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Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat

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  • Xiaojie Xu
  • Yun Zhang

Abstract

Agricultural commodity price forecasting represents a key concern for market participants. We explore the usefulness of neural network modeling for forecasting problems in datasets of daily prices over periods of greater than 50 years for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. By investigating different model settings across the algorithm, delay, hidden neuron, and data‐splitting ratio, we arrive at models leading to a decent performance for each commodity, with the overall relative root mean square error ranging from 1.70% to 3.19%. These results have small advantages over no‐change models due to particular price adjustments in the prices considered here. Our results can be used on a standalone basis or combined with fundamental forecasts in forming perspectives of commodity price trends and conducting policy analysis. Our empirical framework should not be diffucult to implement, which is a critical consideration for many decision‐makers and has the potential to be generalized for price forecasts of more commodities.

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  • Xiaojie Xu & Yun Zhang, 2022. "Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 169-181, July.
    • Handle: RePEc:wly:isacfm:v:29:y:2022:i:3:p:169-181
    DOI: 10.1002/isaf.1519
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    as
    1. Yang, Jian & Cabrera, Juan & Wang, Tao, 2010. "Nonlinearity, data-snooping, and stock index ETF return predictability," European Journal of Operational Research, Elsevier, vol. 200(2), pages 498-507, January.
    2. Roger A. Fujihara & Mbodja Mougoué, 1997. "An examination of linear and nonlinear causal relationships between price variability and volume in petroleum futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 17(4), pages 385-416, June.
    3. Joshua G. Maples & B. Wade Brorsen, 2022. "Handling the discontinuity in futures prices when time series modeling of commodity cash and futures prices," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 70(2), pages 139-152, June.
    4. Dharmaraja Selvamuthu & Vineet Kumar & Abhishek Mishra, 2019. "Indian stock market prediction using artificial neural networks on tick data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.
    5. Christoph Wegener & Christian Spreckelsen & Tobias Basse & Hans‐Jörg Mettenheim, 2016. "Forecasting Government Bond Yields with Neural Networks Considering Cointegration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(1), pages 86-92, January.
    6. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    7. Yang, Jian & Su, Xiaojing & Kolari, James W., 2008. "Do Euro exchange rates follow a martingale? Some out-of-sample evidence," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 729-740, May.
    8. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi, 2020. "Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 877-886, September.
    11. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
    12. David Bessler & Zijun Wang, 2012. "D-separation, forecasting, and economic science: a conjecture," Theory and Decision, Springer, vol. 73(2), pages 295-314, August.
    13. David A Bessler & Jian Yang & Metha Wongcharupan, 2003. "Price Dynamics in the International Wheat Market: Modeling with Error Correction and Directed Acyclic Graphs," Journal of Regional Science, Wiley Blackwell, vol. 43(1), pages 1-33, February.
    14. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    15. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    16. Christopher K. Ma & William H. Dare & Darla R. Donaldson, 1990. "Testing rationality in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 10(2), pages 137-152, April.
    17. Jefferson T. Davis & Athanasios Episcopos & Sannaka Wettimuny, 2001. "Predicting direction shifts on Canadian–US exchange rates with artificial neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 83-96, June.
    18. Cheol‐Ho Park & Scott H. Irwin, 2010. "A reality check on technical trading rule profits in the U.S. futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(7), pages 633-659, July.
    19. Jian Yang & Zheng Li & Tao Wang, 2021. "Price discovery in chinese agricultural futures markets: A comprehensive look," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(4), pages 536-555, April.
    20. Zoran Vojinovic & Vojislav Kecman & Rainer Seidel, 2001. "A data mining approach to financial time series modelling and forecasting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(4), pages 225-239, December.
    21. Li, Miao & Xiong, Tao, 2021. "Dynamic price discovery in Chinese agricultural futures markets," Journal of Asian Economics, Elsevier, vol. 76(C).
    22. Zhipeng Yan & Shenghong Li & Gongmin Zhao & Juanmei Zhou, 2021. "Dynamic price discovery in China’s thermal coal future market," Applied Economics Letters, Taylor & Francis Journals, vol. 28(4), pages 255-259, February.
    23. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    24. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
    25. Kayode Ayankoya & Andre P. Calitz & Jean H. Greyling, 2016. "Real-Time Grain Commodities Price Predictions in South Africa: A Big Data and Neural Networks Approach," Agrekon, Taylor & Francis Journals, vol. 55(4), pages 483-508, October.
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    Cited by:

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    2. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    3. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.

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