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Dynamic R&D Investment Policies


  • Paul D. Childs

    (Gatton College of Business and Economics, University of Kentucky, Lexington, Kentucky 40506-0034)

  • Alexander J. Triantis

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742-1815)


This paper examines dynamic R&D investment policies and the valuation of R&D programs in a contingent claims framework. We incorporate the following characteristics of R&D programs into our model: learning-by-doing, collateral learning between different projects in the program, interaction between project cash flows, periodic reevaluations of the program, different intensities of investment, capital rationing constraints, and competition. We show that a firm may invest in multiple projects even if only one can be implemented after development is complete. Furthermore, the firm may significantly alter its funding policy over time. For example, it may simultaneously develop multiple projects for a period of time, then focus on a lead project, and potentially resume funding of a "backup" project if the lead project fails to deliver on its early promise. We show how a firm can forecast expected R&D spending through time for an optimally executed R&D program. While project volatility plays an important role in determining R&D program value, we find that for high volatility projects the optimal investment policy is not very sensitive to changes in (or misestimation of) volatility. In considering whether to accelerate development of a project, a firm should balance the adverse effects of increased costs and the loss of investment flexibility against the positive effects of rapid uncertainty resolution and accelerated cash flows. In the presence of a budget constraint that prevents the firm from simultaneously accelerating projects and developing projects in parallel, we find that, if one project significantly dominates another early in the development stage, the option to accelerate the lead project is likely to be more valuable than the option to exchange projects. Thus, the backup project would be shelved in order to commit extra resources to development of the lead project. Finally, competition from other firms leads to more parallel investment in the early development stages of projects, less parallel investment in the latter stages of development, and lower overall investment.

Suggested Citation

  • Paul D. Childs & Alexander J. Triantis, 1999. "Dynamic R&D Investment Policies," Management Science, INFORMS, vol. 45(10), pages 1359-1377, October.
  • Handle: RePEc:inm:ormnsc:v:45:y:1999:i:10:p:1359-1377

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    References listed on IDEAS

    1. Hull, John & White, Alan, 1990. "Valuing Derivative Securities Using the Explicit Finite Difference Method," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 25(01), pages 87-100, March.
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    3. Davis, Graham A. & Owens, Brandon, 2003. "Optimizing the level of renewable electric R&D expenditures using real options analysis," Energy Policy, Elsevier, vol. 31(15), pages 1589-1608, December.
    4. Andrea Gamba & Alberto Micalizzi, 2007. "Product Development and Market Expansion: A Real Options Model," Financial Management, Financial Management Association International, vol. 36(1), pages 91-112, March.
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