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Predicting Bank Failure Using Regulatory Accounting Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Predicting Bank Failure Using Regulatory Accounting Data./
Author:	Pruitt, Helen.
Description:	1 online resource (135 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: A.
Contained By:	Dissertation Abstracts International79-01A(E).
Subject:	Accounting. -
Online resource:	click for full text (PQDT)
ISBN:	9780355219654

Predicting Bank Failure Using Regulatory Accounting Data.
Pruitt, Helen.

Predicting Bank Failure Using Regulatory Accounting Data. - 1 online resource (135 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: A.

Thesis (D.B.A.)

Includes bibliographical references

A liquidity shortfall in the United States triggered the bankruptcy of several large commercial banks, and bank failures continue to occur, with 50 banks failing between 2013 and 2015. Therefore, it is critical banking regulators understand the correlates of financial performance measures and the potential for banks to fail. In this study, binary logistic regression was employed to assess the theoretical proposition that banks with higher nonperforming loans, lower Tier 1 leverage capital, and higher noncore funding dependence are more likely to fail. Archival data ranging from 2012--2015 were collected from 250 commercial banks listed on the Federal Deposit Insurance Corporation's website. The results of the logistic regression analyses indicated the model was able to predict bank failure, X2(3, N = 250) = 218.86, p < .001. Nonperforming loans, Tier 1 leverage capital, and noncore funding were all statistically significant, with Tier 1 leverage capital (beta = -1.485), p < .001) accounting for a higher contribution to the model than nonperforming loans (beta = .354, p < .001) and noncore funding dependence (beta = -.057, p = .015). The implication for positive social change of this study includes the potential for bank regulators to enhance job security, wealth creation, and lending within the community by working with bank managers to develop more timely corrective action plans to alleviate the risk of bank failure.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355219654Subjects--Topical Terms:

561166
Accounting.
Index Terms--Genre/Form:

554714
Electronic books.

Predicting Bank Failure Using Regulatory Accounting Data.
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100 1 $a Pruitt, Helen. $3 1181074
245 1 0 $a Predicting Bank Failure Using Regulatory Accounting Data.
264 0 $c 2017
300 $a 1 online resource (135 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: A.
500 $a Adviser: Sean Stanley.
502 $a Thesis (D.B.A.) $c Walden University $d 2017.
504 $a Includes bibliographical references
520 $a A liquidity shortfall in the United States triggered the bankruptcy of several large commercial banks, and bank failures continue to occur, with 50 banks failing between 2013 and 2015. Therefore, it is critical banking regulators understand the correlates of financial performance measures and the potential for banks to fail. In this study, binary logistic regression was employed to assess the theoretical proposition that banks with higher nonperforming loans, lower Tier 1 leverage capital, and higher noncore funding dependence are more likely to fail. Archival data ranging from 2012--2015 were collected from 250 commercial banks listed on the Federal Deposit Insurance Corporation's website. The results of the logistic regression analyses indicated the model was able to predict bank failure, X2(3, N = 250) = 218.86, p < .001. Nonperforming loans, Tier 1 leverage capital, and noncore funding were all statistically significant, with Tier 1 leverage capital (beta = -1.485), p < .001) accounting for a higher contribution to the model than nonperforming loans (beta = .354, p < .001) and noncore funding dependence (beta = -.057, p = .015). The implication for positive social change of this study includes the potential for bank regulators to enhance job security, wealth creation, and lending within the community by working with bank managers to develop more timely corrective action plans to alleviate the risk of bank failure.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Accounting. $3 561166
655 7 $a Electronic books. $2 local $3 554714
690 $a 0272
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Walden University. $b Management. $3 1148485
773 0 $t Dissertation Abstracts International $g 79-01A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10622911 $z click for full text (PQDT)