Higher order and recurrent neural architectures for trading the EUR/USD exchange rate
Jason Laws and
Quantitative Finance, 2010, vol. 11, issue 4, 615-629
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 Euro/Dollar (EUR/USD) exchange rate. This is done by benchmarking three different neural network designs representing a Higher Order Neural Network (HONN), a Psi Sigma Network and a Recurrent Network (RNN) with three successful architectures, the traditional Multilayer Perceptron (MLP), the Softmax and the Gaussian Mixture (GM) models. More specifically, the trading performance of the six models is investigated in a forecast and trading simulation competition on the EUR/USD time series over a period of 8 years. These results are also benchmarked with more traditional models such as a moving average convergence divergence technical model (MACD), an autoregressive moving average model (ARMA) and a logistic regression model (LOGIT). As it turns out, the MLP, the HONN, the Psi Sigma and the RNN models all do well and outperform the more traditional models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the GM network produces remarkable results and outperforms all the other network architectures.
Keywords: Quantitative trading strategies; Volatility modelling; Risk management; Options volatility (search for similar items in EconPapers)
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