IDEAS home Printed from https://ideas.repec.org/a/eee/finmar/v19y2014icp86-109.html
   My bibliography  Save this article

Outperformance in exchange-traded fund pricing deviations: Generalized control of data snooping bias

Author

Listed:
  • Kearney, Fearghal
  • Cummins, Mark
  • Murphy, Finbarr

Abstract

An investigation into exchange-traded fund (ETF) outperformance during the period 2008–2012 is undertaken utilizing a data set of 288 U.S. traded securities. ETFs are tested for net asset value (NAV) premium, underlying index and market benchmark outperformance, with Sharpe, Treynor, and Sortino ratios employed as risk-adjusted performance measures. A key contribution is the application of an innovative generalized stepdown procedure in controlling for data snooping bias. We find that a large proportion of optimized replication and debt asset class ETFs display risk-adjusted premiums with energy and precious metals focused funds outperforming the S&P 500 market benchmark.

Suggested Citation

  • Kearney, Fearghal & Cummins, Mark & Murphy, Finbarr, 2014. "Outperformance in exchange-traded fund pricing deviations: Generalized control of data snooping bias," Journal of Financial Markets, Elsevier, vol. 19(C), pages 86-109.
  • Handle: RePEc:eee:finmar:v:19:y:2014:i:c:p:86-109
    DOI: 10.1016/j.finmar.2013.08.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1386418113000487
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Mark Cummins & Andrea Bucca, 2012. "Quantitative spread trading on crude oil and refined products markets," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1857-1875, December.
    2. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    3. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data-Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    4. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    5. Cheol-Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    6. Gerasimos G. Rompotis, 2011. "Predictable patterns in ETFs' return and tracking error," Studies in Economics and Finance, Emerald Group Publishing, vol. 28(1), pages 14-35, March.
    7. Edwin J. Elton, 2002. "Spiders: Where Are the Bugs?," The Journal of Business, University of Chicago Press, vol. 75(3), pages 453-472, July.
    8. Malkiel, Burton G, 1995. " Returns from Investing in Equity Mutual Funds 1971 to 1991," Journal of Finance, American Finance Association, vol. 50(2), pages 549-572, June.
    9. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.
    10. Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.
    11. Harper, Joel T. & Madura, Jeff & Schnusenberg, Oliver, 2006. "Performance comparison between exchange-traded funds and closed-end country funds," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(2), pages 104-122, April.
    12. Alexander, C. & Barbosa, A., 2008. "Hedging index exchange traded funds," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 326-337, February.
    13. Chanwit Phengpis & Peggy E. Swanson, 2009. "iShares and the US Market Risk Exposure," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 36(7-8), pages 972-986.
    14. Cuthbertson, Keith & Nitzsche, Dirk & O'Sullivan, Niall, 2008. "UK mutual fund performance: Skill or luck?," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 613-634, September.
    15. Richard DeFusco & Stoyu Ivanov & Gordon Karels, 2011. "The exchange traded funds’ pricing deviation: analysis and forecasts," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 35(2), pages 181-197, April.
    16. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    17. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Can commodity futures be profitably traded with quantitative market timing strategies?," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1810-1819, September.
    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. António Afonso & Pedro Cardoso, 2017. "Exchange-traded Funds as an Alternative Investment Option: a Case Study," Working Papers REM 2017/22, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. repec:eee:ecofin:v:42:y:2017:i:c:p:250-265 is not listed on IDEAS

    More about this item

    Keywords

    Exchange-traded fund; ETF performance; Multiple hypothesis testing; Data snooping bias;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    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:finmar:v:19:y:2014:i:c:p:86-109. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/finmar .

    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.