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Optimum Technology Product Life Cycle Technology Innovation Investment-Using Compound Binomial Options

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  • Chuan-Chuan Ko

    (Business School of City College of Dongguan University of Technology, No. 1 Wenchang Road, Liaobu Town, Dongguan 523419, Guangdong, China)

  • Tyrone T. Lin

    (Department of International Business, National Dong Hwa University, No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, Taiwan)

  • Fu-Min Zeng

    (Marketing Department, Data Communications Branch, Chunghwa Telecom Co. Ltd., No. 21-3, Section 1, Xinyi Road, Zhongzheng District, Taipei City10048, Taiwan)

  • Chien-Yu Liu

    (Department of Food and Beverage Management, Jinwen University of Science & Technology, No. 99, Anzhong Rd., Xindian Dist., New Taipei City 23154, Taiwan)

Abstract

The study considers the product life cycle in the stages of technological innovation, and focuses on how to evaluate the optimal investment strategy and the project value. It applies different product stages (three stages including production innovation, manufacture innovation, and business innovation) factors to different risks to build a technology innovation strategy model. This study of option premiums aims for the best strategy timing for each innovation stage. It shows that the variation of business cycle will affect the purchasing power under the uncertainty of Gross Domestic Product (GDP). In application, the compound binomial options for the manufacture innovation will only be considered after the execution of the production innovation, whereas the operation innovation will only be considered after the execution of the manufacture innovation. Thus, this paper constructs the dynamic investment sequential decision model, assesses the feasibility of an investment strategy, and makes a decision on the appropriate project value and option premiums for each stage under the possible change of GDP. Numerically, the result shows the equity value of the investment is greater than 0. Therefore, this paper recommends the case firm to invest in its innovation project known as one-time passwords. Sensitivity analysis shows when the risk-adjusted discounted rate r increases, the risk of the investment market increases accordingly, hence the equity value must also be higher in order to attract the case firm’s investment interest. Also, the average GDP growth rate u sensitivity analysis results in different phenomena. The equity value gradually decreases when the average GDP growth rate rises. When the average GDP growth rate u rises to a certain extent, however, its equity value is gradually growing. The study investigates the product life cycle innovation investment topic by using the compound binomial options method and therefore provide a more flexible strategy decision compared with other trend forecast criteria.

Suggested Citation

  • Chuan-Chuan Ko & Tyrone T. Lin & Fu-Min Zeng & Chien-Yu Liu, 2018. "Optimum Technology Product Life Cycle Technology Innovation Investment-Using Compound Binomial Options," Risks, MDPI, vol. 6(3), pages 1-14, September.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:3:p:98-:d:169829
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    References listed on IDEAS

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    Cited by:

    1. Chuan-Chuan Ko & Chien-Yu Liu & Zan-Yu Chen & Jing Zhou, 2019. "Sustainable Development Economic Strategy Model for Reducing Carbon Emission by Using Real Options Approach," Sustainability, MDPI, vol. 11(19), pages 1-14, October.
    2. Andrey A. Zaytsev & Roman S. Blizkyi & Irina I. Rakhmeeva & Nikolay D. Dmitriev, 2021. "Building a Model for Financial Management of Digital Technologies in the Areas of Combinatorial Effects," Economies, MDPI, vol. 9(2), pages 1-15, April.
    3. Arkadiusz J. Derkacz, 2020. "Fiscal, Investment and Export Multipliers and the COVID-19 Pandemic Slowdowns Uncertainty Factor in the First Half of 2020," Risks, MDPI, vol. 8(4), pages 1-21, November.
    4. Natalia Morozko & Nina Morozko & Valentina Didenko, 2020. "The Use of Real Options in Assessing the Development of Small Energy in Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 205-211.
    5. Arkadiusz J. Derkacz, 2020. "Autonomous Expenditure Multipliers and Gross Value Added," JRFM, MDPI, vol. 13(9), pages 1-18, September.

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