Boyraz 20120406 Mastersthesis V6

AN EMPIRICAL STUDY ON EARLY WARNING SYSTEMS FOR BANKING SECTOR A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF SOCIAL SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY MUSTAFA FATİH BOYRAZ IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN THE DEPARTMENT OF ECONOMICS APRIL 2012 i Approval of the Graduate School of Social Sciences. ____________________ Prof. Dr. Meliha ALTUNIŞIK Director I certify that this thesis satisfies all the requirements as a thesis for the degree of Master of Science. ____________________ Prof. Dr. Erdal ÖZMEN Head of Department This is to certify that we have read this thesis and that in our opinion it is fully adequate, in scope and quality, as a thesis for the degree of Master of Science. ____________________ Asst. Prof. Dr. Esma GAYGISIZ Supervisor Examining Committee Members Asst. Prof. Dr. Esma GAYGISIZ (METU, ECON) ___________________ Prof. Dr. Erdal ÖZMEN (METU, ECON) ___________________ Asst. Prof. Dr. Bedri Kamil Onur TAŞ (TOBB ETU, ECON) __________________ ii PLAGIARISM I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work. Name, Last Name: Mustafa Fatih BOYRAZ Signature: iii ABSTRACT AN EMPIRICAL STUDY ON EARLY WARNING SYSTEMS FOR BANKING SECTOR Boyraz, Mustafa Fatih M.S., Department of Economics Supervisor : Asst. Prof. Dr. Esma GAYGISIZ April 2012, 225 pages Early Warning Systems (EWSs) for banking sectors are used to measure occurrence risks of banking crises, generally observed with a rundown of bank deposits and widespread failures of financial institutions. In countries with a small number of banks, for example Turkey with 48 banks (BDDK, 2011), every bank may be considered to have a systematic importance since the failure of any individual bank may carry a potential threat to lead to a banking crisis. Taking into account this fact the present study focuses on EWSs in Turkey. Since there is no single correct EWS to apply to all cases, in this study, 300 models were constructed and tested to find models as accurate as possible by using a trial-and-error process and by searching optimal feature subset or classifier methods. Empirical results indicate that prediction accuracy did not increase significantly while we got closer to the actual occurrence of bankruptcy. An important finding of the study was that trends of financial ratios were very useful in the prediction of bank failures. Instead of failures as a result of instant shocks, the banks' failures followed through a path: first a downward movement affected the efficiency of the banks' officers and the quality of iv management structure measured with "Activity Ratios", then the profitability of the banks measured with "Profit Ratios" declined. At last, the performance and the stability of banks' earnings stream measured with "Income-Expenditure Structure Ratios" and the level and quality of the banks' capital base, the end line of defense, measured with "Capital Ratios". At the end of study, we proposed an ensemble model which produced probability ratios for the success rates of the banks. The proposed model achieved a very high success rate for the banks we considered. Keywords: Early Warning Systems, Banking Crises, Bank Failures, Turkish Banking System v ÖZ BANKACILIK SEKTÖRÜ İÇİN ERKEN UYARI SİSTEMLERİ ÜZERİNE DENEYSEL BİR ÇALIŞMA Boyraz, Mustafa Fatih Yüksek Lisans, Ekonomi Bölümü Tez Yöneticisi : Yrd.Doç.Dr. Esma GAYGISIZ Nisan 2012, 225 pages Bankacılık sektörü için erken uyarı sistemleri, mevduat stokunda ciddi azalış ve finansal kurum başarısızlıkları olarak tanımlayabileceğimiz bankacılık krizlerinin ortaya çıkış riskinin ölçümü için kullanılmaktadır. Sadece 48 bankası bulunan (BDDK, 2011) Türkiye gibi bir ülkede her bir banka sistematik önem taşıyor olabilir ve herhangi bir bankanın başarısızlığı bir bankacılık krizi ile sonuçlanabilir. Erken uyarı sistemleri tasarlanırken, her durumda uygulanabilecek, tek bir doğru model bulunmamaktadır. Bu çalışmada daha başarılı sonuç verecek değişken altkümesinin ve sınıflandırma metodunun tespiti için 300 model kurulup test edilmiştir. Ampirik sonuçlar göstermiştir ki, tahmin başarısı iflasın olduğu zamana yaklaştıkça kayda değer biçimde artmamaktadır. Ayrıca finansal oranların eğilimlerinin banka başarısızlık tahmininde faydalı olduğu görülmüştür. Bunların yanı sıra bankaların batma süreçlerinin ani bir şok olmadığı, daha ziyade bankaların bir rota takip ederek battığı tespit edilmiştir. Bankalarda aşağı doğru hareketin önce banka personelinin ve yönetiminin kalitesinde ve verimliliğinde (Faaliyet Oranları), daha sonra banka karlılığında (Karlılık Oranları) ve son olarak da banka kazanç sisteminin vi performansında ve istikrarında (Gelir-Gider