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Detection of Financial Time Series Turning Points: A New CUSUM Approach Applied to IPO Cycles

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  • Blondell, David
  • Hoang, Philip
  • Powell, John G.
  • Shi, Jing

Abstract

This paper presents a new Cumulative Sum approach for the detection of turning points in financial time series that are subject to cyclical mean level and volatility regime shifts. The new CUSUM approach is applied to the problem of detecting turning points in "hot issue" markets for Initial Public Offerings (IPOs), thus providing a multi-dimensional characterization of states of the IPO cycle. Copyright 2002 by Kluwer Academic Publishers

Suggested Citation

  • 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.
    • Handle: RePEc:kap:rqfnac:v:18:y:2002:i:3:p:293-315
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Xinyu Wang & Cathy Ning, 2022. "A new Markov regime‐switching count time series approach for forecasting initial public offering volumes and detecting issue cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 118-133, January.
    5. 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.
    6. 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.
    7. 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.
    8. Bartosz Gębka & Michail Karoglou, 2013. "Is there life in the old dogs yet? Making break-tests work on financial contagion," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 485-507, April.
    9. 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.
    10. 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.
    11. Michael D. McKenzie, 2007. "International Evidence on the Determinants of the Decision to List," Australian Journal of Management, Australian School of Business, vol. 32(1), pages 1-28, June.
    12. 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.

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