The application of Geometric Brownian Motion in stock forecasting during the coronavirus outbreak in Indonesia

Fitria, Irma; Handayati, Kristin Nova; Hasanah, Primadina

doi:10.1088/1742-6596/1821/1/012008

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…These results indicate that the industrial sector in the United States has experienced a significantly increased risk due to the Covid-19 pandemic [16]. A high level of volatility results in a greater risk, namely the accuracy of stock price estimates is decreasing [17].…”

Section: Introductionmentioning

confidence: 89%

“…Stock markets in the US, Japan and Europe where agricultural companies such as fertilizer manufacturers, agricultural chemicals and food distributors also show that the sector is experiencing high volatility [18]. A high level of volatility results in a greater risk, namely the decreasing accuracy of stock estimates [17]. With the stock market being more volatile, investors face more uncertain conditions than before the COVID-19 pandemic.…”

Section: Differences In Stock Price Volatility Of Agricultural Compan...mentioning

confidence: 99%

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The Impact of the Covid-19 Pandemic on Stock Prices and Stock Price Volatility in Agricultural Companies on the Indonesia Stock Exchange

Agusman¹,

Mulyo²,

Irham³

2023

Advances in Biological Sciences Research

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The Covid-19 pandemic has had a tremendous impact on various aspects of life, including the fields of health, economy, industry, and agriculture in Indonesia. This study aims to analyze the impact of the Covid-19 pandemic on stock prices and stock price volatility in agricultural companies in Indonesia. The data used in this study is daily stock price data of agricultural companies listed on the Indonesian stock exchange from December 2019 to May 2020. To answer the research objectives, a paired sample t-test was conducted on stock prices and daily stock price volatility of agricultural companies before the Covid-19 pandemic (data from December 5, 2019 to March 1, 2020) compared to stock prices and daily stock price volatility of agricultural companies after the Indonesian government announced cases of Covid-19 (March 2, 2020 to May 31, 2020). The volatility of stock prices of agricultural companies is measured by following the approach taken by Parkinson (1980). The results of the analysis show that the Covid-19 pandemic has a negative and significant impact on stock prices of agricultural companies in Indonesia that is the average stock price of agricultural companies decreased by 34.01%. Furthermore, the results of the study also show that the Covid-19 pandemic has caused the dynamics of stock prices of agricultural companies in Indonesia to become more volatile. This provides an understanding that the COVID-19 pandemic has resulted in increased business risk for agricultural companies in Indonesia along with the dynamics of stock price movements.

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Section: Introductionmentioning

confidence: 89%

Section: Differences In Stock Price Volatility Of Agricultural Compan...mentioning

confidence: 99%

The Impact of the Covid-19 Pandemic on Stock Prices and Stock Price Volatility in Agricultural Companies on the Indonesia Stock Exchange

Agusman¹,

Mulyo²,

Irham³

2023

Advances in Biological Sciences Research

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“…The use of MAPE in outcome evaluation can avoid measurement accuracy of the magnitude of the actual value and the predicted value. MAPE score criteria shown in Table 1 [7].…”

Section: Best Model Selectionmentioning

confidence: 99%

Comparison in Predicting the Short-Term Using the Sarima, Dsarima and Tsarima Methods

Giovani

Anggriani

Simatupang

2022

BAREKENG: J. Math. & App.

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The flow of data and information is growing rapidly and rapidly in various sizes and means which is called Big Data. In the face of a change for the better in the future, a careful analysis and design of a data processing system is needed, in which a predictive framework can formulate the right policy to be one of the efforts to make a good decision. This is one of the appropriate Big Data processing efforts, which can be realized through one of the methods, namely prediction or forecasting is an effort to predict future values or trends as a reference for analyzing conditions in the past. One example of Big Data in the City of Balikpapan, namely the temperature within 2 meters obtained from the NASA satellite published on the website power.larc.nasa.gov. One of the methods used in this research is the ARIMA method and it is developed according to the data used. Based on the data to be used, namely temperature data within a distance of 2 meters in the city of Balikpapan, the development of data processing is carried out to pay attention to three seasonal patterns or the so-called Triple Seasonal ARIMA model. In this study, it can be seen how to build the Triple Seasonal ARIMA model and comparison with alternative models, namely Seasonal ARIMA and Double Seasonal ARIMA, and can see how the results of the Triple Seasonal ARIMA model accuracy when compared with alternative models. The method used in this study is the Seasonal ARIMA, Double Seasonal ARIMA and Triple Seasonal ARIMA methods. The results obtained in this study obtained a comparison of methods in making predictions with a specified time span, the results obtained from the Seasonal ARIMA model that it was very good at predicting a time span of 2 weeks, Double Seasonal ARIMA for a period of 1 month, Double Seasonal ARIMA for a period of 3 months, and Triple Seasonal ARIMA for a period of 6 months.

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“…The article by Farida Agustini W, Ika Restu Affianti and Endah RM Putri arrives at the same model again [12]. The paper by Joel Lidén is extremely detailed and considered multiple models like kernel density estimation, bootstrap and ARMA [13]. It even considered the assumption of the Cauchy distribution, which may lack justification.…”

Section: Introductionmentioning

confidence: 99%

Modeling Stock Price of Transnational companies using Brownian motion

Kucherov

2022

Artificial Societies

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This article is concerned with creating a model for stock price using the Brownian motion. At first, we consider the notion of a discrete time stochastic process, simple random walk, and then move on to its continuous analogue, the Brownian motion. Next we identify the problem with using regular Calculus for stochastic differential equations and derive Ito’s Lemma. After that we derive a model for stock prices and use lognormal distribution to determine its expected value and variance. Finally, we use sample volatility to make predictions for Apple and Gazprom stock prices.

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The application of Geometric Brownian Motion in stock forecasting during the coronavirus outbreak in Indonesia

Cited by 6 publications

References 1 publication

The Impact of the Covid-19 Pandemic on Stock Prices and Stock Price Volatility in Agricultural Companies on the Indonesia Stock Exchange

The Impact of the Covid-19 Pandemic on Stock Prices and Stock Price Volatility in Agricultural Companies on the Indonesia Stock Exchange

Comparison in Predicting the Short-Term Using the Sarima, Dsarima and Tsarima Methods

Modeling Stock Price of Transnational companies using Brownian motion

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