Research On Forecasting Method Of Soybean Futures Price Under Decomposition Integration Framework

Agriculture is the foundation of the national economy,and soybeans occupy an important position in China’s agriculture.The fluctuations in the soybean market affect the development of China’s agriculture and even the stability of the social economy;Futures can effectively avoid risks and improve market transparency in the process of economic development.Therefore,the study of soybean futures prices is of great significance.This thesis combines data decomposition methods with machine learning methods,and the soybean futures price prediction based on the "decomposition integration" framework is divided into the following two parts:The first part is a study on soybean futures price prediction based on a decomposition denoising framework,which decomposes and denoises soybean futures prices and introduces futures price information of soybean meal and corn closely related to soybeans to predict soybean futures prices.The futures price sequence contains noise,which may mask the impact of soybean meal futures prices and corn futures prices on soybean futures prices.Therefore,in this thesis,the introduced soybean meal futures prices and corn futures prices are denoised.The results indicate that decomposition denoising can effectively improve the prediction performance,while introducing information from soybean meal futures and corn futures can further improve the predictive performance of the model.The second part is a study on soybean futures price prediction based on the "decomposition reconstruction integration" framework,mainly studying the impact of different reconstruction methods on soybean futures price prediction,and introducing information from other futures markets on this basis.The reconstruction method in this thesis includes single feature reconstruction and multi feature reconstruction considering three data features: complexity,periodicity,and correlation with the original sequence.This thesis introduces the price information of soybean meal futures and corn futures,decomposes and reconstructs the prices of soybean meal futures and corn futures,and analyzes the correlation between the reconstructed sequence of soybean meal futures prices and corn futures prices and the reconstructed sequence of soybean futures,thereby screening the input variables for predicting the reconstructed sequence of soybean futures.Empirical evidence shows that the multi feature reconstruction method is superior to the single feature reconstruction method,and introducing other futures market information can effectively improve the predictive performance of the model.The research in this thesis is in-depth layer by layer,and the good performance of the proposed prediction model in predicting soybean futures prices provides a new approach for predicting soybean futures prices.