Chinese Financial Institutions Research Articles

Research on Internal Control of Chinese Commercial Banks Based on Comprehensive Risk Management

The dynamic and complex nature of global financial markets has necessitated the evolution of internal control mechanisms within commercial banks, especially within the context of Chinese financial institutions which operate in a rapidly changing economic environment. This study delves into the intricacies of internal control systems in Chinese commercial banks, scrutinizing their effectiveness through the lens of comprehensive risk management practices. Employing a mixed-methods approach that combines quantitative data analysis with qualitative case studies, this research examines the extent to which these banks have integrated risk management into their internal control frameworks. The analysis focuses on financial, operational, compliance, and strategic risks, providing a holistic view of the internal control landscape. Key findings reveal that while many Chinese commercial banks have made significant strides in embedding risk management principles into their internal controls, there remain notable inconsistencies and gaps, particularly in areas related to emerging financial technologies and regulatory compliance. Moreover, the study highlights the critical role of organizational culture and leadership in fostering an environment conducive to effective risk management. This research contributes to the existing body of knowledge by offering insights into the challenges and opportunities facing internal control systems in Chinese commercial banks. It underscores the necessity of adopting an integrated risk management approach to enhance resilience and competitiveness. Furthermore, the findings have practical implications for policymakers, regulators, and banking professionals, suggesting pathways for strengthening the internal control mechanisms to mitigate risks and capitalize on opportunities in the evolving financial landscape.

The cross-sector risk contagion among Chinese financial institutions: Evidence from the extreme volatility spillover perspective

This paper investigates the cross-sector risk contagion among Chinese financial institutions using the quantile connectedness approach, and analyzes the impact of macroeconomic factors on risk spillover effects at different quantiles. The empirical results show that the volatility spillovers in the tail quantiles are higher than those in the median quantile, suggesting that financial risk propagate more intensely during extreme events relative to normal conditions. Furthermore, we demonstrate that macroeconomic factors have a significant impact on volatility spillovers among financial institutions. Our findings provide valuable insights for regulators to effectively manage and mitigate the risk contagion within the Chinese financial system.

The impact of financial institutions' cross-shareholdings on risk-taking

The complex network associations formed by cross-shareholdings of financial institutions have affected the risk-taking of financial institutions, and have gained considerable attention in the realm of finance and corporate governance. This article uses 40 Chinese financial institutions listed on A-shares from 2012 to 2021 as data samples, using social network and panel regression to analyze the impact of cross-shareholdings of financial institutions on risk-taking. The research results show that: First, cross-shareholding intensity can reduce the risk-taking level of financial institutions. Second, the intensity of cross-shareholdings reduces the risk-taking level of financial institutions by increasing the level of diversified income. Third, securities companies have the best effect of reducing risk-taking levels through cross-shareholdings, followed by banks, and insurance institutions are relatively poor.

Multilayer networks in the frequency domain: Measuring volatility connectedness among Chinese financial institutions

We construct multilayer networks in the frequency domain, including short-term, medium-term, and long-term connectedness networks, to examine the volatility connectedness among Chinese financial institutions. We investigate the network topology of intra-layer and inter-layer by the proposed method on the static and dynamic samples. We observe that the volatility connectedness among financial institutions is heterogeneous in the frequency domain. Specifically, we find that long-term connectedness among financial institutions rises significantly during crisis events. In addition, we note that China Life Insurance, Ping An Bank, and most securities tend to play risk emitters at a specific layer during the period of stress rise. Finally, we find that the stock market crisis in 2015 changed the edge structure among financial institutions in the short-term and long-term. Our work provides a new perspective for financial connectedness and risk contagion.

