Corporate governance dynamics in financial institution performance: A panel data analysis

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The study aims to identify the effect of corporate governance factors on financial institution performance in Bangladesh. This study employs annual data for 20 financial institutions, including banks, NBFIs, and insurance companies, data is collected from 2011 to 2022. Here, three corporate governance indicators are utilized – board size, board independence, and director’s ownership. The performance of the financial institutions is measured using return on assets (ROA), return on equity (ROE), and net asset value (NAV). Apart from the corporate governance variables, three company-specific factors, i.e., firm age, financial leverage, and firm size, are used as the control variables. Panel data analysis is conducted through the dynamic Feasible Generalize Least Square (FGLS) method, and the robustness is performed using the random effect model. The results show that corporate governance parameter such as board size has a significant positive influence on financial institution performance in Bangladesh, where board independence and director ownership do not have a significant influence on the performance of financial institutions. Thus, the performance of financial institutions increases when board size increases. This indicates that board members are actively engaged in strategic decision-making and ensure the rights of all stakeholders, which helps improve financial institutions’ overall performance. Therefore, financial institutions may increase their board size to the maximum level to ensure better corporate governance practices in the organizations, which ultimately increases performance.

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    • Table 1. Variable description
    • Table 2. Descriptive statistics
    • Table 3. Correlation matrix
    • Table 4. Regression results of impact of corporate governance on financial institutions’ performance (feasible generalized least squares (FGLS) regression model)
    • Table 5. Regression results of impact of corporate governance on financial institutions’ performance (random effects model regression)
    • Conceptualization
      Shaikh Masrick Hasan, Tawfiq Taleb Tawfiq, K. M. Anwarul Islam
    • Formal Analysis
      Shaikh Masrick Hasan, Md. Mahedi Hasan
    • Investigation
      Shaikh Masrick Hasan, Tawfiq Taleb Tawfiq, Md. Mahedi Hasan, K. M. Anwarul Islam
    • Methodology
      Shaikh Masrick Hasan, Md. Mahedi Hasan
    • Project administration
      Shaikh Masrick Hasan, Md. Mahedi Hasan
    • Resources
      Shaikh Masrick Hasan, Tawfiq Taleb Tawfiq, Md. Mahedi Hasan, K. M. Anwarul Islam
    • Software
      Shaikh Masrick Hasan
    • Supervision
      Shaikh Masrick Hasan
    • Validation
      Shaikh Masrick Hasan, Md. Mahedi Hasan
    • Visualization
      Shaikh Masrick Hasan, Md. Mahedi Hasan
    • Writing – original draft
      Shaikh Masrick Hasan, Tawfiq Taleb Tawfiq, Md. Mahedi Hasan, K. M. Anwarul Islam
    • Writing – review & editing
      Shaikh Masrick Hasan, Tawfiq Taleb Tawfiq, Md. Mahedi Hasan, K. M. Anwarul Islam
    • Funding acquisition
      Tawfiq Taleb Tawfiq
    • Data curation
      Md. Mahedi Hasan