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Comparing Growth Models with Other Investment Methods

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  • Guizhou Wang
  • Kjell Hausken

Abstract

This article introduces five growth models as an investment method. These are conventional logistic growth, Gompertz growth, generalized charged capacitor growth, combined logistic and charged capacitor growth, and combined Gompertz and charged capacitor growth. This article demonstrates how to apply the growth models in investing while taking oscillation and lengthening cycles into consideration. The growth models applied as an investment method are compared with 15 other common investment methods. The growth models can be used to predict the prices of various types of assets, including derivatives, stocks, bonds, real estate, and cryptocurrencies. Other phenomena involving growth and fluctuations can also be analyzed. This article provides insights for researchers and investors for how to predict when investing. Â JEL classification numbers: C5, G11.

Suggested Citation

  • Guizhou Wang & Kjell Hausken, 2023. "Comparing Growth Models with Other Investment Methods," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 12(1), pages 1-1.
  • Handle: RePEc:spt:fininv:v:12:y:2023:i:1:f:12_1_1
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    References listed on IDEAS

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    1. Jimmy E. Hilliard & Julie T. D. Ngo, 2022. "Bitcoin: jumps, convenience yields, and option prices," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 2079-2091, November.
    2. R. K. Jana & Indranil Ghosh & Debojyoti Das, 2021. "A differential evolution-based regression framework for forecasting Bitcoin price," Annals of Operations Research, Springer, vol. 306(1), pages 295-320, November.
    3. Mahboubeh Faghih Mohammadi Jalali & Hanif Heidari, 2020. "Predicting changes in Bitcoin price using grey system theory," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-12, December.
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    More about this item

    Keywords

    Growth models; Investment methods; Price prediction; Oscillatory growth; Lengthening cycle; Differential equation.;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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