Artificial Intelligence Ethics Best Practices Model for Financial Decision-Making in Chinese Financial Institutions

Chinese financial institutions (CFIs) are increasingly embracing artificial intelligence (AI) for their financial decision-making driven by AI's capacity to mitigate risks and enhance efficiency and accuracy. However, there remain ethical challenges related to the integration of AI in financial decision-making. This study develops the AI ethics best practices model (AB-PraM) to mitigate ethical concerns and enhance the application of AI in financial decision-making. By employing a quantitative methodology, this research collected questionnaire data from 320 financial experts in CFIs. Structural equation modelling (SEM) was adopted to identify AI ethics best practices for the implementation of the AB-PraM. The findings of this research will mitigate AI ethics challenges in CFIs and provide a practical framework for transparent and accountable decision-making in alignment with ethical standards and regulations.

Identifying the systemic importance and systemic vulnerability of financial institutions based on portfolio similarity correlation network

The indirect correlation among financial institutions, stemming from similarities in their portfolios, is a primary driver of systemic risk. However, most existing research overlooks the influence of portfolio similarity among various types of financial institutions on this risk. Therefore, we construct the network of portfolio similarity correlations among different types of financial institutions, based on measurements of portfolio similarity. Utilizing the expanded fire sale contagion model, we offer a comprehensive assessment of systemic risk for Chinese financial institutions. Initially, we introduce indicators for systemic risk, systemic importance, and systemic vulnerability. Subsequently, we examine the cross-sectional and time-series characteristics of these institutions’ systemic importance and vulnerability within the context of the portfolio similarity correlation network. Our empirical findings reveal a high degree of portfolio similarity between banks and insurance companies, contrasted with lower similarity between banks and securities firms. Moreover, when considering the portfolio similarity correlation network, both the systemic importance and vulnerability of Chinese banks and insurance companies surpass those of securities firms in both cross-sectional and temporal dimensions. Notably, our analysis further illustrates that a financial institution’s systemic importance and vulnerability are strongly and positively associated with the magnitude of portfolio similarity between that institution and others.

China’s Digital Expansionism in Africa and the US Counter-Strategies

This article explains that digital geopolitics is a key component of Sino-US rivalry, as digital technologies have taken the centre stage in the new global conflict between the United States and China. China’s digital expansionism through its Digital Silk Road (DSR) projects is promoting its geopolitical agenda in African countries. Chinese financial institutions are funding African countries’ digital infrastructure projects, which are being implemented by Chinese companies. Chinese companies, which are dominating the African digital technology space, are also setting new technology standards and norms that challenge the existing US-led global digital order. DSR projects have improved the digital connectivity of African countries, boosted the emergence of local companies, created job opportunities for Africans and enhanced governments’ governance capabilities. However, African countries’ dependence on the Chinese digital technology curtails local companies’ growth capabilities while promoting China’s ‘digital authoritarianism’ and ‘data colonialism’, as China’s companies possess market manipulation tools and capabilities. In addition, unmanageable loans given by Chinese financial institutions to implement DSR projects are creating sovereignty-eroding debt traps for African countries. As China’s digital expansionism poses a severe challenge to US geopolitical interests in the African continent, the United States, as a part of its counter-moves, has started a global campaign against Chinese digital expansionism, imposed a ban on Chinese tech companies, adopted the Digital Connectivity and Cyber-security Partnership (DCCP) and launched the Partnership for Global Infrastructure and Investment (PGII) scheme. Pursuing an aggressive strategy, the United States should launch a ‘Digital Marshal Plan’ to counter the Chinese digital expansionism at the global level in general and Africa in particular. Hence, in this context, using the descriptive-qualitative approach, this study is based on the content analysis method.

