Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction

TL;DR

This paper introduces a Bayesian expert framework for bankruptcy prediction that explicitly incorporates expert judgment, quantifies uncertainty, and demonstrates superior or comparable performance with lower false positive rates on real data.

Contribution

It presents a novel generative Bayesian modeling approach that includes expert knowledge, enhancing interpretability and uncertainty quantification in bankruptcy prediction.

Findings

Model achieves comparable or better accuracy than existing methods.

Proposed model has significantly lower false positive rate.

Framework is flexible, interpretable, and suitable for high-stakes applications.

Abstract

In recent years, bankruptcy forecasting has gained lot of attention from researchers as well as practitioners in the field of financial risk management. For bankruptcy prediction, various approaches proposed in the past and currently in practice relies on accounting ratios and using statistical modeling or machine learning methods. These models have had varying degrees of successes. Models such as Linear Discriminant Analysis or Artificial Neural Network employ discriminative classification techniques. They lack explicit provision to include prior expert knowledge. In this paper, we propose another route of generative modeling using Expert Bayesian framework. The biggest advantage of the proposed framework is an explicit inclusion of expert judgment in the modeling process. Also the proposed methodology provides a way to quantify uncertainty in prediction. As a result the model built using Bayesian framework is highly flexible, interpretable and intuitive in nature. The proposed approach is well suited for highly regulated or safety critical applications such as in finance or in medical diagnosis. In such cases accuracy in the prediction is not the only concern for decision makers. Decision makers and other stakeholders are also interested in uncertainty in the prediction as well as interpretability of the model. We empirically demonstrate these benefits of proposed framework on real world dataset using Stan, a probabilistic programming language. We found that the proposed model is either comparable or superior to the other existing methods. Also resulting model has much less False Positive Rate compared to many existing state of the art methods. The corresponding R code for the experiments is available at Github repository.