Economic value added: The best indicator for measuring value creation or just an illusion?

  • Received December 27, 2022;
    Accepted February 1, 2023;
    Published February 14, 2023
  • Author(s)
    Anouar Faiteh ,
    Mohammed Rachid Aasri
    ORCID
  • DOI
    http://dx.doi.org/10.21511/imfi.20(1).2023.13
  • Article Info
    Volume 20 2023, Issue #1, pp. 138-150
  • TO CITE АНОТАЦІЯ
  • Cited by
    7 articles
  • 881 Views
  • 367 Downloads

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

Value creation has become a very important concept in finance. To this end, value creation metrics, like market value added and economic value added have raised the question of their superiority and ability to reflect the true value of organizations, as opposed to the classic accounting indicators like ROE, ROA and EPS. Nevertheless, EVA can only be calculated for listed companies, which makes it difficult to use this indicator to measure value creation for non-listed companies. In this way, some alternatives have been used such as the accounting beta to calculate the return on equity and subsequently the determination of the EVA. Within this framework, the central point of this research is to empirically verify the idea that the normal EVA and EVA calculated using accounting beta are the better measure than traditional indicators to explain MVA. A panel of 32 companies traded on the Casablanca Stock Exchange over the period 2015–2019 was selected for this study. The regression method on panel data was used. The results show that normal EVA is a superior metric than the classical indicators to explain MVA. In addition, the EVA calculated from the accounting beta could be used as a measure adapted to the case of unlisted companies to measure value creation.

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    • Table 1. Variables
    • Table 2. Descriptive statistics
    • Table 3. Correlation study
    • Table 4. Multicollinearity test
    • Table 5. Specification test
    • Table 6. Hausman test
    • Table 7. Regression analysis
    • Table 8. Skewness and kurtosis test result
    • Table 9. Wooldridge test result
    • Table 10. Wald test result
    • Table 11. Corrected regression for heteroscedasticity and autocorrelation
    • Conceptualization
      Anouar Faiteh, Mohammed Rachid Aasri
    • Data curation
      Anouar Faiteh
    • Formal Analysis
      Anouar Faiteh, Mohammed Rachid Aasri
    • Funding acquisition
      Anouar Faiteh
    • Investigation
      Anouar Faiteh
    • Methodology
      Anouar Faiteh, Mohammed Rachid Aasri
    • Project administration
      Anouar Faiteh, Mohammed Rachid Aasri
    • Resources
      Anouar Faiteh
    • Software
      Anouar Faiteh
    • Writing – original draft
      Anouar Faiteh
    • Supervision
      Mohammed Rachid Aasri
    • Validation
      Mohammed Rachid Aasri
    • Visualization
      Mohammed Rachid Aasri
    • Writing – review & editing
      Mohammed Rachid Aasri
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