Paper Pre-print

[Paul Refenes]

The following pre-print is available. Please send requests to 
H.tracey at lbs.lon.ac.uk.  Paper copy only.

                            
 MEASURING THE PERFORMANCE OF NEURAL NETWORKS IN MODERN
  PORTFOLIO MANAGEMENT: TESTING STRATEGIES AND METRICS
                            
                            
                      A. N. REFENES
             Department of Decision Science
                 London Business School
               Sussex Place, Regents Park
                   London NW1 4SA, UK
                            
                            
                        ABSTRACT

Neural networks have attracted much interest in financial
engineering  and  modern portfolio management  with  many
researchers claiming that they signal the beginning of  a
new  era  in  the evolution of forecasting  and  decision
support  systems. Various performance figures  are  being
quoted  to  support these claims but there  is  rarely  a
comprehensive   testing   strategy   to   quantify    the
performance   of  neural  networks  in  ways   that   are
meaningful  to  the  practitioner in the  field.  In  the
context  of  asset management some of the quoted  figures
could   be  at  best  misleading  and  others  are  often
irrelevant.  In  this  paper we review  some  well  known
metrics  for  measuring  estimator  performance  both  in
absolute  and relative terms, measuring profitability  of
the   final   objective  function,  and   analysing   the
characteristics of the equity curves.