A Foreign Exchange Portfolio Management Mechanism Based on Fuzzy Neural Networks   [FE] [NN]

by

Yao, S., Pasquier, M. and Quek, C.

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Info: 2007 IEEE Congress on Evolutionary Computation (Conference proceedings), 2007, p. 2576-2583
Abstract:
The key in foreign exchange (Forex) trading [FE] is to pick the right currency to trade at the right time, primarily based on accurate forecast of future exchange rates. [ER] This paper presents a novel neuro-fuzzy approach in foreign exchange (Forex) portfolio management [FE] to pick the right pairs of currencies to buy and sell with optimized market timing. [MT] The proposed mechanism forecasts future BUY/SELL signals before matching these offsetting signals across different currencies to maximize trade returns. This mechanism makes use of fuzzy neural network [NN] (FNNs) as a forecasting tool, technical indicators [TI] such as moving averages and a novel Portfolio Trade Timing Optimization (PTTO) algorithm to produce an optimized BUY-SELL schedule for the Forex portfolio under management. Experimental results on real world Forex market data [RW] shows that the proposed mechanism yields significantly higher profits against various popular benchmarks.
Notes:
CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
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BibTex:
@inproceedings(:2007:cec:AFE,
  title =	 {A Foreign Exchange Portfolio Management Mechanism
                  Based on Fuzzy Neural Networks},
  author =	 {Shuo Yao and Michel Pasquier and Chai Quek},
  pages =	 {2576--2583},
  booktitle =	 "2007 IEEE Congress on Evolutionary Computation",
  year =	 2007,
  editor =	 "Dipti Srinivasan and Lipo Wang",
  address =	 "Singapore",
  month =	 "25-28 September",
  organization = "IEEE Computational Intelligence Society",
  publisher =	 "IEEE Press",
  ISBN =	 "1-4244-1340-0",
  file =	 {2078.pdf},
  abstract =	 {The key in foreign exchange (Forex) trading is to
                  pick the right currency to trade at the right time,
                  primarily based on accurate forecast of future
                  exchange rates. This paper presents a novel
                  neuro-fuzzy approach in foreign exchange (Forex)
                  portfolio management to pick the right pairs of
                  currencies to buy and sell with optimized market
                  timing. The proposed mechanism forecasts future
                  BUY/SELL signals before matching these offsetting
                  signals across different currencies to maximize
                  trade returns. This mechanism makes use of fuzzy
                  neural network (FNNs) as a forecasting tool,
                  technical indicators such as moving averages and a
                  novel Portfolio Trade Timing Optimization (PTTO)
                  algorithm to produce an optimized BUY-SELL schedule
                  for the Forex portfolio under
                  management. Experimental results on real world Forex
                  market data shows that the proposed mechanism yields
                  significantly higher profits against various popular
                  benchmarks.},
  notes =	 {CEC 2007 - A joint meeting of the IEEE, the EPS, and
                  the IET.  IEEE Catalog Number: 07TH8963C},
)