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Mathematical Problems in Engineering
Volume 2014, Article ID 101808, 10 pages
http://dx.doi.org/10.1155/2014/101808
Research Article

Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms

1School of Humanities and Economic Management, China University of Geosciences, Beijing 100083, China
2Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources, Beijing 100083, China
3Lab of Resources and Environmental Management, China University of Geosciences, Beijing 100083, China
4Institute of China’s Economic Reform and Development, Renmin University of China, Beijing 100872, China

Received 19 February 2014; Revised 4 May 2014; Accepted 7 May 2014; Published 26 May 2014

Academic Editor: Wei Chen

Copyright © 2014 Lijun Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Z. M. Chen and G. Q. Chen, “Demand-driven energy requirement of world economy 2007: a multi-region input-output network simulation,” Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 7, pp. 1757–1774, 2013. View at Publisher · View at Google Scholar
  2. G. Wu, L.-C. Liu, and Y.-M. Wei, “Comparison of China's oil import risk: results based on portfolio theory and a diversification index approach,” Energy Policy, vol. 37, no. 9, pp. 3557–3565, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. X. H. Xia, G. T. Huang, G. Q. Chen, B. Zhang, Z. M. Chen, and Q. Yang, “Energy security, efficiency and carbon emission of Chinese industry,” Energy Policy, vol. 39, no. 6, pp. 3520–3528, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. X. H. Xia and G. Q. Chen, “Energy abatement in Chinese industry: cost evaluation of regulation strategies and allocation alternatives,” Energy Policy, vol. 45, pp. 449–458, 2012. View at Publisher · View at Google Scholar
  5. N. Cui, Y. Lei, and W. Fang, “Design and impact estimation of a reform program of China's tax and fee policies for low-grade oil and gas resources,” Petroleum Science, vol. 8, no. 4, pp. 515–526, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. L. Lei, N. Cui, and D. Y. Pan, “Economic and social effects analysis of mineral development in China and policy implications,” Resources Policy, vol. 38, pp. 448–457, 2013. View at Publisher · View at Google Scholar
  7. Z. M. Chen and G. Q. Chen, “An overview of energy consumption of the globalized world economy,” Energy Policy, vol. 39, no. 10, pp. 5920–5928, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. N. Nomikos and K. Andriosopoulos, “Modelling energy spot prices: empirical evidence from NYMEX,” Energy Economics, vol. 34, no. 4, pp. 1153–1169, 2012. View at Publisher · View at Google Scholar
  9. G. B. Ning, Z. J. Zhen, P. Wang, Y. Li, and H. X. Yin, “Economic analysis on value chain of taxi fleet with battery-swapping mode using multiobjective genetic algorithm,” Mathematical Problems in Engineering, vol. 2012, Article ID 175912, 15 pages, 2012. View at Publisher · View at Google Scholar
  10. S. Chai, Y. B. Li, J. Wang, and C. Wu, “A genetic algorithm for task scheduling on NoC using FDH cross efficiency,” Mathematical Problems in Engineering, vol. 2013, Article ID 708495, 16 pages, 2013. View at Publisher · View at Google Scholar
  11. G. Q. Chen and B. Chen, “Resource analysis of the Chinese society 1980–2002 based on exergy. Part 1: fossil fuels and energy minerals,” Energy Policy, vol. 35, no. 4, pp. 2038–2050, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Chen, G. Chen, X. Xia, and S. Xu, “Global network of embodied water flow by systems input-output simulation,” Frontiers of Earth Science, vol. 6, no. 3, pp. 331–344, 2012. View at Publisher · View at Google Scholar
  13. Z. M. Chen and G. Q. Chen, “Embodied carbon dioxide emission at supra-national scale: a coalition analysis for G7, BRIC, and the rest of the world,” Energy Policy, vol. 39, no. 5, pp. 2899–2909, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Z. An, X.-Y. Gao, W. Fang, X. Huang, and Y. H. Ding, “The role of fluctuating modes of autocorrelation in crude oil prices,” Physica A, vol. 393, pp. 382–390, 2014. View at Publisher · View at Google Scholar
