IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v525y2019icp1405-1417.html
   My bibliography  Save this article

A method to get a more stationary process and its application in finance with high-frequency data of Chinese index futures

Author

Listed:
  • Li, Long
  • Bao, Si
  • Chen, Jing-Chao
  • Jiang, Tao

Abstract

Technical indicators have been widely used in financial markets for a long time. Wang and Zheng (2014) proposed in their book that the technical indicators can be transformed into the stationary process and investigated the profitability and availability. But in fact, we can only test that a data series form a weakly stationary process but a strongly stationary process. Nevertheless, the convergence of a more stationary process will vanish faster, thus it is much better if we can get a more stationary process. In this paper, we propose a method to get a more strongly (or weakly) process named mean reverting process that based on the original strongly (or weakly) stationary process. We particularly give some examples based on high-frequency data of CSI300 Stock Index Futures to show that some technical indicators are mean reverting process. We talk about its advantage and application in high frequency trading.

Suggested Citation

  • Li, Long & Bao, Si & Chen, Jing-Chao & Jiang, Tao, 2019. "A method to get a more stationary process and its application in finance with high-frequency data of Chinese index futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1405-1417.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:1405-1417
    DOI: 10.1016/j.physa.2019.04.085
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119304509
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.04.085?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Jing-Chao & Zhou, Yu & Wang, Xi, 2018. "Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index Futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1664-1678.
    2. Zhaodong Wang & Weian Zheng, 2014. "High-Frequency Trading and Probability Theory," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9233, August.
    3. Shi, Huai-Long & Zhou, Wei-Xing, 2017. "Time series momentum and contrarian effects in the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 309-318.
    4. Liu, Wei & Huang, Xudong & Zheng, Weian, 2006. "Black–Scholes’ model and Bollinger bands," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 565-571.
    5. Mukherji, Sandip, 2011. "Are stock returns still mean-reverting?," Review of Financial Economics, Elsevier, vol. 20(1), pages 22-27, January.
    6. Ansley, Craig F. & Spivey, W. Allen & Wrobleski, William J., 1977. "On the structure of moving average processes," Journal of Econometrics, Elsevier, vol. 6(1), pages 121-134, July.
    7. Zhu, Hong & Jiang, Zhi-Qiang & Li, Sai-Ping & Zhou, Wei-Xing, 2015. "Profitability of simple technical trading rules of Chinese stock exchange indexes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 75-84.
    8. Huai-Long Shi & Zhi-Qiang Jiang & Wei-Xing Zhou, 2015. "Profitability of Contrarian Strategies in the Chinese Stock Market," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-22, September.
    9. Wang, Shan & Jiang, Zhi-Qiang & Li, Sai-Ping & Zhou, Wei-Xing, 2015. "Testing the performance of technical trading rules in the Chinese markets based on superior predictive test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 114-123.
    10. Papailias, Fotis & Thomakos, Dimitrios D., 2015. "An improved moving average technical trading rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 458-469.
    11. Shan Wang & Zhi-Qiang Jiang & Sai-Ping Li & Wei-Xing Zhou, 2015. "Testing the performance of technical trading rules in the Chinese market," Papers 1504.06397, arXiv.org.
    12. Wang, Xi & Bao, Si & Chen, Jingchao, 2017. "High-frequency stock linkage and multi-dimensional stationary processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 70-83.
    13. 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.
    14. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
    15. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    16. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    2. Chen, Shi & Bao, Si & Zhou, Yu, 2016. "The predictive power of Japanese candlestick charting in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 148-165.
    3. Ni, Yensen & Cheng, Yirung & Huang, Paoyu & Day, Min-Yuh, 2018. "Trading strategies in terms of continuous rising (falling) prices or continuous bullish (bearish) candlesticks emitted," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 188-204.
    4. Yensen Ni & Min-Yuh Day & Yirung Cheng & Paoyu Huang, 2022. "Can investors profit by utilizing technical trading strategies? Evidence from the Korean and Chinese stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    5. Cui, Ling-xiao & Long, Wen, 2016. "Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 498-508.
    6. Ma, Junjun & Xiong, Xiong & He, Feng & Zhang, Wei, 2017. "Volatility measurement with directional change in Chinese stock market: Statistical property and investment strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 169-180.
    7. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    8. Mohamed Es-Sanoun & Jude Gohou & Mounir Benboubker, 2023. "Testing of Herd Behavior In african Stock Markets During COVID-19 Pandemic [Essai de vérification du comportement mimétique dans les marchés boursiers africains au cours de la crise de covid-19]," Post-Print hal-04144289, HAL.
    9. Semenov, Andrei, 2021. "Measuring the stock's factor beta and identifying risk factors under market inefficiency," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 635-649.
    10. Prat, Georges, 2013. "Equity risk premium and time horizon: What do the U.S. secular data say?," Economic Modelling, Elsevier, vol. 34(C), pages 76-88.
    11. Campbell, John Y., 2001. "Why long horizons? A study of power against persistent alternatives," Journal of Empirical Finance, Elsevier, vol. 8(5), pages 459-491, December.
    12. Junsoo Lee & Jen-Chi Cheng & Chyongchiou Lin & Cliff Huang, 1998. "The market efficiency hypothesis on stock prices: international evidence in the 1920s," Applied Financial Economics, Taylor & Francis Journals, vol. 8(1), pages 61-65.
    13. John Y. Campbell & John Cochrane, 1999. "Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 107(2), pages 205-251, April.
    14. Klos, Alexander & Langer, Thomas & Weber, Martin, 2002. "Über kurz oder lang : welche Rolle spielt der Anlagehorizont bei Investitionsentscheidungen?," Papers 02-49, Sonderforschungsbreich 504.
    15. Luc Savard, 2010. "Scaling up infrastructure spending in the Philippines: A CGE top-down bottom-up microsimulation approach," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 43-59.
    16. F. DePenya & L. Gil-Alana, 2006. "Testing of nonstationary cycles in financial time series data," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 47-65, August.
    17. Campbell, John Y & Ammer, John, 1993. "What Moves the Stock and Bond Markets? A Variance Decomposition for Long-Term Asset Returns," Journal of Finance, American Finance Association, vol. 48(1), pages 3-37, March.
    18. Spierdijk, Laura & Bikker, Jacob A. & van den Hoek, Pieter, 2012. "Mean reversion in international stock markets: An empirical analysis of the 20th century," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 228-249.
    19. Mordecai Kurz & Hehui Jin & Maurizio Motolese, 2005. "Determinants of stock market volatility and risk premia," Annals of Finance, Springer, vol. 1(2), pages 109-147, July.
    20. Chiang, Raymond & Davidson, Ian & Okunev, John, 1997. "Some further theoretical and empirical implications regarding the relationship between earnings, dividends and stock prices," Journal of Banking & Finance, Elsevier, vol. 21(1), pages 17-35, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:1405-1417. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.