TY - GEN
T1 - Combining Multiple Kernel Learning and Genetic Algorithm for forecasting short time foreign exchange rate
AU - Deng, Shangkun
AU - Sakurai, Akito
PY - 2011
Y1 - 2011
N2 - This paper proposes a hybrid model named MKL-GA, which combines Multiple Kernel Learning (MKL) and Genetic Algorithm (GA), for modeling and the prediction of FX (foreign exchange) rate on USDJPY currency pair by extracting features from three main FX pairs with three different short time horizons. Firstly, the MKL regression model predicts the change rate based on MACD indicators, and then GA is applied to fuse all the information from the regression model and overbought/oversold technical indicators. Experimental results show that the proposed model outperforms other models in terms of returns and risk-return ratio. In addition, the result of kernel weights for different currency pairs in the step of MKL training should be also advisable for the trading.
AB - This paper proposes a hybrid model named MKL-GA, which combines Multiple Kernel Learning (MKL) and Genetic Algorithm (GA), for modeling and the prediction of FX (foreign exchange) rate on USDJPY currency pair by extracting features from three main FX pairs with three different short time horizons. Firstly, the MKL regression model predicts the change rate based on MACD indicators, and then GA is applied to fuse all the information from the regression model and overbought/oversold technical indicators. Experimental results show that the proposed model outperforms other models in terms of returns and risk-return ratio. In addition, the result of kernel weights for different currency pairs in the step of MKL training should be also advisable for the trading.
KW - FX trading
KW - Genetic Algorithm
KW - MKL-GA hybrid model
KW - Multiple Kernel Learning
UR - http://www.scopus.com/inward/record.url?scp=79958164800&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79958164800&partnerID=8YFLogxK
U2 - 10.2316/P.2011.717-111
DO - 10.2316/P.2011.717-111
M3 - Conference contribution
AN - SCOPUS:79958164800
SN - 9780889868632
T3 - Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011
SP - 200
EP - 209
BT - Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011
T2 - 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011
Y2 - 14 February 2011 through 16 February 2011
ER -