Research on the Application of Financial Timing Model in Agricultural Product Price Prediction

Authors

  • Yunsong Qin

DOI:

https://doi.org/10.54097/y8sgtj28

Keywords:

Agricultural products price, Financial factors, Financial timing model

Abstract

The Fluctuations in agricultural prices getting more and more attention. Domestic and foreign scholars use the financial timing model to analyze and apply this problem in many aspects. This paper summarizes the research on the financial factors affecting the Fluctuations in agricultural prices and the practical application of the financial timing model in the price prediction of agricultural products.

Downloads

Download data is not yet available.

References

[1] Cao Shuang, He Yucheng. The SVM-ARIMA price prediction model for agricultural products based on wavelet decomposition [J]. Statistics and Decision-making, 2015, 31 (13): 92-95

[2] Cui Chang, Li Guowei. Analysis of the structural change characteristics and influencing factors of agricultural product prices in China [J]. Mathematical Statistics and Management, 2019, 38 (1): 1-15

[3] Ding Huijuan, Zhang Jinlei, Chen Jianzhong, Li Juntao, Cui Peng. Comparison of ARIMA model and grey model in agricultural product price prediction [J]. Anhui Agricultural Science, 2018, 46 (24): 191-194

[4] Ding Zhiguo, Li Boyi. Research on the impact of agricultural product price fluctuations on policy-based agricultural insurance —— Based on the subject game model [J]. Rural economy in China, 2020 (6): 115-125

[5] Ernesto León-Castro, Luis F. Espinoza-Audelo, Jose M. Merigó, Enrique Herrera-Viedma, Francisco Herrera, Measuring volatility based on ordered weighted average operators: The case of agricultural product prices,Fuzzy Sets and Systems,Volume 422, 2021, Pages 161-176,

[6] Faruk Urak, Abdulbaki Bilgic, Food insecurity and sovereignty threat to uncontrolled price spillover effects in financialized agricultural products: The red meat case in Turkiye, Borsa Istanbul Review,Volume 23, Issue 3, 2023, Pages 580-599,

[7] G. Avinash, V. Ramasubramanian, Mrinmoy Ray, Ranjit Kumar Paul, Samarth Godara, G.H. Harish Nayak, Rajeev Ranjan Kumar, B. Manjunatha, ShashiDahiya, MirAsif Iquebal, Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices, Applied Soft Computing, Volume 158, 2024,

[8] Héctor M. Núñez, Jesús Otero, Andrés Trujillo-Barrera, Wholesale price rigidities and exchange rate pass-through: Evidence from daily data of agricultural products, International Economics, Volume 176, 2023

[9] Kuai Hao, Liu Ying, Gao Qizheng. Study on the transmission effect of international rice price fluctuation on domestic rice price [J]. Agricultural Resources and regionalization in China, 2019, 40 (10): 129-136

[10] Lai Yulian, Ma Linjuan, Zhang Yanlin. Empirical modal decomposition- -a graph neural network algorithm predicts agricultural product prices [J]. Journal of The University of Jinan (Natural Science edition), 2024, 38 (3): 356-361

[11] Li Jian, Lu Jie, Li Chongguang. Research on real-time early warning of bubble risk in agricultural products futures market [J]. Rural Economy in China, 2019, 0 (3): 53-64

[12] Ma Hongyang, Zhao Xia. Empirical analysis of the price fluctuation characteristics of small agricultural products in China —— Take garlic as an example [J]. Agricultural technology and economy, 2021 (6): 33-48

[13] Meng Jun, Lu Xingchen. Research on the price fluctuation characteristics and rules of small agricultural products in China —— Analysis based on ARCH model [J]. Price Theory and Practice, 2021 (11): 87-90197

[14] Peng Hongjun, Shi Ligang, Pang Tao. Optimal strategy of output random order agricultural supply chain based on CVaR [J]. Statistics and Decision-making, 2019, 0 (20): 46-49

[15] Soumik Ray, Achal Lama, Pradeep Mishra, Tufleuddin Biswas, Soumitra Sankar Das, Bishal Gurung, An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique Image 1,Applied Soft Computing, Volume 149, Part A, 2023,

[16] Sumesh Eratt Parameswaran, Vidhyalavanya Ramachandran, Swati Shukla, Crypto Trend Prediction Based on Wavelet Transform and Deep Learning Algorithm, Procedia Computer Science, Volume 235, 2024, Pages 1179-1189,

[17] Tian Hausen, Feng Hongjuan. Study on the impact effect of monetary policy changes on agricultural product price fluctuations [J]. Financial Theory and Practice, 2021, 42 (1): 33-40

[18] Xia Bing. Verification and aggregation characteristics of agricultural price fluctuation and trend forecast [J]. Statistics and Decision-making, 2015, 31 (20): 145-148

[19] Xu Qigang, Liu Mingjun, Li Hai. Agricultural product price prediction model based on the improved KNN algorithm [J]. Journal of University of Jinan (Natural Science edition), 2014, 28 (2): 114-117

[20] Xu Yaqing, Wei Yihua, Li Xugang. Construction of a price prediction model for agricultural products [J]. Statistics and Decision-making, 2017, 33 (12): 75-77

[21] Zhang Chao, Hou Kai. Inertial study of B-N decomposition and stochastic shock [J]. Business Research, 2019, 0 (7): 127-132

[22] Zhang Dabin, Zeng Liling, Ling Liwen. Integrated prediction model for price decomposition of agricultural futures based on VMD-ELM [J]. Operations Research and Management, 2023, 32 (1): 127-133

[23] Zhang is hopeful, Li Chongguang. Analysis of the financialization factors in the price fluctuation of agricultural products —— Take soybean and sugar as an example [J]. Journal of Huazhong Agricultural University (Social Science edition), 2018 (5): 86-93164, 165

[24] Zhang Junhua, Hua Junguo, Tang Huacang, Wu Yiping. Economic policy uncertainty and agricultural price fluctuations [J]. Agricultural technology and economy, 2019 (5): 110-122

Downloads

Published

24-12-2024

Issue

Section

Articles

How to Cite

Qin, Y. (2024). Research on the Application of Financial Timing Model in Agricultural Product Price Prediction. Frontiers in Business, Economics and Management, 17(3), 139-142. https://doi.org/10.54097/y8sgtj28