Open AccessA Study on Machine Learning Prediction Model for Company Bankruptcy using Features in Time Series Financial Data
Author(s) -
Akira Otsuki,
Shohei Narumi,
Masayoshi Kawamura
Publication year - 2022
Publication title -
global journal of management and business research
Language(s) - English
Resource type - Journals
eISSN - 2249-4588
pISSN - 0975-5853
DOI - 10.34257/gjmbravol22is1pg9
Subject(s) - bankruptcy , bankruptcy prediction , logistic regression , linear discriminant analysis , predictive modelling , soundness , binary classification , financial ratio , going concern , logit , computer science , finance , artificial intelligence , actuarial science , machine learning , econometrics , business , economics , accounting , support vector machine , programming language , audit , auditor's report
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.