Zeta™ methodology and variation in the systemic risk of default: Accounting for the effects of Type II (false negative) errors variation on lending

This paper argues that a standard Bayesian transformation of the ZETA bankruptcy prediction methodology, introduced by Altman in 1968-1977, allows for an a posteriori update of conditional Type I and II errors due to variation in the systemic likelihood of default. The Bayesian transformation can be used both to condition a loan manager’s prior decision (generally based on Basel II-compliant Internal Rating Based system or Credit Agency’s Rating) and to update such a decision on the basis of any posterior hypothesis (based on actuarial frequentist assumptions of conditional hazard rates) regarding the creditworthiness and the probability of default of an underlying pool of securities. At the same time – under a Bayesian framework the ZETA diagnostic test can be conditioned on the new evidence introduced by other tests to increase the total sensitivity of the default prediction models (IRB ratings, TTC ratings, logit, probit, neural) to update the commercial bank’s lending decisions. A ground-state, static meta-analysis of Altman’s et al. ZETA original article (1977) reveals that the odds of the commercial bank detecting a default after the ZETA score has been introduced (posttest) is 13.2 times more effective than the a priori prediction. Under the same assumptions, the odds of the commercial bank detecting a survival after (post-test) the ZETA score has been introduced is 12.2 times more effective than the a priori.