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

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

Akira Otsuki

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Shohei Narumi

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Masayoshi Kawamura

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

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Abstract

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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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite 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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GJMBR Volume 22 Issue A1

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Journal Specifications

Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

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Classification

GJMBR-A Classification JEL Code: G33

Submission ReceivedDecember 7, 2021
Peer Review Double Blind
Handling Editor
Accepted December 30, 2021
Published January 11, 2022

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v1.2

Issue date

February 1, 2022

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