  15. X.-Y. Gao, H.-Z. An, H.-H. Liu, and Y.-H. Ding, “Analysis on the topological properties of the linkage complex network between crude oil future price and spot price,” Acta Physica Sinica, vol. 60, no. 6, Article ID 068902, 2011. View at Google Scholar · View at Scopus
  16. X.-Y. Gao, H. Z. An, and W. Fang, “Research on fluctuation of bivariate correlation of time series based on complex networks theory,” Acta Physica Sinica, vol. 61, no. 9, Article ID 098902, 2012. View at Publisher · View at Google Scholar
  17. S. Yu and Y.-M. Wei, “Prediction of China's coal production-environmental pollution based on a hybrid genetic algorithm-system dynamics model,” Energy Policy, vol. 42, pp. 521–529, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Geisendorf, “Internal selection and market selection in economic genetic algorithms,” Journal of Evolutionary Economics, vol. 21, no. 5, pp. 817–841, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Tehrani and F. Khodayar, “A hybrid optimized artificial intelligent model to forecast crude oil using genetic algorithm,” African Journal of Business Management, vol. 5, pp. 13130–13135, 2011. View at Google Scholar
  20. A. M. Elaiw, X. Xia, and A. M. Shehata, “Minimization of fuel costs and gaseous emissions of electric power generation by model predictive control,” Mathematical Problems in Engineering, vol. 2013, Article ID 906958, 15 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  21. L. Mendes, P. Godinho, and J. Dias, “A Forex trading system based on a genetic algorithm,” Journal of Heuristics, vol. 18, no. 4, pp. 627–656, 2012. View at Publisher · View at Google Scholar
  22. M. C. Roberts, “Technical analysis and genetic programming: constructing and testing a commodity portfolio,” Journal of Futures Markets, vol. 25, no. 7, pp. 643–660, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. J.-Y. Potvin, P. Soriano, and V. Maxime, “Generating trading rules on the stock markets with genetic programming,” Computers and Operations Research, vol. 31, no. 7, pp. 1033–1047, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Esfahanipour and S. Mousavi, “A genetic programming model to generate risk-adjusted technical trading rules in stock markets,” Expert Systems with Applications, vol. 38, no. 7, pp. 8438–8445, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. M. A. H. Dempster, T. W. Payne, Y. Romahi, and G. W. P. Thompson, “Computational learning techniques for intraday FX trading using popular technical indicators,” IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 744–754, 2001. View at Publisher · View at Google Scholar · View at Scopus
  26. T. Nakashima, Y. Yokota, Y. Shoji, and H. Ishibuchi, “A genetic approach to the design of autonomous agents for futures trading,” Artificial Life and Robotics, vol. 11, no. 2, pp. 145–148, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Ghandar, Z. Michalewicz, M. Schmidt, T.-D. To, and R. Zurbrugg, “Computational intelligence for evolving trading rules,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 1, pp. 71–86, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. W. L. Tung and C. Quek, “Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach,” Expert Systems with Applications, vol. 38, no. 5, pp. 4668–4688, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. C.-H. Cheng, T.-L. Chen, and L.-Y. Wei, “A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting,” Information Sciences, vol. 180, no. 9, pp. 1610–1629, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. P.-C. Chang, C.-Y. Fan, and J.-L. Lin, “Trend discovery in financial time series data using a case based fuzzy decision tree,” Expert Systems with Applications, vol. 38, no. 5, pp. 6070–6080, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. G. A. Vasilakis, K. A. Theofilatos, E. F. Georgopoulos, A. Karathanasopoulos, and S. D. Likothanassis, “A genetic programming approach for EUR/USD exchange rate forecasting and trading,” Computational Economics, vol. 42, no. 4, pp. 415–431, 2013. View at Publisher · View at Google Scholar
  32. I. A. Boboc and M. C. Dinica, “An algorithm for testing the efficient market hypothesis,” PLoS ONE, vol. 8, no. 10, Article ID e78177, 2013. View at Publisher · View at Google Scholar
