Global Journal of Management and Business Research, A: Administration and Management, Volume 22 Issue 1

© 2022. Akira Otsuki, Shohei Narumi & Masayoshi Kawamura. This research/review article is distributed under the terms of the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). You must give appropriate credit to authors and reference this article if parts of the article are reproduced in any manner. Applicable licensing terms are at https:// creativecommons.org/licenses/by-nc-nd/4.0/. Global Journal of Management and Business Research: A Administration and Management Volume 22 Issue 1 Version 1.0 Year 2022 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 2249-4588 & Print ISSN: 0975-5853 A Study on Machine Learning Prediction Model for Company Bankruptcy using Features in Time Series Financial Data By Akira Otsuki, Shohei Narumi & Masayoshi Kawamura Nihon University 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. Keywords: machine learning; corporate bankruptcy prediction; time-series financial statement data analysis. GJMBR-A Classification: JEL Code: G33 AStudyonMachineLearningPredictionModelforCompanyBankruptcyusingFeaturesinTimeSeriesFinancialData Strictly as per the compliance and regulations of: