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Can the Leading US Energy Stock Prices be Predicted using the Ichimoku Cloud?

Author

Listed:
  • Ikhlaas Gurrib

    (Faculty of Management, Canadian University Dubai, United Arab Emirates)

  • Firuz Kamalov

    (Faculty of Management, Canadian University Dubai, United Arab Emirates)

  • Elgilani Elshareif

    (Faculty of Management, Canadian University Dubai, United Arab Emirates)

Abstract

The aim of this study is to investigate if Ichimoku Cloud can serve as a technical analysis indicator to improve stock price prediction for leading US energy companies. The methodology centers on the application of the Ichimoku Cloud as a trading system. The daily stock prices of the top ten constituents of the S&P Composite 1500 Energy Index - spanning the period from 12th April, 2012 to 31st July, 2019 - were sourced for experimentation. The performance of the Ichimoku Cloud is measured using both the Sharpe and Sortino ratios to adjust for total and downside risks. The analysis is split into pre and post oil crisis to account for the drop in energy stock prices during the July 2014 - December 2015. The model is also benchmarked against the na ve buy-and-hold strategy. The capacity of the Ichimoku indicator to provide signals during strengthening trends is analyzed. Despite the drop in energy stock prices, number of trades continued to increase along with profit opportunities. The PSX stock ranked first, with the highest Sharpe ratio, Sortino ratio, and Sharpe per number of trade. As expected, a number of buying signals occurred during strengthening bullish periods. Surprisingly, various sell signals also occurred during similar strengthening bullish trends. Most of the buy and sell signals under the Ichimoku indicator occurred outside of strengthening of bullish or bearish trends. The overall findings suggest that speculators can benefit from the use of the Ichimoku Cloud in analyzing energy stock price movements. In addition, it has the potential to reduce susceptibility to changes in energy prices. Last, the strength of the trend in place needs to be captured as it served as an additional layer of information which can improve the decision making process of the trader.

Suggested Citation

  • Ikhlaas Gurrib & Firuz Kamalov & Elgilani Elshareif, 2021. "Can the Leading US Energy Stock Prices be Predicted using the Ichimoku Cloud?," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 41-51.
    • Handle: RePEc:eco:journ2:2021-01-7
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    References listed on IDEAS

    as
    1. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    2. Narayan, Paresh Kumar & Narayan, Seema & Sharma, Susan Sunila, 2013. "An analysis of commodity markets: What gain for investors?," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3878-3889.
    3. Anna Creti & Duc Khuong Nguyen, 2015. "Energy markets׳ financialization, risk spillovers, and pricing models," Post-Print hal-01517413, HAL.
    4. repec:dau:papers:123456789/14774 is not listed on IDEAS
    5. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    6. Olson, Dennis, 2004. "Have trading rule profits in the currency markets declined over time?," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 85-105, January.
    7. Wing-Keung Wong & Meher Manzur & Boon-Kiat Chew, 2003. "How rewarding is technical analysis? Evidence from Singapore stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 543-551.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. Czudaj, Robert L., 2019. "Crude oil futures trading and uncertainty," Energy Economics, Elsevier, vol. 80(C), pages 793-811.
    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. Ioannis Psaradellis & Jason Laws & Athanasios A. Pantelous & Georgios Sermpinis, 2019. "Performance of technical trading rules: evidence from the crude oil market," The European Journal of Finance, Taylor & Francis Journals, vol. 25(17), pages 1793-1815, November.
    12. Smith, David M. & Wang, Na & Wang, Ying & Zychowicz, Edward J., 2016. "Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(6), pages 1991-2013, December.
    13. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Does intraday technical analysis in the U.S. equity market have value?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 199-210, March.
    14. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    15. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Can commodity futures be profitably traded with quantitative market timing strategies?," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1810-1819, September.
    16. Shynkevich, Andrei, 2012. "Performance of technical analysis in growth and small cap segments of the US equity market," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 193-208.
    17. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    18. Shynkevich, Andrei, 2016. "Predictability in bond returns using technical trading rules," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 55-69.
    19. Urquhart, Andrew & Gebka, Bartosz & Hudson, Robert, 2015. "How exactly do markets adapt? Evidence from the moving average rule in three developed markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 127-147.
    20. Szakmary, Andrew C. & Shen, Qian & Sharma, Subhash C., 2010. "Trend-following trading strategies in commodity futures: A re-examination," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 409-426, February.
    21. Ikhlaas Gurrib & Firuz Kamalov, 2019. "The implementation of an adjusted relative strength index model in foreign currency and energy markets of emerging and developed economies," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(2), pages 105-123, May.
    22. Cheol‐Ho Park & Scott H. Irwin, 2010. "A reality check on technical trading rule profits in the U.S. futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(7), pages 633-659, July.
    23. Fama, Eugene F, 1972. "Components of Investment Performance," Journal of Finance, American Finance Association, vol. 27(3), pages 551-567, June.
    24. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    25. Ikhlaas Gurrib, 2019. "Can energy commodities affect energy blockchain-based cryptos?," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 36(4), pages 682-699, July.
    26. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    27. Paresh Kumar Narayan & Huson Ali Ahmed & Seema Narayan, 2015. "Do Momentum‐Based Trading Strategies Work in the Commodity Futures Markets?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(9), pages 868-891, September.
    28. Ikhlaas Gurrib, 2018. "Can an Energy Futures Index Predict US Stock Market Index Movements?," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 230-240.
    29. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    30. Gencay, Ramazan, 1999. "Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules," Journal of International Economics, Elsevier, vol. 47(1), pages 91-107, February.
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    More about this item

    Keywords

    Energy stocks; Price forecasts; Ichimoku Cloud; Trading Performance;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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