|
NeuroCOLT
Technical Report NC-TR-96-046
Use Of Neural
Network Ensembles for Portfolio Selection and Risk Management
D.L.Toulson
Intelligent Financial Systems Ltd
UK
S.P.Toulson
London School Of Economics
UK
Abstract
A well known method of managing the risk whilst maximising the return
of a portfolio is through Markowitz Analysis of the efficient set.
A key pre-requisite for this technique is the accurate estimation
of the future expected returns and risks (variance of re turns) of
the securities contained in the portfolio along with their expected
correlations. The estimates for future returns are typically obtained
using weighted averages of historical returns of the securities involved
or other (linear) techniques. Estimates for the volatilities of the
securities may be made in the same way or through the use of (G)ARCH
or stochastic volatility (SV) techniques. In this paper we propose
the use of neural networks to estimate future returns and risks of
securities. The networks are arranged into committees. Each
committee contains a number of independ ently trained neural networks.
The task of each committee is to estimate either the future return
or risk of a particular security. The inputs to the networks of the
committee make use of a novel discriminant analysis technique we have
called Fuzzy Discriminants Analysis. The estimates
of future returns and risks provided by the committees are then used
to manage a portfolio of 40 UK equities over a five year period (1989-1994).
The management of the portfolio is constrained such that at any time
it should have the same risk characteristic as the FTSE-100 index.
Within this constraint, the portfolio is chosen to provide the maximum
possible return. We show that the managed portfolio significantly
outper forms the FTSE-100 index in terms of both overall return and
volatility.
Download Compressed
Postscript
|