Development of a supervised and transparent prediction model for predicting bond credit rating migrations in real-time for short to moderate time frame

The purpose of this research is to develop a supervised prediction model that will be used to predict whether a bond credit rating will migrate down to a lower credit rating in a short to moderate time frame. The problem will consist of the development and optimization of a bond credit-rating migration prediction model; the development of a framework that gives the model transparency and explain-ability lending weight to the credibility of the results; and the integration of the credit migration prediction into a portfolio allocation optimization for bond portfolios.

The model will provide insight into the short-term creditworthiness of corporate bonds that will be used to develop a platform for which the probability of credit rating migration will be available to a broad network of financial professionals in real-time. This can play a significant role in reducing the losses due to an un-expected bond credit rating downgrade.

Faculty Supervisor:

Roy H Kwon

Student:

Vaughn Edward Gambeta

Partner:

Migrations.ml

Discipline:

Engineering - mechanical

Sector:

University:

University of Toronto

Program: