Support Vector Regression Implementation for Indonesian Private External Debt Analysis

Support Vector Regression Implementation for Indonesian Private External Debt Analysis

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Submitted May 8, 2019
Published May 17, 2019
DOI https://doi.org/10.24018/ejece.2019.3.3.89
Abstract viewed = 319 times
Issue
Vol. 3 No. 3 (2019)
Section
Articles

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  •   Zuherman Rustam

    •    University of Indonesia
  •   Janice Diani

    •    University of Indonesia

Abstract

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.

References

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How to Cite
[1]
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:https://doi.org/10.24018/ejece.2019.3.3.89.