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Modelling and trading commodities with a new deep belief network

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  • Andreas Karathanasopoulos

Abstract

The scope of this project is to study a novel methodology in the task of forecasting and trading the crack spread modelled index. More specifically in this research we are expanding the earlier work carried out by Karathanasopoulos et al. (2016c) and Dunis et al. (2005) who model the Crack Spread with traditional neural networks. In this research paper we provide for first time a more advanced approach to non-linear modelling and trading the ‘Crack’. The selected trading period covers 4500 trading days and the proposed model is a deep belief network (DBN). To model, test and evaluate the crack spread we use an expansive universe of 500 inputs correlated with the main index. Moreover we have used for reasons of comparison a radial basis function combined with partial swarm optimizer and two linear models such as random walk theorem and buy and hold strategy. Â

Suggested Citation

  • Andreas Karathanasopoulos, 2017. "Modelling and trading commodities with a new deep belief network," Economics and Business Letters, Oviedo University Press, vol. 6(2), pages 28-34.
    • Handle: RePEc:ove:journl:aid:11280
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    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/11280
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    1. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    2. Christiane Baumeister & Lutz Kilian, 2014. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 62(1), pages 119-145, April.
    3. Darren Butterworth & Phil Holmes, 2002. "Inter-market spread trading: evidence from UK index futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 12(11), pages 783-790.
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