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[P.Refenes@cs.ucl.ac.uk]

CURRENCY EXCHANGE RATE PREDICTION & NEURAL NETWORK DESIGN STRATEGIES


 A. N. REFENES, M. AZEMA-BARAC, L. CHEN, & S. A. KAROUSSOS

              Department of Computer Science,
                 University College London,
                  Gower Street, WC1, 6BT,
                        London, UK.

ABSTRACT


This  paper  describes  a  non   trivial   application   in
forecasting currency exchange rates, and its implementation
using a multi-layer perceptron network. We show  that  with
careful   network   design,  the  backpropagation  learning
procedure is an effective way of training  neural  networks
for   time  series  prediction.  The  choice  of  squashing
function is an important design  issue  in  achieving  fast
convergence   and   good  generalisation  performance.   We
evaluate the use  of  symmetric  and  asymmetric  squashing
functions   in   the  learning  procedure,  and  show  that
symmetric functions yield  faster  convergence  and  better
generalisation  performance.  We derive analytic results to
show  the  conditions  under  which   symmetric   squashing
functions  yield  faster  convergence,  and to quantify the
upper bounds on the convergence improvement.   The  network
is  evaluated  both for long term forecasting without feed-
back (i.e. only  the  forecast  prices  are  used  for  the
remaining trading days) and for short term forecasting with
hourly feed-back.  The network learns the training set near
perfect  and shows accurate prediction, making at least 22%
profit on the last 60 trading days of 1989.


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