Using Recurrent Neural Networks To Forecasting of Forex

This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates. The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO. Various statistical estimates of forecast quality have been carried out. Obtained results show, that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65. Linear and nonlinear statistical data preprocessing, such as Kolmogorov-Smirnov test and Hurst exponents for each currency were calculated and analyzed.

Subjects:	Disordered Systems and Neural Networks (cond-mat.dis-nn)
Cite as:	arXiv:cond-mat/0304469v1 [cond-mat.dis-nn]

Submission history

From: Yuri A. Kuperin [view email]
[v1] Mon, 21 Apr 2003 22:55:07 GMT (282kb)

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

In a universe with a single currency, there would be no foreign exchange market, no foreign exchange rates, and no foreign exchange. Over the past twenty-five years, the way the market has performed those tasks has changed enormously. The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. In this paper, we attempt to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead. The soft computing models considered are a neural network trained by the scaled conjugate gradient algorithm and a neuro-fuzzy model implementing a Takagi-Sugeno fuzzy inference system. We also considered Multivariate Adaptive Regression Splines (MARS), Classification and Regression Trees (CART) and a hybrid CART-MARS technique. We considered the exchange rates of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed hybrid models could predict the forex rates more accurately than all the techniques when applied individually. Empirical results also reveal that the hybrid hard computing approach also improved some of our previous work using a neuro-fuzzy approach.

Subjects:	Artificial Intelligence (cs.AI)
ACM classes:	1.2.0
Journal reference:	IEEE International Conference on Fuzzy Systems (IEEE FUZZ'02), 2002 IEEE World Congress on Computational Intelligence, Hawaii, ISBN 0780372808, IEEE Press pp. 1616 -1622, 2002
Cite as:	arXiv:cs/0405028v1 [cs.AI]