Evidence of Forward-Looking Loan Loss Provisioning with Credit Market Information

Balasubramanyan, Lakshmi; Thomson, James B.; Zaman, Saeed

doi:10.1007/s10693-016-0255-0

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…The income-smoothing hypothesis concerns the relationship between net income ( ) and LLP, where we find a significant positive relationship in all estimated regressions (the coefficient ranges from 0.478 in the SGMM to 0.231 in the dynamic OLS). This finding is similar to the results across 40 countries by Fonseca and González (2008), European banks by Leventis et al (2011), South East Asian countries by Packer & Zhu (2012), central European banks by Skala (2015) and US banks by Balasubramanyan et al (2016). Such that, "regardless of the commendable or condemnable motives underlying income smoothing" (as shown by a positive relationship) "this behavior contributes to financial soundness of banks and reduces procyclicality" (page.…”

Section: Hypothesis 3: Income Smoothingsupporting

confidence: 82%

European bank loan loss provisioning and technological innovative progress

Simper

Dadoukis

Bryce

2019

International Review of Financial Analysis

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This paper presents an analysis of Loan Loss Provisioning (LLP) behavior of European banks across 26-member states to determine how bad management and Technological Innovative Progress (TIP) has affected bank risk management. Technological improvements in banking have seen advances in both back and front office operations with respect to lending. This is created through increased disembodied technological change capturing improvements in both non-financial and risk management technologies. We find, using a new dynamic LLP model that European banks employed bad management practices in relation to their lending and monitoring practices, leading to higher losses on loans (through increased LLPs). However, the relationship between TIP and LLPs indicates that those banks which increased their technological efficiency with respect to costs had a greater ability to recognize bad loans, and were therefore able to subsequently increase LLPs. That is, improving technology mitigated the impact of bad management practices in European banks.

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Section: Hypothesis 3: Income Smoothingsupporting

confidence: 82%

European bank loan loss provisioning and technological innovative progress

Simper

Dadoukis

Bryce

2019

International Review of Financial Analysis

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“…The data were not trimmed or corrected for outliers, and the means are in line with those in other studies. For example Balasubramanyan et al ( 2017 ), using US-based data from 1997 to 2011, found that all loan loss reserves represented 1% of total assets on average, while in our study it was 0.844%.…”

Section: Empirical Investigationcontrasting

confidence: 62%

“…Prior empirical research mainly has focused on other effects on lending, e.g. GDP (Salas and Saurina 2002 ), unemployment rates (Balasubramanyan et al 2017 ) or interest rates (Delis and Kouretas 2011 ) or issues that are directly attributable to a single loan such as collateral (e.g. Berger and Udell 1990 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…As Balasubramanyan et al ( 2017 ) pointed out, estimating NPLs using loan loss provisions (LLPs) can be biased, as LLPs may be based on expectations for NPLs, and thus LLPs are not independent from future loan performance. Endogeneity, therefore, has another stake in estimating the equation.…”

Section: Empirical Investigationmentioning

confidence: 99%

“… 10 Balasubramanyan et al ( 2017 ) use a 2SLS procedure to overcome this problem by first estimating LLP using current NPLs and using the estimates when quantifying NPL. …”

mentioning

confidence: 99%

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Real Estate Markets and Lending: Does Local Growth Fuel Risk?

Zurek

2021

J Financ Serv Res

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Real estate price growth affects credit risk for several reasons: it provides input for economic forecasts as it’s closely tied to economic growth; when used as collateral by banks, rising real estate prices may decrease both expected and actual losses; and banks may become less risk averse in lending practices in the presence of rising property prices. Therefore, we analyze these effects on loan portfolios’ estimated and realized risks on a local level. Using data of 390 German savings banks, however, we find that real estate prices have little or no impact on savings banks’ credit portfolio risk or risk precautions.

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How fintech impacts pre‐ and post‐loan risk in Chinese commercial banks

Wang

Liu

2020

Int. J Fin Econ

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Due to information asymmetry in the lending market, credit activities inevitably cause pre-and post-loan risk in banks. To explore whether these risks can be alleviated by fintech and study its specific mechanism from the perspective of managers, we investigate on a sample from the establishment of fintech subsidiaries in Chinese commercial banks during 2014-2018. The results show that fintech can alleviate pre-loan risk associated with credit activities, and this negative effect is more pronounced in banks with higher level of managerial ownership. As the control of post-loan risk is more dependent on the regulators, fintech in banks has no significant effect on reducing post-loan risk associated with credit activities. This paper adds empirical evidence on the role of fintech in banks.

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Evidence of Forward-Looking Loan Loss Provisioning with Credit Market Information

Cited by 12 publications

References 20 publications

European bank loan loss provisioning and technological innovative progress

European bank loan loss provisioning and technological innovative progress

Real Estate Markets and Lending: Does Local Growth Fuel Risk?

How fintech impacts pre‐ and post‐loan risk in Chinese commercial banks

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