/FINTECH_ANALYTICS_ACPM_2016

Fintech Analytics probability of default model for ACPM Fall 2016 INTA-GB.2320.10

Primary LanguageR

Credit Default Modeling Algorithm

This code was based on a term long project for Fintech Analytics with Professor Roger M. Stein. The model estimates the probability of default for 16,509 unique US banks between 1993 and 2016. You can view the presentation for results and summaries.

Methods used within the code:

  • Winterization transformation of key variables
  • Logistic Regression Modeling
  • Bayesian Priors Calibration
  • Bootstrap Testing
  • Walk-forward Testing

Scripting Languages and Libraries Used

  • R Studio
  • GGplot2: Great tool for data visualization and clean charts
  • dplyr: For dealing with data
  • lubridate: Dealing with dates and stepping forward or back by quarters

Instructions

I will need to update with instructions on how to fully use all of the functions but they are well defined in the beginning of the code.

Questions

Don't hesitate to reach out. You can find my twitter here for any suggestions! You can also reach out via linkedin

Data

Dataset will be uploaded shortly.