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dc.contributor.author
Lazaridis, Nikolaos
en
dc.date.accessioned
2016-12-06T14:08:04Z
dc.date.available
2016-12-07T01:00:15Z
dc.date.issued
2016-12-06
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/14565
dc.rights
Default License
dc.title
Predicting Failure of Eurozone Banks using Logistic Regression and Multiple Discriminant Analysis: Evidence from 2008 to 2015
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heal.type
masterThesis
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heal.creatorID.email
n.lazaridis1@ihu.edu.gr
heal.keywordURI.LCSH
Logistic regression analysis
heal.keywordURI.LCSH
Logistic regression analysis--Data processing
heal.keywordURI.LCSH
Regression analysis
heal.keywordURI.LCSH
Discriminant analysis
heal.keywordURI.LCSH
Bank failures
heal.keywordURI.LCSH
Banks and banking
heal.keywordURI.LCSH
Bank failures--Research
heal.language
en
en
heal.access
free
el
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en
heal.references
Agar wal Vineet, Taffler Richard J. (2007) “Twenty - five years of the Taffler z - score model: does it really have predictive ability?” . Accounting and Business Research , Vol. 37, No. 4, pp. 285, 287  Altman Edward I. ( 1968 ) “ Financial r atios, discriminant analysis and the p re diction of corporate b ankruptcy”. The Journal of Finance , Vol. XXIII, No. 4, pp. 594, 609  Altman Edward I. , Haldeman Robert G., Narayanan P. ( 1977 ) “Zeta analysis - A new model to identify bankruptcy risk of corporations” . Journal of Banking and Finance, North - Holland Publishing Company , pp. 34 - 35, 50  Aziz M. Adnan, Dar Humayon A. ( 2006 ) “Predicting corporate bankruptcy: where we stand?” . Corporate Governance , Vol. 6, No. 1, 2006, Emerald Group Publishing Limited, pp. 19, 29  Baldwin Jane, Glezen G. William ( 1992 ) , “Bankruptcy Prediction Using Quarterly Financial Statement Data” . Journal of Accounting, Auditing and Finance , pp . 269 , 282  Beaver William H. ( 1966 ) “Financial Ratios as Predictors of Failure” . Journal of Accounting Research , Vol. 4, Issue 3, pp. 71 - 111  C asey Cornelius, Bartczak Norman ( 1985 ) “Using Operating Cash Flow Data to Predict Financial Distress: Some Extensions” . Journal of Accounting Research , Vol. 23, No. 1, pp. 394 - 395  Celli Massimili ano ( 2015 ) “Can Z - Score Model Predict Listed Companies’ Failures in Italy? An Empirical Test” . International Journal of Business and Management , Vol. 10, No. 3, 2015, Published by Canadian Center of Science and Education, pp. 63  Charitou Andreas, Neophytou Evi, Charalambous Chris ( 2004 ) “Predicting Corporate Failure: Empirical Evidence for the UK” . European Accounting Review , Vol. 13, No. 3, pp. 1, 467 - 468, 478 , 481, 493  Cosma Daniela ( 2013 ) “The need to reform the banking system - A premise for the implementation of the Basel III Accord” . Annals of the University of Oradea, Economic Science Series , Vol. 22, Issue 1, pp . 1078  Davis E. Philip, Karim Dilruba ( 2008 ) “Comparing early warning systems for banking crises” . Journal of Financial Stability , pp. 1 Predicting Failure of Eurozone Banks using Logistic Regression and Multiple Discriminant Analysis: Evidence from 2008 to 2015 34  Deakin Edward B. ( 1972 ) “A Discrimin ant Analysis of Predictors of Business Failure” . Journal of Accounting Research , Vol. 10, Issue 1, pp . 167 , 178  Dielman T. E. ( 1996 ) “Applied Regression for Business and Economics” . Duxbury Press  Demyanyk Yuliya, Hasan Iftekhar ( 2009 ) “Financial Crises and Bank Failures: A Review of Prediction Methods” . Federal Reserve Bank of Cleveland , pp. 15  Jesswein Kurt R. ( 2009 ) “Bank failure models: A preliminary examination of the ‘Texas’ Ratio” . Proceedings of the Academy of Banking Studies , Vol. 9, No. 1, pp. 1, 3, 5  Jordan Dan J., Rice Douglas, Sanchez Jacques, Wal ker Christopher, Donald H. Wort ( 2012 ) “Predicting Bank Failures: Evidence from 2007 to 2010” . Journal of Banking & Finance , pp. 1 - 2, 5, 23  Ka rels Gordon V., Prakash Arun J. ( 1987 ) “Multivariate norm ality and forecasting of business bankruptcy” . Journal of Business, Finance and Accounting , pp. 1  Moosa Imad A. ( 2010 ) “Basel II as a casualty of the global financial crisis” . Journal of Banking Regulation , Vol. 11, 2, pp. 95  O zkan - Gunay E. Nur, Ozkan Mehmed ( 2007 ) “Prediction of bank failures in emerging financial markets: an ANN approach” . Journal of Risk Finance (Emerald Group Publishing Limited). 2007 , pp. 465 - 480  Papadopoulos Theodoros ( 2014 ) “Prediction Bankruptcy for European Banks in 2008 - 2012” . MSc, International Hellenic University, pp. 1 6  Pla tt Harlan D., Platt Marjorie B. ( 1990 ) “Development of a class of stable predictive variables: The case of bankruptcy prediction” . Journal of Business Finance and Accounting , pp. 43, 47  Poghosyan Tigran, Cihak Martin ( 2009 ) “Distress in European Banks: An Analysis Based on a New Dataset” . International Monetary Fund , pp. 5, 11 , 12  Rodriguez L. Jacobo ( 2002 ) “International Banking Regulation: Where’s the Market Discipline in Basel II?” . Policy Analysis , No. 455, October 15 2002, pp. 1  West Robert Craig ( 1985 ) “A factor - analytic approach to bank condition” . Journal of Banking & Finance, 1985 , Vol. 9, Issue 2, pp. 253 - 266  Yim Juliana, Mitchell Heather ( 2007 ) “Predicting financial distress in the Australian fin ancial service industry” . Australian Economic Papers , Vol. 46, Issue 4, pp . 