Financial Institutions In China Research Articles

Analyzing Opportunities and Challenges for Financial Institutions in the Area of Carbon Finance

Human industrial activities not only promote human social and economic development, but also intensify carbon dioxide emissions, and their impact on the global climate has threatened the fate of human beings and the Earth. The issue of climate change is not only a matter of environmental governance but also a political and economic issue that determines whether a country can take the lead in the global energy revolution. This paper mainly analyzes the opportunities and challenges of financial institutions in the field of carbon to understand the development status of financial institutions in China, especially the banking industry, as well as the opportunities and challenges, to put forward some innovations and measures to promote the development of financial institutions in the field of carbon finance in China.

Financial Density and Innovation Quality: Evidence from the Geographic Distribution of Financial Institutions in China

Financial Density and Innovation Quality: Evidence from the Geographic Distribution of Financial Institutions in China

Green Banking Concept Implementation in Banking Credit Governance in Indonesia: Comparison Between Indonesia and China

2Universitas IndonesiaEnvironmental issues nowadays are becoming the world’s attention. Banks are now transforming their business activities as well as minimizing them on the environment. This concept is called the green banking concept. However, in Indonesia, there is no adequate regulation yet on the implementation of green banking, especially in banking credit governance. On the other hand, China has a green credit policy which has now become a guideline to ensure that loan applications and investments are considering environmental management and protection. The objective of this policy issuance is to encourage green credit among financial institutions in China.
 Keywords: green banking, sustainable finance, credit governance

A financial risk meter for China

The study presents FRM@China, a novel risk meter that uses quantile-lasso regression to detect systemic financial risk and dependencies of tail-events among critical financial institutions in China. The analysis demonstrates the high level of robustness of FRM@China in predicting tail-event risks and a negative correlation between FRM@China and FIs' TE risk interconnectedness. To address limitations of the current FRM approach, the study employs the Shapley value to assess the impact of different macroeconomic variables. The results offer policymakers a valuable tool for monitoring market liquidity and evaluating financial policy responses to prevent severe financial risks.

Systemic risk spillover of financial institutions in China: A copula-DCC-GARCH approach

In this paper, we study the systemic risk spillover between several financial sectors and the stock market in China from 2009 to 2021 using the copula-DCC-GARCH model. The financial sectors concerned include banking, security, insurance, real estate, and other finance sectors. We first obtain time-varying correlations between financial sectors and the stock market from the copula-DCC-GARCH model. Furthermore, we find evidence of high dynamic correlations between the financial sectors and the stock market, varying from 0.7 to 0.9, indicating a strong risk linkage between the financial sectors and the stock market. Simultaneously, we analyze the spillovers between the financial sectors and the stock market by conditional VaR and the delta CoVaR and employ the bivariate Student-t-copula function to calculate CoVaR and ΔCoVaR. When we consider the risk spillovers from financial sectors to the stock markets, the results suggest that the highest risk spillover occurs in the banking sector, followed by the security and real estate sectors, showing that the banking sector remains a significant source of systemic risk in China. Besides, we discover that among the financial sector risk spillovers, the security industry has the highest risk spillover. As a result of the real estate sector's significant role in the financial system, it has now emerged as one of the security and banking sectors' most significant sources of risk. From the dynamic of the stock market CoVaR, it seems that the CoVaR of the stock market by a different risk source will drop rapidly (loss rise) when a major risk event occurs. Our findings provide important risk warnings and management implications for regulators, investors, and portfolio managers.

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.

Research on Risk Spillovers of Financial Institutions in China—Taking China’s A-Share Banking Industry as an Example

Research on Risk Spillovers of Financial Institutions in China—Taking China’s A-Share Banking Industry as an Example

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.

How Does ESG Affect the Stock Prices of Financial Institutions in China?

Purpose - As “green growth”, “sustainable development”, and “carbon peaking” frequently come up, research on the environment, society, and corporate governance (ESG) is gradually increasing. This study aims to explore how ESG affects the stock prices of financial institutions through empirical analysis, and how the attention of newspapers, magazines, and online media, as well as the number of shareholders, affect the relationship between the ESG performance and stock prices of financial institutions.
 Design/Methodology/Approach - This study uses a fixed-effect regression method to conduct an empirical study on the data of financial institutions listed on China’s A-shares from 2015 to 2020.
 Findings - The research conclusion shows that the better the ESG performance of financial institutions in China, the higher the stock price of financial institutions. At the same time, newspapers and periodicals, online media, and the number of shareholders play a positive regulatory role in the mechanism by which ESG performance promotes stock prices.
 Research Implications - This article enriches the relevant research in the field of ESG, especially research on the ESG performance and stock price of financial institutions with less research, fills the gaps in research in related fields, and provides a theoretical basis.

