IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v22y2009i7p2535-2558.html
   My bibliography  Save this article

Testing Portfolio Efficiency with Conditioning Information

Author

Listed:
  • Wayne E. Ferson
  • Andrew F. Siegel

Abstract

We develop asset pricing models' implications for portfolio efficiency with conditioning information in the form of lagged instruments. A model identifies a portfolio that should be minimum-variance efficient with respect to the conditioning information. Our framework refines tests of portfolio efficiency by using the given conditioning information optimally. The optimal use of the lagged variables is economically important; by using the instruments optimally, we reject several efficiency hypotheses that are not otherwise rejected. The Sharpe ratios of a sample of hedge fund indexes appear consistent with the optimal use of conditioning information. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Suggested Citation

  • Wayne E. Ferson & Andrew F. Siegel, 2009. "Testing Portfolio Efficiency with Conditioning Information," Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2535-2558, July.
  • Handle: RePEc:oup:rfinst:v:22:y:2009:i:7:p:2535-2558
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhn112
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fletcher, Jonathan, 2011. "Do optimal diversification strategies outperform the 1/N strategy in U.K. stock returns?," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 375-385.
    2. Peñaranda, Francisco & Sentana, Enrique, 2016. "Duality in mean-variance frontiers with conditioning information," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 762-785.
    3. Ferson, Wayne E., 2013. "Investment Performance: A Review and Synthesis," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 969-1010, Elsevier.
    4. Darolles, Serge & Gourieroux, Christian, 2010. "Conditionally fitted Sharpe performance with an application to hedge fund rating," Journal of Banking & Finance, Elsevier, vol. 34(3), pages 578-593, March.
    5. Caio Vigo Pereira & Marcio Laurini, 2020. "Portfolio Efficiency Tests with Conditioning Information - Comparing GMM and GEL Estimators," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202014, University of Kansas, Department of Economics, revised Sep 2020.
    6. Jonathan Fletcher, 2011. "An Examination of Dynamic Trading Stategies in UK and US Stock Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(9-10), pages 1290-1310, November.
    7. Galvani, Valentina & Gubellini, Stefano, 2013. "Mean–variance dominant trading strategies," Finance Research Letters, Elsevier, vol. 10(3), pages 142-150.
    8. Jonathan Fletcher & Andrew Marshall, 2014. "Investor Heterogeneity and the Cross-section of U.K. Investment Trust Performance," Journal of Financial Services Research, Springer;Western Finance Association, vol. 45(1), pages 67-89, February.
    9. Galvani, Valentina & Plourde, André, 2013. "Spanning with futures contracts," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(1), pages 61-72.
    10. Jiawei Wang & Zhen Chen, 2023. "Exploring Low-Risk Anomalies: A Dynamic CAPM Utilizing a Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    11. Potì, Valerio & Levich, Richard M. & Pattitoni, Pierpaolo & Cucurachi, Paolo, 2014. "Predictability, trading rule profitability and learning in currency markets," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 117-129.
    12. Valentina Galvani & Stuart Landon, 2013. "Riding the yield curve: a spanning analysis," Review of Quantitative Finance and Accounting, Springer, vol. 40(1), pages 135-154, January.
    13. Eiling, Esther & Gerard, Bruno & Hillion, Pierre & de Roon, Frans A., 2012. "International portfolio diversification: Currency, industry and country effects revisited," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1249-1278.
    14. Fabian Hollstein & Marcel Prokopczuk, 2023. "Managing the Market Portfolio," Management Science, INFORMS, vol. 69(6), pages 3675-3696, June.
    15. Carlo A. Favero & Fulvio Ortu & Andrea Tamoni & Haoxi Yang, 2020. "Implications of Return Predictability for Consumption Dynamics and Asset Pricing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 527-541, July.
    16. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2017. "Predictability and diversification benefits of investing in commodity and currency futures," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 52-66.
    17. Levich, Richard M. & Potì, Valerio, 2015. "Predictability and ‘good deals’ in currency markets," International Journal of Forecasting, Elsevier, vol. 31(2), pages 454-472.
    18. Vigo Pereira, Caio, 2021. "Portfolio efficiency with high-dimensional data as conditioning information," International Review of Financial Analysis, Elsevier, vol. 77(C).
    19. Roussanov, Nikolai, 2014. "Composition of wealth, conditioning information, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 111(2), pages 352-380.
    20. Fletcher, Jonathan, 2018. "Betas V characteristics: Do stock characteristics enhance the investment opportunity set in U.K. stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 114-129.
    21. Mikhail Chernov & Magnus Dahlquist & Lars Lochstoer, 2023. "Pricing Currency Risks," Journal of Finance, American Finance Association, vol. 78(2), pages 693-730, April.
    22. Fletcher, Jonathan & Basu, Devraj, 2016. "An examination of the benefits of dynamic trading strategies in U.K. closed-end funds," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 109-118.
    23. Favero, Carlo A. & Tamoni, Andrea & Ortu, Fulvio & Yang, Haoxi, 2016. "Implications of Return Predictability across Horizons for Asset Pricing Models," CEPR Discussion Papers 11645, C.E.P.R. Discussion Papers.
    24. Fletcher, Jonathan, 2019. "Model comparison tests of linear factor models in U.K. stock returns," Finance Research Letters, Elsevier, vol. 28(C), pages 281-291.

    More about this item

    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:oup:rfinst:v:22:y:2009:i:7:p:2535-2558. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.html .

    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.