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Geometric Brownian Motion (GBM) of Stock Indexes and Financial Market Uncertainty in the Context of Non-Crisis and Financial Crisis Scenarios

Author

Listed:
  • Vasile Brătian

    (Department of Finance and Accounting, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Ana-Maria Acu

    (Department of Mathematics and Informatics, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Diana Marieta Mihaiu

    (Department of Finance and Accounting, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Radu-Alexandru Șerban

    (Department of Management, Marketing and Business Administration, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

Abstract

The present article proposes a methodology for modeling the evolution of stock market indexes for 2020 using geometric Brownian motion (GBM), but in which drift and diffusion are determined considering two states of economic conjunctures (states of the economy), i.e., non-crisis and financial crisis. Based on this approach, we have found that the GBM proved to be a suitable model for making forecasts of stock market index values, as it describes quite well their future evolution. However, the model proposed by us, modified geometric Brownian motion (mGBM), brings some contributions that better describe the future evolution of stock indexes. Evidence in this regard was provided by analyzing the DAX, S&P 500, and SHANGHAI Composite stock indexes. Throughout the research, it was also found that the entropy of these markets, analyzed in the periods of non-crisis and financial crisis, does not differ significantly for DAX—German Stock Exchange (EU) and S&P 500—New York Stock Exchange (US), and insignificant differences for SHANGHAI Composite—Shanghai Stock Exchange (Asia). Given the fact that there is a direct link between market efficiency and their entropy (high entropy—high efficiency; low entropy—low efficiency), it can be deduced that the analyzed markets are information-efficient in both economic conjunctures, and, in this case, the use of GBM for forecasting is justified, as the prices have a random evolution (random walk).

Suggested Citation

  • Vasile Brătian & Ana-Maria Acu & Diana Marieta Mihaiu & Radu-Alexandru Șerban, 2022. "Geometric Brownian Motion (GBM) of Stock Indexes and Financial Market Uncertainty in the Context of Non-Crisis and Financial Crisis Scenarios," Mathematics, MDPI, vol. 10(3), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:309-:d:728635
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    References listed on IDEAS

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    1. Oussama Tilfani & Paulo Ferreira & Andreia Dionisio & My Youssef El Boukfaoui, 2020. "EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients," JRFM, MDPI, vol. 13(5), pages 1-23, May.
    2. Robert F. Engle & Tianyue Ruan, 2019. "Measuring the probability of a financial crisis," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(37), pages 18341-18346, September.
    3. Risso, Wiston Adrián, 2008. "The informational efficiency and the financial crashes," Research in International Business and Finance, Elsevier, vol. 22(3), pages 396-408, September.
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    Cited by:

    1. Curto, José Dias & Serrasqueiro, Pedro, 2022. "Averaging financial ratios," Finance Research Letters, Elsevier, vol. 48(C).

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