Environmental, social, and governance (ESG) and market efficiency of China's commercial banks under market competition.

As the leading financial institutions in China, it is crucial for commercial banks to pay attention to environmental protection (E), social responsibility (S), and corporate governance (G) in order to enhance operational efficiency and to advance the high-quality development of the country's social economy. This research explores the market share of banks as exogenous variables in the profit stage and the market and sustainability stage to investigate the efficiency of 20 listed banks in China over 2016-2020 and innovatively incorporates indicators such as green credit, social giving, executive compensation, and ESG score into the meta-dynamic two-stage SBM under the exogenous variable DEA model.The results demonstrate the following. (1) By integrating market share as an exogenous variable in the model, the efficiency estimate is more precise. (2) In overall, UCBs are the most efficient type of banks, JSCBs are the second, SOCBs are the least efficient. All three types of banks are more efficient in profit stage versus the market and sustainability stage, JSCBs perform best in the profit stage, where SOCBs perform best in the market and sustainability stage. The three different bank types' TGR performance is comparable to their efficiency value performance. (3) SOCBs lead in ESG investment and have the best ESG performance due to their distinct state-owned background. With their ongoing dedication to profit maximization and disregard for social responsibility and sustainable development, JSCBs have the worst ESG performance. (4) Policy recommendations are made based on the study's findings for commercial banks, stakeholders, and regulators to support ESG investment and to bring about long-term sustainable development. Finally, as ESG develops in China, future research can consider longer time scales and larger perspectives to investigate the sustainability efficiency of commercial banks themselves, as well as their role in the local economy and industrial transformation.

Spatial Spillovers of Financial Risk and Their Dynamic Evolution: Evidence from Listed Financial Institutions in China.

This paper investigates the multidimensional spatial effects of risk spillovers among Chinese financial institutions and the dynamic evolution of financial risk contagion in the tail risk correlation network over different time periods. We first measure risk spillovers from financial submarkets to the stock market, identifying five periods using structural breakpoint tests. Then, we construct a spatial error financial network panel model by combining complex network and spatial econometric theory to explore the spatial spillover variability. Finally, we calculate the Bonacich centrality of nodes in the tail risk network and analyze the dynamic evolution of the financial impact path during the different time periods. The results show that the multidimensional spatial spillovers of financial risk among financial institutions are obvious and time varying. The spatial spillovers of financial institutions are positively correlated with the turnover rate and negatively correlated with the exchange rate, interest rate and return volatility. Financial institutions of the same type in the tail risk network display intraindustry risk clustering, and the systemically important institutions identified based on Bonacich centrality differ significantly across time. Moreover, when risk spillovers increase, external shocks' destructive power and speed of transmission to the network rise.

THE RIGHT MEASUREMENTS FOR THE APPROPRIATE INTERVENTIONS: JOB BURNOUT AMONG EMPLOYEES IN CHINA’S FINANCIAL INSTITUTIONS

Job burnout has been coined as the main cause of turnover cases. Financial institutions in China were not excluded from reporting high case of turnover due to job burnout. The way job burnout was measured were discussed further to identify the appropriate interventions. Hence, literature reviews were carried out from sources of local studies and abroad. Results show that job burnout and external working environment are among the factors influencing employees’ feeling of uneasiness and dissatisfaction leading to job burnout. Unless job burnout is mitigated, China’s financial institutions will keep on facing the shortfall of respective talents.

Institutional investor attention and venture capital valuation: evidence from China

ABSTRACT This paper analyzes how institutional investor attention affects firm valuations in the venture capital market. Relying on research reports of financial institutions in China, we propose a direct measure of abnormal institutional attention to industries (AIA) after eliminating industry differences and heteroscedasticity. We find that firm valuation is positively associated with institutional investor attention. Our conclusion continues to hold after controlling for selection bias and validating different measures of AIA. In addition, we find a negative relationship between AIA and the performance of VC investments measured by the likelihood of successful exits. Our findings support the notion that an increase in venture valuations is overpricing caused by institutional attention rather than a fundamental premium.

How does intelligent technology investment affect employment compensation and firm value in Chinese financial institutions?

