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Financial Decision Making Using Computational Intelligence pp 103–127Cite as

Modelling and Trading the Greek Stock Market with Hybrid ARMA-Neural Network Models

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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 70))

Abstract

The motivation for this chapter is to investigate the use of alternative novel 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 six different neural network designs representing aHigher Order Neural Network (HONN), aRecurrent Network (RNN), a classicMultilayer Perceptron (MLP), a Hybrid Higher Order Neural Network, a Hybrid Recurrent Neural Network and a Hybrid Multilayer Perceptron Neural Network with some traditional techniques, either statistical such as 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 fixing time series over the period 2001–2008 using the last one and a half year for out-of-sample testing. We use the ASE 20 daily fixing 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 hybrid-HONNs do remarkably well and outperform all other models in a simple trading simulation exercise. However, when more sophisticatedtrading strategies usingconfirmation filters andleverage are applied, the hybrid-HONN network produces better results and outperforms all other neural network and traditional statistical models in terms of annualised return.

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Notes

  1. 1.

    We examine the ASE 20 since its first trading day on 21 January 2001, and until 31 December 2008, using the continuous data available from datastream.

  2. 2.

    Confirmation of its stationary property is obtained at the 1% significance level by both the Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) test statistics.

  3. 3.

    A “long” ASE 20 position means buying the index at the current price, while a “short” position means selling the index at the current price.

  4. 4.

    For a full discussion on the procedure, refer to [3, 30].

  5. 5.

    Backpropagation networks are the most common multilayer networks and are the most commonly used type in financial time series forecasting [20].

  6. 6.

    Associative recall is the act of associating two seemingly unrelated entities, such as smell and colour. For more information see [22].

  7. 7.

    Since most of the models have a volatility of about 20%, we have chosen this level as our basis. The leverage factors retained are given in Table 4.8.

  8. 8.

    The interest costs are calculated by considering a 4% interest rate p.a. divided by 252 trading days. In reality, leverage costs also apply during non-trading days so that we should calculate the interest costs using 360 days per year. But for the sake of simplicity, we use the approximation of 252 trading days to spread the leverage costs of non-trading days equally over the trading days. This approximation prevents us from keeping track of how many non-trading days we hold a position.

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Author information

Authors and Affiliations

  1. Liverpool John Moores University, Liverpool, UK

    Christian L. Dunis

  2. University of Liverpool Management School, Liverpool, UK

    Jason Laws

  3. London Metropolitan University, London, UK

    Andreas Karathanasopoulos

Authors
  1. Christian L. Dunis
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  2. Jason Laws
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  3. Andreas Karathanasopoulos
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Correspondence to Christian L. Dunis .

Editor information

Editors and Affiliations

  1. Dept. Production Engineering, and Management, Technical University of Crete, University Campus, Chania, 731 00, Greece

    Michael Doumpos

  2. , Department of Production Engineering and, Technical University of Crete, University Campus, Chania, 73100, Greece

    Constantin Zopounidis

  3. , Department of Industrial and Systems Eng, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

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Dunis, C.L., Laws, J., Karathanasopoulos, A. (2012). Modelling and Trading the Greek Stock Market with Hybrid ARMA-Neural Network Models. In: Doumpos, M., Zopounidis, C., Pardalos, P. (eds) Financial Decision Making Using Computational Intelligence. Springer Optimization and Its Applications, vol 70. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3773-4_4

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