ABSTRACT
The traditional SVM method has the problem of kernel function's parameters and dynamic optimization of penalty coefficient C. This paper constructs a hybrid model by extending the SVM method with PCA method to solve the problem. Finally we use the daily date of the exchange rate to test the high prediction accuracy of PCA-SVM model. In order to achieve better prediction accuracy, four kernel functions are used to construct different SVM. The empirical results show that SVR based on RBF kernel has the highest prediction accuracy. This result illustrates that the relevant government can take use of the model to monitor the smooth fluctuations in the exchange rate market.
- Sofiane Aboura, Julien Chevallier. A cross-volatility index for hedging the country risk J. Journal of International Financial Markets, Institutions & Money, 2015, 38: 25--41.Google ScholarCross Ref
- Yaofu Ouyang, Peng Li. On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach J. Energy Economics, 2018, 71: 238--252.Google ScholarCross Ref
- Ray Yeutien Chou, Tso-Jung Yen, Yu-Min Yen. Risk evaluations with robust approximate factor models J. Journal of Banking and Finance, 2017, 82: 244--264.Google ScholarCross Ref
- Mohcine Chraibi, Tim Ensslen, Hanno Gottschalk, Mohamed Saadi, Armin Seyfried. Assessment of models for pedestrian dynamics with functional principal component analysis J. Physica A: Statistical Mechanics and its Applications, 2016, 451: 475--489.Google ScholarCross Ref
- Xiao Zhong, Dvid Enke. Forecasting daily stock market return using dimensionality reduction J. Expert Systems With Applications, 2017, 67: 126--139. Google ScholarDigital Library
- João Nadkarni, Rui Ferreira Neves. Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets J. Expert Systems With Applications, 2018, 103: 184--195.Google ScholarCross Ref
- Ningning Zhang, Aijing Lin, Pengjian Shang. Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting J. Physica A: Statistical Mechanics and its Applications, 2017, 477: 161--173.Google ScholarCross Ref
- Mariusz Podsiadlo, Henryk Rybinski. Financial time series forecasting using rough sets with time-weighted rule voting J. Expert Systems With Applications, 2016, 66: 219--233. Google ScholarDigital Library
- P.P. Das, R. Bisoi, P.K. Dash. Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis J. Expert Systems With Applications, 2018, 96: 427--449. Google ScholarDigital Library
- Vapnik, Cortes, Corinna, Vladimir N. Support-vector networks J. Machine Learning, 1995, 20(3): 273--297. Google ScholarDigital Library
- Hotelling, H. Analysis of a complex of statistical variables into principal components J. Journal of Educational Psychology, 1933, 24: 417--441 and 498--520.Google ScholarCross Ref
Index Terms
- Research for construction and application of PCA-SVM for exchange rate forecasting
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