exchange and industry fixed effects. Table 7 presents in column (1) the details of this estimation, and shows that all variables are significantly associated with net income deflated by total assets, and the adjusted R2 of the regression is 41%. The firm-year-specific residuals of this regression represent the part of net income that cannot be explained by the factors we include in the regression. To control for the underlying variability of sample firms' earnings process, we compare the variability of net income to the variability of the underlying OCF for both the segment and non-segment firms in the pre- vs. post-merger periods. We expect that for a given level of volatility in OCF, higher volatility of net income indicates that it has been subject to less earnings management and is therefore of higher quality. We measure the variability of net income as the standard deviation of the residuals from Eq. (3), and the variability of OCF as residual standard deviation from the following regression: OCF_Assets = + + Growth + + + 0 1Size 2Sales 3Leverage 4Turn 5Audit + + FExchanges + + + (Eq. 6USCROSSLISTED 7# kExchange kIndustry it 4) estimated at the firm-year level, where firm and year subscripts are suppressed. Table 7 presents in column (2) the details of this estimation, and shows that all variables except Auditor and the number of foreign exchange listings are associated with OCF deflated by total assets, and the adjusted R2 is 11%. [Insert Table 7 About Here] We also compare the Spearman correlation between accruals and OCF pre- and post-merger. We expect firms with higher accounting quality to have less negative correlation between accruals and OCF, because less earnings smoothing means that fewer discretionary accruals have to be reversed in the following year. Following Barth et al. (2007), we compute the correlation of accruals and OCF as the correlation of firm-year-specific residuals from Eq. (4) and those from the following accruals regression: Accruals_Assets = 0 + 1Size + 2Sales Growth + 3Leverage + 4Turn +
exchange and industry fixed effects. Table 7 presents in column (1) the details of this estimation, and shows that all variables are significantly associated with net income deflated by total assets, and the adjusted R2 of the regression is 41%. The firm-year-specific residuals of this regression represent the part of net income that cannot be explained by the factors we include in the regression. To control for the underlying variability of sample firms' earnings process, we compare the variability of net income to the variability of the underlying OCF for both the segment and non-segment firms in the pre- vs. post-merger periods. We expect that for a given level of volatility in OCF, higher volatility of net income indicates that it has been subject to less earnings management and is therefore of higher quality. We measure the variability of net income as the standard deviation of the residuals from Eq. (3), and the variability of OCF as residual standard deviation from the following regression: OCF_Assets = 0 + 1Size + 2Sales Growth + 3Leverage + 4Turn + 5Audit + 6USCROSSLISTED + 7# FExchanges + kExchange + kIndustry + it (Eq. 4) estimated at the firm-year level, where firm and year subscripts are suppressed. Table 7 presents in column (2) the details of this estimation, and shows that all variables except Auditor and the number of foreign exchange listings are associated with OCF deflated by total assets, and the adjusted R2 is 11%. [Insert Table 7 About Here] We also compare the Spearman correlation between accruals and OCF pre- and post-merger. We expect firms with higher accounting quality to have less negative correlation between accruals and OCF, because less earnings smoothing means that fewer discretionary accruals have to be reversed in the following year. Following Barth et al. (2007), we compute the correlation of accruals and OCF as the correlation of firm-year-specific residuals from Eq. (4) and those from the following accruals regression: Accruals_Assets = 0 + 1Size + 2Sales Growth + 3Leverage + 4Turn +