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Forecasting foreign exchange rates using Support Vector Regression. / Bahramy, Farhad
; Crone, Sven F. 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2013. p. 34-41 6611694 (Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Bahramy, F
& Crone, SF 2013,
Forecasting foreign exchange rates using Support Vector Regression. in
2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)., 6611694, Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, IEEE, pp. 34-41, 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, Singapore,
16/04/13.
https://doi.org/10.1109/CIFEr.2013.6611694APA
Vancouver
Bahramy F
, Crone SF.
Forecasting foreign exchange rates using Support Vector Regression. In 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE. 2013. p. 34-41. 6611694. (Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). doi: 10.1109/CIFEr.2013.6611694
Author
Bahramy, Farhad
; Crone, Sven F. /
Forecasting foreign exchange rates using Support Vector Regression. 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2013. pp. 34-41 (Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).
Bibtex
@inproceedings{3aa782235747435a8c160106c7f7fc22,
title = "Forecasting foreign exchange rates using Support Vector Regression",
abstract = "Support Vector Regression (SVR) algorithms have received increasing interest in forecasting, promising nonlinear, non-parametric and data driven regression capabilities for time series prediction. But despite evidence on the nonlinear properties of foreign exchange markets, applications of SVR in price or return forecasting have demonstrated only mixed results. However, prior studies were limited to using only autoregressive time series inputs to SVR. This paper evaluates the efficacy of SVR to predict the Euro-US Dollar exchange rate using input vectors enhanced with explanatory variables on mean-reversion movements derived from Bollinger Bands technical indicators. Using a rigorous empirical out-of-sample evaluation of multiple rolling forecast origins, we assess the accuracy of different SVR input vectors, including upper and lower BB, binary trading signals of BB, and combinations of the above. As a result, a local SVR model using autoregressive lags in conjunction with BB bands and BB indicators, and recalibrated yearly, outperforms the random walk on directional and all other error metrics, showing some promise for an SVR application.",
keywords = "Bollinger Bands, financial forecasting, foreign exchange rates, Support Vector Regression, technical indicator",
author = "Farhad Bahramy and Crone, {Sven F.}",
year = "2013",
month = oct,
day = "28",
doi = "10.1109/CIFEr.2013.6611694",
language = "English",
series = "Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013",
publisher = "IEEE",
pages = "34--41",
booktitle = "2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)",
note = "2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 ; Conference date: 16-04-2013 Through 19-04-2013",
}
RIS
TY - GEN
T1 - Forecasting foreign exchange rates using Support Vector Regression
AU - Bahramy, Farhad
AU - Crone, Sven F.
PY - 2013/10/28
Y1 - 2013/10/28
N2 - Support Vector Regression (SVR) algorithms have received increasing interest in forecasting, promising nonlinear, non-parametric and data driven regression capabilities for time series prediction. But despite evidence on the nonlinear properties of foreign exchange markets, applications of SVR in price or return forecasting have demonstrated only mixed results. However, prior studies were limited to using only autoregressive time series inputs to SVR. This paper evaluates the efficacy of SVR to predict the Euro-US Dollar exchange rate using input vectors enhanced with explanatory variables on mean-reversion movements derived from Bollinger Bands technical indicators. Using a rigorous empirical out-of-sample evaluation of multiple rolling forecast origins, we assess the accuracy of different SVR input vectors, including upper and lower BB, binary trading signals of BB, and combinations of the above. As a result, a local SVR model using autoregressive lags in conjunction with BB bands and BB indicators, and recalibrated yearly, outperforms the random walk on directional and all other error metrics, showing some promise for an SVR application.
AB - Support Vector Regression (SVR) algorithms have received increasing interest in forecasting, promising nonlinear, non-parametric and data driven regression capabilities for time series prediction. But despite evidence on the nonlinear properties of foreign exchange markets, applications of SVR in price or return forecasting have demonstrated only mixed results. However, prior studies were limited to using only autoregressive time series inputs to SVR. This paper evaluates the efficacy of SVR to predict the Euro-US Dollar exchange rate using input vectors enhanced with explanatory variables on mean-reversion movements derived from Bollinger Bands technical indicators. Using a rigorous empirical out-of-sample evaluation of multiple rolling forecast origins, we assess the accuracy of different SVR input vectors, including upper and lower BB, binary trading signals of BB, and combinations of the above. As a result, a local SVR model using autoregressive lags in conjunction with BB bands and BB indicators, and recalibrated yearly, outperforms the random walk on directional and all other error metrics, showing some promise for an SVR application.
KW - Bollinger Bands
KW - financial forecasting
KW - foreign exchange rates
KW - Support Vector Regression
KW - technical indicator
U2 - 10.1109/CIFEr.2013.6611694
DO - 10.1109/CIFEr.2013.6611694
M3 - Conference contribution/Paper
AN - SCOPUS:84886079773
T3 - Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 34
EP - 41
BT - 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)
PB - IEEE
T2 - 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -
