THE EFFECT OF KERNEL VALUES IN SUPPORT VECTOR MACHINE TO FORECASTING PERFORMANCE OF FINANCIAL TIME SERIES

Year 2019, Volume: 4 Issue: 1, 17 - 21, 30.06.2019

Aytaç Altan , Seçkin Karasu

Abstract

Nowadays, one of the most
important research topics in economic sciences is the estimation of different
financial exchange rates. The reliable and accurate forecasting of the exchange
rate in the financial markets is of great importance, particularly after the
recent global economic crises. In addition, the high accuracy forecasting of
the financial exchange rates causes that investors are less affected by
financial bubbles and crashes. In this paper, a financial time series
forecasting model is identified by support vector machine (SVM), which is one
of the machine learning methods, for estimating the closing price of USD/TRY
and EUR/TRY exchange rates. The closing price values and commodity channel index
(CCI) indicator value are used as inputs in financial time series forecasting
model. Various models are obtained with different kernel scale values in SVM
and the model that estimates financial time series with the highest accuracy is
proposed. The performance of the obtained models is measured by means of
Pearson correlation and statistical indicators such as mean absolute error
(MAE), mean squared error (MSE), and root mean squared error (RMSE). It is seen
that the forecasting performance of the proposed SVM model for the financial
time series data set is higher than that the performance of the compared other
models.  

Keywords

Financial Time Series, Support Vector Machine, Kernel Scale, Exchange Rate Forecasting

References

• [1] E. C. Hui, X. Zheng, and H. Wang, “A Dynamic Mathematical Test of International Property Securities Bubbles and Crashes”, Physica A: Statistical Mechanics and its Applications, Vol. 389, No. 7, 2010, pp. 1445-1454.
• [2] B. Deviren, Y. Kocakaplan, M. Keskin, M. Balcılar, Z. A. Özdemir, and E. Ersoy, “Analysis of Bubbles and Crashes in the TRY/USD, TRY/EUR, TRY/JPY and TRY/CHF Exchange Rate within the Scope of Econophysics”, Physica A: Statistical Mechanics and its Applications, Vol. 410, 2014, pp. 414-420.
• [3] O. Onalan, “Currency Exchange Rate Estimation using Grey Markov Prediction Model”, Journal of Economics Finance and Accounting, Vol. 1, No. 3, 2014, 205-217.
• [4] M. Khashei, S. Torbat, and Z. H. Rahimi, “An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting”, Turkish Journal of Forecasting, Vol. 1, No. 1, 2017, pp. 16-29.
• [5] A. N. Kia, S. Haratizadeh, and H. Zare, “Prediction of USD/JPY Exchange Rate Time Series Directional Status by KNN with Dynamic Time Warping as Distance Function”, Bonfring International Journal of Data Mining, Vol. 3, No. 2, 2013, pp. 12-16.
• [6] S. Galeshchuk, “Neural Networks Performance in Exchange Rate Prediction”, Neurocomputing, Vol. 172, 2016, pp. 446-452.
• [7] A. D. Aydin and S. C. Cavdar, “Comparison of Prediction Performances of Artificial Neural Network (ANN) and Vector Autoregressive (VAR) Models by using the Macroeconomic Variables of Gold Prices, Borsa Istanbul (BIST) 100 Index and US Dollar-Turkish Lira (USD/TRY) Exchange Rates”, Procedia Economics and Finance, Vol. 30, 2015, pp. 3-14.
• [8] A. S. Babu and S. K. Reddy, “Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron”, Journal of Stock & Forex Trading, Vol. 3, No. 4, 2015, pp. 1-5.
• [9] E. H. Fırat, “SETAR (Self-Exciting Threshold Autoregressive) Non-linear Currency Modelling in EUR/USD, EUR/TRY and USD/TRY Parities”, Mathematics and Statistics, Vol. 5, No. 1, 2017, pp. 33-55.
• [10] M. Fathian and A. Kia, “Exchange Rate Prediction with Multilayer Perceptron Neural Network using Gold Price as External Factor”, Management Science Letters, Vol. 2, No. 2, 2002, pp. 561-570.
• [11] M. Li and F. Suohai, “Forex Prediction based on SVR Optimized by Artificial Fish Swarm Algorithm”, presented at the Fourth Global Congress on Intelligent Systems, pp. 47-52, Hong Kong, 2013.
• [12] S. Karasu, A. Altan, Z. Saraç, and R. Hacioğlu, “Prediction of Bitcoin Prices with Machine Learning Methods using Time Series Data”, presented at IEEE 26th Signal Processing and Communications Applications Conference, pp. 1-4, Izmir, 2018.
• [13] X. Hu, Z. Xiao, and N. Zhang, “Removal of Baseline Wander from ECG Signal based on a Statistical Weighted Moving Average Filter”, Journal of Zhejiang University Science C, Vol. 12, No. 5, 2011, pp. 397-403.
• [14] URL1:https://www.investopedia.com/terms/c/commoditychannelindex.asp, “Commodity Channel Index - CCI Definition and Uses”, May 23, 2019.
• [15] A. A. Abdoos, P. K. Mianaei, and M. R. Ghadikolaei, “Combined VMD-SVM based Feature Selection Method for Classification of Power Quality Events”, Applied Soft Computing, Vol. 38, 2016, pp. 637-646.
• [16] R. J. Hyndman and A. B. Koehler, “Another Look at Measures of Forecast Accuracy”, International Journal of Forecasting, Vol. 22, No. 4, 2006, pp. 679-688.
• [17] S. Karasu, A. Altan, Z. Saraç, and R. Hacioğlu, “Prediction of Solar Radiation based on Machine Learning Methods”, The Journal of Cognitive Systems, Vol. 2, No. 1, pp. 16-20, 2017.