IDEAS home Printed from https://ideas.repec.org/a/taf/uteexx/v61y2016i1p44-56.html
   My bibliography  Save this article

The IRR of a project with many potential outcomes

Author

Listed:
  • Morris G. Danielson

Abstract

This article shows that the internal rate of return (IRR) of a project's expected cash flow stream is a weighted average of the IRRs offered by the project's (many) possible future outcomes, where the weights are calculated using the outcome probabilities and invested capital balances. Because the invested capital associated with a particular realization is a function of the Macaulay duration of the cash flows in that outcome, the weights depend on the outcome probabilities and the effective length of each cash flow stream.

Suggested Citation

  • Morris G. Danielson, 2016. "The IRR of a project with many potential outcomes," The Engineering Economist, Taylor & Francis Journals, vol. 61(1), pages 44-56, January.
    • Handle: RePEc:taf:uteexx:v:61:y:2016:i:1:p:44-56
    DOI: 10.1080/0013791X.2015.1095383
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0013791X.2015.1095383
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0013791X.2015.1095383?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
    ---><---

    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. Magni, Carlo Alberto, 2016. "Capital depreciation and the underdetermination of rate of return: A unifying perspective," Journal of Mathematical Economics, Elsevier, vol. 67(C), pages 54-79.
    2. Simona Hašková & Petr Fiala, 2023. "Internal Rate of Return Estimation of Subsidised Projects: Conventional Approach Versus fuzzy Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1233-1249, October.

    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:taf:uteexx:v:61:y:2016:i:1:p:44-56. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UTEE20 .

    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.