A model proposal for estimating banks’ future value: Evidence from Turkey

  • Received November 12, 2021;
    Accepted December 11, 2021;
    Published December 21, 2021
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  • Article Info
    Volume 16 2021 , Issue #4, pp. 169-178
  • Cited by
    2 articles

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

Investors make solid decisions when evaluating their investments based on positive indicators the firm may show in the future, rather than based on its past performance. Accordingly, this study aims to investigate the relationship between performance criteria and the most significant value-based criterion; Economic Value Added (EVA). Further, it evaluates the impact of future EVA values on the bank value. Panel Data Analysis and the OLS Regression model are used to estimate the regression equation. The analysis is performed using data of 10 banks on the BIST Banks Index over the period 2011 to 2020. Furthermore, the EVA criterion was converted into standardized EVA(SEVA) by dividing EVA by total assets. The OLS regression analysis results revealed that the model’s explanatory power for the SEVA variable is 71.92%. The three variables that have positive correlation with SEVA are earnings per share (EPS) and TOBINQ rates at the 1% significance level and the price to sales growth rate with a degree of significance at 10%. Regarding the Panel Data Analysis results, while the explanatory power of the SEVA variable is 72.14%, its association with the EPS and TOBINQ criteria was found to be significant at the 1% significance level. The empirical investigations reveal that the model developed using the future SEVA as a proxy for bank value is found to be promising, and it is accepted that the SEVA variable can be used instead of the bank value.

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    • Table 1. The Information of banks used in the analysis
    • Table 2. The definition of variables
    • Table 3. Descriptive statistics
    • Table 4. Correlation coefficients
    • Table 5. Diagnostic tests results
    • Table 6. OLS regression results
    • Table 7. Panel regression results
    • Conceptualization
      Burhan Günay, Ayten Turan Kurtaran, Sara Faedfar
    • Data curation
      Burhan Günay
    • Formal Analysis
      Burhan Günay, Sara Faedfar
    • Investigation
      Burhan Günay, Ayten Turan Kurtaran, Sara Faedfar
    • Methodology
      Burhan Günay, Ayten Turan Kurtaran
    • Software
      Burhan Günay
    • Validation
      Burhan Günay, Sara Faedfar
    • Funding acquisition
      Ayten Turan Kurtaran, Sara Faedfar
    • Project administration
      Ayten Turan Kurtaran
    • Resources
      Ayten Turan Kurtaran, Sara Faedfar
    • Supervision
      Ayten Turan Kurtaran
    • Visualization
      Ayten Turan Kurtaran
    • Writing – review & editing
      Ayten Turan Kurtaran, Sara Faedfar
    • Writing – original draft
      Sara Faedfar