Performance evaluation using the CAMELS model: A comparative study of local commercial banks in Qatar and Kuwait

  • 477 Views
  • 149 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Nowadays, the banking system is undergoing significant changes. Digitalization that appears in Industry 4.0 also pioneers in the banking system, so we can also talk about Bank 4.0 as a new development direction. In this shift in the digital age, it becomes even more critical to examine the performance of banks. The case study approach was based on an attempt to diagnose the performance of a sample of local commercial banks in Qatar and Kuwait based on their financial statements for the period 2013–2017, and approve the existing accounting data as sources for the financial analysis process, by using essential financial analysis tools such as financial ratios. The output of the analysis was used to measure performance. All this is applicable when using the CAMELS rating model to evaluate the financial performance of the banking sector. The results show statistically significant differences between countries for four factors (Asset quality, Management efficiency, Earnings quality and Sensitivity) and none for the remaining two (Capital adequacy and Liquidity management) because the significant level is higher than 5%. However, the two factors with no significant differences are vital to the prudent operation of banks, mainly that Qatari banks perform better than Kuwaiti banks.

view full abstract hide full abstract
    • Table 1. Sample of the selected banks ‎
    • Table 2. CAMELS factors and measurement ratios
    • Table 3. Average ratios of years and rankings in the case of the Capital adequacy factor in selected banks
    • Table 4. Average ratios of years and rankings in the case of the Asset quality factor at selected banks
    • Table 5. Average ratios of years and rankings in the case of the Management efficiency factor at selected banks
    • Table 6. Average ratios of years and rankings in the case of the Earnings quality factor at selected banks
    • Table 7. Average ratios of years and rankings in the case of the Liquidity management factor at selected banks
    • Table 8. Average ratios of years and rankings in the case of the Sensitivity factor at selected banks
    • Table 9. The overall ranking of the selected banks from Qatar and Kuwait for the period 2013–2017
    • Table 10. The result of multivariate analysis of variance comparing the banks’ attributes in two countries
    • Conceptualization
      Rawan Abuzarqa, Tibor Tarnóczi
    • Data curation
      Rawan Abuzarqa
    • Formal Analysis
      Rawan Abuzarqa, Tibor Tarnóczi
    • Investigation
      Rawan Abuzarqa
    • Methodology
      Rawan Abuzarqa
    • Funding acquisition
      Rawan Abuzarqa
    • Project administration
      Rawan Abuzarqa, Tibor Tarnóczi
    • Resources
      Rawan Abuzarqa
    • Validation
      Rawan Abuzarqa
    • Visualization
      Rawan Abuzarqa, Tibor Tarnóczi
    • Writing – original draft
      Rawan Abuzarqa
    • Supervision
      Tibor Tarnóczi
    • Writing – review & editing
      Tibor Tarnóczi