Can Corporate Governance Variables Enhance the Prediction Power of Accounting-Based Financial Distress Prediction Models?
Author InfoLee, Tsun-Siou Yeh, Yin-Hua Liu, Rong-Tze Abstract We integrated accounting, corporate governance, and macroeconomic variables to build up a binary logistic regression model for the prediction of financially distressed firms. Debt ratio and ROA are found to be the most explanatory accounting variables while the percentage of directors controlled by the largest shareholder (which measures negative entrenchment effect), management participation, and the percentage of shares pledged for loans by large shareholders are shown to have positive contribution to the probability of financial distress. For macroeconomic sensitivities, firms with higher sensitivities to the annualized growth rates of manufacturing production index and money supply (M2) are more vulnerable to financial distress. As to the issue of sampling technique, we find that oversampling of distressed firms is subject to the problem of choice-based sample bias pointed out by Zmijewski (1984). The classification accu
