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Bank Bankruptcy in Ukraine: W Hat Are the Determinants and Can Bank Failure Be Forecasted?

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Bank Bankruptcy in Ukraine: W Hat Are the Determinants and Can Bank Failure Be Forecasted? BANK BANKRUPTCY IN UKRAINE: W HAT ARE THE DETERMINANTS AND CAN BANK FAILURE BE FORECASTED? by Oleksandr Nikolsko-Rzhevskyy A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Economics National University of “Kyiv-Mohyla Academy” Economics Education and Research Consortium Master’s Program in Economics 2003 Approved by ___________________________________________________ Ms.Svitlana Budagovska (Head of the State Examination Committee) __________________________________________________ __________________________________________________ __________________________________________________ Program Authorized to Offer Degree Master’s Program in Economics, NaUKMA Date __________________________________________________________ National University of “Kyiv-Mohyla Academy” Abstract BANK BANKRUPTCY IN UKRAINE: W HAT ARE THE DETERMINANTS AND CAN BANK FAILURE BE FORECASTED? by Oleksandr Nikolsko-Rzhevskyy Head of the State Examination Committee: Ms.Svitlana Budagovska, Economist, W orld Bank of Ukraine The paper examines the causes of bank failures in Ukraine during 1998–2003 using micro-level data. Two basic models are used and their performance compared. The first model is the Giant Logit model, which models the probability of bank failure. The second model focuses on estimating time-to- failure, and uses an Accelerated Failure Time framework with time-varying covariates. In addition to standard financial ratios suggested by the C.A.M.E.L. scheme, I concentrate especially on the role of managerial quality, reflected by DEA efficiency. In this paper, DEA efficiency is preliminarily tested and adjusted for environmental differences. Finally, a bank ranking is built and the forecast for potential failures in July 2003 is made. The results show that inefficient banks tend to fail; so do banks, which over-invest in securities, and hold significant value of demand deposits; bank’s size has the opposite effect. Further, an analysis of hazard function shows that for an average bank the probability of bankruptcy increases until the bank reaches the age of approximately 2.5 years, then steadily declines. TABLE OF CONTENTS Chapter 1: Introduction .......................................................................................................1 Chapter 2: Literature Review..............................................................................................5 Chapter 3: Data Description.............................................................................................11 Chapter 4: The Measurement of Productive Efficiency..............................................15 Overview......................................................................................................................15 Data Envelopment Analysis....................................................................................16 i. Introduction to the Data Efficiency Analysis (DEA)...............................16 ii. Efficiency Estimation using DEA...............................................................18 Implementation and the Results.............................................................................23 i. Specifying inputs-outputs sets.......................................................................23 ii. Correcting for heterogeneity.........................................................................26 Chapter 5: Econometric Model........................................................................................31 Theory overview.........................................................................................................31 Multiperiod Logit .......................................................................................................35 i. Theoretical background..................................................................................35 ii. Estimation using logit model........................................................................36 Duration Analysis.......................................................................................................42 i. Theoretical background..................................................................................42 ii. Estimation using log-logistic Hazard model..............................................45 Chapter 6: Discussion and Conclusions.........................................................................54 i. Discussion.........................................................................................................54 ii. Conclusion .......................................................................................................55 Bibliography.......................................................................................................................57 LIST OF FIGURES Number Page Figure 1 The concept of efficiency................................................................................20 Figure 2 Measure of the efficiency under CRS, NIRS, and VRS assumptions.....22 Figure 3 Hazard function for a mean newly-established bank comparing with the probability of fail of a 5-year old large and 3-year old medium banks......48 Figure 4 Comparison of patterns of Marginal Effects and Hazard.........................51 LIST OF TABLES Number Page Table 1 Data summary....................................................................................................13 Table 2 Various techniques of efficiency estimation.................................................16 Table 3 Definition of input and output sets used for the DEA analysis...............26 Table 4 Testing the hypothesis of no environmental differences...........................27 Table 5 Comparative efficiency measurement results for failed and sound banks from Jan 1999 to Jan 2003 ................................................................................28 Table 6 Statistics on Variables Used.............................................................................34 Table 7 Logit estimation.................................................................................................37 Table 8 Calculation of the modified marginal effects for a median bank.............38 Table 9 Goodness of fit of logit models......................................................................40 Table 10 List of the banks with the smallest and the highest probability to fail until July, 2003 predicted by Logit model......................................................41 Table 11 AFT Output Produced by STATA 7.0.......................................................47 Table 12 Modified Marginal Effects for a mean bank, % ........................................50 Table 13 Goodness-of-fit for the AFT model...........................................................52 Table 14 List of the banks with the smallest and the highest probability to fail until July, 2003 predicted by AFT model.......................................................53 ACKNOW LEDGMENTS I wish to thank my thesis advisor, Prof. Laurel Adams, who devoted a great deal of her time to this work and without whose valuable and remarkable comments this work would not appear. I also appreciate a lot the help of Prof. Tom Coupé, for numerous suggestions and improvements. I also wish to thank workshop research professors Roy Gardner, Michael Bowe and Magdalena Sokalska for their helpful comments. I wish to express my special gratitude to Prof. Peter Kennedy and Prof. Valentin Zelenyuk, for interesting and useful courses, which inspired me a lot in conducting this research, and for reviewing early versions of this work. ALOSSARY AFT. Accelerated Failure Time (AFT) model is an econometric model where the natural logarithm of the survival time is expressed as a linear function of the covariates; the distributional form of the error term determines the regression model Allocative Efficiency. Allocative efficiency in input selection involves selecting that mix of inputs which produces a given quantity of outputs at minimum costs Assets. In the accounting sense, all the assets which are or should be of service to an undertaking, valued in accordance with normal accounting conventions Authorized Capital. The total amount of share capital with which a company has been registered in accordance with its Memorandum of Association. Part of the authorized capital may remain as unissued capital C.A.M .E.L. Scheme. Bank’s situation analysis based on capital, assets, management, earnings, and liquidity examination Capital. Accumulated wealth or property used in the production of further wealth, i.e. one of the factors of production, the others being land and labour Data Efficiency Analysis (“DEA”). An econometric non-parametric method which provides measures of relative efficiency among the decision making units, e.g. firms. Demand Deposit. A deposit which is available to the depositor at any time, such as funds in current accounts Decision M aking Unit (“DM U”). – an operating unit under analysis. see DEA above Hryvnya (“UAH”). Official currency of Ukraine. 1 USD = 5.33 UAH1 Liability. Insurance guarding against damage or loss that the policyholder, may cause another person in the form of bodily injury or property damage. 1 January, 2003; Source: Monthly Economic Monitor of Ukraine #2 (28) 2003 National Bank of Ukraine (“NBU”). The central bank of Ukraine, an independent monetary authority of the Ukrainian government Net Assets. The difference between total assets on the one hand and current liabilities and noncapitalized long-term liabilities on
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