Open AccessForecasting Nestle Stock Price by using Brownian Motion Model during Pandemic Covid-19
Author(s) -
Siti Raihana Hamzah,
Hazirah Halul,
Assan Jeng,
Umul Ain’syah Sha’ari
Publication year - 2021
Publication title -
malaysian journal of science, health and technology
Language(s) - English
Resource type - Journals
ISSN - 2601-0003
DOI - 10.33102/mjosht.v7i2.214
Subject(s) - geometric brownian motion , econometrics , mean absolute percentage error , stock (firearms) , covid-19 , python (programming language) , economics , stock market , mean squared error , computer science , actuarial science , financial economics , statistics , mathematics , geography , economy , medicine , context (archaeology) , archaeology , diffusion process , disease , pathology , infectious disease (medical specialty) , service (business) , operating system
In 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.