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A Model for Aggregate Financial Planning

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  • Clement G. Krouse

    (University of California, Los Angeles)

Abstract

Financial planning in the firm is a dynamic process requiring that a variety of liquidity, profitability, capital structure, growth and stability variables be simultaneously considered. In recent years widespread attention has been given the time-wise interdependencies involved, but the focus has been conceptual and not directed to yield an implementable model. In the present paper a workable, multi-stage planning model is developed by combining these developments in corporate finance with techniques from optimal systems control. The firm is viewed as attempting to optimize a multiple-objective criterion of time-dated variables involving both short-and long-term financial goals. Random elements are considered to be part of the process by which decisions affect the firm's financial state and an optimal decision strategy developed for an important first-approximation model. The paper concentrates on the general model structure and specific means for implementation; rigorous mathematical analysis is suppressed. An illustrative application is presented and potential uses discussed.

Suggested Citation

  • Clement G. Krouse, 1972. "A Model for Aggregate Financial Planning," Management Science, INFORMS, vol. 18(10), pages 555-566, June.
    • Handle: RePEc:inm:ormnsc:v:18:y:1972:i:10:p:b555-b566
    DOI: 10.1287/mnsc.18.10.B555
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    Cited by:

    1. Edward A. Stohr & Mohan Tanniru, 1978. "The Design of a Corporate Planning System Simulator," Discussion Papers 342, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    2. J. M. Blin, 1977. "Development of a Corporate Information System," Discussion Papers 304, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    3. Jean-Marie Blin, 1978. "A Structure for Computer-Aided Corporate Planning," Discussion Papers 350, Northwestern University, Center for Mathematical Studies in Economics and Management Science.

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