Support Vector Machine (SVM) as Financial Distress Model Prediction in Property and Real Estate Companies

DOI

10.2991/978-2-494069-83-1_72How to use a DOI?

Keywords

Financial distress; Machine learning; Support vector machine; Property and real estate companies

Abstract

Financial distress prediction is an interesting topic to be studied because of its significant impact on various stakeholders. Various methods have been developed to predict the company's financial distress. Among the famous models, the Support Vector Machine (SVM) is claimed to be the most successful model in prediction and classification. SVM is a machine learning method that works on the principle of Structural Risk Minimization (SRM) with the aim of finding the best hyperplane that separates two classes in the input space by maximizing the hyperplane margin and obtaining the best support vector. This study applies the SVM model in predicting the financial distress of property and real estate companies listed on the Indonesia Stock Exchange. There were 18 variables of financial ratios used in this study. By Using Principal Component Analysis (PCA) in feature selections there are five variables selected in this study, namely Return on Assets, Return on Equity, Net Profit Margin, Earning Per Share, and Operating Profit Margin. The SVM model is formed by dividing the training and testing data with 10-fold cross-validation and using three kernels: linear kernel, polynomial, and Radial Basis Function (RBF). The best SVM model formed is the SVM model with RBF kernel type with parameters sigma = 1 and C = 1.0 which can predict financial distress with an accuracy value of 82.99% and an error rate of 17.01%.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ni Wayan Dewinta Ayuni
AU  - Ni Nengah Lasmini
AU  - Agus Adi Putrawan
PY  - 2022
DA  - 2022/12/30
TI  - Support Vector Machine (SVM) as Financial Distress Model Prediction in Property and Real Estate Companies
BT  - Proceedings of the International Conference on Applied Science and Technology on Social Science 2022 (iCAST-SS 2022)
PB  - Atlantis Press
SP  - 397
EP  - 402
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-83-1_72
DO  - 10.2991/978-2-494069-83-1_72
ID  - Ayuni2022
ER  -