Welcome to Francis Academic Press

Academic Journal of Business & Management, 2022, 4(11); doi: 10.25236/AJBM.2022.041102.

Corn Price Prediction in China's Futures Market during COVID-19

Author(s)

Junxue Lv1, Xi Wu2

Corresponding Author:
Xi Wu
Affiliation(s)

1College of Science, Anhui Agricultural University, Hefei, 230036, China

2School of Business Administration, Northeastern University, Shenyang, 110169, China

Abstract

Using data from the Dalian Commodity Exchange from January 2010 to December 2019 as a training set, this study develops an optimal seasonal autoregressive integrated moving average model (SARIMA) in Python to predict the settlement price of corn futures. Further, the model’s forecast accuracy and applicability are tested by predicting the price of corn futures from January 2020 to December 2020 and comparing it with the actual settlement price of active corn futures contracts in 2020 after the outbreak of COVID-19. The results show that the SARIMA (2,1,0) (3,1,1)12 model can accurately predict the settlement price. Moreover, COVID-19 had a positive short-term impact on the settlement prices.

Keywords

Corn Futures, SARIMA Time Series, COVID-19, Price Prediction

Cite This Paper

Junxue Lv, Xi Wu. Corn Price Prediction in China's Futures Market during COVID-19. Academic Journal of Business & Management (2022) Vol. 4, Issue 11: 7-12. https://doi.org/10.25236/AJBM.2022.041102.

References

[1] Hua Yutao. Analysis of price fluctuation factors in agricultural futures market -- a case study of corn futures [J]. Commercial Economics Research,2021 (05): 182-184.

[2] Fang Y, Guan B, Wu S. Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices [J]. Journal of Forecasting, 2020, 39 (6).

[3] Hu Zhongxing. Analysis and prediction of influencing factors of corn price fluctuation in Heilongjiang Province [D]. Northeast Agricultural University, 2019, 1-80.

[4] Zhang Shouping. Research on corn futures price Prediction method based on machine learning [D]. Northeast Agricultural University, 2018, 1-83.

[5] Wang Wenjing. Prediction of corn market price based on data mining [D]. Qingdao University, 2017, 1-54.

[6] Wang Yanqing, Wang Xiaoshu, Wu Laping. Analysis on price trend and influencing factors of corn futures market [J]. Agricultural outlook, 2014, 10 (11): 30-35.

[7] Monk, M. J., Jordaan, Henry B. Factors affecting the price volatility of July futures contracts for white maize in South Africa [J]. Agrekon, 2010, 49 (4)