Support Vector Machine (SVM) as Financial Distress Model Prediction in Property and Real Estate Companies
- 10.2991/978-2-494069-83-1_72How to use a DOI?
- Financial distress; Machine learning; Support vector machine; Property and real estate companies
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%.
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Cite this article
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 -