financial institutions such as industrial and Commercial Bank of China, social responsibility has the highest weight of social responsibility, indicating that the performance of banks and financial institutions in the social level is much attention. The weight of environmental and corporate governance dimensions is relatively low, but still important. Financial institutions need to consider their environmental impact and sustainability while pursuing economic benefits. Corporate governance responsibility involves the internal governance structure, transparency, compliance, and risk management of the company, which are key factors in maintaining long-term stability and trust.

ADVANCING DATA SECURITY IN GLOBAL BANKING: INNOVATIVE BIG DATA MANAGEMENT TECHNIQUES

In the context of the rapidly evolving digital landscape, the banking sector faces unprecedented challenges in data security due to the massive volumes of sensitive information they manage. This paper examines the implementation and efficacy of innovative big data management techniques within global banking institutions to enhance data security. It discusses the integration of predictive analytics, the impact of regulatory changes, and the adoption of emerging technologies like blockchain and advanced encryption, which collectively redefine data security strategies. The study utilises a qualitative case study approach focusing on three central banks—JPMorgan Chase & Co., HSBC Holdings plc, and the Industrial and Commercial Bank of China—highlighting how each bank utilises big data techniques to address specific security challenges, comply with regulations, and enhance customer trust. The findings underscore the crucial role of innovative data management strategies in mitigating risks and safeguarding data against cyber threats, suggesting that these technologies fulfil security needs and offer competitive advantages in customer trust and regulatory compliance. The paper concludes with strategic recommendations for banks to enhance their data security measures and suggests directions for future research in data security within the banking industry.

Systemic Importance and Risk Characteristics of Banks Based on a Multi-Layer Financial Network Analysis.

Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China's multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China's banking system data in 2021. An improved PageRank algorithm is proposed to identify systemically important banks and other economic sectors, and a stress test is conducted. This study finds that China's multi-layer financial network is sparse, and the distribution of transactions across financial markets is uneven. Regulatory authorities should support economic recovery and adjust the money supply, while banks should differentiate competition and manage risks better. Based on the PageRank index, this paper assesses the systemic importance of large commercial banks from the perspective of network structure, emphasizing the role of banks' transaction behavior and market participation. Different industries and asset classes are also assessed, suggesting that increased attention should be paid to industry risks and regulatory oversight of bank investments. Finally, stress tests confirm that the improved PageRank algorithm is applicable within the multi-layer financial network, reinforcing the need for prudential supervision of the banking system and revealing that the degree of transaction concentration will affect the systemic importance of financial institutions.

Application of SOM-GAN based deep learning technology in the security protection of rural bank depositors' funds information

Since the reform and opening up, the status of China's banking industry has been significantly improved, and the traditional banking industry has gradually cross-integrated with Internet technology [1], which has become an important support for national economic development and a core component of the financial system. For example, in the original physical bank and online bank, new Internet financial models have been expanded, such as online financial management, bank electronic accounts, mobile banking, P2P models, third-party payment platforms, crowdfunding, etc. [2]. However, in the period of rapid development of the transformation and upgrading of traditional banks facilitated by information technology, many problems have emerged, such as the relaxation of supervision, the lag of policies and regulations, and the difficulty of reasonable protection of private data, etc. These problems may affect the interests of depositors and even the state. For example, in 2022, China's Henan rural bank 40 billion event, this event involved about 400,000 depositors, and platforms such as Du Xiaoman and Xiaomi Finance have related cooperation with these rural banks. Therefore, reasonable security protection of depositors' fund information and information processing and encryption under certain deep learning technology have long become the focus of experts' research in information technology. For example, Tian Gengwen has conducted an in-depth discussion on customer information security in the digital transformation of banks [3]. For example, Yang Wanrong conducted an in-depth study on the path and measures of data security governance of small and medium-sized banks [4]. For example, Zhang Wenli, Peng Xiaolei et al have made an in-depth elaboration on the bank data security solution [5]. Following hot issues closely, this paper provides a method of deep learning technology based on SOM-GAN in the security protection of rural bank depositors' funds information, aiming to combine the advantages of self-organizing mapping neural network and generative adantagonistic network unsupervised learning. At the same time, it can complement the competition mode of self-organized neural network, generate the discriminant characteristics of adversarial network and generate real data to process and review the relevant data of the fund information security of rural bank depositors, ensure the fund management security of rural bank depositors, and realize the monitoring and analysis of their funds and the surrounding environment when depositors are storing funds. Thus, the security, confidentiality, authenticity and integrity of depositors' funds information are guaranteed, and hidden dangers such as theft of depositors' funds, loss of depositors' personal information and threats to depositors' personal information security brought about by the 40bn deposit red code incident of Henan Bank in 2022 are eliminated.