377, 385 Predicting Failure of Eurozone Banks using Logistic Regression and Multiple Discriminant Analysis: Evidence from 2008 to 2015 35 World Wide Web  EViews statistical software package (9.5 Student Lite Version)  Bankscope d atabase web site: https://bankscope.bvdinfo.com/version - 201689/Login.serv?product=scope2006  Investopedia http://www.investopedia.com  Wikipedia https://en.wikipedia.org
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heal.fileFormat
pdf
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heal.recordProvider
School of Economics, Business Administration and Legal Studies, MSc in Banking and Finance
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heal.publicationDate
2016-10-12
heal.abstract
The main purpose of this study is to examine a dataset of 32 matched pairs of failed and non - failed Eurozone banks , according to the size of total assets, over the period 2008 - 2015. Logistic Regression and Multiple Discriminant Analysis (M DA), based on the CAMEL rating system, were employed on yearly report data for one, two and three years prior to failure in order to determine whether reliable failure prediction models for Eurozone banks can be developed. Logistic Regression Analysis outperformed Multiple Discriminant Analysis when yearly data for one year prior to failure were employed. Notably, the logit model yielded an overall correct classification accuracy of 82.81% compared to 81.25% for the MDA. On the other hand, MDA was superior when yearly data for two and three years were employed. It yielded an overall correct classification accuracy of 73.44% and 64.06% respectively, compared to 71.88% and 59.38% for the logit model.
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heal.tableOfContents
1. INTRODUCTION ................................ ................................ ................................ ........... 1 2. LITERATURE REVIEW ................................ ................................ ................................ .... 3 3. METHODOLO GY ................................ ................................ ................................ ........... 7 3.1. Sample Data ................................ ................................ ................................ .......... 7 3.2. Selection of the financial ratios ................................ ................................ ........... 11 3.3. Description of the application of Logistic Regression Analysis ............................. 14 3.4. Description of the application of Multiple Discri minant Analysis .......................... 16 4. DATA ANALYSIS AND DISCUSSION ................................ ................................ ............. 17 4.1. Descriptive Statistics ................................ ................................ ........................... 18 4.2. Univariate Analysis ................................ ................................ .............................. 20 4.3. Implementation of multivariate Logistic Regression Analysis .............................. 23 4.4. Implementation of Multiple Discriminant Analysis ................................ .............. 26 4.5. Comparative results of multivariate Logistic Regression Analysis and Multiple Discriminant Analysis ................................ ................................ ................................ . 29 5. SUMMARY AND CONCLUSIONS ................................ ................................ ................. 31 Limitations of this study ................................ ................................ ............................. 32 REFERE NCES ................................ ................................ ................................ .................. 33 Bibliography ................................ ................................ ................................ ............... 33 World Wide Web ................................ ................................ ................................ ....... 35
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heal.advisorName
Artikis, Panayiotis
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heal.committeeMemberName
Grose, Christos
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heal.committeeMemberName
Sikalidis, Alexandros
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heal.academicPublisher
IHU
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heal.academicPublisherID
ihu
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heal.numberOfPages
39
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heal.spatialCoverage
Europe
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heal.temporalCoverage
2008-2015
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