Afiantara, Agus and Mahawan, Eng Bagus and Budiarto, Eka (2019) Modeling Banking Stability Index Using Machine Learning Technique. Masters thesis, Swiss German University.

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Abstract

The purpose of this research is to create an early warning detection model of financial stability index from selected variables to detect instability of financial system for the banking sector of Indonesia using PCA and machine learning algorithm Random Forest (Random Forest Classifier and Regressor). Economic Crisis are unexpected and unpredicted events that can have serious implications with consequences seriously affecting country’s condition of economy. The capability of data and information processing of economic indicators is absolutely required as a challenge to avoid the instability of financial system in the future. There are a lot of variables used to describe instability of financial system in many ways, but there is a big question which variables can precisely predict the instability of financial system. The diverse data characteristics of types and units should be normalized before processing data in order to speed up the calculation need in the model. A very important point of concern in this research is to reduce dimension of variables selected using PCA as generally used to compress the data into a new dataset with fewer dimension. Bagged decisions trees like Random Forest is used to estimate the importance of features. Thorough the research Random Forest as machine learning technique and combined with PCA is shown to have the best result to classify and select the core variables of economic indicators and predict the instability of financial system in Indonesia. Thirty three variables are used to predict the instability of financial system signal that constructed from Financial Stability Index (FSI) of Central Bank of Indonesia with monthly data. The algorithm is trained over the data from 2004-2011 period. As the result nine most components analysis are obtained as input for random forest machine learning to predict the instability of financial system especially banking system with explained variance ratio (the capability to explain) around 97%, accuracy around 89%, and precision 91% and mean absolute error around 11%.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Model ; Independent Variables ; Un-Supervised Machine Learning ; PCA ; Data Processing
Subjects:	T Technology > T Technology (General) > T58.6 Management information systems
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Adityatama Ratangga
Date Deposited:	19 May 2020 04:05
Last Modified:	19 May 2020 04:05
URI:	http://repository.sgu.ac.id/id/eprint/490

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