IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v8y1995i1p1-53.html
   My bibliography  Save this article

Bayesian Inference and Portfolio Efficiency

Author

Listed:
  • Kandel, Shmuel
  • McCulloch, Robert
  • Stambaugh, Robert F

Abstract

A Bayesian approach is used to investigate a sample's information about a portfolio's degree of inefficiency. With standard diffuse priors, posterior distributions for measures of portfolio inefficiency can concentrate well away from values consistent with efficiency, even when the portfolio is exactly efficient in the sample. The data indicate that the NYSE - AMEX market portfolio is rather inefficient in the presence of a riskless asset, although this conclusion is justified only after an analysis using informative priors. Including a riskless asset significantly reduces any sample's ability to produce posterior distributions supporting small degrees of inefficiency. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Suggested Citation

  • Kandel, Shmuel & McCulloch, Robert & Stambaugh, Robert F, 1995. "Bayesian Inference and Portfolio Efficiency," The Review of Financial Studies, Society for Financial Studies, vol. 8(1), pages 1-53.
    • Handle: RePEc:oup:rfinst:v:8:y:1995:i:1:p:1-53
    as

    Download full text from publisher

    File URL: http://www.jstor.org/fcgi-bin/jstor/listjournal.fcg/08939454
    File Function: full text
    Download Restriction: Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.
    ---><---

    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. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Kandel, Shmuel & Stambaugh, Robert F, 1989. "A Mean-Variance Framework for Tests of Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 2(2), pages 125-156.
    3. Shanken, Jay, 1987. "A Bayesian approach to testing portfolio efficiency," Journal of Financial Economics, Elsevier, vol. 19(2), pages 195-215, December.
    4. Stambaugh, Robert F., 1982. "On the exclusion of assets from tests of the two-parameter model : A sensitivity analysis," Journal of Financial Economics, Elsevier, vol. 10(3), pages 237-268, November.
    5. McCulloch, Robert & Rossi, Peter E., 1990. "Posterior, predictive, and utility-based approaches to testing the arbitrage pricing theory," Journal of Financial Economics, Elsevier, vol. 28(1-2), pages 7-38.
    6. Ferson, Wayne E & Kandel, Shmuel & Stambaugh, Robert F, 1987. "Tests of Asset Pricing with Time-Varying Expected Risk Premiums and Market Betas," Journal of Finance, American Finance Association, vol. 42(2), pages 201-220, June.
    7. Roll, Richard, 1977. "A critique of the asset pricing theory's tests Part I: On past and potential testability of the theory," Journal of Financial Economics, Elsevier, vol. 4(2), pages 129-176, March.
    8. Harvey, Campbell R. & Zhou, Guofu, 1990. "Bayesian inference in asset pricing tests," Journal of Financial Economics, Elsevier, vol. 26(2), pages 221-254, August.
    9. Kandel, Shmuel & Stambaugh, Robert F., 1987. "On correlations and inferences about mean-variance efficiency," Journal of Financial Economics, Elsevier, vol. 18(1), pages 61-90, March.
    10. Oldfield, George S. & Rogalski, Richard J., 1987. "The stochastic properties of term structure movements," Journal of Monetary Economics, Elsevier, vol. 19(2), pages 229-254, March.
    11. Breeden, Douglas T., 1979. "An intertemporal asset pricing model with stochastic consumption and investment opportunities," Journal of Financial Economics, Elsevier, vol. 7(3), pages 265-296, September.
    12. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    13. Shanken, Jay, 1987. "Multivariate proxies and asset pricing relations : Living with the Roll critique," Journal of Financial Economics, Elsevier, vol. 18(1), pages 91-110, March.
    14. McCulloch, Robert & Rossi, Peter E., 1991. "A bayesian approach to testing the arbitrage pricing theory," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 141-168.
    15. Gibbons, Michael R & Ross, Stephen A & Shanken, Jay, 1989. "A Test of the Efficiency of a Given Portfolio," Econometrica, Econometric Society, vol. 57(5), pages 1121-1152, September.
    16. Black, Fischer, 1972. "Capital Market Equilibrium with Restricted Borrowing," The Journal of Business, University of Chicago Press, vol. 45(3), pages 444-455, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Geweke, John & Zhou, Guofu, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," The Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 557-587.
    2. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    3. Dimson, Elroy & Mussavian, Massoud, 1999. "Three centuries of asset pricing," Journal of Banking & Finance, Elsevier, vol. 23(12), pages 1745-1769, December.
    4. 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.
    5. Wang, Zhenyu, 1998. "Efficiency loss and constraints on portfolio holdings," Journal of Financial Economics, Elsevier, vol. 48(3), pages 359-375, June.
    6. Shanken, Jay & Weinstein, Mark I., 2006. "Economic forces and the stock market revisited," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 129-144, March.
    7. Wayne E. Ferson & Ravi Jagannathan, 1996. "Econometric evaluation of asset pricing models," Staff Report 206, Federal Reserve Bank of Minneapolis.
    8. Grauer, Robert R. & Janmaat, Johannus A., 2009. "On the power of cross-sectional and multivariate tests of the CAPM," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 775-787, May.
    9. Wayne E. Ferson & Andrew F. Siegel, 2006. "Testing Portfolio Efficiency with Conditioning Information," NBER Working Papers 12098, National Bureau of Economic Research, Inc.
    10. Enrique Sentana, 2009. "The econometrics of mean-variance efficiency tests: a survey," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 65-101, November.
    11. Walsh, David M. & Walsh, Kathleen D. & Evans, John P., 1998. "Assessing estimation error in a tracking error variance minimisation framework," Pacific-Basin Finance Journal, Elsevier, vol. 6(1-2), pages 175-192, May.
    12. Fletcher, Jonathan, 2018. "Bayesian tests of global factor models," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 279-289.
    13. DeRoon, Frans A. & Nijman, Theo E., 2001. "Testing for mean-variance spanning: a survey," Journal of Empirical Finance, Elsevier, vol. 8(2), pages 111-155, May.
    14. Ukhov, Andrey D., 2006. "Expanding the frontier one asset at a time," Finance Research Letters, Elsevier, vol. 3(3), pages 194-206, September.
    15. Shanken, Jay & Zhou, Guofu, 2007. "Estimating and testing beta pricing models: Alternative methods and their performance in simulations," Journal of Financial Economics, Elsevier, vol. 84(1), pages 40-86, April.
    16. Rocciolo, Francesco & Gheno, Andrea & Brooks, Chris, 2022. "Explaining abnormal returns in stock markets: An alpha-neutral version of the CAPM," International Review of Financial Analysis, Elsevier, vol. 82(C).
    17. Todd Prono, 2006. "GARCH-based identification of triangular systems with an application to the CAPM: still living with the roll critique," Working Papers 07-1, Federal Reserve Bank of Boston.
    18. Ouysse, Rachida & Kohn, Robert, 2010. "Bayesian variable selection and model averaging in the arbitrage pricing theory model," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3249-3268, December.
    19. Diacogiannis, George & Ioannidis, Christos, 2022. "Linear beta pricing with efficient/inefficient benchmarks and short-selling restrictions," International Review of Financial Analysis, Elsevier, vol. 81(C).
    20. Francisco Barillas & Jay Shanken, 2018. "Comparing Asset Pricing Models," Journal of Finance, American Finance Association, vol. 73(2), pages 715-754, April.

    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:8:y:1995:i:1:p:1-53. 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.

    If CitEc recognized a bibliographic 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.

    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.