Abstract
The ASEAN-5 region has experienced significant economic growth and development in recent years, but this growth has been accompanied by increasing volatility in financial markets. To better understand the dynamics of these markets and identify potential risks and opportunities, it is important to analyze the historical data to evaluate the stock market performance. This research aims to determine the parameters of Geometric Brownian Motion (GBM) for stock indexes in ASEAN-5 countries from 2017 to 2022 and model GBM using this data. Subsequently, the data is split into two partitions, with 70% used as the in-sample in data management for the starting of the analysis, while the other 30% will be applied in future works. Note that GBM, drift, and volatility parameters are calculated based on the in-sample data. The findings of this research provide insights into the performance of GBM in modeling stock index data and contribute to understanding the behavior of stock indexes in the ASEAN-5 countries.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chaqmakchi, D.W.: Stock market and financial market efficiency (2022)
Ma, L. et al.: Fundamental analysis in China: an empirical study of the relationship between financial ratios and stock prices. arXiv preprint arXiv:1910.06746 (2019)
Kumar, S., Srivastava, M., Prakash, V.: Challenges and opportunities for mutual fund investment and the role of industry 4.0 to recommend the individual for speculation. New Horiz. Ind. 4.0 Mod. Bus. 69–98 (2023)
Ross, S.M.: Introduction to probability models. Acad. Press (2014)
Lidén, J.: Stock price predictions using a geometric Brownian motion (2018)
Brown, R.: Brownian motion. Unpublished Exper. 38 (1827)
Hersugondo, H. et al.: ASEAN-5 stock price index valuation after COVID-19 outbreak through GBM-MCS and VaR-SDPP methods. Int. J. Finan. Stud. 10(4), 112 (2022)
Mukherjee, S., Wei, Z.: Suicide disparities across metropolitan areas in the US: a comparative assessment of socio-environmental factors using a data-driven predictive approach. PLoS ONE 16(11), e0258824 (2021)
Abidin, S.N.Z., Jaffar, M.M.: Forecasting share prices of small size companies in Bursa Malaysia using geometric Brownian motion. Appl. Math. & Inf. Sci. 8(1), 107 (2014)
Massey, F.J.: The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46(253), 68–78 (1951)
Wilmott, P.: Paul Wilmott on quantitative finance. Wiley (2000)
Lee, S.L., et al.: Geometric Brownian motion-based time series modeling methodology for statistical autocorrelated process control: logarithmic return model. Int. J. Math. Math. Sci. (2022)
Sinha, A.K.: The reliability of geometric Brownian motion forecasts of S&P500 index values. J. Forecast. 40(8), 1444–1462 (2021)
Fasanya, I.O.: Pandemic uncertainty and sectoral stock return predictability in South Africa. Afr. J. Econ. Manage. Stud. (2022)
Estember, R.D., Maraña, M.J.R.: Forecasting of stock prices using Brownian motion–Monte Carlo simulation. Int. Conf. Ind. Eng. Oper. Manage. (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nordin, N., Ishak, N., Halim, N.A., Hamzah, S.R., Rasadee, A.F.N. (2024). A Geometric Brownian Motion of ASEAN-5 Stock Indexes. In: Alareeni, B., Elgedawy, I. (eds) AI and Business, and Innovation Research: Understanding the Potential and Risks of AI for Modern Enterprises. Studies in Systems, Decision and Control, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-42085-6_67
Download citation
DOI: https://doi.org/10.1007/978-3-031-42085-6_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42084-9
Online ISBN: 978-3-031-42085-6
eBook Packages: EngineeringEngineering (R0)