PurposeThe authors intend to expand the literature on the relationship between intelligent technology and human resources in operating cost, and firm value of financial institutions in emerging markets by integrating the influence of intelligent technology and contribute to a growing body of literature on the determinants of firm value.Design/methodology/approachThis paper empirically investigates the impact of intelligent technology investment on employment compensation and firm value using a sample of 86 listed financial institutions in China from 2010 to 2019.FindingsThis paper reports robust evidence that an increase in intelligent technology investment has a significantly negative effect on employment compensation in financial institutions. In addition, this inhibitory effect is persistent. The increase in intelligent investment has a significant two-year lag effect on firm value, which is positive and sustained. This indicates that intelligent technology investment has a short-term “useless” effect but brings long-term “gains” for Chinese financial institutions.Originality/valueThese findings may shed light on the decision-making processes of financial institutions, which helps practitioners better understand that firms need to reasonably deal with the subsequent cost of growth caused by intelligent technology input. Alternatively, they may wish to select the appropriate accounting method of depreciation or amortization to smooth its impact.

Systemically important financial institutions in China: from view of tail risk spillover network

ABSTRACT The investigation of the systemically important financial institutions (SIFIs) plays a key role in coping with systemic risk. We first adopt the GARCH-Copula-CoVaR model to establish tail risk spillover networks of China’s financial institutions. We then employ the systemic risk emitters (SRE) and receivers (SRR) to measure the SIFIs. Finally, we utilize the panel data regression model to analyse the determinants of the rank of SRE and SRR. We find that state-owned banks and large insurance companies show systemic importance, while some commercial banks are also SIFIs due to their high value of SRR. Furthermore, the growth rate of total assets, leverage, nonperforming loans, price-earnings ratio and firm size are the common factors that affect the SIFIs.

Identifying systemically important financial institutions in China: new evidence from a dynamic copula-CoVaR approach

Identifying systemically important financial institutions in China: new evidence from a dynamic copula-CoVaR approach

China’s forthcoming digital currency: implications for foreign companies and financial institutions in China

PurposeTo answer key questions about China’s forthcoming digital currency, the Digital Currency Electronic Payment (DCEP) or “digital yuan.”Design/methodology/approachDiscusses prospective legal standards and guidelines; expected features compared with traditional payment methods and other digital currencies; how DCEP works; status of pilot programs; use of DCEP for cross-border payments; transparency, data protection and cybersecurity issues; and key implications for foreign businesses and financial institutions in China.FindingsWhen DCEP is officially launched in China, there is little doubt that the population can easily adapt to its use. The launch of DCEP can have significant ramifications on a global scale, as it could reduce China’s reliance on the SWIFT system for international banking and offers the first glimpse of the internationalization of the renminbi (RMB).Practical implicationsForeign companies operating in China, hi-tech businesses, retailers, financial institutions, and mobile app developers need to track the development and acceptance of DCEP, monitor arising risks, assess how their financial products fit, and adjust business operations, reporting requirements and financial reserves related to the requirements and use of DCEP, expected growth in fintech surrounding digital currencies.Originality/valuePractical advice from experienced mergers and acquisitions, private equity, strategic investment and capital markets lawyers.

Research on the Influence of Network Public Opinion on the Systematic Risk of Listed Financial Institutions in China

Research on the Influence of Network Public Opinion on the Systematic Risk of Listed Financial Institutions in China

A method to measure the efficiency of industry finance integration of manufacturing enterprises based on SFA model

This study aims to analyze the efficiency and influencing factors of participating in financial institutions in China’s listed manufacturing enterprises, and uses the stochastic frontier model (SFA) to carry out empirical research. Firstly, with the

Business connectedness or market risk? Evidence from financial institutions in China

We utilize the spectral representation of generalized forecast error variance decomposition to investigate the frequency dynamics of volatility connectedness and systemic risk of financial institutions in China from 2011 to 2018. We find that, first, high-frequency components account for the largest part of volatility connectedness (48.33%), followed by low-frequency components, and finally the medium-frequency components. Second, the low-frequency components reflect the business connectedness among financial institutions, while the high-frequency components capture the market risk. Third, the business connectedness among financial institutions will lead to a rise in overall connectedness as well as the accumulation of potential risks. Further, once a crisis breaks out, the potential risks have realized and the business connectedness among institutions declines; while market risk increases rapidly, which helps systemic financial risk stay at a high level. Lastly, among the financial sectors, the banking sector possesses a relatively higher level of business connectedness which plays an important role in the accumulation of potential financial risks; the securities sector features with higher market risk; while the insurance sector has both comparatively lower business connectedness and market risk.