Zhang, Jin (2006-12). Three essays on business failure: causality and prediction. Doctoral Dissertation.
This dissertation investigates three issues on business failure causality and prediction. First, a nonlinear model for mathematical programming based discriminant analysis is studied. This study proposes a nonlinear model that builds on the existing linear and quadratic models and allows for a more flexible degree of nonlinearity through a set of power parameters. The proposed nonlinear model is solved using a genetic algorithm and is tested against linear and quadratic models using real financial data. The results show that each model is better in certain cases, but the nonlinear model turns out to be the best overall among the three. Better performance of this nonlinear model appears likely, but a more robust solver would be required. Second, the relationship between aggregate business failures and macroeconomic conditions is studied from a causality perspective. A structural Vector Autoregression (VAR) is used while incorporating the recently developed causal inference method Directed Acyclic Graph (DAG). Particularly, DAG is used to provide a contemporaneous causal structure and the VAR results are summarized using innovation accounting techniques. The results show that during the period from 1980 to 2004 in the U.S., aggregate business failures were influenced by interest rates, but overall these failures appear to be far more exogenous than was found previously. Third, the effect of incorporating macroeconomic variables into business failure prediction models is investigated with a focus on the U.S. airline industry from 1995 to 2005. The attention is placed on prediction accuracy, parameter stability, and the effect of particular macroeconomic variables. The results show that the stability of parameters in the prediction model is improved when macro variables are added. In terms of prediction accuracy, the model augmented with a macro variable performed better in a jackknife prediction, but not in out-of-sample predictions. The macroeconomic variable found to be significant is the change of interest rate, which is probably related to the high level of leverage common in this particular industry. Also, the results demonstrate that a probability score can be used as a more informative evaluation measure than the current one based on cutoff probabilities.