The full text of this article
Intraday high-frequency FX trading with adaptive neuro-fuzzy inference systems
by Abdalla Kablan, Wing Lon Ng
International Journal of Financial Markets and Derivatives (IJFMD), Vol. 2, No. 1/2, 2011
Abstract: This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for financial trading, which learns to predict price movements from training data consisting of intraday tick data sampled at high frequency. The empirical data used in our investigation are five-minute mid-price time series from FX markets. The ANFIS optimisation involves back-testing as well as varying the number of epochs, and is combined with a new method of capturing volatility using an event-driven approach that takes into consideration directional changes within pre-specified thresholds. The results show that the proposed model outperforms standard strategies such as buy-and-hold or linear forecasting.
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Financial Markets and Derivatives (IJFMD):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable).
See our Orders page to subscribe.
If you still need assistance, please email email@example.com