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        <title>Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines</title>
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            <title>Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)</title>
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            <namePart type="given">Chung-Chi</namePart>
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            <namePart type="given">Hen-Hsen</namePart>
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            <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines
%A Arno, Henri
%A Mulier, Klaas
%A Baeck, Joke
%A Demeester, Thomas
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F arno-etal-2022-next
%X 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.
%R 10.18653/v1/2022.finnlp-1.25
%U https://aclanthology.org/2022.finnlp-1.25
%U https://doi.org/10.18653/v1/2022.finnlp-1.25
%P 187-195

Markdown (Informal)

[Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines](https://aclanthology.org/2022.finnlp-1.25) (Arno et al., FinNLP 2022)

• Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines (Arno et al., FinNLP 2022)

ACL

• Henri Arno, Klaas Mulier, Joke Baeck, and Thomas Demeester. 2022. Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 187–195, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.