OPTIMAL MACHINE LEARNING BASED FORECASTING MODEL TO ANALYZE THE IMPACT OF TRADE LIBERALIZATION IN CHINA’S ECONOMY ON EXPORT PERFORMANCE

Abstract

China's economic trajectory has garnered significant attention globally, driven by its impressive growth and increased integration into the international trade arena. In response to this, China has strategically implemented a series of trade liberalization policies aimed at fostering economic development, attracting foreign investment, and enhancing global competitiveness. Despite the importance of these policies and their potential impact on the nation's export performance, there is a discernible gap in the literature that necessitates a more sophisticated and forward-looking approach. Traditional forecasting methods applied in economic analyses, while valuable, face challenges in capturing the inherent complexity of economic variables influenced by rapidly changing policies and the dynamic nature of global market forces. Acknowledging this gap, our research is inherently motivated by the urgent need for an accurate and efficient forecasting model capable of navigating the intricate and ever-evolving economic landscape of China. In this study, we introduce an optimal machine learning-based forecasting model to analyze the impact of trade liberalization on China's economy and its export performance. Initially, we extract meaningful features from the provided China's economic dataset, optimizing these features through the modified chicken swarm optimization (MCSO) algorithm. Furthermore, we design the convolutional neural network–bagged decision tree (CNN-BDT) for China's economic forecasting, specifically designed to reduce the false positive rate. Finally, we validate the performance of the proposed CNN-BDT model using sample data of China's exports to the US from 2015 to 2021. The results demonstrate the effectiveness of the proposed CNN-BDT model in terms of performance metrics, including accuracy, precision, recall, and F-measure.