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Estimation of the Allowance for Doubtful Accounts by Markov Chains

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Author Info

  • R. M. Cyert

    (Graduate School of Industrial Administration, Carnegie Institute of Technology)

  • H. J. Davidson

    (Graduate School of Industrial Administration, Carnegie Institute of Technology)

  • G. L. Thompson

    (Graduate School of Industrial Administration, Carnegie Institute of Technology)

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    Abstract

    Investigation of more accurate and efficient methods of estimating the allowance for doubtful accounts was begun in the early 1950's by R. M. Cyert and R. M. Trueblood [Cyert, R. M., R. M. Trueblood. 1957. Statistical sampling techniques in the aging of accounts receivable in a department store. Management Sci. 3 (2, January) 185-195.]. At this time, studies were confined primarily to investigation of more efficient methods of performing the first step in the estimation procedure: determining the age distribution of accounts. In particular, the applicability of statistical sampling techniques was evaluated. A number of retail establishments now use scientific sampling methods to perform the first step in the estimation of the allowance for doubtful accounts. As a continuation of research, the second phase of the allowance estimation problem was a logical area for investigation. While it did not seem likely that all of the judgment factors involved in the setting of loss expectancy rates could be eliminated, it did appear feasible to develop a scientific approach to determining these rates. Accordingly, research into this problem was initiated. This paper discusses a method which has been developed. In addition, some of the managerial implications of the method are discussed.

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    File URL: http://dx.doi.org/10.1287/mnsc.8.3.287
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 8 (1962)
    Issue (Month): 3 (April)
    Pages: 287-303

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    Handle: RePEc:inm:ormnsc:v:8:y:1962:i:3:p:287-303

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    Cited by:
    1. Kelly, Robert, 2011. "The Good, The Bad and The Impaired - A Credit Risk Model of the Irish Mortgage Market," Research Technical Papers 13/RT/11, Central Bank of Ireland.
    2. L. Smith & Baiqiang Jin, 2007. "Modeling exposure to losses on automobile leases," Review of Quantitative Finance and Accounting, Springer, vol. 29(3), pages 241-266, October.
    3. Heger, Diana, 2004. "The Link Between Firms? Innovation Decision and the Business Cycle: An Empirical Analysis," ZEW Discussion Papers 04-85, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    4. 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.
    5. Smith, L. Douglas & Lawrence, Edward C., 1995. "Forecasting losses on a liquidating long-term loan portfolio," Journal of Banking & Finance, Elsevier, vol. 19(6), pages 959-985, September.
    6. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305.
    7. Stefan Hlawatsch & Sebastian Ostrowski, 2009. "Economic Loan Loss Provision and Expected Loss," FEMM Working Papers 09013, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    8. Schechtman, Ricardo, 2013. "Default matrices: A complete measurement of banks’ consumer credit delinquency," Journal of Financial Stability, Elsevier, vol. 9(4), pages 460-474.
    9. Dariusz Wedzki, 2007. "Trade credit portfolio selection – a markovian approach," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 2, pages 105-119.
    10. Seifert, Daniel & Seifert, Ralf W. & Protopappa-Sieke, Margarita, 2013. "A review of trade credit literature: Opportunities for research in operations," European Journal of Operational Research, Elsevier, vol. 231(2), pages 245-256.

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