What Determines The Probability Of Default?
Real Estate / Trading and Investing
Today we are taking a look at one of our favorite machine learning minds, Professor Arka Bandyopadhyay of the University of Miami! In his research, Professor Bandyopadhyay is able to; “identify the determinants of default and the incentives for strategic default using Deep Neural Network (DNN) methodology on proprietary Trepp panel data of commercial mortgages from 1998-2021.”
Moreover, an important period of time as we have a number of important global financial events. We have Covid and the Great Financial Crisis included in the time period as well as the internet crash.
Furthermore, the good professor finds that; “Net Operating Income (NOI), appraisal reduction amount, prepayment penalty clause, balloon payment amongst others co-determine the delinquency behavior in highly nonlinear ways compared to more statistically significant variables such as LTV.”
Lastly, Professor Bandyopadhyay finds that; “DNN( Deep Neural Network ) results are the most stable even among non-parametric machine learning models.”
Read the professor’s full paper at SSRN:
Deep Learning for Determinants of Default by Arka Bandyopadhyay :: SSRN
For further coverage on Professor Arka Bandyopadhyay, see our piece: What are the practical applications of reinforcement learning?
What Determines The Probability Of Default?
