# Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines

arXiv: https://arxiv.org/abs/2208.11334 · HTML: https://mlsanity.com/paper/2208.11334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [EDGAR-CORPUS: Billions of Tokens Make The World Go Round](https://mlsanity.com/paper/2109.14394/) (2021)
- [Predicting Distresses using Deep Learning of Text Segments in Annual Reports](https://mlsanity.com/paper/1811.05270/) (2018)

## Built on by (most cited first, 1 of 1)

- [News Signals: An NLP Library for Text and Time Series](https://mlsanity.com/paper/2312.11399/) (2023)

## Abstract (arXiv)

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.