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Sales forecasting in financial distribution: a comparison of quantitative forecasting methods

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  • Jiří Šindelář

    (University of Finance and Administration Prague)

Abstract

This paper deals with the issue of forecastability of sales activities of independent financial advisers (agents). Employing the most common quantitative methods on a diverse sample of timelines from multiple advisory companies, we have found that under most settings, these methods offer sub-par performance with high relative errors and no statistical differences between them. When a more granular approach is applied (reflecting sales unit size), ARIMA and the simple moving average emerge as significantly less accurate. This outcome is true for all sales units regardless of their size, when relative error is concerned. Thus, our analysis confirms the difficult forecastability of financial sales, speaking against the utilisation of more sophisticated forecasting methods, which mostly fail when compared to their much simpler and less costly counterparts.

Suggested Citation

  • Jiří Šindelář, 2019. "Sales forecasting in financial distribution: a comparison of quantitative forecasting methods," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(3), pages 69-80, December.
    • Handle: RePEc:pal:jofsma:v:24:y:2019:i:3:d:10.1057_s41264-019-00068-3
    DOI: 10.1057/s41264-019-00068-3
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    References listed on IDEAS

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    1. Mohammad M. Ali & John E. Boylan, 2010. "The Value of Forecast Information Sharing in Supply Chains," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 18, pages 14-18, Summer.
    2. Dalrymple, Douglas J., 1975. "Sales forecasting methods and accuracy," Business Horizons, Elsevier, vol. 18(6), pages 69-73, December.
    3. Peter M. Catt, 2009. "Forecastability: Insights from Physics, Graphical Decomposition, and Information Theory," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 13, pages 24-33, Spring.
    4. Robert Salomon & J. Myles Shaver, 2005. "Export and domestic sales: their interrelationship and determinants," Strategic Management Journal, Wiley Blackwell, vol. 26(9), pages 855-871, September.
    5. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    6. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    7. Paul Goodwin, 2011. "High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem?," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 23, pages 10-12, Fall.
    8. Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
    9. Dalrymple, Douglas J., 1987. "Sales forecasting practices: Results from a United States survey," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 379-391.
    10. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    11. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    12. Lee, Jongsu & Lee, Chul-Yong & Lee, Kichun Sky, 2012. "Forecasting demand for a newly introduced product using reservation price data and Bayesian updating," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1280-1291.
    13. Donna F. Davis & John T. Mentzer & Teresa M. Mccarthy & Susan L. Golicic, 2006. "The evolution of sales forecasting management: a 20-year longitudinal study of forecasting practices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 303-324.
    14. Green, Kesten C. & Armstrong, J. Scott & Soon, Willie, 2009. "Validity of climate change forecasting for public policy decision making," International Journal of Forecasting, Elsevier, vol. 25(4), pages 826-832, October.
    15. Adam Gordon, 2010. "The Boundaries of Quantitative Forecasting Methods: Respecting the Limits of Determinism," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 19, pages 9-15, Fall.
    16. Peter Hofer & Christoph Eisl & Albert Mayr, 2015. "Forecasting in Austrian companies," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 16(3), pages 359-382, November.
    17. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
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