基于GA-MLP的玉米期货价格预测模型

Corn Futures Price Prediction Model Based on GA-MLP

摘要: 在我国金融经济体系中，农产品期货市场不仅能引导市场自我调节，还为监管者提供了高效的信息传递渠道。有效、准确地预测期货价格有助于指导农业生产、监控价格波动带来的经营风险，并提升宏观调控政策的预见性与精准性。本文主要探讨粮食期货市场中的玉米期货价格预测问题，研究以大连商品交易所2005至2023年期间的玉米连续期货日度基本指标和技术指标数据为样本，构建了一种基于多层感知器(MLP)的玉米期货价格预测模型，用于拟合玉米期货的收盘价。考虑到预测精度与计算效率的平衡，本文还引入了遗传算法(GA)对MLP模型的参数进行优化，以提高预测结果的准确性。此外，为了全面评估所提出模型的表现，本文将优化后的MLP模型与决策树(DT)、随机森林(RF)、XGBoost和LightGBM等多种主流机器学习算法进行了对比分析。实证结果表明，MLP模型在MSE、MAE、R²等多个评估指标上优于其他四个基准模型，表现出更强的预测能力。而通过遗传算法(GA)对MLP模型参数进行优化后，模型的预测性能得到了进一步提升，尤其在价格波动较大的市场环境下，优化后的MLP模型展现了较好的稳健性和精确性。

Abstract: In China’s financial and economic system, the agricultural futures market not only guides market self-regulation, but also provides efficient information transmission channels for regulators. Effectively and accurately predicting futures prices helps guide agricultural production, monitor operational risks caused by price fluctuations, and enhance the predictability and precision of macroeconomic regulation policies. This article mainly explores the problem of predicting corn futures prices in the grain futures market. Using the daily basic and technical indicators of corn futures from 2005 to 2023 on the Dalian Commodity Exchange as samples, a corn futures price prediction model based on multi-layer perceptron (MLP) is constructed to fit the closing price of corn futures. Considering the balance between prediction accuracy and computational efficiency, this article also introduces genetic algorithm (GA) to optimize the parameters of the MLP model to improve the accuracy of prediction results. In addition, in order to comprehensively evaluate the performance of the proposed model, this paper compared and analyzed the optimized MLP model with various mainstream machine learning algorithms such as decision tree (DT), random forest (RF), XGBoost, and LightGBM. The empirical results show that the MLP model outperforms the other four benchmark models in multiple evaluation indicators such as MSE, MAE, and R², demonstrating stronger predictive ability. After optimizing the parameters of the MLP model through genetic algorithm (GA), the predictive performance of the model was further improved, especially in market environments with significant price fluctuations. The optimized MLP model demonstrated good robustness and accuracy.