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.

References

15 Cites in Article

Reference Format

Seigotasaka Empirical study about a bankruptcy company: It is based on the model of Beaver and Altman.
Adrian Wagner (2009). Language Transfer as a Cause of Translation and Interlanguage Errors Between L1 Japanese and L2 English.
Ayaka Okubo (2010). Study on Black-in Bankruptcy Mechanism through Financial Statements focused on Cash Flow.
Masaru Ishikawa,Ngai Chung Sze (2012). A Study of Corporate Bankruptcies Based on the Cash Flow Information.
Ryuji Mizoguchi,Shunsuke Nakajima (2012). Discriminant analysis of companies and bankruptcy probability estimation.
Jiang Feihong (2003). Financial Forecast and Cash Flow Information.
Koji Jidaisho,Hamido Fujita,Masaki Kurematsu,Jun Hakura (2017). Experiment of Risk Prediction on Japanese Companies Using a Bankruptcy Risk Prediction Model.
Yuichi Masuyama (2017). The Conditions for Corporate Growth in the Context of Corporate Bankruptcy Analysis.
Yasuhiro Saigo,Kazutoyo Nakano (2012). Discounted Cash Flow Valuation.
(2019). O’Kelly, Edward Peter, (died 22 July 1914), JP Co. Wicklow.
Natalia Ilyevich (2019). E. Voraussetzungen für ein Delisting nach der aktuellen Gesetzeslage.
(2019). Hussey, Edward Windsor, (1855–17 Oct. 1952), DL, JP.
(2019). COSMOS1 corporate finance database.
Tokuoiwaisako (2018). Serial Correlation of Stock Price Indexes and Cross-autocorrelation of Portfolios by Size.
Akira Otsuki (2020). Supplemental Information 11: Clustering sequences based on principal component analysis using Jalview v2.10..

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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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