Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage

Sermpinis, Georgios; Dunis, Christian L.; Laws, Jason

doi:10.1016/j.dss.2012.05.039

Cited by 75 publications

(31 citation statements)

References 42 publications

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“…The activation function of the nodes in the hidden layer is the summing function, while the activation function of the output layer is a sigmoid one. For more information on MLP, RNN and PSN architectures see Zhang et al (1998), Ghazali et al (2006) and Sermpinis et al (2012).…”

Section: Neural Network (Nns)mentioning

confidence: 99%

Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects

Sermpinis

Dunis²

2014

Journal of International Financial Markets, Institutions and Mo

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The motivation of this paper is threefold. Firstly, we apply a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN) and a Psi-Sigma Network (PSN) architecture in a forecasting and trading exercise on the EUR/USD, EUR/GBP and EUR/CHF exchange rates and explore the utility of Kalman Filter, Genetic Programming (GP) and Support Vector Regression (SVR) algorithms as forecasting combination techniques. Secondly, we introduce a hybrid leverage factor based on volatility forecasts and market shocks and study if its application improves the trading performance of our models. Thirdly we introduce a specialized loss function for Neural Networks (NNs) in financial applications. In terms of our results, the PSN from the individual forecasts and the SVR from our forecast combination techniques outperform their benchmarks in statistical accuracy and trading efficiency. We also note that our trading strategy is successful, as it increased the trading performance of most of our models, while our NNs loss function seems promising.

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Section: Neural Network (Nns)mentioning

confidence: 99%

Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects

Sermpinis

Dunis²

2014

Journal of International Financial Markets, Institutions and Mo

Self Cite

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show abstract

“…The activation function of the nodes in the hidden layer is the summing function, while the activation function of the output layer is a sigmoid one. For more information on MLP, RNN and PSN architectures see Zhang et al (1998) and Sermpinis et al (2012). The summary of the structure and the training characteristics of those networks are presented in the Appendix A.…”

Section: Nn Benchmarksmentioning

confidence: 99%

Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions

2014

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“…There is abundance of effort focused on the accuracy of exchange rate forecast [2]. There are two broad efforts in this direction namely exchange rate forecast by using the Markov-switching model and artificial neural network.…”

mentioning

confidence: 99%

“…Market-based forecasting explores the expectation of the market on the future exchange rate. Machine-learning based forecasting involves forecasting by using artificial neural network, which data are assumed to be non-linear [2]. Mixed forecasting is a composite of two or more methods.…”

mentioning

confidence: 99%

Exchange Rate Forecast: A Note

Tsen

2013

J Stock Forex Trad

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Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage

Cited by 75 publications

References 42 publications

Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects

Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects

Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions

Exchange Rate Forecast: A Note

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