The Ability of Altman’s Z”-score Model to Detect the Economic Distress of Kazakh Banks

Aigul P. Salina, Xin Zhang, Tong Jiao and Omaima A. G. Hassan

Chapter 13 in Banking Resilience and Global Financial Stability, 2024, pp 347-373 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This study contributes to the literature by evaluating the ability of Altman’s Z”-score model to predict the economic distress of 12 Kazakh banks over the period 2008–2014. The original Z”-score model with a cut-off point implied by Altman (2005) produced a prediction accuracy ratio of 44.05% and correctly classifies 76.19% of the observations as an economically distressed group. This study then re-estimates the model using three approaches, namely, the “leave-one-out”, Direct, and Wilks’ methods, and identifies new, optimal cut-off points for the re-estimated models. The re-estimated models, together with the new, optimal cut-off points, improved the prediction accuracy ratio to 70% and correctly classified over 90% of the observations originally assigned to the economically distressed group. The results imply that the Kazakh banking regulator and other market participants could use Altman’s Z”-score model to detect economically distressed banks.

Keywords: Bank capital; Capital Buffers; Financial Stability; Macroprudential Regulation; Systemic Risks; Accounting Standards; Bank Stability; Financial Reporting; Local GAAP; IAS/IFRS; Lending Risk; Bank Capitalization; Bank Risk-Taking; Credit Risk; Asian Banks; Stability; Financial Development; Banking System; Growth; Regression; Human Resources; Trade: Population; Emerging Economies; Human Capital; Financial Stability; Intellectual Capital Efficiency; Human Capital Efficiency; Structural Capital Efficiency; Relational Capital Efficiency; Resources Based Theory; System Generalize Method of Moments; Competitive Environment; Islamic Banks; Asian Countries; Banking; Regulatory Capital; Bank Performance; Basel Accord; Profitability; Risk; Minimal Capital Requirements; Political Instability; Bank Supervision; 2SLS; Financial Institutions; Bank Holding Companies; Great Recession: FDIC; Financial Markets; Financial Crises; Economic Recessions; Heterogeneity; Systemic Risk; Dodd Frank; Foreign Exchange; Systemic Risk; Turkish Banking Sector; Volatility; Foreign Loans; Domestic Loans; Total Assets; Total Credits; Total Deposits; Interest Rates; Financial Crisis; Risk Management; Competency Development; Banks; Integrated; Resilience; Uncertainty; Pandemic; Risk; Actionable; Financial Stability; Systemic Risk; Crisis Management; Bank Recovery and Resolution; Capital Requirements; Climate Physical Risk; Climate Transition Risk; Digital Finance; Safety Net; Twin Transition; Market Illiquidity; Monetary Policy; Bank Lending Channel; Banks; Shocks; Loan Supply; OECD Countries; Financial Crisis; Interest Rates; Transmission Mechanism; European Monetary Union; European Central Bank; ECB Governing Council; Expansionary Monetary Policy; Trilemma; TARGET2-Balance; Inflation; Transmission Protection Instrument (TPI); ECB Anti-Fragmentation Instrument; Pandemic Emergency Purchase Program (PEPP); Credit-to-GDP Gap; Out-of-Sample Forecasts; Augmented Credit Gap; Countercyclical Capital Buffer; Credit Gap; Decision-making Process; Basel Gap; Forecasting Gaps; One-Sided Gap Series; Two-Sided Gap Series; Altman; Z-Score; Economic Distress; Kazakhstan; Banks; Emerging Market; Multiple Discriminant Analysis; Financial Health; Prediction Accuracy; Wilks' Approach; Direct Approach; Microfinance; Microfinance Institutions; Global Financial Crisis; Covid-19; Social Outreach; Financial Sustainability; Operational Self-Sufficiency; NGOs; Capital Structure; Legal Status; Liquidity Hoarding; Economic Policy Uncertainty; Qatari Banks; Islamic Banks; Endogeneity; Instrumental Variable Approach; Economic Blockade; Asset-Side Liquidity Hoarding; Liability-Side Liquidity Hoarding; Gulf Cooperation Council (search for similar items in EconPapers)
JEL-codes: G2 G21 G24 G28 (search for similar items in EconPapers)
Date: 2024
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