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We reintroduce an early theoretical distinction between measurement comparability and reporting comparability; we argue that extant comparability research has focused exclusively on measurement comparability. We develop an intuitive reporting comparability measure based on a firm’s non-missing COMPUSTAT data items that are common with its peer firms in the same industry. We demonstrate that our reporting comparability measure is positively associated with analyst coverage and analyst forecast accuracy, and negatively associated with analyst forecast dispersion, consistent with reporting comparability being related to information processing efficiencies. These associations are robust to controlling for measurement comparability. We partition our reporting comparability measure into separate income statement and balance sheet measures. We find that income statement comparability is associated with greater analyst coverage and lower analyst forecast dispersion, while balance sheet comparability is associated with better analyst forecast accuracy and lower dispersion. In partitioned analysis, we examine differential effects of income statement and balance sheet components of reporting comparability for firms with higher earnings volatility, and for firms with frequent losses.
Marcus L. Caylor, Kennesaw State University
Dennis J. Chambers, Kennesaw State University
Sunay Mutlu, Kennesaw State University