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Value and risk dynamics over the innovation cycle

Author

Listed:
  • Engelbert J. Dockner

    (Department of Finance, Accounting and Statistics and Vienna Graduate School of Finance (Vienna University of Economics and Business Administration (Wirtschaftsuniversität Wien - WU)))

  • Baran Siyahhan

    (IMT-BS - DEFI - Département Droit, Économie et Finances - TEM - Télécom Ecole de Management - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - EESC-GEM Grenoble Ecole de Management - UEVE - Université d'Évry-Val-d'Essonne - TEM - Télécom Ecole de Management)

Abstract

This paper studies investment in intellectual capital and corresponding value and risk dynamics over the innovation cycle. We assume that the innovation cycle consists of three phases, R&D, trial, and market introduction phases. We use a real option investment model to characterize firm value and risk dynamics over the innovation cycle and find that firm value is the sum of the value of assets in place and non-linear option values related to breakthrough, exit, and market introduction options. Firm risk over the innovation cycle is highly non-linear and quite distinct in different phases. During the R&D phase risk is high as the firm faces high operating leverage originating from R&D fixed costs together with technological uncertainty. During the trial phase risk is significantly lower and dominated by option risk to launch the product in the market while after the introduction of the product in the market risk is equivalent to the asset risk of the company. Our model is consistent with the view that positive excess returns of R&D intensive firms are a compensation for risk. Based on this insight we derive several testable predictions.

Suggested Citation

  • Engelbert J. Dockner & Baran Siyahhan, 2015. "Value and risk dynamics over the innovation cycle," Grenoble Ecole de Management (Post-Print) hal-01504374, HAL.
    • Handle: RePEc:hal:gemptp:hal-01504374
    DOI: 10.1016/j.jedc.2015.07.005
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    Cited by:

    1. Gao, Kang & Yuan, Yijun, 2022. "Government intervention, spillover effect and urban innovation performance: Empirical evidence from national innovative city pilot policy in China," Technology in Society, Elsevier, vol. 70(C).
    2. Colombo, Luca & Labrecciosa, Paola, 2021. "A stochastic differential game of duopolistic competition with sticky prices," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
    3. Xueling Li & Xiaoyan Zhang & Yuan Liu & Yuanying Mi & Yong Chen, 2022. "The impact of artificial intelligence on users' entrepreneurial activities," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 597-608, May.
    4. Caggese, Andrea & Pérez-Orive, Ander, 2022. "How stimulative are low real interest rates for intangible capital?," European Economic Review, Elsevier, vol. 142(C).

    More about this item

    Keywords

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    JEL classification:

    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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