LAUSR

While financial statement analysis is a rich tool, there is no widely used holistic measure of the amount of change in corporate financial statements. Statistical decomposition analysis has been employed as an index of the amount of change, but has fallen into disuse because it does not allow negative accounting numbers. As a remedy, this paper suggests three distance measures adapted from cluster analysis that avoid this critical data limitation. We successfully apply these proposed distance measures to explain the total and systematic risk of stock returns (in the CAPM and Fama–French model), corporate bond ratings, and corporate distress.