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

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.