Constructive higher-order network that is polynomial time

Redding, N.J.; Kowalczyk, Adam; Downs, T.

doi:10.1016/s0893-6080(09)80009-9

Cited by 88 publications

(18 citation statements)

References 23 publications

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“…Although their function approximation superiority over the more traditional architectures is well documented in the literature (see among others Redding et al (1993), Kosmatopoulos et al (1995) and Psaltis et al (1998)), their use in finance so far has been limited. This has changed when scientists started to investigate not only the benefits of Neural Networks (NNs) against the more traditional statistical techniques but also the differences between the different NNs model architectures.…”

Section: Literature Reviewmentioning

confidence: 99%

GP algorithm versus hybrid and mixed neural networks

Dunis

Laws

Karathanasopoulos

2013

The European Journal of Finance

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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 inputoutput 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.

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Section: Literature Reviewmentioning

confidence: 99%

GP algorithm versus hybrid and mixed neural networks

Dunis

Laws

Karathanasopoulos

2013

The European Journal of Finance

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“…Although their function approximation superiority over the more traditional architectures is well documented in the literature (see, among others, studies by Redding et al, 12 Kosmatopoulos et al 13 …”

Section: Literature Reviewmentioning

confidence: 99%

The robustness of neural networks for modelling and trading the EUR/USD exchange rate at the ECB fixing

Dunis¹,

Laws²,

Sermpinis³

2009

J Deriv Hedge Funds

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The objective of this study is to investigate the use, the stability and the robustness of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate using the European Central Bank (ECB) fixing series with only autoregressive terms as inputs. This is achieved by benchmarking the forecasting performance of three different NN designs representing a Higher Order Neural Network (HONN), a Recurrent Neural Network (RNN) and the classic Multilayer Perceptron (MLP) with some traditional techniques, either statistical, such as an autoregressive moving average model , or technical, such as a moving average convergence/divergence model, plus a naïve strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on the EUR/USD ECB fixing time series over the period January 1999 -August 2008 using the last 8 months for out-of-sample testing. Our results in terms of their robustness and stability are compared with a previous study by the authors, who apply the same models and follow the same methodology forecasting the same series, using as out-of-sample the period from July 2006 to December 2007. As it turns out, the HONN and MLP networks present a robust performance and do remarkably well in outperforming all other models in a simple trading simulation exercise in both studies. Moreover, when transaction costs are considered and leverage is

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“…To overcome these limitations some researchers have proposed the use of Higher Order Neural Networks (HONNs) [6,7]. HONNs are networks in which the net input to a computational neuron is a weighted sum of products of its inputs.…”

Section: Introductionmentioning

confidence: 99%