Financial Structure and Systemic Risk of Banks: Evidence from Chinese Reform

Ji, Guseon; Kim, Daniel Sungyeon; Ahn, Kwangwon

doi:10.3390/su11133721

Cited by 13 publications

(5 citation statements)

References 80 publications

(108 reference statements)

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“…To contribute further, the study aims to highlight how these risks can be measured, reduced if not eliminated with the support and integration of modern AI and ML mechanisms. There already exists a large pool of dominant literature in this particular field, (Ji et al, 2019; Kaserer & Klein, 2019; Schwarcz, 2008) identifying pros and cons, reasons and methods of computing or forecasting risks. However, when the research was incorporated with AI, only a few pieces of literature came to light.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Measuring systemic and systematic risk in the financial markets using artificial intelligence

2022

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Financial markets are exposed to sharp price volatility, and defaults and errors, which results in high risks for relevant stakeholders. In such markets, success depends upon the quality and quantity of information available to assist the decision‐making. Artificial intelligence (AI), in this regard, can measure or predict systemic and systematic risk in the financial markets. This research study aims to highlight how the risks can be measured and controlled with the support and integration of modern AI and machine learning mechanisms. By performing a review‐based methodology, the study first presents an explanation of the models, which is followed by proposing a new AI‐based model. The model relies on several financial system's inputs, which include portfolio data, trade data, market data, financial reports, market condition, and sector‐wise data. The systemic and systematic risk is then assessed through a number of outputs that the AI algorithm will process, in the form of an interactive dashboard. The article further tests that model and presents benefits and implications.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Measuring systemic and systematic risk in the financial markets using artificial intelligence

2022

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“…Financial system risk is an important issue in the banking industry. Ji et al (2019) reported that the transformation of the financial system to a more market-oriented structure could reduce banks' systemic risk. Kou et al (2019) introduced the current situation of using machine learning methods to study financial system risk, and put forward the direction of future work.…”

Section: Management Of Commercial Banksmentioning

confidence: 99%

The impact of gross domestic product on the financing and investment efficiency of China’s commercial banks

et al. 2021

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China’s commercial banks have developed at a very rapid speed in recent decades. However, with global economic development slowing down, the impact of gross domestic product growth as an exogenous factor cannot be ignored. Most existing studies only consider the internal factors of banks, and neglect their external economic factors. This study thus adopts an undesirable dynamic slacks-based measure under an exogenous model in combination with the Kernel density curve to explore the efficiency of state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs), and urban commercial banks (UCBs) in China from 2012 to 2018. The results show that SOCBs have the highest overall efficiency, followed by JSCBs, then UCBs. The efficiencies of SOCBs, JSCBs, and UCBs in the financing stage are greater than those in the investment stage, indicating that the latter stage brings down overall efficiency. Thus, all commercial banks need to focus on the efficiency of non-performing loans and return on capital. Finally, SOCBs need to strengthen internal controls, reduce non-performing loans and improve return on capital. JSCBs should actively expand its business while controlling costs, and UCBs should optimize its management.

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“…A compilation of a study on 'Literature of efficiency of the financial institution' (Berger & Mester, 1997)showed that less than 5% researches had studied the efficiency of the bank in a developing nation and that majority of the research are directed to developed countries such as the US and the European nations. Some of the studies, which have helped understand the banking system of developing nations include (Asmild et al, 2018;Baten et al, 2015), which are some of the prominent studies from Bangladesh, Tecles & Tabak, 2010; are studies from Brazil, (Avkiran, 2015;Ji et al, 2019;Ma et al, 2019;Zhou et al, 2008) are studies from China, (Anand & Kumar, 2020;Bawa et al , 2019;Behera, 2019;George, 2016;Kumar & Dhingra, 2016b;Kumar, 2013;Padake & Soni, 2015) are some prevalent studies in Indian banking sector, (Abbas et al, 2019;Ahmad, 2011;Akhtar, & Nishat, 2002;Tahir et al, 2016) are some studies in Pakistan, (Miencha & Selvam, 2013) is study from Kenya, (Seelanatha, 2010;Wijesiri et al, 2015) are studies from Sri Lanka, (Mahathanaseth & Tauer, 2014;Sufian et al, 2010) are some studies from Thailand, (Eyceyurt et al, 2017;Gunes & Yilmaz, 2016) are some studies from Turkey, and many other similar studies (Banna et al, 2017;Le, 2020;Nguyen, 2020)from different countries. These studies have highlighted the inefficiencies and their causes for the banking sector of developing nations.…”

Section: Literature Reviewmentioning

confidence: 99%

Trends in Indian Private Sector Bank Efficiency: Non-Stochastic Frontier DEA Window Analysis Approach

Kumar¹,

Anand²,

Batra³

2020

JAFEB

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The study examines the efficiency of private sector banks in India with the help of Window DEA (Data Envelopment Analysis) for a period from 2005 to 2017. With a window of three years, the period was divided into 11 windows. The study outcomes show that 59.9% of all private sector banks in India operate at more than 0.9 level of efficiency, and there are only three occasions when banks were operating at the efficiency value between 0.6 to 0.7. Further, the consistency in the efficiency scores of the banks has also been analyzed using an efficiency mapping matrix, and the mean efficiency score of the bank in each window is studied. The score of standard deviation was interpreted accordingly for these banks. Banks that are showing the highest efficiency scores also have a higher variance of efficiency scores. There was no bank identified in the matrix that promises high-efficiency ratings with low variability. The study concludes that the analysis of the efficiency mapping matrix indicates that, as a DMU escalates in the efficiency scores, the standard deviation reflecting the risk in overall efficiency scores also tends to rise. The findings complement the concept of higher risk to higher return or greater efficiency.

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Financial Structure and Systemic Risk of Banks: Evidence from Chinese Reform

Cited by 13 publications

References 80 publications

RETRACTED: Measuring systemic and systematic risk in the financial markets using artificial intelligence

RETRACTED: Measuring systemic and systematic risk in the financial markets using artificial intelligence

The impact of gross domestic product on the financing and investment efficiency of China’s commercial banks

Trends in Indian Private Sector Bank Efficiency: Non-Stochastic Frontier DEA Window Analysis Approach

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