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Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach

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  • Wang, Lijun
  • An, Haizhong
  • Liu, Xiaojia
  • Huang, Xuan

Abstract

Strategies to increase profit from investments in crude oil futures markets are an important issue for investors in energy finance. This paper proposes an approach to generate dynamic moving average trading rules in crude oil futures markets. An adaptive moving average calculation is used to better describe the fluctuations, and trading rules can be adjusted dynamically in the investment period based on the performance of four reference rules. We use genetic algorithms to select optimal dynamic moving average trading rules from a large set of possible parameters. Our results indicate that dynamic trading rules can help traders make profit in the crude oil futures market and are more effective than the BH strategy in the price decrease process. Moreover, dynamic moving average trading rules are more favorable to traders than static trading rules, and the advantage becomes more obvious over long investment cycles. The lengths of the two periods of dynamic moving average trading rules are closely associated with price volatility. The dynamic trading rules will have outstanding performance when market is shocked by significant energy related events. Investment advices are given out and these advices are helpful for traders when choosing technical trading rules in actual investments.

Suggested Citation

  • Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
    • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:1608-1618
    DOI: 10.1016/j.apenergy.2015.08.132
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    1. D'Ecclesia, Rita L. & Magrini, Emiliano & Montalbano, Pierluigi & Triulzi, Umberto, 2014. "Understanding recent oil price dynamics: A novel empirical approach," Energy Economics, Elsevier, vol. 46(S1), pages 11-17.
    2. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    3. Massoud Metghalchi & Juri Marcucci & Yung-Ho Chang, 2012. "Are moving average trading rules profitable? Evidence from the European stock markets," Applied Economics, Taylor & Francis Journals, vol. 44(12), pages 1539-1559, April.
    4. Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
    5. Nazlioglu, Saban & Soytas, Ugur & Gupta, Rangan, 2015. "Oil prices and financial stress: A volatility spillover analysis," Energy Policy, Elsevier, vol. 82(C), pages 278-288.
    6. Serban, Alina F., 2010. "Combining mean reversion and momentum trading strategies in foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2720-2727, November.
    7. Bassam Fattouh, Lutz Kilian, and Lavan Mahadeva, 2013. "The Role of Speculation in Oil Markets: What Have We Learned So Far?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    8. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(4), pages 405-426, December.
    9. Jammazi, Rania, 2012. "Cross dynamics of oil-stock interactions: A redundant wavelet analysis," Energy, Elsevier, vol. 44(1), pages 750-777.
    10. Neely, Christopher J. & Weller, Paul A. & Ulrich, Joshua M., 2009. "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 467-488, April.
    11. Cotter, John & Hanly, Jim, 2015. "Performance of utility based hedges," Energy Economics, Elsevier, vol. 49(C), pages 718-726.
    12. Kilian, Lutz & Baumeister, Christiane, 2014. "A General Approach to Recovering Market Expectations from Futures Prices With an Application to Crude Oil," CEPR Discussion Papers 10162, C.E.P.R. Discussion Papers.
    13. Zhang, Bing & Li, Xiao-Ming & He, Fei, 2014. "Testing the evolution of crude oil market efficiency: Data have the conn," Energy Policy, Elsevier, vol. 68(C), pages 39-52.
    14. Chiarella, Carl & He, Xue-Zhong & Pellizzari, Paolo, 2012. "A Dynamic Analysis Of The Microstructure Of Moving Average Rules In A Double Auction Market," Macroeconomic Dynamics, Cambridge University Press, vol. 16(4), pages 556-575, September.
    15. Holmberg, Ulf & Lönnbark, Carl & Lundström, Christian, 2013. "Assessing the profitability of intraday opening range breakout strategies," Finance Research Letters, Elsevier, vol. 10(1), pages 27-33.
    16. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    17. Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006. "A dynamic analysis of moving average rules," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1729-1753.
    18. Bjursell, Johan & Gentle, James E. & Wang, George H.K., 2015. "Inventory announcements, jump dynamics, volatility and trading volume in U.S. energy futures markets," Energy Economics, Elsevier, vol. 48(C), pages 336-349.