The effect shipper, carrier, and supervisory management data mining of online freight platforms on the innovation of financing model for Chinese financial institutions

The application of e-commerce and digitization in the logistics industry has given rise to the emergence of online freight forwarding platforms that connect shippers and carriers for the efficient transportation of goods. Online transactions on the platforms generate a large amount of data, creating opportunities to gain valuable insights through data mining techniques. However, against the backdrop of the Chinese government's accelerated efforts to create a new three-dimensional dynamic logistics landscape of "Internet + Logistics," the traditional financial service models of Chinese financial institutions are struggling to meet the evolving needs of these platforms. This research and innovation project explores the impact of data mining on the operation of online freight transportation platforms and the development of innovative financing models by Chinese financial institutions. A mixed research methodology was used to collect primary data, combining quantitative analysis, qualitative questionnaire analysis, and relevant interviews to collect and analyze the data. By analyzing financial data such as transaction volumes, creditworthiness indicators, and risk factors on these platforms, various data mining algorithms are utilized to mine and predict models. Further evidence is presented to analyze the relationship between shippers, actual carriers, freight platform managers, and financial service innovations that transact through online freight platforms and to propose new types of financing service solutions that can support the growth and sustainability of the logistics industry. These financing models have the potential to increase financial inclusion, reduce credit risk, and facilitate capital flows in the freight industry.

Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective

Accurately measuring systemic financial risk and analyzing its sources are important issues. This study focuses on the frequency dynamics of volatility connectedness in Chinese financial institutions using a spectral representation framework of generalized forecast error variance decomposition with the least absolute shrinkage and selection operator vector autoregression. It assesses the volatility connectedness network using complex network analysis techniques. The data are derived from 31 publicly traded Chinese financial institutions between 4 January 2011 and 31 August 2023, encompassing the Chinese stock market crash in 2015 and the COVID-19 pandemic. The frequency dynamics of the volatility connectedness results indicate that long-term connectedness peaks and cross-sectoral connectedness rises during periods of financial instability, especially in the recent bull market (2014–2015) and the 2015 Chinese stock market crash. The volatility connectedness of Chinese financial institutions declined during the COVID-19 pandemic but rose during the post-COVID-19 pandemic period. Network estimation results show that securities triggered the 2015 bull market, whereas banks were the main risk transmitters during the 2015 market crash. These results have important practical implications for supervisory authorities.

Systemic Risk Spillover Effects among China’s Financial Institutions: Evidence from the Spatial Econometric Model

This study investigates the spillover and contagion effects of systemic risk among Chinese financial institutions in terms of the conditional Value-at-Risk method and spatial econometric techniques. We construct different representative spatial weight matrices to demonstrate various risk connective categories and contagion channels. The spatial autoregression model is built to reveal the different magnitudes of systemic risk contagion effects and extended as the spatial quantile regression model to measure the change in spillovers across quantiles. The results highlight that the spatial agglomeration pattern of institution-level systemic risk is highly concentrated within the same sector but highly disparate between the different sectors. The closeness of the asset price channel and the information channel would enhance the systemic risk spillover effects among institutions. The higher the single institution’s systemic risk level is, the stronger its spillovers among all of them within the same financial department, yet contrarily, the spillovers are larger at lower quantiles between different sectors due to the disparate spatial tendency of systemic risk. Besides, the spillover effects across quantiles indicate the higher tail contagion of systemic risk spatial spillovers, especially during 2015 A-share market crash and 2020 COVID-19 outbreak.

Governing the Extraterritorial: Global Environmentalities of China’s Green Belt and Road Initiative

This article proposes a global environmentality framework to critique efforts to “green” the Belt and Road Initiative (or Green BRI) by examining the Chinese state’s environmental governance of extraterritorial spaces. The article transcends a focus within governmentality studies on domestic processes to reveal the relations between governance techniques and environmental subjects, including state and nonstate actors, beyond sovereign borders. Drawing on interviews, observations, and analysis of policies and reports, we identify three ways in which global environmentalities operate and are negotiated through the Green BRI. First, the Chinese state is embracing international sustainable development criteria to gain global legitimacy while seeking to export its domestic environmental governance model, making the Green BRI a dialectic policy. Second, the state is targeting and disciplining BRI participants, including Chinese financial institutions, construction companies, the renewable industry, and foreign state actors in BRI countries. Third, Chinese and BRI partner country participants’ variegated subjectivities arise out of the negotiation of their own interests, Chinese state interests, and BRI host country concerns. Our analysis contributes to understanding of how China, as a rising power, engages in global environmental governance and produces extraterritorial environmental subjects.

