Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times
Tibor Tatay () and
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László Vancsura: Doctoral School of Management and Business Administration, Hungarian University of Agriculture and Life Sciences, Kaposvári Campus, 7400 Kaposvár, Hungary
Tibor Tatay: Department of Statistics, Finances and Controlling, Széchenyi István University, 9026 Győr, Hungary
Tibor Bareith: Department of Investment, Finance and Accounting, Hungarian University of Agriculture and Life Sciences, Kaposvári Campus, 7400 Kaposvár, Hungary
Risks, 2023, vol. 11, issue 2, 1-16
The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21- and 125-day periods. The main findings of the study are that in a calm economic environment, the estimation accuracy is higher (1.5% vs. 4%), and that the AI-based estimation methods provide the most accurate estimates for both time horizons. These models provide the most accurate forecasts over short and medium time periods. Incorporating these forecasts into the ERM can significantly help to hedge purchase prices. Artificial intelligence-based models are becoming increasingly widely available, and can achieve significantly better accuracy than other approximations.
Keywords: commodity market; price forecast; risk management; time series; artificial intelligence; neural network; planning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:11:y:2023:i:2:p:27-:d:1044238
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