The impact of CEO overconfidence on discretionary deferred tax assets: Evidence from Korea

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This study examines the relationship between CEO overconfidence and discretionary deferred tax assets (DTAs) using a method that separates DTAs into discretionary and non-discretionary components. Based on data from publicly listed companies in South Korea between 2017 and 2021, the study analyzes how overconfident CEOs influence the recognition of DTAs. Under K-IFRS 1012, DTAs should be recognized only when there is sufficient future taxable income with a high probability; however, the lack of explicit guidelines on what constitutes “high probability” leaves room for subjective interpretation by management. Overconfident CEOs, driven by excessive optimism and upward-biased forecasts of future cash flows, may over-recognize DTAs. The main analysis, incorporating industry and year-fixed effects, reveals a positive relationship between CEO overconfidence and discretionary DTAs (coef = 0.003, p-value < 0.05). This tendency is more pronounced in firms with higher marginal tax rates compared to those with lower rates (coef = 0.004, p-value < 0.005) and in firms with lower levels of outside directors (coef = 0.002, p-value < 0.1). Additionally, analyses using alternative variables for CEO overconfidence and discretionary DTAs, as well as propensity score matching (PSM) models, yield consistent results. Overall, this study underscores the critical role of managerial characteristics in shaping accounting judgments and decisions. By providing empirical evidence on the impact of cognitive biases such as overconfidence on financial reporting quality, the findings contribute to the broader discourse on corporate governance and offer practical implications for policymakers, investors, and regulators.

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    • Table 1. Sample selection process
    • Table 2. Descriptive statistics (N = 5,734)
    • Table 3. Pearson correlation (N = 5,734)
    • Table 4. CEO overconfidence and discretionary deferred tax assets
    • Table 5. Cross-sectional test
    • Table 6. Alternative proxies
    • Table 7. Propensity score matching
    • Conceptualization
      Sun-ae Cho, Seul Gi Oh, Grace Goun Kim
    • Investigation
      Sun-ae Cho, Seul Gi Oh, Grace Goun Kim
    • Methodology
      Sun-ae Cho, Seul Gi Oh, Grace Goun Kim
    • Project administration
      Sun-ae Cho
    • Visualization
      Sun-ae Cho
    • Writing – original draft
      Sun-ae Cho, Grace Goun Kim
    • Writing – review & editing
      Sun-ae Cho, Seul Gi Oh, Grace Goun Kim
    • Data curation
      Seul Gi Oh, Grace Goun Kim
    • Formal Analysis
      Seul Gi Oh, Grace Goun Kim
    • Supervision
      Seul Gi Oh
    • Validation
      Seul Gi Oh
    • Software
      Grace Goun Kim