Carbon footprints of the equity portfolios of Chinese fund firms

Under the dual carbon goals in China, the transition to a net-zero carbon economy requires massive amounts of capital, which must be provided and facilitated by financial institutions. Yet, there are no unified, publicly available disclosures of the investment carbon footprint of Chinese financial institutions, leaving them facing great climate-related risks. Here we demonstrate that the aggregated financed emissions show an upward trend from 2015, and the investment portfolios are more exposed to carbon-intensive assets, based on the equity portfolios of China’s 105 fund firms. We further explore the decarbonization potential for fund firms and show that sustainability-aware fund firms are characterized by financed emission reductions and carbon efficiency gains. To fulfill sustainable investing, China’s institutional investors should focus on investment allocations shifting to high-tech sectors and target the improvement of self-reported carbon emissions.

Entry modes for Chinese enterprises in Latin America

AbstractThe Belt and Road Initiative (BRI) has been an important project for President Xi Jinping on foreign soil. The initiative gained momentum through colossal investments in Eurasia and Africa, mainly through infrastructure projects, backed by generous funding from Chinese financial institutions. A few years later, Latin America emerged as a new target for geographically extending the BRI. The region provides sufficient motivation to entice Chinese investors; it is well endowed with natural resources, offers a market of approximately 670 million people, and has many unmet needs. In addition, recent geopolitical reasons such as the ongoing trade war between the United States and China may give Chinese enterprises indirect access to the world's largest consumer market. We present a brief overview of the relationship between China and Latin America, focusing on the mode of entry of Asian companies into these territories. We analyze the primary entry mode of Chinese enterprises and whether they do so through Greenfield Investment (GI) or Mergers and Acquisitions (M&A). For this purpose, we use data from the China Global Investment Tracker. Thus, we intend to provide a complete picture of how public and private Chinese organizations are gaining a foothold in Latin America, and present its managerial implications.

The Analysis of Risk Measurement and Association in China’s Financial Sector Using the Tail Risk Spillover Network

This study focused on analyzing the complexities and risk spillovers that arise among financial institutions due to the development of financial markets. The research employed the conditional value at risk (CoVaR) methodology to quantify the extent of tail risk spillover and constructed a risk spillover network encompassing Chinese financial institutions. The study further investigated the characteristics, transmission paths, and dynamic evolution of this network under different risk conditions. The empirical findings of this research highlighted several important insights. First, financial institutions play distinct roles in the risk spillover process, with the securities and banking sectors as risk exporters and the insurance and diversified financial sectors as risk takers. The closest risk spillover relationships were observed between banking and insurance and between securities and diversified financial sectors. Second, in high-risk scenarios, there is significant intrasectoral risk transmission between banks and the diversified financial sector, as well as dual-sectoral risk contagion between banks and securities, with the most-common transmission occurring between diversified financial and securities sectors. Finally, the securities sector acts as the pivotal node for risk spillovers, being the main transmitter of intersectoral risks. The formation and evolution of risk spillover networks are influenced by endogenous mechanisms, in particular the convergence effect.

Share pledge financing network and systemic risks: Evidence from China

We document a unique determinant of financial systemic risks in China, share pledge financing (SPF) network, by studying all listed Chinese financial institutions that provide SPF business. As one of the most popular refinancing tools in China, SPF formulates a network among financial institutions with common collaterals. We propose a centrality measure to quantify such network effects, and document that banks are more important than securities in the SPF network before 2018, but this pattern has reversed afterwards. SPF network effects, rather than SPF margin closeout risks, significantly increase systemic risks of China's financial institutions, by increasing both individual risks of financial institutions and connectedness among them. Such an impact of the SPF network on systemic risks is more pronounced in OTC markets than in exchanges. The SPF network affects systemic risks in more extreme cases, while SPF margin closeout risks affect systemic risks in more moderate cases.

