GP Algorithm versus Hybrid and Mixed Neural Networks

Karathanasopoulos, Andreas (2011) GP Algorithm versus Hybrid and Mixed Neural Networks. Centre for EMEA Banking, Finance and Economics Working Paper Series, 2011 (16). pp. 1-36.

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Abstract

In the current paper we present an integrated genetic programming
environment, called java GP Modelling. The java GP Modelling environment is an implementation of the steady-state genetic programming algorithm. That
algorithm evolves tree based structures that represent models of input –
output relation of a system. The motivation of this paper is to compare the GP
algorithm with neural network architectures when applied to the task of
forecasting and trading the ASE 20 Greek Index using only autoregressive
terms as inputs. This is done by benchmarking the forecasting performance of
the GP algorithm and 6 different ARMA-Neural Network combination designs
representing a Hybrid, Mixed Higher Order Neural Network (HONN), a Hybrid,
Mixed Recurrent Network (RNN), a Hybrid, Mixed classic Multilayer
Perceptron (MLP) with some traditional techniques, either statistical such as a
an autoregressive moving average model (ARMA), or technical such as a
moving average convergence/divergence model (MACD), plus a naïve trading strategy. More specifically, the trading performance of all models is
investigated in a forecast and trading simulation on ASE 20 time series
closing prices over the period 2001-2008 using the last one and a half years
for out-of-sample testing. We use the ASE 20 daily series as many financial
institutions are ready to trade at this level and it is therefore possible to leave
orders with a bank for business to be transacted on that basis.

As it turns out, the GP model does remarkably well and outperforms all other
models in a simple trading simulation exercise. This is also the case when
more sophisticated trading strategies using confirmation filters and leverage
are applied, as the GP model still produces better results and outperforms all
other neural network and traditional statistical models in terms of annualised
return.

Item Type: Article
Uncontrolled Keywords: Centre for EMEA Banking, Finance and Economics Working Paper Series, Greece
Subjects: 300 Social sciences > 330 Economics
Department: Guildhall School of Business and Law
Depositing User: David Pester
Date Deposited: 20 Apr 2015 12:55
Last Modified: 20 Apr 2015 12:55
URI: http://repository.londonmet.ac.uk/id/eprint/428

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