Paper: arXiv 2212.03454

Authors: Harrison Mateika, Juannan Jia, Linda Lillard, Noah Cronbaugh, Will Shin

Abstract

The primary aim of this research was to find a model that best predicts which fallen angel bonds would either potentially rise up back to investment grade bonds and which ones would fall into bankruptcy. To implement the solution, we thought that the ideal method would be to create an optimal machine learning model that could predict bankruptcies. Among the many machine learning models out there we decided to pick four classification methods: logistic regression, KNN, SVM, and NN. We also utilized an automated methods of Google Cloud’s machine learning. The results of our model comparisons showed that the models did not predict bankruptcies very well on the original data set with the exception of Google Cloud’s machine learning having a high precision score. However, our over-sampled and feature selection data set did perform very well. This could likely be due to the model being over-fitted to match the narrative of the over-sampled data (as in, it does not accurately predict data outside of this data set quite well). Therefore, we were not able to create a model that we are confident that would predict bankruptcies. However, we were able to find value out of this project in two key ways. The first is that Google Cloud’s machine learning model in every metric and in every data set either outperformed or performed on par with the other models. The second is that we found that utilizing feature selection did not reduce predictive power that much. This means that we can reduce the amount of data to collect for future experimentation regarding predicting bankruptcies.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper applies standard off-the-shelf machine learning techniques and AutoML to fallen angel corporate bond defaults without presenting deep mathematical innovation. The empirical rigor suffers from issues with over-fitting on over-sampled data, and the authors ultimately fail to build a model with strong out-of-sample predictive confidence.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict Fallen Angel Bond<br/>Bankruptcy & Rising Potential] --> B[Data Inputs: Original vs.<br/>Oversampled & Feature Selected]
    
    B --> C[Manual Classification Models<br/>Logistic Regression, KNN, SVM, NN]
    B --> D[Automated Machine Learning<br/>Google Cloud AutoML]
    
    C --> E[Model Evaluation & Comparison]
    D --> E
    
    E --> F[Outcome 1: Low Confidence in Bankruptcy Prediction<br/>Oversampled data showed over-fitting]
    E --> G[Outcome 2: Google Cloud AutoML<br/>Outperformed or matched manual models]
    E --> H[Outcome 3: Feature Selection Maintained Power<br/>Reduces required data for future research]