Does financial institutions assure financial support in a digital economy for energy transition? Empirical evidences from Markov chain and DEA technique.

Enhancing the energy transition of the Chinese economy toward digitalization gained high importance in realizing SDG-7 and SDG-17. For this, the role of modern financial institutions in China and their efficient financial support is highly needed. While the rise of the digital economy is a promising new trend, its potential impact on financial institutions and financial support is unproven. For this, this research intended to study how financial institutions assure financial support for China's energy transition toward digitalization. To attain this purpose, DEA analysis and Markov chain techniques are applied to the Chinese data from 2011 to 2021. The results estimated that the transition of the Chinese economy toward digitalization significantly depends upon the digital services of financial institutions and extended digital financial support. The extent of the digital energy transition in China can enhance economic sustainability. The role of Chinese financial institutions accounted for 29.86% of the total effect in transiting China's digital economy. In comparison, the part of digital financial services is found to be significant, with a score of 19.77%. The Markov chain estimates revealed that the digitalization of financial institutions is 86.1%, and financial support is 28.6% important for the digital energy transition of China. The Markov chain result caused a digital energy transition of 28.2% in China from 2011 to 2021. The findings highlighted that China still warrants more prudent and active efforts for financial and economic digitalization, for which the primary research also presents multiple policy recommendations.

Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions

We propose multilayer networks in the frequency domain, including the short-term, medium-term, and long-term layers, to investigate the extreme risk connectedness among financial institutions. Using the conditional autoregressive value at risk (CAViaR) tool to measure the extreme risk of financial institutions, we construct extreme risk networks and inter-sector extreme risk networks of 36 Chinese financial institutions through the proposed approach. We observe that the extreme risk connectedness across financial institutions is heterogeneous in the short-, medium-, and long-term. In general, the long-term connectedness among financial institutions rises sharply during times of financial stress, such as the 2015 Chinese stock market turbulence and the 2020 COVID-19 pandemic. Moreover, we note that the insurers are key players in driving the inter-sector extreme risk networks, because the inter-sector systemic importance of insurance institutions is dominant. Finally, our conclusions provide valuable information for regulators to prevent systemic risk.

An Empirical Study on the Impact of Green Credit on Financial Performance of China’s Listed Banks

Abstract The concept of green environmental protection and sustainability is deeply rooted in China. Green credit has become an important social issue in recent years. Most Chinese financial institutions are also actively participating and investing in this field, especially banks. In order to explore the correlation between banks' participation in green credit business and banks' financial performance, this paper collects the green credit balance, green credit ratio, return on total assets, non-performing loan ratio and other data of 16 major listed commercial banks in China from 2011 to 2019. At the same time, econometric modelling is built using the system GMM regression analysis method. This focuses on the impact of commercial banks' green credit business on their profitability. The final research results certify that banks' increasing investment in green credit has a positive impact on promoting profitability, both showed a positive correlation. Therefore, this paper suggests that banks should focus on the development of green credit business, which is beneficial to both the financial performance of banks and the construction of an environmentally sustainable society. JEL classification numbers: P34. Keywords: Green credit, Green credit, Bank financial performance.

Efficient Market Hypothesis, Covid-19 and Accumulated Return of Common Shares – Event Study of The Top Chinese Financial Institutions by Market Value Criterion

This work analyzed, via event study methodology, the impact of the disclosure of two relevant decrees in 2020 (1. quarantine in the city of Wuhan by the Chinese government on January 22; 2. COVID-19 pandemic by the WHO on March 11 ) in the cumulative return of the common shares of the four largest Chinese banks by the market value criterion. Event 1 presented p-values greater than 0.05 in the estimation window, showing no statistically significant impact on the behavior of the abnormal cumulative return of selected common shares, characterizing informational inefficiency. For event 2, the opposite is in the days close to the disclosure (p-values lower than 0.05) under the expected behavior of price adjustment in efficient markets.

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.