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Modelling Stock Market Prices Using the Open, High and Closes Prices. Evidence from International Financial Markets

Author

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  • Samuel Tabot Enow

    (The IIE Vega school, South Africa)

Abstract

Purpose: Modelling security prices seem to be an ending debate in finance literature due to no clear consensus on behavioral patterns. Knowledge of stock price movement has always been an important source of information that is much needed in asset pricing and trading strategies. The aim of this study was to model stock market prices using six international markets as a sample. Design/methodology/approach: This study made use of the Bayesian Time-Varying coefficient for a five-year period from January 2, 2018, to January 2, 2023. Finding: The findings of this study revealed that there is strong empirical evidence that the returns of a security can be modelled using the open, high and low prices. Research limitations/implications: This implies that the drift in stock price movement can be better explained by observing the lag values of the open, high and low prices which may be an important tool for short term traders and incorporated in volatility estimation. Also, the lag values of the open, high and low price movements explain more than 98% of changes in the closing price. Originality/value: As per the author’s knowledge, this study is the first to model stock market prices using the open, high and low prices for multiple international markets.

Suggested Citation

  • Samuel Tabot Enow, 2022. "Modelling Stock Market Prices Using the Open, High and Closes Prices. Evidence from International Financial Markets," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 15(3), pages 52-59, December.
    • Handle: RePEc:tei:journl:v:15:y:2022:i:3:p:52-59
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    References listed on IDEAS

    as
    1. Samuel Tabot Enow, 2021. "The Impact of Covid-19 on Market Efficiency: A Comparative Market Analysis," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 9(4), pages 235-244.
    2. Rasika Yatigammana & Shelton Peiris & Richard Gerlach & David Edmund Allen, 2018. "Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants," Risks, MDPI, vol. 6(2), pages 1-22, May.
    3. Pinar KAYA & Bulent GULOGLU, 2017. "Modeling and Forecasting the Markets Volatility and VaR Dynamics of Commodity," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 11(1), pages 9-49.
    4. Erginbay Ugurlu & Eleftherios Thalassinos & Yusuf Muratoglu, 2014. "Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 72-87.
    5. Samuel Tabot Enow, 2022. "Price Clustering in International Financial Markets during the COVID-19 Pandemic and Its Implications," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 10(2), pages 46-53.
    6. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.
    7. Karlsson, Sune & Österholm, Pär, 2020. "A hybrid time-varying parameter Bayesian VAR analysis of Okun’s law in the United States," Economics Letters, Elsevier, vol. 197(C).
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    Cited by:

    1. Li-Chuan Liao & Tzu-Pu Chang & Ping-Huang Wang, 2023. "Earnings Management Ethicality and Application in the Kenyan Public Sector: A Critical Review," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 16(1), pages 71-86, October.

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    More about this item

    Keywords

    Modelling stock price; Bayesian Model; Stock market; Financial markets; VAR;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G4 - Financial Economics - - Behavioral Finance

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