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Technological Forecasting at the Stock Market

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  • MODIS, THEODORE

Abstract

Under the assumption that competition (Darwinian in nature) reigns in the stock market, we analyze the behavior of company stocks as if they were species competing for investors’ resources. The approach requires the study of dollar values and share volumes, daily exchanged in the stock market, via logistic growth functions. These two variables, in contrast to prices, obey the law of natural growth in competition, which like every natural law, is endowed with predictability. A number of unexpected insights about the stock market emerge. The forecasts indicate that whereas there is no looming crash in the near future, no significant growth should be expected either. The DJIA is to hover around 9500 depicting large erratic excursions above and below this level for a few years. The use of Volterra-Lotka equations demonstrates that the 1987 crash altered the stock-bond interaction from a symbiotic to a predator-prey relationship with stocks acting as predators. This research work has lead to the publication of the book "An S-Shaped Trail to Wall Street" by T. Modis, (Growth Dynamics, Geneva, 1999).

Suggested Citation

  • Modis, Theodore, 1999. "Technological Forecasting at the Stock Market," OSF Preprints ctd6s, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:ctd6s
    DOI: 10.31219/osf.io/ctd6s
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    Cited by:

    1. Meixia Pan & Wanming Chen & Shengyuan Wang & Xiaolan Wu, 2022. "The Influence of Low Carbon Emission Engine on the Life Cycle of Automotive Products: A Case Study of Three-Cylinder Models in the Chinese Market," Energies, MDPI, vol. 15(18), pages 1-15, September.
    2. Addolorata Marasco & Alessandro Romano, 2018. "Deterministic modeling in scenario forecasting: estimating the effects of two public policies on intergenerational conflict," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2345-2371, September.
    3. Lin, Chiun-Sin, 2013. "Forecasting and analyzing the competitive diffusion of mobile cellular broadband and fixed broadband in Taiwan with limited historical data," Economic Modelling, Elsevier, vol. 35(C), pages 207-213.
    4. Nokhaiz Tariq Khan & Javed Aslam & Ateeq Abdul Rauf & Yun Bae Kim, 2022. "The Case of South Korean Airlines-Within-Airlines Model: Helping Full-Service Carriers Challenge Low-Cost Carriers," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    5. de Groot, E.A. & Segers, R. & Prins, D., 2022. "Non-resonating cycles in a dynamic model for investment behavior," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    6. Shengyuan Wang & Meixia Pan & Xiaolan Wu, 2023. "Sustainable Development in the Export Trade from a Symbiotic Perspective on Carbon Emissions, Exemplified by the Case of Guangdong, China," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    7. Avila, Luz Angelica Pirir & Lee, Deok-Joo & Kim, Taegu, 2018. "Diffusion and competitive relationship of mobile telephone service in Guatemala: An empirical analysis," Telecommunications Policy, Elsevier, vol. 42(2), pages 116-126.
    8. Marasco, A. & Picucci, A. & Romano, A., 2016. "Market share dynamics using Lotka–Volterra models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 49-62.
    9. Goran Dominioni & Addolorata Marasco & Alessandro Romano, 2018. "A mathematical approach to study and forecast racial groups interactions: deterministic modeling and scenario method," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1929-1956, July.
    10. Zhang, Wei & Lam, Jasmine Siu Lee, 2017. "An empirical analysis of maritime cluster evolution from the port development perspective – Cases of London and Hong Kong," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 219-232.
    11. Gupta, Ruchita & Jain, Karuna, 2016. "Competition effect of a new mobile technology on an incumbent technology: An Indian case study," Telecommunications Policy, Elsevier, vol. 40(4), pages 332-342.
    12. Grinin, Leonid & Grinin, Anton & Korotayev, Andrey, 2020. "A quantitative analysis of worldwide long-term technology growth: From 40,000 BCE to the early 22nd century," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    13. Xiaoxia Fu & Ping Zhang & Juzhi Zhang, 2017. "Forecasting and Analyzing Internet Users of China with Lotka–Volterra Model," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-18, February.
    14. Shengyuan Wang, 2022. "Exploring the Sustainability of China’s New Energy Vehicle Development: Fresh Evidence from Population Symbiosis," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    15. Pao, Hsiao-Tien & Fu, Hsin-Chia, 2015. "Competition and stability analyses among emissions, energy, and economy: Application for Mexico," Energy, Elsevier, vol. 82(C), pages 98-107.
    16. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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