  33. W. Cheung, K. S. K. Lam, and H. F. Yeung, “Intertemporal profitability and the stability of technical analysis: evidences from the Hong Kong stock exchange,” Applied Economics, vol. 43, no. 15, pp. 1945–1963, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. H. Dewachter and M. Lyrio, “The economic value of technical trading rules: a nonparametric utility-based approach,” International Journal of Finance and Economics, vol. 10, no. 1, pp. 41–62, 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Fernández-Pérez, F. Fernández-Rodríguez, and S. Sosvilla-Rivero, “Exploiting trends in the foreign exchange markets,” Applied Economics Letters, vol. 19, no. 6, pp. 591–597, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. M. Metghalchia, J. Marcucci, and Y.-H. Chang, “Are moving average trading rules profitable? Evidence from the European stock markets,” Applied Economics, vol. 44, no. 12, pp. 1539–1559, 2012. View at Publisher · View at Google Scholar · View at Scopus
  37. C. J. Neely, P. A. Weller, and J. M. Ulrich, “The adaptive markets hypothesis: evidence from the foreign exchange market,” Journal of Financial and Quantitative Analysis, vol. 44, no. 2, pp. 467–488, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. W. E. Shambora and R. Rossiter, “Are there exploitable inefficiencies in the futures market for oil?” Energy Economics, vol. 29, no. 1, pp. 18–27, 2007. View at Publisher · View at Google Scholar · View at Scopus
  39. F. Allen and R. Karjalainen, “Using genetic algorithms to find technical trading rules,” Journal of Financial Economics, vol. 51, no. 2, pp. 245–271, 1999. View at Google Scholar · View at Scopus
  40. J. Wang, “Trading and hedging in S&P 500 spot and futures markets using genetic programming,” Journal of Futures Markets, vol. 20, no. 10, pp. 911–942, 2000. View at Google Scholar · View at Scopus
  41. J. How, M. Ling, and P. Verhoeven, “Does size matter? A genetic programming approach to technical trading,” Quantitative Finance, vol. 10, no. 2, pp. 131–140, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  42. F. Wang, P. L. H. Yu, and D. W. Cheung, “Combining technical trading rules using particle swarm optimization,” Expert Systems with Applications, vol. 41, no. 6, pp. 3016–3026, 2014. View at Publisher · View at Google Scholar
  43. J. Andrada-Félix and F. Fernández-Rodríguez, “Improving moving average trading rules with boosting and statistical learning methods,” Journal of Forecasting, vol. 27, no. 5, pp. 433–449, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  44. T. T.-L. Chong and W.-K. Ng, “Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30,” Applied Economics Letters, vol. 15, no. 14, pp. 1111–1114, 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. I. Cialenco and A. Protopapadakis, “Do technical trading profits remain in the foreign exchange market? Evidence from 14 currencies,” Journal of International Financial Markets, Institutions and Money, vol. 21, no. 2, pp. 176–206, 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. A. E. Milionis and E. Papanagiotou, “Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non-linear dependencies in stock returns,” Journal of Applied Statistics, vol. 40, no. 11, pp. 2480–2494, 2013. View at Publisher · View at Google Scholar
  47. Y. S. Ni, J. T. Lee, and Y. C. Liao, “Do variable length moving average trading rules matter during a financial crisis period?” Applied Economics Letters, vol. 20, no. 2, pp. 135–141, 2013. View at Publisher · View at Google Scholar
  48. V. Pavlov and S. Hurn, “Testing the profitability of moving-average rules as a portfolio selection strategy,” Pacific-Basin Finance Journal, vol. 20, no. 5, pp. 825–842, 2012. View at Publisher · View at Google Scholar
  49. C. Chiarella, X.-Z. He, and C. Hommes, “A dynamic analysis of moving average rules,” Journal of Economic Dynamics & Control, vol. 30, no. 9-10, pp. 1729–1753, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  50. X.-Z. He and M. Zheng, “Dynamics of moving average rules in a continuous-time financial market model,” Journal of Economic Behavior and Organization, vol. 76, no. 3, pp. 615–634, 2010. View at Publisher · View at Google Scholar · View at Scopus
  51. C. Neely, P. Weller, and R. Dittmar, Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach, Cambridge University Press, 1997.