A Novel Hybrid Technique for Exchange Rate Forecasting


Abbas Mohamed Salah1, Tarik Ahmed Rashid1 & Shareef Maulod Shareef1

1College of Engineering, Salahaddin University





Abstract
Exchange rate prices play a significant role in economic and financial systems. The
precise forecasting of the exchange rate will aid to predict the situations of other
aspects in the future. This offers useful information for economist traders to fulfill vital
actions to eliminate risks that might lead to financial losses. One vital and common
approach for forecasting is a quantitative technique. The quantitative technique is split
into two approaches; fundamental and technical analysis. Both approaches have one
different, which is the economic factor. The fundamental analysis uses economic
factors while the other not, but both of them have drawbacks. In fundamental
technique, the forecasters cannot understand the economic factors totally, the values
and quantity of the factors unstable, while the technical analysis neglects the factors
totally. This paper suggests a new technique which is appeared to be as a hybridized of
the both techniques for forecasting exchange rate prices to overcome the issues
mentioned above. The proposed technique makes new factors that have real values and
have a real impact on the accurate result. It also derives the inputs and factors in the
prediction models by a feature extraction technique which produces very accurate
results.
 

Key Words: Exchange Rate, Forecasting, egovernment, Feature Extraction, Technical and Fundamental Analysis 



References

[1]. Shareef S, Hamid J., Muhammad D., "E-Government Stage Model: Based on Citizen-Centric Approach in the
Regional Government in Developing Countries", International Journal Of Electronic Commerce Studies,
Vol.3, No.1, Pp.145-164, (2012).
[2]. Hoshman, A.R., "Business Forecasting", Hong Kong, China, (2010).

[3]. Granvik , A. R.,"Forecasting Exchange Rates", Msc Thesis, International Business, (2010).
[4]. Liu , B., Wan H., Chen, X.,"Exchange Rate Forecasting Method Based on Particle Swarm Optimization And
Probabilistic Neural Network Model", IEEE, (2011).
[5]. Eddelbuttel, D., Mccurdy, T.H., "The Impact Of News On Foreign Exchange Rates", Evidence from High
Frequency Data, Manuscript, Rotman School Of Management, University Of Toronto. Forex News, Data
Gathered Since Sep, (2008).
[6]. Alamili, M., "Exchange Rate Prediction Using Support Vector Machines", Msc Thesis, January, 2011.
[7]. Meng L., "Exchange Rate Forecasting Based On Neural Network With Revised Weight", IEEE, 2011.
[8]. Bhatia N., Vandana SSCS., "Survey Of Nearest Neighbor Techniques", International Journal Of Computer
Science And Information Security, Vol. 8, No. 2, (2010).
[9]. Anguilli, F., "Fast Condensed Nearest Neighbor Rule", Proceedings Of The 22nd International Conference
On Machine Learning, Bonn, Germany, (2005).

[10]. Kanungo, T., Mount D. M., Netanyahu N. S., Piatko C. D., Silverman R., Wu A. Y., "An Efficient K-
Means Clustering Algorithm: Analysis And Implementation", Ieee Transactions On Pattern Analysis And

Machine Intelligence, Vol. 24, No. 7, July (2002).
[11]. Hamming, R. W., "Error Detecting And Error Correcting Codes", Bell System Technical
Journal Vol. (29), No. 2, pp. 147–160, (1950).
[12]. Deza, E. , Deza, M. M., “ Encyclopedia Of Distances", Springer. pp. 94, (2009).
[13]. Jenjira Tipyan, "Quantitative Models For Forecasting Vehicle Fuel Prices In Thailand", Computer
Science And Information Engineering, 2009 Wri World Congress, Vol (2), (2009).
[14]. Huang B.Q. , Kechadi T.-M. , Buckley B., Kiernan G. , Keogh E. , Rashid T. , “A New Feature Set
With New Window Techniques For Customer Churn Prediction”, Expert Systems With Applications, Expert
Systems With Applications, Vol. (37), pp. 3657–3665, (2010).
[15]. Isabelle G., "An Introduction To Variable And Feature Selection", pp. 1157-1182, (2003).