scholarly journals Bank Failure Prediction: A Two-Step Survival Time Approach

2006 ◽  
Author(s):  
Michael Halling ◽  
Evelyn Hayden
Keyword(s):  
Survival Time ◽  
Failure Prediction ◽  
Bank Failure

scholarly journals Bank Failure Prediction Model for Zimbabwe

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
Victor Gumbo ◽  
Simba Zoromedza
Keyword(s):  
Prediction Model ◽  
Failure Prediction ◽  
Bank Failure

scholarly journals The Information Content Of Loan Default Disclosures In The Prediction Of Bank Failure

2011 ◽  
Vol 4 (9) ◽  
Author(s):  
Marilyn Waldron ◽  
Charles Jordan ◽  
Alan MacGregor
Keyword(s):  
Statistical Analysis ◽  
Information Content ◽  
Regional Variation ◽  
Prediction Models ◽  
Failure Prediction ◽  
Geographic Region ◽  
Bank Failure ◽  
Font Size ◽  
Loan Default ◽  
Logit Regression

<p class="MsoBodyText" style="text-align: justify; margin: 0in 34.2pt 0pt 0.5in; mso-pagination: none;"><span style="font-size: 10pt;" lang="EN-AU"><span style="font-family: Times New Roman;">Bank failure prediction remains an important economic issue.<span style="mso-spacerun: yes;">&nbsp; </span>Although prior research investigates bank failure prediction, the opportunity to improve predictions exists.<span style="mso-spacerun: yes;">&nbsp; </span>The purpose of this present study is to investigate the possibility of improving prediction of bank failure by including loan default variables and regional variation in prediction of bank failure. The results of statistical analysis indicate loan default measures contain information content both in their own right and also incrementally above that of traditional CAMEL measures.<span style="mso-spacerun: yes;">&nbsp; </span>Furthermore, statistical analysis utilizing logit regression shows the superiority of bank failure prediction models that include consideration of geographic region.<span style="mso-spacerun: yes;">&nbsp; </span></span></span></p>

FUZZY BRIDGED REFINEMENT DOMAIN ADAPTATION: LONG-TERM BANK FAILURE PREDICTION

2013 ◽  
Vol 12 (01) ◽  
pp. 1350003 ◽  
Author(s):  
VAHID BEHBOOD ◽  
JIE LU ◽  
GUANGQUAN ZHANG
Keyword(s):  
Test Data ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Domain Adaptation ◽  
Failure Prediction ◽  
Bank Failure ◽  
Training Data ◽  
Support Vector ◽  
Target Domain

Machine learning methods, such as neural network (NN) and support vector machine, assume that the training data and the test data are drawn from the same distribution. This assumption may not be satisfied in many real world applications, like long-term financial failure prediction, because the training and test data may each come from different time periods or domains. This paper proposes a novel algorithm known as fuzzy bridged refinement-based domain adaptation to solve the problem of long-term prediction. The algorithm utilizes the fuzzy system and similarity concepts to modify the target instances' labels which were initially predicted by a shift-unaware prediction model. The experiments are performed using three shift-unaware prediction models based on nine different settings including two main situations: (1) there is no labeled instance in the target domain; (2) there are a few labeled instances in the target domain. In these experiments bank failure financial data is used to validate the algorithm. The results demonstrate a significant improvement in the predictive accuracy, particularly in the second situation identified above.

A novelty detection machine and its application to bank failure prediction

2014 ◽  
Vol 130 ◽  
pp. 63-72 ◽  
Author(s):  
Shukai Li ◽  
Whye Loon Tung ◽  
Wee Keong Ng
Keyword(s):  
Novelty Detection ◽  
Failure Prediction ◽  
Bank Failure
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