IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v21y2017icp115-125.html
   My bibliography  Save this article

Real and complex wavelets in asset classification: An application to the US stock market

Author

Listed:
  • Bruzda, Joanna

Abstract

In the paper we suggest the use of wavelets to classify equities and industries into defensive and cyclical categories. We demonstrate that real- and complex-valued wavelets better serve the purpose of equity classification than more traditional approaches, and that this takes place through a more reliable and detailed dependence measurement and risk assessment. In particular, we introduce a family of wavelet-based tests of the random walk hypothesis exploring local features of spectra, which enable examining mean reversion and cyclicality of prices. The suggested approach is illustrated with an analysis of daily and monthly US industry indexes.

Suggested Citation

  • Bruzda, Joanna, 2017. "Real and complex wavelets in asset classification: An application to the US stock market," Finance Research Letters, Elsevier, vol. 21(C), pages 115-125.
  • Handle: RePEc:eee:finlet:v:21:y:2017:i:c:p:115-125
    DOI: 10.1016/j.frl.2017.02.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612317300661
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2017.02.004?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. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    2. Thomas Conlon & John Cotter, 2012. "An empirical analysis of dynamic multiscale hedging using wavelet decomposition," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(3), pages 272-299, March.
    3. Martin, John D & Klemkosky, Robert C, 1976. "The Effect of Homogeneous Stock Groupings on Portfolio Risk," The Journal of Business, University of Chicago Press, vol. 49(3), pages 339-349, July.
    4. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    5. Frederick C. Dirks, 1958. "Recent Investment Return On Industrial Stocks," Journal of Finance, American Finance Association, vol. 13(3), pages 370-385, September.
    6. Claessens, Stijn & Kose, M. Ayhan & Terrones, Marco E., 2012. "How do business and financial cycles interact?," Journal of International Economics, Elsevier, vol. 87(1), pages 178-190.
    7. Balázs Égert & Douglas Sutherland, 2014. "The Nature of Financial and Real Business Cycles: The Great Moderation and Banking Sector Pro-Cyclicality," Scottish Journal of Political Economy, Scottish Economic Society, vol. 61(1), pages 98-117, February.
    8. Viviana Fernandez, 2008. "Multi‐period hedge ratios for a multi‐asset portfolio when accounting for returns co‐movement," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(2), pages 182-207, February.
    9. Rua, António & Nunes, Luís C., 2009. "International comovement of stock market returns: A wavelet analysis," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 632-639, September.
    10. Farrell, James L, Jr, 1974. "Analyzing Covariation of Returns to Determine Homogeneous Stock Groupings," The Journal of Business, University of Chicago Press, vol. 47(2), pages 186-207, April.
    11. Fernandez Viviana P, 2005. "The International CAPM and a Wavelet-Based Decomposition of Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-37, December.
    12. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    13. 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.
    14. In, Francis & Kim, Sangbae, 2006. "Multiscale hedge ratio between the Australian stock and futures markets: Evidence from wavelet analysis," Journal of Multinational Financial Management, Elsevier, vol. 16(4), pages 411-423, October.
    15. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2005. "Multiscale systematic risk," Journal of International Money and Finance, Elsevier, vol. 24(1), pages 55-70, February.
    16. Claudio Borio, 2014. "The financial cycle and macroeconomics: what have we learned and what are the policy implications?," Chapters, in: Ewald Nowotny & Doris Ritzberger-Grünwald & Peter Backé (ed.), Financial Cycles and the Real Economy, chapter 2, pages 10-35, Edward Elgar Publishing.
    17. Mathias Drehmann & Claudio Borio & Kostas Tsatsaronis, 2012. "Characterising the financial cycle: don't lose sight of the medium term!," BIS Working Papers 380, Bank for International Settlements.
    18. Conlon, T. & Crane, M. & Ruskin, H.J., 2008. "Wavelet multiscale analysis for Hedge Funds: Scaling and strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5197-5204.
    19. M. Vermorken & A. Szafarz & H. Pirotte, 2010. "Sector classification through non-Gaussian similarity," Applied Financial Economics, Taylor & Francis Journals, vol. 20(11), pages 861-878.
    20. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    21. Borio, Claudio, 2014. "The financial cycle and macroeconomics: What have we learnt?," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 182-198.
    22. Bruzda Joanna, 2015. "Amplitude and phase synchronization of European business cycles: a wavelet approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 625-655, December.
    23. Gençay, Ramazan & Signori, Daniele, 2015. "Multi-scale tests for serial correlation," Journal of Econometrics, Elsevier, vol. 184(1), pages 62-80.
    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. Shahzad, Syed Jawad Hussain & Aloui, Chaker & Jammazi, Rania, 2020. "On the interplay between US sectoral CDS, stock and VIX indices: Fresh insights from wavelet approaches," Finance Research Letters, Elsevier, vol. 33(C).
    2. Fan He & Xuansen He, 2019. "A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 729-761, August.
    3. Bruzda, Joanna, 2019. "Complex analytic wavelets in the measurement of macroeconomic risks," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    4. Maciej Ryczkowski, 2020. "Money and credit during normal times and house price booms: evidence from time-frequency analysis," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 835-861, November.

