IFRS and stock exchange development in sub-Saharan Africa: a logistic model

  • Received August 24, 2020;
    Accepted September 30, 2020;
    Published October 9, 2020
  • Author(s)
    Ochuko B. Emudainohwo
    ORCID
  • DOI
    http://dx.doi.org/10.21511/imfi.17(3).2020.30
  • Article Info
    Volume 17 2020, Issue #3, pp. 397-407
  • TO CITE АНОТАЦІЯ
  • Cited by
    2 articles
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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

This study examines the impact of International Financial Reporting Standards (IFRS) on the stock exchange development (SED) in sub-Saharan Africa (SSA). The essence is to offer suggestions on how the adoption of IFRS in the SSA region can benefit their SED. The study employed logistic regression analysis of data for 40 SSA countries for the period 2010–2018. Data were extracted from the World Bank’s World Development Index (WDI) database, sampled countries’ stock exchange websites, and the IFRS website. The dependent variable (SED) took two values: 1 – if a stock exchange is established in the observed country’s period, otherwise – 0. The model result was well fitted: p < 0.0001, correctly classified an overall SED accuracy up to 84.84% and excellent area predictive power at a receiver operator characteristic of 0.9347. The study observed that IFRS had high degree of co-movement with SED, and changes in IFRS had a strong positive impact on SED. Besides, changes in market size, ICT infrastructure, and public sector management and institution (PSMI) had a positive and significant impact on SED. The odd ratio of SED compared to non-SED is greatest with IFRS (40.67 times), and for the other variables, the ratios are: market size (4.02), ICT infrastructure (1.26), and PSMI (2.73), respectively. On a greater extent, SSA countries should allow the use of IFRS for financial reporting to expedite SED.

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    • Figure 1. ROC curve
    • Table 1. Variable descriptions
    • Table 2. SED description and the use of IFRS (2010–2018)
    • Table 3. Variables statistic (2010–2018)
    • Table 4. Correlation matrix
    • Table 5. Stock exchange fit test classification
    • Table 6. Regression results
    • Conceptualization
      Ochuko B. Emudainohwo
    • Data curation
      Ochuko B. Emudainohwo
    • Formal Analysis
      Ochuko B. Emudainohwo
    • Methodology
      Ochuko B. Emudainohwo
    • Resources
      Ochuko B. Emudainohwo
    • Validation
      Ochuko B. Emudainohwo
    • Writing – original draft
      Ochuko B. Emudainohwo
    • Writing – review & editing
      Ochuko B. Emudainohwo
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