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Dunis, Christian L., Laws, Jason and Karathanasopoulos, Andreas (2011) Modelling and trading the Greek stock market with mixed neural network models. Centre for EMEA Banking, Finance and Economics Working Paper Series, 2011 (15). pp. 1-32.
In this paper, a mixed methodology that combines both the ARMA and NNR models is proposed to take advantage of the unique strength of ARMA and NNR models in linear and nonlinear modelling. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately. The motivation for this paper 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 a Higher Order Neural Network (HONN), a Recurrent Network (RNN), a classic Multilayer Percepton (MLP), a Mixed Higher Order Neural Network, a Mixed Recurrent Neural Network and a Mixed Multilayer Percepton Neural Network 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 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.
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