IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v12y2006i6-7p529-552.html
   My bibliography  Save this article

Return-based style analysis with time-varying exposures

Author

Listed:
  • Laurens Swinkels
  • Pieter Van Der Sluis

Abstract

This paper focuses on the estimation of mutual fund styles by return-based style analysis. Often the investment style is assumed to be constant through time. Alternatively, time variation is sometimes implicitly accounted for by using rolling regressions when estimating the style exposures. The former assumption is often contradicted empirically, and the latter is inefficient due to its ad hoc chosen window size. Here, the Kalman filter is used to model time-varying exposures of mutual funds explicitly. This leads to a testable model and more efficient use of the data, which reduces the influence of spurious correlation between mutual fund returns and style indices. Several stylized examples indicate that more reliable style estimates can be obtained by modelling the style exposure as a random walk, and estimating the coefficients with the Kalman filter. The differences with traditional techniques are substantial in these stylized examples. The results from the empirical analyses indicate that the structural model estimated by the Kalman filter improves style predictions and influences results on performance measurement.

Suggested Citation

  • Laurens Swinkels & Pieter Van Der Sluis, 2006. "Return-based style analysis with time-varying exposures," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 529-552.
  • Handle: RePEc:taf:eurjfi:v:12:y:2006:i:6-7:p:529-552
    DOI: 10.1080/13518470500248508
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/13518470500248508
    Download Restriction: Access to full text is restricted to subscribers.

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lakonishok, Josef, et al, 1991. "Window Dressing by Pension Fund Managers," American Economic Review, American Economic Association, vol. 81(2), pages 227-231, May.
    2. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, June.
    3. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    4. Huberman, Gur, 2001. "Familiarity Breeds Investment," Review of Financial Studies, Society for Financial Studies, vol. 14(3), pages 659-680.
    5. ter Horst, Jenke R. & Nijman, Theo E. & de Roon, Frans A., 2004. "Evaluating style analysis," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 29-53, January.
    6. French, Kenneth R & Poterba, James M, 1991. "Investor Diversification and International Equity Markets," American Economic Review, American Economic Association, vol. 81(2), pages 222-226, May.
    7. Tae-Hwan Kim, 2005. "Asymptotic and Bayesian Confidence Intervals for Sharpe-Style Weights," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(3), pages 315-343.
    8. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    9. Alexander, Gordon J. & Benson, P. George & Eger, Carol E., 1982. "Timing Decisions and the Behavior of Mutual Fund Systematic Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(04), pages 579-602, November.
    10. Fisher, Lawrence & Kamin, Jules H., 1985. "Forecasting Systematic Risk: Estimates of “Raw” Beta that Take Account of the Tendency of Beta to Change and the Heteroskedasticity of Residual Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 20(02), pages 127-149, June.
    11. Ter Horst, J.R. & Nijman, T.E. & de Roon, F.A., 2004. "Evaluating style analysis," Other publications TiSEM 8a501733-7a06-4399-8a43-0, Tilburg University, School of Economics and Management.
    12. Heston, Steven L. & Rouwenhorst, K. Geert, 1994. "Does industrial structure explain the benefits of international diversification?," Journal of Financial Economics, Elsevier, vol. 36(1), pages 3-27, August.
    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. Kathryn Holmes & Robert Faff, 2008. "Style analysis, customized benchmarks, and managed funds: new evidence," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 4(4), pages 253-258.
    2. repec:sbe:breart:v:26:y:2006:i:1:a:2497 is not listed on IDEAS
    3. Darolles, Serge & Vaissié, Mathieu, 2012. "The alpha and omega of fund of hedge fund added value," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1067-1078.
    4. Takaya Fukui & Seisho Sato & Akihiko Takahashi, 2016. "Style Analysis with Particle Filtering and Generalized Simulated Annealing," CIRJE F-Series CIRJE-F-1010, CIRJE, Faculty of Economics, University of Tokyo.
    5. repec:wsi:ijfexx:v:04:y:2017:i:02n03:n:s2424786317500372 is not listed on IDEAS
    6. Takaya Fukui & Seisho Sato & Akihiko Takahashi, 2017. "This paper proposes a new approach to style analysis of mutual funds in a general state space framework with particle filtering and generalized simulated annealing (GSA). Speci cally, we regard the ex," CARF F-Series CARF-F-383, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    7. Martin Bohl & Philipp Kaufmann & Patrick Stephan, 2012. "From Hero to Zero: Evidence of Performance Reversal and Speculative Bubbles in German Renewable Energy Stocks," CQE Working Papers 2412, Center for Quantitative Economics (CQE), University of Muenster.
    8. Laura Andreu & Cristina Ortiz & José Sarto, 2014. "Herding in the strategic allocations of Spanish pension plan managers," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 38(4), pages 658-671, October.
    9. Salotti, Simone & Trecroci, Carmine, 2014. "Multifactor risk loadings and abnormal returns under uncertainty and learning," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(3), pages 393-404.
    10. Kathryn Holmes & Robert Faff & Iain Clacher, 2010. "Style analysis and dominant index timing: an application to Australian multi-sector managed funds," Applied Financial Economics, Taylor & Francis Journals, vol. 20(4), pages 293-301.
    11. Stephanos Papadamou & Nikolaos A. Kyriazis & Lydia Mermigka, 2017. "Japanese Mutual Funds before and after the Crisis Outburst: A Style- and Performance-Analysis," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 5(1), pages 1-20, March.
    12. Bohl, Martin T. & Kaufmann, Philipp & Stephan, Patrick M., 2013. "From hero to zero: Evidence of performance reversal and speculative bubbles in German renewable energy stocks," Energy Economics, Elsevier, vol. 37(C), pages 40-51.
    13. Holmes, Kathryn A. & Faff, Robert, 2008. "Estimating the performance attributes of Australian multi-sector managed funds within a dynamic Kalman filter framework," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 998-1011, December.
    14. Bodson, Laurent & Cavenaile, Laurent & Sougné, Danielle, 2013. "A global approach to mutual funds market timing ability," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 96-101.
    15. Laurent Bodson & Alain Coën & Georges Hübner, 2010. "Dynamic Hedge Fund Style Analysis With Errors-In-Variables," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(3), pages 201-221.

    More about this item

    Keywords

    Dynamic models; Kalman filter; Mutual funds; Style analysis;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    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:taf:eurjfi:v:12:y:2006:i:6-7:p:529-552. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/REJF20 .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.