Support Vector Regression Implementation for Indonesian Private External Debt Analysis


  •   Zuherman Rustam

  •   Janice Diani


Indonesian corporations have been borrowing large sums of money from foreign investors in the past decade, such that private debt ratio has reached 49% of Indonesia’s total external debt by the end of 2017. This act of borrowing might improve the borrowing firms’ performance which leads to increase in profit, but in other hand it might result on debt value expansion, due to the exchange rate depreciation trend in Indonesia. This paper employs Support Vector Regression, a machine-learning method, to study the relationship between factors that might affect corporate performance, and compares the results with that of the conventional panel data regression method. The study was done using data from annual financial statements of 189 firms in Indonesia during 2011-2017.

It is shown that the machine-learning approach discussed in this study gave better accuracy than the previously employed panel data regression method. Both methods generally showed that balance-sheet effect is more dominant in Indonesian corporations, and it is recommended for companies to minimize their foreign debts and imported purchases, and if possible, export more of their products.

Keywords: depreciation; exchange rate; external debt; machine learning; support vector regression.


Kementerian Keuangan Republik Indonesia, Statistik Utang Luar Negeri Indonesia edisi Maret 2018, v.02

Biro Riset Ekonomi Bank Indonesia, Analisa Perilaku Pembiayaan Asing dan Dampaknya Terhadap Ketahanan Perusahaan, Working Paper WP/12/2011 Bank Indonesia.

J.A. Frenkel and A Razin, The Mundell-Fleming Model: A Quarter Century Later. National Bureau of Economic Research, 1987. Working Paper no. 2321

P Krugman, Balance Sheets, the Transfer Problem, and Financial Crises. (Springer: Dordrecht, 1999) pp 31-55

P Kintandani and Z Rustam, Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation, Modelling and Simulation in Engineering 2019.

Z. Rustam, D. F. Vibranti, and D. Widya, Predicting the direction of Indonesian stock price movement using support vector machines and fuzzy kernel C-means, Proceedings of 3rd International Symposium on Current Progress in Mathematics and Sciences, Bali, Indonesia, 2017

Z. Rustam, Nurrimah, and R. Hidayat, Indonesia Composite Index Prediction using Fuzzy Support Vector Regression with Fisher Score Feature Selection, International Journal on Advanced Science, Engineering and Information Technology, vol 9, no 1 2019.

V.C. Vapnik, Statistical Learning Theory: Adaptive and learning systems for signal processing, communications, and control (Wiley, 1998)

C.M. Bishop, Pattern Recognition and Machine Learning (UK: Springer, 2006)

J. Smola and B. Scholkopf, A tutorial on support vector regression, Journal of Statistics and Computing, vol. 1, pp. 199–222, 2002.

D.C. Montgomery, E.A. Peck and G.G. Vining, Introduction to Linear Regression Analysis (New Jersey: Wiley, 2012).


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How to Cite
Rustam, Z. and Diani, J. 2019. Support Vector Regression Implementation for Indonesian Private External Debt Analysis. European Journal of Electrical Engineering and Computer Science. 3, 3 (May 2019). DOI: