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Forecasting Nestle Stock Price by using Brownian Motion Model during Pandemic Covid-19

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dc.contributor.authorSiti Raihana Hamzahen_US
dc.contributor.authorHazirah Halulen_US
dc.contributor.authorAssan Jengen_US
dc.contributor.authorUmul Ain’syah Sha’arien_US
dc.date.accessioned2024-05-30T02:07:58Z
dc.date.available2024-05-30T02:07:58Z
dc.date.issued2021
dc.date.submitted2022-1-28
dc.descriptionVol. 7 No. 2 (2021)en_US
dc.description.abstractIn the modern financial market, investors have to make quick and efficient investment decisions. The problem arises when the investor does not know the right tools to use in investment decision making. Different tools can be implemented in trading strategies to predict future stock prices. Therefore, the primary objective of this paper is to analyse the performance of the Geometric Brownian Motion (GBM) model in forecasting Nestle stock price by assessing the performance evaluation indicators. To analyse the stocks, two software were used, namely Microsoft Excel and Python. The model is trained for 16 weeks (4 months) of data from May to August 2019 and 2020. The simulated sample is for four weeks (1 month) which is for September 2019 and 2020. The findings show that during the Pandemic Covid-19, short-term prediction using GBM is more efficient than long-term prediction as the lowest Mean Square Error (MSE) value is at one week period. In addition, the Mean Absolute Percentage Error (MAPE) for all GBM simulations is highly accurate as it shows that MAPE values are less than 10%, indicating that the GBM method can be used to predict Nestle stock price during an economic downturn.en_US
dc.identifier.citationHamzah, S. R., Halul, H., Jeng, A., & Umul Ain’syah Sha’ari. (2021). Forecasting Nestle Stock Price by using Brownian Motion Model during Pandemic Covid-19. Malaysian Journal of Science Health & Technology, 7(2), 58–64. https://doi.org/10.33102/mjosht.v7i2.214en_US
dc.identifier.doihttps://doi.org/10.33102/mjosht.v7i2.214
dc.identifier.epage13
dc.identifier.issn2601-0003
dc.identifier.issueSpecial Is
dc.identifier.other2048-7
dc.identifier.spage1
dc.identifier.urihttps://mjosht.usim.edu.my/index.php/mjosht/article/view/214
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/15493
dc.identifier.volume7
dc.language.isoenen_US
dc.publisherUSIM Pressen_US
dc.relation.ispartofMalaysian Journal of Science Health & Technology,en_US
dc.subjectBrownian Motion, Stochastic Model, Python Programming, Covid 19, Nestle, Stock Priceen_US
dc.titleForecasting Nestle Stock Price by using Brownian Motion Model during Pandemic Covid-19en_US
dc.typeArticleen_US
dspace.entity.typePublication

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