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

Georgios Sermpinis, Charalampos Stasinakis () and Christian Dunis

Journal of International Financial Markets, Institutions and Money, 2014, vol. 30, issue C, 21-54

Abstract: The motivation of this paper is 3-fold. 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.

Keywords: Forecast combinations; Kalman Filter; Genetic programming; Support vector regression; Trading strategies (search for similar items in EconPapers)
JEL-codes: C44 C45 C53 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1016/j.intfin.2014.01.006

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Journal of International Financial Markets, Institutions and Money is currently edited by I. Mathur and C. J. Neely

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