Predicting long-term stock movements with fused textual features of Chinese research reports

Zhang, Ming; Yang, Jiahao; Wan, Meilin; Zhang, Xuejun; Zhou, Jun

doi:10.1016/j.eswa.2022.118312

Cited by 8 publications

(1 citation statement)

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“…However, existing studies on corn future prices have largely overlooked the potential forecasting information embedded in policy regulations and agricultural‐related news due to the difficulty of extracting and quantifying such information. Recent forecasting studies have shown that qualitative analysis of politics, disasters, emergencies, and social media can provide valuable insights that enhance financial, economic, and political forecasts (Aprigliano et al, 2023; Jiao et al, 2022; Zhang, Yang, et al, 2022). For example, the Russo‐Ukrainian war outbreak in February 2022 caused a significant decrease in grain supply from Russia and Ukraine, which account for one‐third of global grains, leading to concerns about global food supply and subsequent market panic, resulting in increased global food prices.…”

Section: Introductionmentioning

confidence: 99%

Interpretable corn future price forecasting with multivariate time series

Wu,

Wang,

Wang

2024

Journal of Forecasting

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Efforts in corn future price forecasting and early warning play a vital role in guiding the high‐quality development of the agricultural economy. However, recent years have witnessed significant fluctuations in global corn future prices due to the impact of COVID‐19 and the escalating risks associated with geopolitical conflicts. Therefore, there is an urgent need for accurate and efficient methods to forecast corn future prices. To address this challenge, a novel and comprehensive framework for explainable corn future price forecasting is designed. This framework takes into account multiple factors contributing to corn price volatility, including supply and demand dynamics, policy adjustments, international market shocks, global geopolitical risks, and investor concerns within the corn market. During the data processing stage, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to thoroughly explore the volatility characteristics of historical corn future prices. Additionally, a convolutional neural network (CNN) is employed to extract essential forecasting information from corn news data. To enhance interpretability, a novel JADE–TFT interpretable corn future price prediction model is proposed. This model combines adaptive differential evolution with optional external archiving (JADE) to intelligently and efficiently optimize the parameters of the temporal fusion transformers (TFTs). Furthermore, in the empirical study, the introduction of a global geopolitical risk coefficient, Baidu indices such as “corn” and “corn price,” and quantized corn news text features is shown to improve the accuracy of corn future price predictions. The proposed corn future price prediction framework contributes to the healthy development of the global grain futures market, thereby fostering the growth and well‐being of enterprises involved in the grain industry.

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Section: Introductionmentioning

confidence: 99%