    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. repec:zbw:bofrdp:2016_014 is not listed on IDEAS
    2. Verona, Fabio, 2016. "Time–frequency characterization of the U.S. financial cycle," Economics Letters, Elsevier, vol. 144(C), pages 75-79.
    3. Verona, Fabio, 2016. "Time–frequency characterization of the U.S. financial cycle," Economics Letters, Elsevier, vol. 144(C), pages 75-79.
    4. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    5. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    6. Scharnagl Michael & Mandler Martin, 2019. "Real and Financial Cycles in Euro Area Economies: Results from Wavelet Analysis," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(5-6), pages 895-916, October.
    7. Yan, Chuanpeng & Huang, Kevin X.D., 2020. "Financial cycle and business cycle: An empirical analysis based on the data from the U.S," Economic Modelling, Elsevier, vol. 93(C), pages 693-701.
    8. Jitka Pomenkova & Eva Klejmova & Zuzana Kucerova, 2019. "Cyclicality in lending activity of Euro area in pre- and post- 2008 crisis: a local-adaptive-based testing of wavelets," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 19(1), pages 155-175.
    9. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    10. Thomas Conlon & Brian M. Lucey & Gazi Salah Uddin, 2018. "Is gold a hedge against inflation? A wavelet time-scale perspective," Review of Quantitative Finance and Accounting, Springer, vol. 51(2), pages 317-345, August.
    11. Johanna Amberger & Ralf Fendel, 2017. "Understanding inflation dynamics in the Euro Area: deviants and commonalities across member countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(2), pages 261-293, May.
    12. Bredin, Don & Conlon, Thomas & Potì, Valerio, 2015. "Does gold glitter in the long-run? Gold as a hedge and safe haven across time and investment horizon," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 320-328.
    13. Meller, Barbara & Metiu, Norbert, 2017. "The synchronization of credit cycles," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 98-111.
    14. McNevin, Bruce D. & Nix, Joan, 2018. "The beta heuristic from a time/frequency perspective: A wavelet analysis of the market risk of sectors," Economic Modelling, Elsevier, vol. 68(C), pages 570-585.
    15. Schüler, Yves S. & Hiebert, Paul P. & Peltonen, Tuomas A., 2020. "Financial cycles: Characterisation and real-time measurement," Journal of International Money and Finance, Elsevier, vol. 100(C).
    16. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
    17. Silvana Bartoletto & Bruno Chiarini & Elisabetta Marzano & Paolo Piselli, 2018. "Banking crises and business cycle: evidence for Italy(1861-2016)," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 11(1), pages 34-61, October.
    18. Ijaz Younis & Cheng Longsheng & Muhammad Farhan Basheer & Ahmed Shafique Joyo, 2020. "Stock market comovements among Asian emerging economies: A wavelet-based approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-23, October.
    19. Christian Menden & Christian R. Proaño, 2017. "Dissecting the financial cycle with dynamic factor models," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1965-1994, December.
    20. Herwartz, Helmut & Ochsner, Christian & Rohloff, Hannes, 2020. "The credit composition of global liquidity," University of Göttingen Working Papers in Economics 409, University of Goettingen, Department of Economics.

    More about this item

    Keywords

    Wavelet transform; Hilbert transform; Defensive asset; Cyclical asset; Financial cycle; Stock grouping;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    Statistics

    Access and download statistics

    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:finlet:v:21:y:2017:i:c:p:115-125. 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.elsevier.com/locate/frl .

    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.