8. Using Public Information to Predict Corporate Default Risk

Corporate defaults are often affected by many factors that are roughly divided into the two types: internal factors and external factors. Internal factors can be measured precisely with firm-specific financial statistics while external factors contain qualitative data, like related news. There are large amount of timely information from news which affects the default probability of corporates. Efficient extraction information contained in the news is the main focus of this study and we propose to use empirical Bayes and Bayesian Networks to achieve this goal. First, we retrieve both macroeconomic and firm-specific news published by major newspapers in Taiwan. Then, word segmentation is applied, keywords are extracted and then the news variables are computed. Instead of adding the news variables to the logistic regression model, we convert them into prior distribution for the parameters in the corporate default model. Finally, we compute the posterior distribution of the model parameters to predict the corporate default. The estimation is performed using the integrated nested Laplace approximations which, to our belief, is better than the traditional Markov Chain Monte Carlo for our model. Empirical analysis using Taiwanese data finds that news has a significant impact on the corporate default rate prediction. Adding the news variable does improve the forecast precision and prove its usefulness.