Effect of board characteristics on real and accrual-based earnings management: Evidence from Vietnamese listed non-financial firms

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Type of the article: Research Article

Abstract
Financial reporting quality is important for maintaining investor confidence, but earnings management remains a persistent concern in developing markets where corporate governance mechanisms are still being strengthened. Vietnam offers a suitable context for this issue because listed firms operate in an environment marked by evolving governance practices, uneven disclosure quality, and concentrated ownership structures. This study examines whether board quality helps limit real earnings management (REM) and accrual-based earnings management (AEM) among Vietnamese non-financial listed firms. The dataset includes 3,697 firm-year observations for companies listed on the Ho Chi Minh Stock Exchange and the Hanoi Stock Exchange from 2017 to 2023. Board quality is captured by an unweighted Board Characteristics Index based on ten board-related attributes. AEM is proxied by performance-matched discretionary accruals, while REM is derived from abnormal cash flows from operations, abnormal production costs, and abnormal discretionary expenses. Panel regression models are estimated, and Feasible Generalized Least Squares (FGLS) is applied to address heteroskedasticity. The main results show that board quality is negatively and significantly related to REM, with a coefficient of –0.0701 and a z-value of –3.19 at the 1% level. In contrast, the relationship between board quality and AEM is negative but statistically insignificant, with a coefficient of –0.0194. These findings indicate that boards are better able to constrain earnings manipulation through operating activities than through accrual choices. For Vietnamese listed firms, stronger board monitoring over real business decisions may help improve the transparency of financial reporting.

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    • Table 1. Construction of the Board Characteristics Index
    • Table 2. Operational definition and measurement of research variables
    • Table 3. Descriptive statistics
    • Table 4. Correlation matrix and VIF values
    • Table 5. Preliminary regression estimates using OLS and panel data specifications
    • Table 6. Diagnostic tests and model selection
    • Table 7. Baseline FGLS results for the R_EM and KA models
    • Table A1. Robustness check by stock exchange
    • Table A2. Robustness check by industry
    • Conceptualization
      Cao Thi Nhan Anh
    • Data curation
      Cao Thi Nhan Anh
    • Formal Analysis
      Cao Thi Nhan Anh
    • Writing – original draft
      Cao Thi Nhan Anh
    • Writing – review & editing
      Cao Thi Nhan Anh