Application of News Analysis Based on Large Language Models in Supply Chain Risk Prediction

Jun Sun; Xin Wen; Gang Ping; Mingxuan Zhang

doi:10.5281/zenodo.13377298

Authors

Jun Sun University of Connecticut
Xin Wen University of Southern California
Gang Ping Hong Kong Metropolitan University
Mingxuan Zhang University of California San Diego

DOI:

https://doi.org/10.5281/zenodo.13377298

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n3a08

Keywords:

Supply Chain Risk Management, Large Language Models, News Analysis, Predictive Analytics

Abstract

This study investigates using large-scale linguistic models (LLM) in media analysis to predict stock market risk. This research combines the best language processing techniques with traditional real-time analysis to create a comprehensive approach to identifying and predicting product impact. A database of 200,000 articles from 2018 to 2023 is used to train and evaluate the needs of the LLM. The model's performance has been rigorously compared with the baseline methods, including TF-IDF with logistic regression, BERT, and LSTM with monitoring methods. The results showed the best performance of the LLM-based method, achieving an F1 score of 0.883 for risk classification and a percentage of uncertainty of 9.3 % for risk estimation. Case studies of specific events, including the COVID-19 pandemic and the Suez Canal blockage, add validity to the forecasting model's capabilities, with operational time ranging from 1 to 4 weeks. The research also addresses the interpretation of the model through visualisation and factor analysis, providing insight into the principal risks of different groups. This study contributes to the risk management industry by providing a new, data-driven approach that leverages the power of LLMs for early risk detection and awareness. Decision-making in the global supply chain is complex.

Author Biographies

Jun Sun, University of Connecticut

Business Analytics and Project Management, University of Connecticut, CT, USA.

Xin Wen, University of Southern California

Applied Data Science, University of Southern California, CA, USA.

Gang Ping, Hong Kong Metropolitan University

Global Business and Marketing, Hong Kong Metropolitan University, Hong Kong, China.

Mingxuan Zhang, University of California San Diego

Computer Science, University of California San Diego, CA, USA.

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