Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models

Mati, Sagiru; Radulescu, Magdalena; Saqib, Najia; Samour, Ahmed; Ismael, Goran Yousif; Aliyu, Nazifi

doi:10.1016/j.heliyon.2023.e21439

Cited by 7 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the Autoregressive Integrated Moving Average (ARIMA) model which is denoted by ARIMA (p, d, q). The three parameters p, d, and q correspond to the Autoregressive sequence, the integration order, and the moving average sequence respectively [15].…”

Section: Arima Model Testmentioning

confidence: 99%

Bitcoin Potential after Brexit Referendum and Price Prediction

Rahman,

Astuti

2024

AJARR

View full text Add to dashboard Cite

In a world of economic volatility and geopolitical tensions, the crypto market is emerging as a opportunity for investors seeking alternatives to traditional assets. Bitcoin also experienced an extraordinary price increase, as an alternative to stocks and gold. The purpose of this study is to determine price movements before and after the 2016 financial crisis as well as bitcoin price predictions in 2024. This study uses secondary data on Bitcoin prices from January 2011 to January 2024. The methods used are ARCH and ARIMA to determine the volatility of the bitcoin price. For predictions using the category of predictors of Bitcoin historical data at the level. In this research divide into two calculations for volatility, from 2011 until 2016 the best model was the ARIMA model, while after 2016 until 2024 the best model was the ARCH. After 2016 until now, there are many phenomena such as uncertain global conditions such as the China-US trade war and British Exit (Brexit). The conclusion on this research about the price of one Bitcoin coin in 2024 February until April from 43,075 to 44,066 or uptrend.

show abstract

Section: Arima Model Testmentioning

confidence: 99%

Bitcoin Potential after Brexit Referendum and Price Prediction

Rahman,

Astuti

2024

AJARR

View full text Add to dashboard Cite

show abstract

“…Autoregressive Models: Autoregressive models, such as ARIMA (autoregressive integrated moving average), are popular tools for time series forecasting [15]. These models analyze the relationship between an observation and a number of lagged observations to predict future values.…”

Section: Traditional Approachesmentioning

confidence: 99%

Harnessing the Power of Prophet Algorithm for Advanced Predictive Modeling of Grab Holdings Stock Prices

Hery

2024

J. Appl. Data Sci.

View full text Add to dashboard Cite

This study investigates the effectiveness of the Prophet algorithm in predicting Grab Holdings' stock prices dataset from Kaggle. By meticulously analyzing historical closing, high, low, and volume data, the research aims to uncover market patterns and gain insights into investor sentiment based on short-term forecasting. The findings reveal a dynamic trajectory for Grab Holdings' stock, characterized by significant fluctuations and evolving investor confidence. The stock reached a peak of $14 in early 2022, indicating optimism, but subsequently experienced a decline to $4 by late 2023, reflecting a shift in sentiment. Notably, 2023 witnessed heightened volatility compared to 2022, evident in more significant price swings and increased trading volume. The Prophet algorithm demonstrated promising potential for prediction better than traditional methods, which overlook the presence of seasonality or fail to adapt to evolving market conditions, leading to less accurate forecasts. The excellent performance of Prophet is indicated by a Mean Absolute Percentage Error (MAPE) of 10.45511%, a Mean Absolute Error (MAE) of 3.112026, and a Root Mean Squared Error (RMSE) of 3.516969. Compared to the traditional ARIMA, MAE and RMSE resulting from Prophet are much lower than their counterparts, which are 14.49675 and 16.079898, respectively. These widely used metrics suggest moderate accuracy in predicting future stock prices. This research offers valuable insights for investors that they can use to understand the trend of Grab Holdings' stock price and make more informed investment decisions regarding buying or selling opportunities. However, it is crucial to acknowledge the inherent limitations of such models and interpret results cautiously, considering the ever-changing dynamics of the financial market.

show abstract

“…Forecasting Brent crude oil prices is a difficult task. Thus, incorporating the consequences of conflict with the Brent price simulation may prove useful for policy makers, especially in oil-producing countries, as it may help them formulate appropriate fiscal policies (Mati et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Impact of Oil Shocks on the Oil, Agricultural and Food Industry - Quantile and Ols Regression

Bakić

2024

View full text Add to dashboard Cite

This paper determines the impact of Brent oil shocks on the price of shares of companies from the oil, agricultural and food industries that includes the period of the COVID-19 pandemic. For this purpose, they use a quantile regression approach and compare its findings with a standard Ordinary Least Squares (OLS) regression model. Moreover, in this research they use quantile regression, which enables them to analyze different quantiles of share prices of companies from the oil industry, the agricultural industry, and the food industry. They observe three different periods - a period of recession, a normal period and a period of expansion. Finally, empirical evaluations using quantile regression and OLS models show us that shocks from the oil market are more pronounced in companies from the oil industry compared to companies from the agricultural and food industries. The findings of this research provide important information for investors, economic policy makers, and other parties.

show abstract

Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models

Cited by 7 publications

References 27 publications

Bitcoin Potential after Brexit Referendum and Price Prediction

Bitcoin Potential after Brexit Referendum and Price Prediction

Harnessing the Power of Prophet Algorithm for Advanced Predictive Modeling of Grab Holdings Stock Prices

Impact of Oil Shocks on the Oil, Agricultural and Food Industry - Quantile and Ols Regression

Contact Info

Product

Resources

About