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The Use of Exponentially-Smoothed Transition Matrices to Improve Forecasting of Cash Flows from Accounts Receivable

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  • A. Wayne Corcoran

    (Virginia Polytechnic Institute and State University)

Abstract

A pioneering application of a Markov chain to forecast account receivable flows (Cyert, Davidson, and Thompson [Cyert, R. M., H. J. Davidson, G. L. Thompson. 1962. Estimation of the allowance for doubtful accounts by Markov chains. Management Sci. (August) 287-303.]) employed an unusual (the oldest balance) method of aging accounts and an assumption that the resulting transition matrix was stable. Forecasting steady state results was the primary focus of the application. The present research uses a commonly found (partial balance) method of aging, an assumption of dynamic changes in the transition matrix, and does not focus on steady state results. A manufacturer's data were analyzed and exponential smoothing was introduced to update the average transition matrix thereby enabling a tracking of changing customer payment behavior. The actual and Markovian estimates of the company's cash collections checked favorably. The paper also discusses how modifications such as the seasonal and trend adjustments introduced by Winters (Winters, P. R. 1960. Forecasting sales by exponentially weighted moving averages. Management Sci. (April) 324-342.) may be incorporated into the model. Extensions to other areas are offered.

Suggested Citation

  • A. Wayne Corcoran, 1978. "The Use of Exponentially-Smoothed Transition Matrices to Improve Forecasting of Cash Flows from Accounts Receivable," Management Science, INFORMS, vol. 24(7), pages 732-739, March.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:7:p:732-739
    DOI: 10.1287/mnsc.24.7.732
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    Cited by:

    1. Schechtman, Ricardo, 2013. "Default matrices: A complete measurement of banks’ consumer credit delinquency," Journal of Financial Stability, Elsevier, vol. 9(4), pages 460-474.
    2. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    3. Chih-Yang Tsai, 2017. "The impact of cost structure on supply chain cash flow risk," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6624-6637, November.
    4. Arno Botha & Conrad Beyers & Pieter de Villiers, 2020. "The loss optimisation of loan recovery decision times using forecast cash flows," Papers 2010.05601, arXiv.org.
    5. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    6. Yair M. Babad & Bala V. Balachandran, 1989. "Operational matrix accounting," Contemporary Accounting Research, John Wiley & Sons, vol. 5(2), pages 775-792, March.
    7. Chen, Shou & Jiang, Xiangqian & He, Hongbo & Zhou, Xi, 2020. "A pricing model with dynamic repayment flows for guaranteed consumer loans," Economic Modelling, Elsevier, vol. 91(C), pages 1-11.
    8. Tangsucheeva, Rattachut & Prabhu, Vittaldas, 2014. "Stochastic financial analytics for cash flow forecasting," International Journal of Production Economics, Elsevier, vol. 158(C), pages 65-76.

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