Bankruptcy prediction : static logit and discrete hazard models incorporating macoreconomic dependencies and industry effects

摘要

In this thesis, we present firm default prediction models based on firm financial statementsand macroeconomic variables. We seek to develop reliable models to forecast out-of-sampledefault probability, and we are particularly interested in exploring the impact ofincorporating macroeconomic variables and industry effects. To the best of our knowledge,this is the first study to account for both macroeconomic dependencies and industry effectsin one analysis. Additionally, we investigate the impact of the 2008 financial crisis onbankruptcies.We develop five models, one static logit model and four hazard models, and compare theout-of-sample predictive performance of these models. To explore the impact of industryeffects and the financial crisis, our study includes 562 U.S. public companies across allsectors (except financial) that filed for bankruptcy between 2003 and 2013. These werematched to a control group of non-bankrupt firms.We find that the cash flow, profitability, leverage, liquidity, solvency, and firm size are allsignificant determinants of bankruptcy. The ratio of cash flow from operations to totalliabilities, and total debt to total assets, are the most significant variables in the static logitmodel. In addition to these ratios, cash to total assets and net income to total assets arealso among the most important covariates in the hazard models. Next, we find that theforecasting results are improved by incorporating macroeconomic variables. Finally, we findthat the hazard model with macroeconomic variables and industry effects has the best outof-sampleaccuracy.