IDEAS home Printed from
   My bibliography  Save this article

Analytical debiasing of corporate cash flow forecasts


  • Blanc, Sebastian M.
  • Setzer, Thomas


We propose and empirically test statistical approaches to debiasing judgmental corporate cash flow forecasts. Accuracy of cash flow forecasts plays a pivotal role in corporate planning as liquidity and foreign exchange risk management are based on such forecasts. Surprisingly, to our knowledge there is no previous empirical work on the identification, statistical correction, and interpretation of prediction biases in large enterprise financial forecast data in general, and cash flow forecasting in particular. Employing a unique set of empirical forecasts delivered by 34 legal entities of a multinational corporation over a multi-year period, we compare different forecast correction techniques such as Theil’s method and approaches employing robust regression, both with various discount factors. Our findings indicate that rectifiable mean as well as regression biases exist for all business divisions of the company and that statistical correction increases forecast accuracy significantly. We show that the parameters estimated by the models for different business divisions can also be related to the characteristics of the business environment and provide valuable insights for corporate financial controllers to better understand, quantify, and feedback the biases to the forecasters aiming to systematically improve predictive accuracy over time.

Suggested Citation

  • Blanc, Sebastian M. & Setzer, Thomas, 2015. "Analytical debiasing of corporate cash flow forecasts," European Journal of Operational Research, Elsevier, vol. 243(3), pages 1004-1015.
  • Handle: RePEc:eee:ejores:v:243:y:2015:i:3:p:1004-1015
    DOI: 10.1016/j.ejor.2014.12.035

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    1. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    2. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    3. Goodwin, P., 1996. "Statistical correction of judgmental point forecasts and decisions," Omega, Elsevier, vol. 24(5), pages 551-559, October.
    4. Robin M. Hogarth & Spyros Makridakis, 1981. "Forecasting and Planning: An Evaluation," Management Science, INFORMS, vol. 27(2), pages 115-138, February.
    5. Harvey, Nigel, 1995. "Why Are Judgments Less Consistent in Less Predictable Task Situations?," Organizational Behavior and Human Decision Processes, Elsevier, vol. 63(3), pages 247-263, September.
    6. Gilchrist, Warren, 1974. "Statistical forecasting--The state of the art," Omega, Elsevier, vol. 2(6), pages 733-750, December.
    7. Graham, John R. & Harvey, Campbell R., 2001. "The theory and practice of corporate finance: evidence from the field," Journal of Financial Economics, Elsevier, vol. 60(2-3), pages 187-243, May.
    8. Lawrence, Michael & O'Connor, Marcus & Edmundson, Bob, 2000. "A field study of sales forecasting accuracy and processes," European Journal of Operational Research, Elsevier, vol. 122(1), pages 151-160, April.
    9. Andreassen, Paul B., 1988. "Explaining the price-volume relationship: The difference between price changes and changing prices," Organizational Behavior and Human Decision Processes, Elsevier, vol. 41(3), pages 371-389, June.
    10. Goodwin, Paul, 2000. "Correct or combine? Mechanically integrating judgmental forecasts with statistical methods," International Journal of Forecasting, Elsevier, vol. 16(2), pages 261-275.
    11. Basu, Sudipta & Markov, Stanimir, 2004. "Loss function assumptions in rational expectations tests on financial analysts' earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 38(1), pages 171-203, December.
    12. O'Connor, Marcus & Remus, William & Griggs, Ken, 1993. "Judgemental forecasting in times of change," International Journal of Forecasting, Elsevier, vol. 9(2), pages 163-172, August.
    13. Gormley, Fionnuala M. & Meade, Nigel, 2007. "The utility of cash flow forecasts in the management of corporate cash balances," European Journal of Operational Research, Elsevier, vol. 182(2), pages 923-935, October.
    14. Enns, S. T., 2002. "MRP performance effects due to forecast bias and demand uncertainty," European Journal of Operational Research, Elsevier, vol. 138(1), pages 87-102, April.
    15. Giloni, Avi & Simonoff, Jeffrey S. & Sengupta, Bhaskar, 2006. "Robust weighted LAD regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3124-3140, July.
    16. Kim, Chang-Soo & Mauer, David C. & Sherman, Ann E., 1998. "The Determinants of Corporate Liquidity: Theory and Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(03), pages 335-359, September.
    17. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    18. Pieter T. Elgers & May H. Lo & Dennis Murray, 1995. "Note on Adjustments to Analysts' Earnings Forecasts Based Upon Systematic Cross-Sectional Components of Prior-Period Errors," Management Science, INFORMS, vol. 41(8), pages 1392-1396, August.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. repec:eee:jrpoli:v:55:y:2018:i:c:p:103-112 is not listed on IDEAS
    2. repec:eee:ejores:v:272:y:2019:i:1:p:162-175 is not listed on IDEAS


    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:eee:ejores:v:243:y:2015:i:3:p:1004-1015. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.