Yapısı Oranları) ve bankanın son savunma hattı olan sermaye tabanını seviyesinde ve kalitesinde (Sermaye Oranları) hissedilmekte olduğu gözlemlenmiştir. Son olarak da bankalar için başarı olasılığı üreten bir “birleşik model” önerilmiştir ve bu model ile %97,50 başarı oranı yakalanmıştır. Anahtar sözcükler: Erken Uyarı Sistemleri, Bankacılık Krizleri, Banka Başarısızlığı, Türk Bankacılık Sistemi vii DEDICATION to my love viii ACKNOWLEDGMENTS The author wishes to express his deepest gratitude to his supervisor Asst. Prof. Dr. Esma GAYGISIZ for her guidance, advice, criticism, encouragements and insight throughout the research. The author would also like to thank Prof. Dr. Erdal ÖZMEN and Asst. Prof. Dr. Bedri Kamil Onur TAŞ for their suggestions and comments. The technical assistance of Mr. Fatih ALTINEL, Ms. Rukiye YAYLA, and Ms. Ekin ÖNŞAN are gratefully acknowledged. ix TABLE OF CONTENTS PLAGIARISM ........................................................................................................................ iii ABSTRACT ........................................................................................................................... iv ÖZ ....................................................................................................................................... vi DEDICATION ..................................................................................................................... viii ACKNOWLEDGMENTS ........................................................................................................ ix TABLE OF CONTENTS ........................................................................................................... x LIST OF TABLES ................................................................................................................. xiii LIST OF FIGURES ................................................................................................................xiv LIST OF ABBREVIATIONS .................................................................................................... xv CHAPTER 1. INTRODUCTION ............................................................................................................... 1 2. EARLY WARNING SYSTEMS .............................................................................................. 4 2. 1. Financial stability and early warning systems ..................................................... 5 2. 2. CAMELS, Off-site Monitoring and Early Warning Systems .................................. 8 2. 3. Business failure prediction ................................................................................ 10 2. 4. Why do banks fail? ............................................................................................ 12 3. LITERATURE REVIEW ON BUSINESS FAILURE PREDICTION ............................................ 14 3. 1. Classifiers ........................................................................................................... 14 3. 1. 1. Statistical techniques ................................................................................. 16 3. 1. 2. Neural networks ........................................................................................ 25 3. 1. 3. Case-based reasoning ................................................................................ 35 3. 1. 4. Decision trees ............................................................................................ 39 3. 1. 5. Operational research ................................................................................. 44 x 3. 1. 6. Evolutionary approaches ........................................................................... 50 3. 1. 7. Rough set based techniques ...................................................................... 54 3. 1. 8. Fuzzy logic based techniques .................................................................... 58 3. 1. 9. Support vector machines .......................................................................... 60 3. 1. 10. Other techniques ................................................................................... 63 3. 1. 11. Soft computing techniques ................................................................... 66 3. 2. Feature selection and feature extraction .......................................................... 81 3.2.1. Feature selection methods ........................................................................ 82 3.2.2. Limitation of feature selection methods ................................................... 84 3.2.3. Literature ..................................................................................................

Load more