IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v33y2006i7p647-661.html
   My bibliography  Save this article

CUSUM method in predicting regime shifts and its performance in different stock markets allowing for transaction fees

Author

Listed:
  • G. Yi
  • S. Coleman
  • Q. Ren

Abstract

Statistical Process Control (SPC) is a scientific approach to quality improvement in which data are collected and used as evidence of the performance of a process, organisation or set of equipment. One of the SPC techniques, the cumulative sum (CUSUM) method, first developed by E.S. Page (1961), uses a series of cumulative sums of sample data for online process control. This paper reviews CUSUM techniques applied to financial markets in several different ways. The performance of the CUSUM method in predicting regime shifts in stock market indices is then studied in detail. Research in this field so far does not take the transaction fees of buying and selling into consideration. As the study in this paper shows, the performances of the CUSUM when taking account of transaction fees are quite different to those not taking transaction fees into account. The CUSUM plan is defined by parameters h and k. Choosing the parameters of the method should be based on studies that take transaction fees into account. The performances of the CUSUM in different stock markets are also compared in this paper. The results show that the same CUSUM plan has remarkably different performances in different stock markets.

Suggested Citation

  • G. Yi & S. Coleman & Q. Ren, 2006. "CUSUM method in predicting regime shifts and its performance in different stock markets allowing for transaction fees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(7), pages 647-661.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:647-661
    DOI: 10.1080/02664760600708590
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708590
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760600708590?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. Blondell, David & Hoang, Philip & Powell, John G. & Shi, Jing, 2002. "Detection of Financial Time Series Turning Points: A New CUSUM Approach Applied to IPO Cycles," Review of Quantitative Finance and Accounting, Springer, vol. 18(3), pages 293-315, May.
    2. Kahya, Emel & Theodossiou, Panayiotis, 1999. "Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology," Review of Quantitative Finance and Accounting, Springer, vol. 13(4), pages 323-345, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. A R Brentnall & M J Crowder & D J Hand, 2010. "Likelihood-ratio changepoint features for consumer-behaviour models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 462-472, March.
    2. Wu, Zhang & Yang, Mei & Jiang, Wei & Khoo, Michael B.C., 2008. "Optimization designs of the combined Shewhart-CUSUM control charts," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 496-506, December.

    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. Michael O'Connor Keefe & David Gallagher, 2014. "Does the effect of revealed private information on initial public offering (IPO) first trading day return differ by IPO market heat?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 54(3), pages 921-964, September.
    2. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.
    3. Zhiqiang HU & Yizhu WANG, 2013. "The IPO Cycles in China's A-share IPO Market: Detection Based on a Three Regimes Markov Switching Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 115-131, October.
    4. Bock, David & Andersson, Eva & Frisén, Marianne, 2007. "Similarities and differences between statistical surveillance and certain decision rules in finance," Research Reports 2007:8, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    5. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
    6. Michael Berlemann & Julia Freese & Sven Knoth, 2020. "Dating the start of the US house price bubble: an application of statistical process control," Empirical Economics, Springer, vol. 58(5), pages 2287-2307, May.
    7. Michael Berlemann & Julia Freese & Sven Knoth, 2012. "Eyes Wide Shut? The U.S. House Market Bubble through the Lense of Statistical Process Control," CESifo Working Paper Series 3962, CESifo.
    8. Amit Sareen & Sudhi Sharma, 2022. "Assessing Financial Distress and Predicting Stock Prices of Automotive Sector: Robustness of Altman Z-score," Vision, , vol. 26(1), pages 11-24, March.
    9. Elisa Ughetto & Andrea Vezzulli, 2011. "What role can mutual guarantee consortia play for financing innovation? A firm-level study for Italy," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 3(4), pages 294-319.
    10. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    11. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 196(1), pages 289-311, July.
    12. Aaro Hazak & Kadri Männasoo, 2007. "Indicators of corporate default : an EU based empirical study," Bank of Estonia Working Papers 2007-10, Bank of Estonia, revised 04 Sep 2007.
    13. Ch. Spathis & M. Doumpos & C. Zopounidis, 2002. "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques," European Accounting Review, Taylor & Francis Journals, vol. 11(3), pages 509-535.
    14. Richard Chung & Scott Fung & Jayendu Patel, 2015. "Alpha–beta–churn of equity picks by institutional investors and the robust superiority of hedge funds," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 363-405, August.
    15. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.
    16. E. Andersson & D. Bock & M. Frisen, 2006. "Some statistical aspects of methods for detection of turning points in business cycles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(3), pages 257-278.
    17. Brindescu-Olariu Daniel & Golet Ionut, 2013. "Prediction Of Corporate Bankruptcy In Romania Through The Use Of Logistic Regression," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 976-986, July.
    18. Eka Bertuah & Erlane K. Ghani*, 2018. "Bulls and Bears and Bankruptcy- An Early Warning of Distress," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 962-969:5.
    19. repec:arp:tjssrr:2019:p:95-102 is not listed on IDEAS
    20. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    21. Kovacs Kevin & Boulier Bryan & Stekler Herman, 2017. "Nowcasting: Identifying German Cyclical Turning Points," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(4), pages 329-341, August.

    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:taf:japsta:v:33:y:2006:i:7:p:647-661. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.