# Composition of bankruptcy probability and firm size

I'm using neural network for a binary classification problem of bankruptcy prediction using patternnet function in MATLAB, so i have probability of bankruptcy for out sample (final report). I'm searching for a method to combine probability of bankruptcy and size ( for example total assets) of companies. I want have biggest companies with biggest bankruptcy probability so i need a hybrid method to combine these features. (the probability is probability of being in bankrupt class for a specific firm). How can i do that?

• What is your target variable in your training data? Do you have a set of companies, for which you know, whether they went bankrupt or not, or you are trying to model expert judgement, that larger companies are more likely to go bankrupt? In the first case you would have discrete target variable, e.g. 0=non bankrupt, 1=bankrupt. In the second case, you would construct the target variable yourself, e.g. for company A the probability that it WILL go bankrupt is 0.7 (whereas it is not known from the data, whether it is bankrupt). Which case are you dealing with? – inzl Aug 28 '14 at 13:16
• Thank you for your comment. My input variables are financial ratios and variables and my target variable is bankrupt (1), otherwise (0). After training neural network (or SVM model) and inserting out sample data (new year data), now I can have probabilities of bankruptcy besides binary output ( 0 and 1). Now i want a hybrid method to sort data. I don't want only sorting based on size of firm or probability. Suppose that you want help bankrupt companies. You want know relatively biggest companies with higher probability. Someone recommended me to use PCA for this but i don't know how. – user2991243 Aug 28 '14 at 13:21

• As I mentioned in main question, if i want bigger companies based on log(total assets) besides higher probability of bankruptcy, what should i do? – user2991243 Aug 28 '14 at 16:00