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Testing Portfolio Efficiency with Conditioning Information

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  • 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
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    File URL: http://hdl.handle.net/10.1093/rfs/hhn112
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Peñaranda, Francisco & Sentana, Enrique, 2016. "Duality in mean-variance frontiers with conditioning information," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 762-785.
    4. Ferson, Wayne E., 2013. "Investment Performance: A Review and Synthesis," Handbook of the Economics of Finance, Elsevier.
    5. 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.
    6. Galvani, Valentina & Gubellini, Stefano, 2013. "Mean–variance dominant trading strategies," Finance Research Letters, Elsevier, vol. 10(3), pages 142-150.
    7. 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.
    8. Galvani, Valentina & Plourde, André, 2013. "Spanning with futures contracts," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(1), pages 61-72.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Levich, Richard M. & Potì, Valerio, 2015. "Predictability and ‘good deals’ in currency markets," International Journal of Forecasting, Elsevier, vol. 31(2), pages 454-472.
    14. 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.
    15. 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.
    16. Favero, Carlo A. & Ortu, Fulvio & Tamoni, Andrea & Yang, Haoxi, 2016. "Implications of Return Predictability across Horizons for Asset Pricing Models," CEPR Discussion Papers 11645, C.E.P.R. Discussion Papers.

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