    19. R. Rosillo & D. de la Fuente & J. A. L. Brugos, 2013. "Technical analysis and the Spanish stock exchange: testing the RSI, MACD, momentum and stochastic rules using Spanish market companies," Applied Economics, Taylor & Francis Journals, vol. 45(12), pages 1541-1550, April.
    20. Tong Zhang & B. Brorsen, 2009. "Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 34(4), pages 399-417, November.
    21. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    22. Jiang, Zhi-Qiang & Xie, Wen-Jie & Zhou, Wei-Xing, 2014. "Testing the weak-form efficiency of the WTI crude oil futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 235-244.
    23. Ruslan Bikbov & Mikhail Chernov, 2011. "Yield Curve and Volatility: Lessons from Eurodollar Futures and Options," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 66-105, Winter.
    24. He, Xue-Zhong & Zheng, Min, 2010. "Dynamics of moving average rules in a continuous-time financial market model," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 615-634, December.
    25. Kolodziej, Marek & Kaufmann, Robert K. & Kulatilaka, Nalin & Bicchetti, David & Maystre, Nicolas, 2014. "Crude oil: Commodity or financial asset?," Energy Economics, Elsevier, vol. 46(C), pages 216-223.
    26. Janice How & Martin Ling & Peter Verhoeven, 2010. "Does size matter? A genetic programming approach to technical trading," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 131-140.
    27. Yudong Wang & Chongfeng Wu, 2013. "Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 393-414, December.
    28. Kaufmann, Robert K., 2011. "The role of market fundamentals and speculation in recent price changes for crude oil," Energy Policy, Elsevier, vol. 39(1), pages 105-115, January.
    29. William Cheung & Keith Lam & HangFai Yeung, 2011. "Intertemporal profitability and the stability of technical analysis: evidences from the Hong Kong stock exchange," Applied Economics, Taylor & Francis Journals, vol. 43(15), pages 1945-1963.
    30. Alexandros E. Milionis & Evangelia Papanagiotou, 2013. "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, Taylor & Francis Journals, vol. 40(11), pages 2480-2494, November.
    31. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    32. Elder, John & Miao, Hong & Ramchander, Sanjay, 2014. "Price discovery in crude oil futures," Energy Economics, Elsevier, vol. 46(S1), pages 18-27.
    33. Adrián Fernández-P�rez & Fernando Fernández-Rodr�guez & Simón Sosvilla-Rivero, 2012. "Exploiting trends in the foreign exchange markets," Applied Economics Letters, Taylor & Francis Journals, vol. 19(6), pages 591-597, April.
    34. Auer, Benjamin R., 2014. "Daily seasonality in crude oil returns and volatilities," Energy Economics, Elsevier, vol. 43(C), pages 82-88.
    35. Jun Wang, 2000. "Trading and hedging in S&P 500 spot and futures markets using genetic programming," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(10), pages 911-942, November.
    36. Hans Dewachter & Marco Lyrio, 2005. "The economic value of technical trading rules: a nonparametric utility-based approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(1), pages 41-62.
    37. Subrata Kumar Mitra, 2011. "How rewarding is technical analysis in the Indian stock market?," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 287-297.
    38. Zhang, Yue-Jun, 2013. "Speculative trading and WTI crude oil futures price movement: An empirical analysis," Applied Energy, Elsevier, vol. 107(C), pages 394-402.
    39. Yen-Sen Ni & Jen-Tsai Lee & Yi-Ching Liao, 2013. "Do variable length moving average trading rules matter during a financial crisis period?," Applied Economics Letters, Taylor & Francis Journals, vol. 20(2), pages 135-141, February.
    40. Yu, Hao & Nartea, Gilbert V. & Gan, Christopher & Yao, Lee J., 2013. "Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 356-371.
    41. Cialenco, Igor & Protopapadakis, Aris, 2011. "Do technical trading profits remain in the foreign exchange market? Evidence from 14 currencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(2), pages 176-206, April.
    42. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    43. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    44. Zhang, Yue-Jun & Wang, Zi-Yi, 2013. "Investigating the price discovery and risk transfer functions in the crude oil and gasoline futures markets: Some empirical evidence," Applied Energy, Elsevier, vol. 104(C), pages 220-228.
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    2. Liu, Xueyong & An, Haizhong & Huang, Shupei & Wen, Shaobo, 2017. "The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH–BEKK model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 374-383.
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