Narrow Results

This paper examines the association between CEO compensation and tangible long-lived assets impairment. We find that the level of CEO compensation is negatively associated with the tangible long-lived assets impairment charges. We also document that in firms with CEOs who have more decision-making power, the negative association between CEO compensation and tangible long-lived assets impairment charges is mitigated. Specifically, the negative association between CEO compensation and tangible long-lived assets impairment charges is less pronounced (1) when CEO chairs the board, (2) when CEO is the founder of the firm, (3) when the CEO is involved in the director selection process, and (4) when overall board independence is low.

This study develops a diagnosing model to examine the outcomes of assets write-off in enriching the literatures of assets impairment. Prior studies employed the Logit, linear and Tobit regression models to classify the determination of assets impairment and to diagnose the magnitude of the impairment, respectively. However, the drivers of assets write-off are somewhat complicated explicitly or implicitly, these models are unlikely to provide fairly satisfactory results. To improve the diagnosis, the Random Forests model is used for the classification determining and the magnitude diagnosing of assets impairment in this study. The result reveals that the Random Forests model outperforms the Logit and linear regression models in each case with variables selected by individual wrapping approach. This study also demonstrates diagnostic checks for both models with similar selected variables. The results are robust to these various specifications.