As far as traditional stock selection strategies are concerned, financial factors and market behavior indicators dominate, but the supply chain structure and resilience of an enterprise are becoming increasingly valuable in a dynamic economic environment. With the help of supply chain resilience theory, this paper designs an innovative framework system that introduces enterprise supply chain resilience factors into machine learning stock selection models. It builds multi-dimensional resilience indicators through text mining and inter-enterprise knowledge graph recognition, covering the dimensions of supply diversity, key node concentration, and flexibility of alternative paths. The traditional financial factors are integrated with the supply chain resilience factors, and the XGBoost and LSTM models are used to build and backtest the stock selection strategy. The results of the empirical research show that the strategy has significant excess returns and robust characteristics in different market cycles, especially in stages where market fluctuations are frequent or supply chain disruptions occur frequently. Further economic value analysis shows that supply chain resilience not only improves the stability of the portfolio returns, but can also be used as a key measure of a company's long-term competitiveness. The study expands the boundaries of non-financial information in financial modeling and creates new factor design ideas for quantitative investment.

Xiaoqing Wu. Supply Chain Resilience-Driven Machine Learning Stock Selection Strategy and Economic Value Analysis. Socio-Economic Statistics Research (2025), Vol. 6, Issue 1: 1-12. https://doi.org/10.38007/SESR.2025.060101.

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