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Profiling high-frequency equity price movements in directional changes

Author

Listed:
  • Edward P. K. Tsang
  • Ran Tao
  • Antoaneta Serguieva
  • Shuai Ma

Abstract

Market prices are traditionally sampled in fixed time intervals to form time series. Directional change (DC) is an alternative approach to record price movements. Instead of sampling at fixed intervals, DC is data driven: price changes dictate when a price is recorded. DC provides us with a complementary way to extract information from data. It allows us to observe features that may not be recognized in time series. The argument is that time series and DC-based analysis complement each other. With data sampled at irregular time intervals in DC, however, some of the time series indicators cannot be used in DC-based analysis. For example, returns must be time adjusted and volatility must be amended accordingly. A major objective of this paper is to introduce indicators for profiling markets under DC. We analyse empirical high-frequency data on major equities traded on the UK stock market, and through DC profiling extract information complementary to features observed through time series profiling.

Suggested Citation

  • Edward P. K. Tsang & Ran Tao & Antoaneta Serguieva & Shuai Ma, 2017. "Profiling high-frequency equity price movements in directional changes," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 217-225, February.
    • Handle: RePEc:taf:quantf:v:17:y:2017:i:2:p:217-225
    DOI: 10.1080/14697688.2016.1164887
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    References listed on IDEAS

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    1. S. Masry & A. Dupuis & R. B. Olsen & E. Tsang, 2013. "Time zone normalization of FX seasonality," Quantitative Finance, Taylor & Francis Journals, vol. 13(7), pages 1115-1123, January.
    2. T. Bisig & A. Dupuis & V. Impagliazzo & R. B. Olsen, 2012. "The scale of market quakes," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 501-508, July.
    3. J. B. Glattfelder & A. Dupuis & R. B. Olsen, 2010. "Patterns in high-frequency FX data: discovery of 12 empirical scaling laws," Quantitative Finance, Taylor & Francis Journals, vol. 11(4), pages 599-614.
    4. Aloud, Monira & Tsang, Edward & Olsen, Richard & Dupuis, Alexandre, 2012. "A directional-change event approach for studying financial time series," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-17.
    5. Abdalla Kablan & Wing Lon Ng, 2011. "Intraday high-frequency FX trading with adaptive neuro-fuzzy inference systems," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(1/2), pages 68-87.
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    Citations

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    Cited by:

    1. Gapeev, Pavel V. & Rodosthenous, Neofytos & Chinthalapati, V.L Raju, 2019. "On the Laplace transforms of the first hitting times for drawdowns and drawups of diffusion-type processes," LSE Research Online Documents on Economics 101272, London School of Economics and Political Science, LSE Library.
    2. Zhenglong Li & Vincent Tam & Kwan L. Yeung, 2024. "Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management," Papers 2402.00515, arXiv.org, revised Feb 2024.
    3. Shengnan Li & Edward P. K. Tsang & John O'Hara, 2022. "Measuring relative volatility in high‐frequency data under the directional change approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 86-102, April.
    4. Pavel V. Gapeev & Neofytos Rodosthenous & V. L. Raju Chinthalapati, 2019. "On the Laplace Transforms of the First Hitting Times for Drawdowns and Drawups of Diffusion-Type Processes," Risks, MDPI, vol. 7(3), pages 1-15, August.
    5. Hu, Shicheng & Zhang, Weijie & Li, Danping & Wu, Bing, 2023. "Incorporating improved directional change and regime change detection to formulate trading strategies in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).

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