A Review of Factors Affecting Palm Oil Futures Prices and Forecasting Models

References

[1] Malaysian Palm Oil Board (MPOB), Palm Oil Statistics. Kuala Lumpur, Malaysia: MPOB, 2023. [Online]. Available: https://mpob.gov.my

[2] Z. A. Zainal, N. Zulkifli, and S. Hassan, “Factors affecting palm oil prices: A review,” J. Agribusiness Marketing, vol. 11, no. 2, pp. 45–53, 2019.

[3] A. Ghaffari and M. Asadi, “Forecasting palm oil prices using ARIMA and ANN models,” J. Oil Palm Res., vol. 28, no. 3, pp. 325–333, 2016.

[4] M. H. Ibrahim, “Oil and food prices in Malaysia: A threshold cointegration approach,” Energy Econ., vol. 44, pp. 51–61, 2015.

[5] N. H. Azman, N. H. Ahmad, and M. N. M. Nor, “Effect of exchange rate on Malaysia palm oil export,” Int. J. Econ. Manage., vol. 16, no. 1, pp. 23–35, 2022.

[6] B. N. Yusof, “Climate change and agricultural productivity in Malaysia: Rainfall effects on palm oil,” Climate Dev., vol. 12, no. 4, pp. 303–312, 2020.

[7] K. P. Tan and H. B. Tan, “The impact of geopolitics and global trade on palm oil futures,” J. Int. Trade Econ. Dev., vol. 30, no. 6, pp. 819–832, 2021.

[8] C. Chatfield, The Analysis of Time Series: An Introduction, 6th ed. Boca Raton, FL, USA: CRC Press, 2003.

[9] G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.

[10] A. Choudhury and A. Ghosh, “Deep learning for commodity price forecasting: LSTM and DBN approaches,” Appl. Soft Comput., vol. 105, Art. no. 107234, 2021.

[11] Z. Hu and W. Lin, “Multi-source data fusion for agricultural price forecasting using Transformer networks,” Expert Syst. Appl., vol. 202, Art. no. 117123, 2022.

[12] T. Li and X. Zhao, “The impact of soybean price movements on palm oil market dynamics,” J. Agric. Commodity Markets, vol. 18, no. 3, pp. 102–115, 2020. [Online]. Available: https://doi.org/10.1016/j.jagm.2020.03.007

[13] S. K. Liew and N. Ahmad, “Exchange rate dynamics and its effect on palm oil exports: A case study of MYR/CNY,” Int. J. Appl. Econ. Finance, vol. 4, no. 1, pp. 29–45, 2017. [Online]. Available: https://doi.org/10.1016/j.ijafe.2017.01.010

[14] M. F. Hadi, F. Idris, and J. S. Tan, “The impact of crude oil price fluctuations on the palm oil futures market,” Energy Econ., vol. 58, no. 4, pp. 102–114, 2020. [Online]. Available: https://doi.org/10.1016/j.eneco.2020.02.014

[15] A. H. Fadhil, A. K. Abdullah, and R. Ismail, “Global supply and demand for palm oil: The role of trade policies in price volatility,” Int. J. Palm Oil Res., vol. 19, no. 1, pp. 21–39, 2021. [Online]. Available: https://doi.org/10.1016/j.palmo.2021.01.005

[16] K. S. Liew, A. R. Tan, and S. Y. Lee, “Price transmission between rapeseed oil and palm oil in the global market,” Agric. Econ. Policy, vol. 31, no. 2, pp. 65–80, 2019. [Online]. Available: https://doi.org/10.1016/j.agri.2019.02.007

[17] K. J. Goh and Z. Wei, “The influence of USD/CNY exchange rate on palm oil prices,” Asian Econ. Policy Rev., vol. 16, no. 3, pp. 175–189, 2021. [Online]. Available: https://doi.org/10.1111/aepr.12345

[18] W. K. Teoh, J. L. Tan, and W. F. Ng, “The role of precipitation patterns in influencing palm oil yields and its impact on market pricLMNYZ\efhyz{|}~€�‚Ÿ ¡¢£­ôäÕÕɼÕɼÕÉ´©šˆšˆšocVC%hðIhðIhðIhðIhðIhðIhðIhðIhðIhðIhðIhðIhðIhðICJ(OJQJaJ(MN{ ¡¢£45µ¶ÃÄ·¸êëÅƳ´õççõõÝÝÝÝÝÝÆÝÝÝÝÝÝÝÝÝÝÝ$ &F„7„Éý¤^„7`„Éýa$gdðIm$ $¤a$gdðI [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

[19] [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

@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},
  }