IDEAS home Printed from https://ideas.repec.org/p/ags/aaea05/19469.html
   My bibliography  Save this paper

Efficient Portfolios of Market Advisory Services: An Application of Shrinkage Estimators

Author

Listed:
  • Cabrini, Silvina M.
  • Irwin, Scott H.
  • Good, Darrel L.

Abstract

A Bayesian hierarchical model was employed to estimate individual expected pricing performance for market advisory programs in corn and soybeans. Performance is defined as the difference between the price/revenue obtained by following the program's marketing recommendations and the average price/revenue offered by the market along the marketing window. The estimates obtained from this model are weighted averages of traditional separate and pooled estimates. Based on the sample employed, the most reasonable individual estimates for expected pricing performance imply a substantial shrinkage towards pooled values. The Bayesian estimates for expected pricing performance range from ¢9/bu to ¢9/bu for corn, from ¢11/bu to ¢17/bu for soybeans and $0.4/acre to $11/acre for revenue. Bayesian estimates are employed in the construction of efficient portfolios of advisory programs. Results suggest that farmers can benefit from following the marketing recommendations of advisory programs portfolios.

Suggested Citation

  • Cabrini, Silvina M. & Irwin, Scott H. & Good, Darrel L., 2005. "Efficient Portfolios of Market Advisory Services: An Application of Shrinkage Estimators," 2005 Annual meeting, July 24-27, Providence, RI 19469, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea05:19469
    DOI: 10.22004/ag.econ.19469
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/19469/files/sp05ca04.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.19469?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jorion, Philippe, 1986. "Bayes-Stein Estimation for Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 279-292, September.
    2. Andrew Gelman, 2004. "Prior distributions for variance parameters in hierarchical models," EERI Research Paper Series EERI_RP_2004_06, Economics and Econometrics Research Institute (EERI), Brussels.
    3. Good, Darrel L. & Martines-Filho, Joao Gomes & Irwin, Scott H., 2002. "The Pricing Performance Of Market Advisory Services In Corn And Soybeans Over 1995-2000," AgMAS Project Research Reports 14784, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    4. Silvina M. Cabrini & Brian G. Stark & Hayri Önal & Scott H. Irwin & Darrel L. Good & João Martines-Filho, 2004. "Efficiency Analysis of Agricultural Market Advisory Services: A Nonlinear Mixed-Integer Programming Approach," Manufacturing & Service Operations Management, INFORMS, vol. 6(3), pages 237-252, December.
    5. Frost, Peter A. & Savarino, James E., 1986. "An Empirical Bayes Approach to Efficient Portfolio Selection," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 293-305, September.
    6. Hagedorn, Lewis A. & Irwin, Scott H. & Martines-Filho, Joao Gomes & Good, Darrel L. & Sherrick, Bruce J. & Schnitkey, Gary D., 2003. "New Generation Grain Marketing Contracts," AgMAS Project Research Reports 14782, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    7. Isengildina, Olga & Pennings, Joost M.E. & Irwin, Scott H. & Good, Darrel L., 2004. "Crop Farmers’ Use of Market Advisory Services," AgMAS Project Research Reports 37489, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    8. Andrew Gelman, 2004. "Prior distributions for variance parameters in hierarchical models," Econometrics 0404001, University Library of Munich, Germany.
    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. Cabrini, Silvina M. & Stark, Brian G. & Irwin, Scott H. & Good, Darrel L. & Martines-Filho, Joao, 2005. "Portfolios of Agricultural Market Advisory Services: How Much Diversification Is Enough?," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 37(1), pages 101-114, April.
    2. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    3. Hsiao-Fen Hsiao & Jiang-Chuan Huang & Zheng-Wei Lin, 2020. "Portfolio construction using bootstrapping neural networks: evidence from global stock market," Review of Derivatives Research, Springer, vol. 23(3), pages 227-247, October.
    4. Michael W. Brandt & Pedro Santa-Clara & Rossen Valkanov, 2009. "Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns," Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3411-3447, September.
    5. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
    6. Junming Li & Xiulan Han & Xiao Li & Jianping Yang & Xuejiao Li, 2018. "Spatiotemporal Patterns of Ground Monitored PM 2.5 Concentrations in China in Recent Years," IJERPH, MDPI, vol. 15(1), pages 1-15, January.
    7. Sergio H. Lence & Dermot J. Hayes, 1994. "The Empirical Minimum-Variance Hedge," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(1), pages 94-104.
    8. Andrew Paskaramoorthy & Tim Gebbie & Terence van Zyl, 2021. "The efficient frontiers of mean-variance portfolio rules under distribution misspecification," Papers 2106.10491, arXiv.org, revised Jul 2021.
    9. Ciprian Crainiceanu & David Ruppert & Raymond Carroll, 2004. "Spatially Adaptive Bayesian P-Splines with Heteroscedastic Errors," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1061, Berkeley Electronic Press.
    10. Rudi Schafer & Nils Fredrik Nilsson & Thomas Guhr, 2010. "Power mapping with dynamical adjustment for improved portfolio optimization," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 107-119.
    11. Egelkraut, Thorsten M. & Woodard, Joshua D. & Garcia, Philip & Pennings, Joost M.E., 2005. "Portfolio Diversification with Commodity Futures: Properties of Levered Futures," 2005 Conference, April 18-19, 2005, St. Louis, Missouri 19047, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    12. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    13. Thomas J. Brennan & Andrew W. Lo, 2010. "Impossible Frontiers," Management Science, INFORMS, vol. 56(6), pages 905-923, June.
    14. Stambaugh, Robert F., 1997. "Analyzing investments whose histories differ in length," Journal of Financial Economics, Elsevier, vol. 45(3), pages 285-331, September.
    15. Fabio Caccioli & Imre Kondor & Matteo Marsili & Susanne Still, 2014. "$L_p$ regularized portfolio optimization," Papers 1404.4040, arXiv.org.
    16. repec:jss:jstsof:14:i05 is not listed on IDEAS
    17. Frahm, Gabriel & Memmel, Christoph, 2008. "Dominating estimators for the global minimum variance portfolio," Discussion Papers in Econometrics and Statistics 2/08, University of Cologne, Institute of Econometrics and Statistics.
    18. Kolm, Petter N. & Tütüncü, Reha & Fabozzi, Frank J., 2014. "60 Years of portfolio optimization: Practical challenges and current trends," European Journal of Operational Research, Elsevier, vol. 234(2), pages 356-371.
    19. Penaranda, Francisco, 2007. "Portfolio choice beyond the traditional approach," LSE Research Online Documents on Economics 24481, London School of Economics and Political Science, LSE Library.
    20. Sumanjay Dutta & Shashi Jain, 2023. "Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation," Papers 2305.11298, arXiv.org.
    21. Lorenzo Garlappi & Raman Uppal & Tan Wang, 2007. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," Review of Financial Studies, Society for Financial Studies, vol. 20(1), pages 41-81, January.

    More about this item

    Keywords

    Marketing;

    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:ags:aaea05:19469. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.