A novel portfolio optimization method for foreign currency investment

Authors:
Yuan Cao;Haibo He;Rajarathnam Chandramouli
Affiliations:
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, New Jersey;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, New Jersey;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, New Jersey
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

In this paper, we present the research of a foreign currency investment framework involving the prediction of the foreign currency exchange rates and the portfolio optimization under certain constrains. We adopt two machine learning methods, support vector machines (SVMs) and neural networks (NNs), as well as the traditional moving average method, to predict the exchange rates for three foreign currencies including Australia Dollars (ADD), European Euro (EDR), and Swiss Francs (CHF). Based on these forecastings, we choose two out of the three currencies listed above and build a portfolio by adopting multi-objective portfolio optimization techniques by maximizing the return and minimizing the risk. Karush-Kuhn-Tucker (KKT) theorem guarantees that the optimal portfolio is reachable. Simulation results show that the optimal portforlio investment can achieve superior return performance compared with three single currency investment benchmarks.