Design and Application of a Credit Bank Based on Blockchain Technology

Credit banks are an important way to build a lifelong learning system and a learning society. At present, various types of credit banks in China generally have limitations such as low efficiency in learning achievement certification and conversion, difficulty in ensuring the quality of learning achievement certification, and vague identification of credit banks. Blockchain technology has the characteristics of openness, consensus, transparent transactions, anonymity, tamper resistance, and traceability, which can effectively solve the centralized governance problems faced by the current construction of credit banks. This article elaborates on the current development status and characteristics of credit banks at home and abroad, analyzes the feasibility of combining credit banks with blockchain, and designs a blockchain based credit bank system architecture and processing flow to address the challenges of inconsistent credit conversion standards, low conversion efficiency, and data security risks in traditional credit banks. It proposes using block structure design to achieve secure and tamper proof credit data, proposes a smart contract based credit recognition and conversion method to achieve decentralized governance of credit banks, and establishes a blockchain based credit bank certification, conversion, and other application design models, which can provide technical support for the large-scale application of credit banks in the future.

Climate policy uncertainty and bank liquidity creation

This paper examines the influence of Climate Policy Uncertainty (CPU) on the liquidity creation of banks in China. Through an empirical analysis of data from 387 commercial banks in China, our findings indicate that CPU notably diminishes bank liquidity creation. The effect is predominantly observed on the asset side of liquidity, indicating a reduction in credit supply by banks as a response to increased CPU, while the impact on liability side liquidity appears negligible. The study further uncovers significant regional variations in the vulnerability of banks to CPU, highlighting that foreign banks, eastern and middle banks are disproportionately affected.

High-end Customers Loyalty among Selected Public and Private Banks in Yantai City, Shandong Province, China: A Service Marketing Guide for Financial Institutions

The ultimate goal of studying high-end customer loyalty is to identify their shortcomings in customer maintenance and provide appropriate service marketing guidelines for public and private banks. This study selected the high-end customer groups of Agricultural Bank of China and Hengfeng Bank as specific research objects and collected a total of 380 data for mean, standard deviation, and correlation analysis. Through statistical analysis of data, it was found that high-end customers have varying degrees of perception towards service quality, perceived value, and customer satisfaction. There is a positive correlation between potential factors of customer loyalty and various dimensions to varying degrees. In order to improve the correlation between variables, this study provides relevant suggestions for public and private banks from three aspects: service quality, perceived value, and customer satisfaction, guiding them to maintain and improve the loyalty of high-end customers to financial institutions.

The Impact of Fed's Interest Rate Hike on RMB Exchange Rate: Based on Stepwise Regression Modelling

In the late stage of the epidemic, the Federal Reserve raised interest rates several times in a row in response to domestic inflation, which had a significant impact on the international financial environment, as exemplified by the fluctuation of the RMB exchange rate. Although several worldwide and domestic scholars have used various models to study the relationship between them, there are no studies that have used linear regression models t to clarify how the two are connected. This study uses stepwise regression on the basis of linear regression to establish an optimal model for studying RMB exchange rate fluctuations and the Fed's monetary policy adjustment, which fills this research gap. It is found that Fed rate hikes cause the RMB exchange rate to decline., while the RMB exchange rate is also negatively affected by the seven-day repo rate in China's interbank market and the People's Bank of China's foreign exchange reserves. Therefore, this study concludes by recommending that investors, Chinese foreign trade firms and People’s bank of China take appropriate measures to minimize the impact of RMB depreciation in anticipation of the Fed's interest rate hike.