Proceedings of the 3rd Asia Pacific International Conference of Management and Business Science (AICMBS 2019)

Classification of Dynamic Financial Distress Manufacturing Company Listed in Indonesia Stock Exchange Using Binary Logistic Regression and Classification Analysis Regression Tree

Authors
Nugroho Priyo Negoro, Aldia Wira Trispantia
Corresponding Author
Nugroho Priyo Negoro
Available Online 13 April 2020.
DOI
https://doi.org/10.2991/aebmr.k.200410.033How to use a DOI?
Keywords
financial distress, classification manufacturing company, binary logistic regression, CART
Abstract

Financial Distress (FD) is a large concept consists several situations where the enterprise facing financial difficulty. Some manufacturing enterprise that are experiencing financial difficulties trying to overcome these problems by make loans and merger, or otherwise shut down their company. If the condition of FD is known, it is expected to take action to improve the situation so that the enterprise will not experience more severe difficulties such as bankruptcy or liquidation. The purpose of this study is to determine the factors that allegedly significantly influence for financial distress, and knowing classification model of financial distress. Thus, this study will try to classify the dynamic financial distress companies that listed on stock exchanges in Indonesia 2012-2014 using binary logistic regression and classification and regression tree analysis (CART). Before analyzing the binary logistic regression and CART, first the data must be balanced using SMOTE. Binary logistic regression developed the model of FD to see the factors that allegedly significantly influence for financial distress, while CART method is to solve the classification. Experimental results show that binary logistic regression has two predictor variables that significantly influence for FD condition for manufacturing enterprise and that variable is liquidity ratio and the activity ratio. CART classification method produces that maximum classification tree is equal to the optimum classification tree, with the primary node is variable solvency ratio, and the value of a classification. CART method outperforms the binary logistic regression with the classification’s accuracy has more than ten percent.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd Asia Pacific International Conference of Management and Business Science (AICMBS 2019)
Series
Advances in Economics, Business and Management Research
Publication Date
13 April 2020
ISBN
978-94-6252-954-0
ISSN
2352-5428
DOI
https://doi.org/10.2991/aebmr.k.200410.033How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Nugroho Priyo Negoro
AU  - Aldia Wira Trispantia
PY  - 2020
DA  - 2020/04/13
TI  - Classification of Dynamic Financial Distress Manufacturing Company Listed in Indonesia Stock Exchange Using Binary Logistic Regression and Classification Analysis Regression Tree
BT  - Proceedings of the 3rd Asia Pacific International Conference of Management and Business Science (AICMBS 2019)
PB  - Atlantis Press
SP  - 214
EP  - 220
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200410.033
DO  - https://doi.org/10.2991/aebmr.k.200410.033
ID  - Negoro2020
ER  -