Can statistics-based early warning systems detect problem banks before markets?

Kimmel, Randall K.; Thornton, John; Bennett, Sara E.

doi:10.1016/j.najef.2016.04.004

Cited by 8 publications

(2 citation statements)

References 58 publications

(101 reference statements)

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“…Currently, there are different forecasting models that can be used (Zigraiova and Jakubik, 2015;Kimmel et al, 2016) depending on where a company is situated on the spectrum of a possible financial failure. For instance, on the basis of the paradigm of business failure, Inmaculada (2017) categorized companies as chronic failure companies, a revenue financing failure company, or an acute failure company.…”

Section: Business Failure Models In Tourism and Hospitality Sectorsmentioning

confidence: 99%

Examining Ownership Equity as a Psychological Factor on Tourism Business Failure Forecasting

Korol

Spyridou

2020

Front. Psychol.

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This paper examines ownership equity as a predictor of future business failure within the tourism and hospitality sectors. The main goals of this study were to examine which ratios are the most important for a tourism business failure forecasting model and how significant is the "total percentage of equity ownership by company directors" ratio compared with other ratios associated with the probability of bankruptcy. A stepwise weight assessment ratio analysis (SWARA) was applied, and 12 tourism bankruptcy experts evaluated key ratios. Total percentage of equity ownership by company directors is considered a psychological factor, and it was identified as the fourth most important ratio for a business failure forecasting model. Academicians and practitioners can use the findings of this study whenever developing a forecasting model for tourism and hospitality enterprises.

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Section: Business Failure Models In Tourism and Hospitality Sectorsmentioning

confidence: 99%

Examining Ownership Equity as a Psychological Factor on Tourism Business Failure Forecasting

Korol

Spyridou

2020

Front. Psychol.

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“…Model Kimmel, Thornton, and Bennet (2016) prove that newer, more technically sophisticated methods of predicting bank failure do not necessarily do a better job of forecasting imminent failure, leaving the choice of statistical technique to be determined by the data available and the needs of the research study. Lane, Looney and Wansley (1986), Wheelock and Wilson (2005), Kiefer (2014) and Kimmel et al (2016) choose to apply the Cox (1972) proportional hazards model to bank failure. This semiparametric model does not require distributional assumptions for the estimation of the baseline hazard function or probability that an average bank will fail.…”

Section: Literature Reviewmentioning

confidence: 99%

Proportional Hazards Model of Bank Failure: Evidence from USA

Cox

Kimmel

Wang

2017

JEFS

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This study uses the Cox Proportional Hazards Model, examining the operating and financial characteristics of banks as well as market and economic conditions, to demonstrate what caused US bank failures. Consistent effects indicate US banks were more likely to survive when having higher capital, loan to assets, short term debt securities, and return on assets. The failure rate was greater when their loan loss allowances and past due accounts were high. The results of this research will help banks, central banks, governments, and regulators to forecast which banks are in financial trouble and understand why. They can then take effective action to shore up the financial strength of the affected banks as well as the financial system.

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Determinants of financial crises—An early warning system based on panel logit regression

Jemović

Marinković

2019

Int. J Fin Econ

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Despite the fact that different types of financial crises are rooted in similar weaknesses of economy or may have common determinants, the very transmission mechanism may determine one category as leading or lagging behind others. We are focused on financial crises that necessarily have the features of systemic banking crises and assess econometric early warning system of 64 systemic banking crises that occurred in the period from 1977 to 2013. The paper employs two different procedures, based on panel logit regression. The dynamic discrete‐choice (binary) early warning model clearly outperformed the static model. The set of significant explanatory variables changed relative to the findings of the static model. The most significant predictor of the crises in the better performing model is deposit insurance system, followed by international reserves, M2‐to‐international reserves ratio, M2 multiplier, bank deposits, and bank reserves ratio. The statistical significance of the lagged variable confirmed the necessity to take the effect of crisis persistence into account.

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Can statistics-based early warning systems detect problem banks before markets?

Cited by 8 publications

References 58 publications

Examining Ownership Equity as a Psychological Factor on Tourism Business Failure Forecasting

Examining Ownership Equity as a Psychological Factor on Tourism Business Failure Forecasting

Proportional Hazards Model of Bank Failure: Evidence from USA

Determinants of financial crises—An early warning system based on panel logit regression

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