DOI: https://doi.org/10.14710/medstat.15.2.163-174

MEASUREMENT OF SUPPORT VECTOR REGRESSION PERFORMANCE WITH CLUSTER ANALYSIS FOR STOCK PRICE MODELING

*Izza Dinikal Arsy  -  Statistics Study Program, Universitas Gadjah Mada, Indonesia, Indonesia
Dedi Rosadi orcid  -  Statistics Study Program, Universitas Gadjah Mada, Indonesia, Indonesia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Risk-averse investors will seek out stock investments with the minimum risk. One step that can be taken is to develop a model of stock prices and predict their fluctuations in the coming months. Significant studies on the modeling of stock movements have used the ARCH/GARCH method, but this method requires some assumptions. This paper will discuss the performance of stock modeling using Support Vector Regression. The performance is measured using the root mean square error value in two stock clusters based on its volatility value, e.g., stocks with large volatility and stocks with small volatility. This case study makes use of daily closing price data from 10 LQ-45 index shares from October 12, 2018 to October 11, 2019. In conclusion, SVR's performance on stocks with high volatility produces RMSE, which is considerably higher than SVR's performance on stocks with low volatility.
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Keywords: Support Vector Regression; Stock; Cluster; Volatility.

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Section: Articles
Language : EN
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  1. Bain, L. J. and Engelhardt, M. (1992). Introduction to Probability and Mathematical Statistics. California: Duxbury Press
  2. Basak, D., Pal, S., and Patranabis, D. C. (2007). Support Vector Regression. Neural Information Processing, Vol. 11, No. 10
  3. Bini, B. S. and Mathew, T. (2016). Clustering and Regression Techniques for Stock Prediction. Procedia Technology, 24, 1248–1255
  4. Biri, R., Langi, Y. A., and Paendong, M. S. (2013). Penggunaan Metode Smoothing Eksponensial dalam Meramal Pergerakan Inflasi Kota Palu. Jurnal Ilmiah Sains, 13(1), 68
  5. Choudhury, S., Ghosh, S., Bhattacharya, A., Fernandes, K. J., and Tiwari, M. K. (2014). A Real Time Clustering and SVM Based Price-Volatility Prediction for Optimal Trading Strategy. Neurocomputing, 131, 419–426
  6. Cortes, C. and Vapnik, V. (1995). Support-Vector Networks. Machine Learning 20, 273–297
  7. Henrique, B. M., Sobreiro, V. A., and Kimura, H. (2018). Stock Price Prediction Using Support Vector Regression on Daily and up to the Minute Prices. Journal of Finance and Data Science, 4(3), 183–201
  8. Kowalczyk, A. (2017). Support Vector Machines Succintctly, Syncfusion. Succinctly E-Book Series, 114. www.syncfusion.com
  9. Maharesi, R. (2013). Penggunaan Support Vector Regression (SVR) pada Prediksi Return Saham Syariah BEI. Proceeding PESAT, 5, 8–9
  10. Mishra, S. and Padhy, S. (2019). An Efficient Portfolio Construction Model using Stock Price Predicted by Support Vector Regression. North American Journal of Economics and Finance, 50(May), 101027
  11. Prahutama, A., Utami, T. W., and Yasin, H. (2014). Prediksi Harga Saham Menggunakan Support Vector Regression Dengan Algoritma Grid Search. Media Statistika, 7(1), 29–35
  12. Rosadi, D. (2014). Analisis Runtun Waktu dan Aplikasinya dengan R. Yogyakarta. Gadjah Mada University Press
  13. Saputra, G. H., Wigena, A. H., and Sartono, B. (2019). Penggunaan Support Vector Regression dalam Pemodelan Indeks Saham Syariah Indonesia dengan Algoritme Grid Search. Indonesian Journal of Statistics and Its Applications, 3(2), 148–160. https://doi.org/10.29244/ijsa.v3i2.172
  14. Subagyo, P. (1986). Forecasting Konsep dan Aplikasi, Yogyakarta: BPFE Yogyakarta,
  15. Smola, A. J. and Schölkopf, B. (2004). A Tutorial for Support Vector Regression. Statistics and Computing 14, 199–222
  16. Winston, W. L. (2004). Operations Research: Application. Boston: Duxbury Press
  17. Yahoo Inc. (2019). Yahoo! Finance. URL: http://www.finance.yahoo.com/

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