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Assessing the profitability of intraday opening range breakout strategies

Author

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  • Holmberg, Ulf

    (Department of Economics, Umeå University)

  • Lönnbark, Carl

    (Department of Economics, Umeå University)

  • Lundström, Christian

    (Department of Economics, Umeå University)

Abstract

Is it possible to beat the market by mechanical trading rules based on historical and publicly known information? Such rules have long been used by investors and in this paper, we test the success rate of trades and profitability of the Open Range Breakout (ORB) strategy. An investor that trades on the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of Low, High, Open and Close over a given time horizon.

Suggested Citation

  • Holmberg, Ulf & Lönnbark, Carl & Lundström, Christian, 2012. "Assessing the profitability of intraday opening range breakout strategies," Umeå Economic Studies 845, Umeå University, Department of Economics.
    • Handle: RePEc:hhs:umnees:0845
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    Cited by:

    1. Mitra, Subrata Kumar & Bawa, Jaslene & Kannadhasan, M. & Goyal, Vinay & Chattopadhyay, Manojit, 2017. "Can profitability through momentum strategies be enhanced applying a range to standard deviation filter?," Finance Research Letters, Elsevier, vol. 20(C), pages 269-273.
    2. Day, Min-Yuh & Ni, Yensen, 2023. "Be greedy when others are fearful: Evidence from a two-decade assessment of the NDX 100 and S&P 500 indexes," International Review of Financial Analysis, Elsevier, vol. 90(C).
    3. 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.
    4. Mu-En Wu & Wei-Ho Chung, 2019. "Empirical Evaluations on Momentum Effects of Taiwan Index Futures via Stop-Loss and Stop-Profit Mechanisms," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 629-648, March.
    5. Dong, Xi & Feng, Shu & Ling, Leng & Song, Pingping, 2017. "Dynamic autocorrelation of intraday stock returns," Finance Research Letters, Elsevier, vol. 20(C), pages 274-280.
    6. Loginov, Alexander & Heywood, Malcolm, 2020. "On the different impacts of fixed versus floating bid-ask spreads on an automated intraday stock trading," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Caporin, Massimiliano & Ranaldo, Angelo & Santucci de Magistris, Paolo, 2013. "On the predictability of stock prices: A case for high and low prices," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5132-5146.
    8. Kuo, Wei-Yu & Lin, Tse-Chun, 2013. "Overconfident individual day traders: Evidence from the Taiwan futures market," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3548-3561.
    9. Vince Vella & Wing Lon Ng, 2015. "A Dynamic Fuzzy Money Management Approach for Controlling the Intraday Risk‐Adjusted Performance of AI Trading Algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 153-178, April.

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    Keywords

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    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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