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Predicting bankruptcy of firms using earnings call data and transfer learning
Business collapse is a common event in economies, small and big alike. A firm’s health is crucial to its stakeholders like creditors, investors, partners, etc. and prediction of the upcoming financial crisis is significantly important to devise appropriate strategies to avoid business collapses. Ban...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280182/ https://www.ncbi.nlm.nih.gov/pubmed/37346732 http://dx.doi.org/10.7717/peerj-cs.1134 |
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author | Siddiqui, Hafeez Ur Rehman de Abajo, Beatriz Sainz Díez, Isabel de la Torre Rustam, Furqan Raza, Amjad Atta, Sajjad Ashraf, Imran |
author_facet | Siddiqui, Hafeez Ur Rehman de Abajo, Beatriz Sainz Díez, Isabel de la Torre Rustam, Furqan Raza, Amjad Atta, Sajjad Ashraf, Imran |
author_sort | Siddiqui, Hafeez Ur Rehman |
collection | PubMed |
description | Business collapse is a common event in economies, small and big alike. A firm’s health is crucial to its stakeholders like creditors, investors, partners, etc. and prediction of the upcoming financial crisis is significantly important to devise appropriate strategies to avoid business collapses. Bankruptcy prediction has been regarded as a critical topic in the world of accounting and finance. Methodologies and strategies have been investigated in the research domain for predicting company bankruptcy more promptly and accurately. Conventionally, predicting the financial risk and bankruptcy has been solely achieved using the historic financial data. CEOs also communicate verbally via press releases and voice characteristics, such as emotion and tone may reflect a company’s success, according to anecdotal evidence. Companies’ publicly available earning calls data is one of the main sources of information to understand how businesses are doing and what are expectations for the next quarters. An earnings call is a conference call between the management of a company and the media. During the call, management offers an overview of recent performance and provides a guide for the next quarter’s expectations. The earning calls summary provided by the management can extract CEO’s emotions using sentiment analysis. This article investigates the prediction of firms’ health in terms of bankruptcy and non-bankruptcy based on emotions extracted from earning calls and proposes a deep learning model in this regard. Features extracted from long short-term memory (LSTM) network are used to train machine learning models. Results show that the models provide results with a high score of 0.93, each for accuracy and F1 when trained on LSTM extracted feature from synthetic minority oversampling technique (SMOTE) balanced data. LSTM features provide better performance than traditional bag of words and TF-IDF features. |
format | Online Article Text |
id | pubmed-10280182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102801822023-06-21 Predicting bankruptcy of firms using earnings call data and transfer learning Siddiqui, Hafeez Ur Rehman de Abajo, Beatriz Sainz Díez, Isabel de la Torre Rustam, Furqan Raza, Amjad Atta, Sajjad Ashraf, Imran PeerJ Comput Sci Artificial Intelligence Business collapse is a common event in economies, small and big alike. A firm’s health is crucial to its stakeholders like creditors, investors, partners, etc. and prediction of the upcoming financial crisis is significantly important to devise appropriate strategies to avoid business collapses. Bankruptcy prediction has been regarded as a critical topic in the world of accounting and finance. Methodologies and strategies have been investigated in the research domain for predicting company bankruptcy more promptly and accurately. Conventionally, predicting the financial risk and bankruptcy has been solely achieved using the historic financial data. CEOs also communicate verbally via press releases and voice characteristics, such as emotion and tone may reflect a company’s success, according to anecdotal evidence. Companies’ publicly available earning calls data is one of the main sources of information to understand how businesses are doing and what are expectations for the next quarters. An earnings call is a conference call between the management of a company and the media. During the call, management offers an overview of recent performance and provides a guide for the next quarter’s expectations. The earning calls summary provided by the management can extract CEO’s emotions using sentiment analysis. This article investigates the prediction of firms’ health in terms of bankruptcy and non-bankruptcy based on emotions extracted from earning calls and proposes a deep learning model in this regard. Features extracted from long short-term memory (LSTM) network are used to train machine learning models. Results show that the models provide results with a high score of 0.93, each for accuracy and F1 when trained on LSTM extracted feature from synthetic minority oversampling technique (SMOTE) balanced data. LSTM features provide better performance than traditional bag of words and TF-IDF features. PeerJ Inc. 2023-01-04 /pmc/articles/PMC10280182/ /pubmed/37346732 http://dx.doi.org/10.7717/peerj-cs.1134 Text en ©2022 Siddiqui et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Siddiqui, Hafeez Ur Rehman de Abajo, Beatriz Sainz Díez, Isabel de la Torre Rustam, Furqan Raza, Amjad Atta, Sajjad Ashraf, Imran Predicting bankruptcy of firms using earnings call data and transfer learning |
title | Predicting bankruptcy of firms using earnings call data and transfer learning |
title_full | Predicting bankruptcy of firms using earnings call data and transfer learning |
title_fullStr | Predicting bankruptcy of firms using earnings call data and transfer learning |
title_full_unstemmed | Predicting bankruptcy of firms using earnings call data and transfer learning |
title_short | Predicting bankruptcy of firms using earnings call data and transfer learning |
title_sort | predicting bankruptcy of firms using earnings call data and transfer learning |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280182/ https://www.ncbi.nlm.nih.gov/pubmed/37346732 http://dx.doi.org/10.7717/peerj-cs.1134 |
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