Home / Current Issue / Paper 1707827
A Review of Factors Affecting Palm Oil Futures Prices and Forecasting Models
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Palm oil futures play a crucial role in the global edible oil market, and their price fluctuations significantly impact food security, energy costs, and economic stability in many regions. Accurately forecasting the price of palm oil futures is essential for government policy-making, enterprise risk management, and investor decision-making. This review comprehensively examines the key factors influencing palm oil futures prices, including the daily closing prices of palm oil, soybean, rapeseed, crude oil, and palm kernel, currency exchange rates (Malaysian Ringgit/Chinese Yuan and United States Dollar/Chinese Yuan), export volumes from Malaysia and Indonesia, and regional precipitation data in areas such as Pahang, Johor, Sarawak, and Sabah. Furthermore, this paper reviews various prediction models applied in this field, such as Linear Regression (LR), Neural Networks (NN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN). Despite considerable research progress, two major challenges remain. First, the large number of influencing factors increases the complexity of government regulation and policy response. Second, the prediction accuracy of existing models is still relatively low, especially under volatile or extreme market conditions. To address these issues, future research could explore hybrid modeling approaches and incorporate multi-source data. This review aims to collect possible factors affecting palm oil futures prices and provide a reference for researchers to find data sources for palm oil futures price forecasts. Additionally, it provides insights for researchers to select and improve palm oil futures forecast models.
Keywords
Palm Oil Futures, Prediction Models, Key Factors Influencing Palm Oil Futures Prices, Hybrid Modeling Approaches
How to cite this paper
@article{1707827,
author = {Yang Yuhong, Song Zhuo, Thelma D. Palaoag},
title = {A Review of Factors Affecting Palm Oil Futures Prices and Forecasting Models},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {262-270},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707827.pdf},
abstract = {Palm oil futures play a crucial role in the global edible oil market, and their price fluctuations significantly impact food security, energy costs, and economic stability in many regions. Accurately forecasting the price of palm oil futures is essential for government policy-making, enterprise risk management, and investor decision-making. This review comprehensively examines the key factors influencing palm oil futures prices, including the daily closing prices of palm oil, soybean, rapeseed, crude oil, and palm kernel, currency exchange rates (Malaysian Ringgit/Chinese Yuan and United States Dollar/Chinese Yuan), export volumes from Malaysia and Indonesia, and regional precipitation data in areas such as Pahang, Johor, Sarawak, and Sabah. Furthermore, this paper reviews various prediction models applied in this field, such as Linear Regression (LR), Neural Networks (NN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN). Despite considerable research progress, two major challenges remain. First, the large number of influencing factors increases the complexity of government regulation and policy response. Second, the prediction accuracy of existing models is still relatively low, especially under volatile or extreme market conditions. To address these issues, future research could explore hybrid modeling approaches and incorporate multi-source data. This review aims to collect possible factors affecting palm oil futures prices and provide a reference for researchers to find data sources for palm oil futures price forecasts. Additionally, it provides insights for researchers to select and improve palm oil futures forecast models.},
keywords = {Palm Oil Futures, Prediction Models, Key Factors Influencing Palm Oil Futures Prices, Hybrid Modeling Approaches},
month = {April},
}