Modelling and trading commodities with a new deep belief network
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.
Alquist, R., Kilian, L. And Vigfusson, R.J. (2013) Forecasting the Price of Oil, Handbook of Economic Forecasting, Elsevier.
Baumeister, C. and Kilian, L. (2012) Real-Time Analysis of Oil Price Risks Using Forecast Scenarios, Staff Working Papers, 12, 1, Bank of Canada.
Butterworth, D. and Holmes, P. (2002) Inter-Market Spread Trading: Evidence from UK Index Futures Markets, Applied Financial Economics, 12(11), 783-791.
Chao, J., Shen, F. and Zhao, J. (2011) Forecasting exchange rate with deep belief networks, in Proceedings of the International Joint Conference on Neural Networks (IJCNN ’11), 1259–1266, San Jose, Calif, USA,
Chen, H. and Murray, A.F. (2002) A continuous restricted Boltzmann machine with hardware-amenable learning algorithm, Proceedings of the 12th International Conference on Artificial Neural Networks, 358–363.
Ding, H., Xiao, Y. and Yue, J. (2005) Adaptive Training of Radial Basis Function Networks Using Particle Swarm Optimization Algorithm, Lecture Notes in Computer Science, 3610, 119- 128.
Dunis, C.L., Laws, J. and Evans, B. (2005) Modelling and Trading The Gasoline Crack Spread: A Non-Linear Story, Derivatives Use, Trading & Regulation, 12 (1-2), 126-145.
Dunis, C.L, Laws, J. And Evans, B. (2006) Modelling and Trading the Soybean-Oil Crush Spread with Recurrent and Higher Order Networks: A Comparative Analysis, Neural Network World, 13(3/6), 193-213.
Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the dimensionality of data with neural networks, Science, 313(5786), 504–507.
Hinton, G.E., Osindero, S. and Teh, Y.W. (2006) A fast learning algorithm for deep belief nets, Neural Computation, 18(7), 1527–1554,
Kang, Y. And Choi, S. (2011) Restricted deep belief networks for multi-view learning, Lecture Notes in Computer Science, 6912, 130–145.
Karathanasopoulos, A., Dunis, C. and Khalil, S. (2016a) Modelling, forecasting and trading with a new sliding window approach: the crack spread example, Quantitative Finance, 16(12), 1875-1886.
Karathanasopoulos, A., Dunis, C., Likothanassis, S., Sermpinis, G. and Theofilatos, K. (2013) A Hybrid Genetic Algorithm-Support Vector Machine Approach in the Task of Forecasting and Trading, Journal of Asset Management, 14, 52-71.
Karathanasopoulos, A., Mitra, S., Skindilias, K. and Lo, C.C. (2016b) Modelling and Trading the English and German Stock Markets with Novelty Optimization Techniques, Journal of Forecasting, in press.
Karathanasopoulos, A., Sermpinis, G., Laws, J. and Dunis, C. (2014) Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms, Journal of Forecasting, 33(8), 596-610.
Karathanasopoulos, A., Sermpinis, G., Stasinakis, C. and Theofilatos, K. (2015) Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions, Computational Economics, 1-19.
Karathanasopoulos, A. (2016c) Modelling and trading the English stock market with novelty optimization techniques, Economics and Business Letters, 5(2), 50-57.
Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948.
Lee, H., Grosse, R., Ranganath, R. and Ng, A.Y. (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proceedings of the 26th International Conference On Machine Learning, 609–616.
Liu, T. (2010) A novel text classification approach based on deep belief network, Lecture Notes in Computer Science, 6443, 314–321,
Nekoukar, V. and Beheshti, H. (2010) A Local Linear Radial Basis Function Neural Network for Financial Time-Series Forecasting, Applied Intelligence, 33(3), 352-356.
Sermpinis, G., Theofilatos, K., Karathanasopoulos, A., Georgopoulos, E. and Dunis, C. (2013) Forecasting Foreign Exchange Rates with Adaptive Neural Networks Using Radial-Basis Functions and Particle Swarm Optimization, European Journal of Operational Research, 225(3), 528-540.
Shen, W., Guo, X., Wu, C. and Wu, D. (2011) Forecasting Stock Indices Using Radial Basis Function Neural Networks Optimized by Artificial Fish Swarm Algorithm, Knowledge-Based Systems, 24(3), 378-385.
Zhou, S., Chen, Q. and Wang, X. (2010) Discriminative Deep Belief networks for image classification, Proceedings of the 17th IEEE International Conference on Image Processing (ICIP ’10), 1561–1564.
How to Cite
The works published in this journal are subject to the following terms:
1. Oviedo University Press (the publisher) retains the property rights (copyright) of published works, and encourages and enables the reuse of the same under the license specified in paragraph 2.
© Ediuno. Ediciones de la Universidad de Oviedo / Oviedo University Press
2. The works are published in the online edition of the journal under a Creative Commons Attribution-Non Commercial-Non Derives 3.0 Spain (legal text). You can copy, use, distribute, transmit and publicly display, provided that: i) you cite the author and the original source of publication (journal, publisher and URL of the work), ii) they are not used for commercial purposes, iii) mentions the existence and specifications of this license.
3. Conditions of self-archiving. The author can archive the post-print version of the article (publisher’s version) on the author’s personal website and/or on the web of the institution where he belong, including a link to the page of the journal and putting the way of citation of the work. Economics and Business Letters and its URL https://reunido.uniovi.es/index.php/EBL/index are the only authorized source for correctly giving the reference of the publisher’s version in every mention of the article.