PRE-PRINT: Financial modeling using neural networks

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

The following preprint is available - hard copies by
surface mail only. 
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        FINANCIAL FORECASTING USING NEURAL NETWORKS



      A. N. REFENES, M. AZEMA-BARAC & P. C. TRELEAVEN

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


  Modeling  of financial systems  has traditionally  been
  done  with  models  assuming  partial equilibrium. Such
  models  have  been  very  useful   in   expanding   our
  understanding of the capital markets; nevertheless many
  empirical    financial    anomalies    have    remained
  unexplainable.  It  is possible that this may be due to
  the  partial  equilibrium  nature  of   these   models.
  Attempting  to  model  the capital markets in a general
  equlibrium   framework   still   remains   analytically
  intractable.

  Because of their inductive  nature,  dynamical  systems
  such  as  neural networks can bypass the step of theory
  formulation, and  they  can  infer  complex  non-linear
  relationships   between  input  and  output  variables.
  Neural Networks have now been applied to  a  number  of
  live   systems   and   have   demonstrated  far  better
  performance than conventional approaches.

  In this paper review the state-of-the art in  financial
  modeling  using  neural  networks  and describe typical
  applications in key areas  of  univariate  time  series
  forecasting,      multivariate      data      analysis,
  classification,   and   pattern    recognition.     The
  applications  cover  areas  such  as  asset allocation,
  foreign exchange, stock ranking and  bond  trading.  We
  describe   the   parameters   that   influence   neural
  performance, and identify intervals of parameter values
  over  which  statistical stability can be achieved.


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