A Study on Machine Learning Prediction Model for Company Bankruptcy Using Features in Time Series Financial Data

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Akira Otsuki
Akira Otsuki
2
Shohei Narumi
Shohei Narumi
3
Masayoshi Kawamura
Masayoshi Kawamura

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Based on such methods as a discriminant analysis and logistic regression, corporate bankruptcy prediction models have been developed as a means to determine the soundness of a company’s operational status based on its financial statements. However, such analytical methods work with binary variables, and thus, as the only outcome of machine learning, the company in question is considered either likely or unlikely to go bankrupt. However, this is insufficient for business operators who would need to know the possible risk factors of a bankruptcy, allowing them to plan and implement measures to avoid any misfortunes. We have therefore developed a prediction model that not only predicts but also identifies the financial variables that can possibly drive the company to bankruptcy.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Akira Otsuki. 2026. \u201cA Study on Machine Learning Prediction Model for Company Bankruptcy Using Features in Time Series Financial Data\u201d. Global Journal of Management and Business Research - A: Administration & Management GJMBR-A Volume 22 (GJMBR Volume 22 Issue A1): .

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Extensive research on machine learning models enhancing financial decision-making and banking stability.
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Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

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GJMBR-A Classification: JEL Code: G33
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February 1, 2022

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English

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Based on such methods as a discriminant analysis and logistic regression, corporate bankruptcy prediction models have been developed as a means to determine the soundness of a company’s operational status based on its financial statements. However, such analytical methods work with binary variables, and thus, as the only outcome of machine learning, the company in question is considered either likely or unlikely to go bankrupt. However, this is insufficient for business operators who would need to know the possible risk factors of a bankruptcy, allowing them to plan and implement measures to avoid any misfortunes. We have therefore developed a prediction model that not only predicts but also identifies the financial variables that can possibly drive the company to bankruptcy.

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A Study on Machine Learning Prediction Model for Company Bankruptcy Using Features in Time Series Financial Data

Akira Otsuki
Akira Otsuki
Shohei Narumi
Shohei Narumi
Masayoshi Kawamura
Masayoshi Kawamura

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