  Corporate credit ratings issued by third-party rating agencies are quantified
assessments of a company's creditworthiness. Credit Ratings highly correlate to
the likelihood of a company defaulting on its debt obligations. These ratings
play critical roles in investment decision-making as one of the key risk
factors. They are also central to the regulatory framework such as BASEL II in
calculating necessary capital for financial institutions. Being able to predict
rating changes will greatly benefit both investors and regulators alike. In
this paper, we consider the corporate credit rating migration early prediction
problem, which predicts the credit rating of an issuer will be upgraded,
unchanged, or downgraded after 12 months based on its latest financial
reporting information at the time. We investigate the effectiveness of
different standard machine learning algorithms and conclude these models
deliver inferior performance. As part of our contribution, we propose a new
Multi-task Envisioning Transformer-based Autoencoder (META) model to tackle
this challenging problem. META consists of Positional Encoding,
Transformer-based Autoencoder, and Multi-task Prediction to learn effective
representations for both migration prediction and rating prediction. This
enables META to better explore the historical data in the training stage for
one-year later prediction. Experimental results show that META outperforms all
baseline models.
