Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines
This provides a standardized evaluation framework for researchers in finance and machine learning, though it is incremental as it focuses on benchmarking rather than novel model development.
The paper tackles the lack of a common benchmark for bankruptcy prediction using textual data by introducing a new benchmark and evaluating baseline models, finding that a lightweight bag-of-words model with static in-domain word representations performs well, especially with multi-year data.
Models for bankruptcy prediction are useful in several real-world scenarios, and multiple research contributions have been devoted to the task, based on structured (numerical) as well as unstructured (textual) data. However, the lack of a common benchmark dataset and evaluation strategy impedes the objective comparison between models. This paper introduces such a benchmark for the unstructured data scenario, based on novel and established datasets, in order to stimulate further research into the task. We describe and evaluate several classical and neural baseline models, and discuss benefits and flaws of different strategies. In particular, we find that a lightweight bag-of-words model based on static in-domain word representations obtains surprisingly good results, especially when taking textual data from several years into account. These results are critically assessed, and discussed in light of particular aspects of the data and the task. All code to replicate the data and experimental results will be released.