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

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Borio, Claudio, 2014. "The financial cycle and macroeconomics: What have we learnt?," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 182-198.
    14. 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.
    15. 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.
    16. 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.
    17. Gençay, Ramazan & Signori, Daniele, 2015. "Multi-scale tests for serial correlation," Journal of Econometrics, Elsevier, vol. 184(1), pages 62-80.
    18. Frederick C. Dirks, 1958. "Recent Investment Return On Industrial Stocks," Journal of Finance, American Finance Association, vol. 13(3), pages 370-385, September.
    19. 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.
    20. 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.
    21. 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.
    22. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2005. "Multiscale systematic risk," Journal of International Money and Finance, Elsevier, vol. 24(1), pages 55-70, February.
    23. 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.
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    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.

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    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

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