Market Reaction to News Flows in Supply Chain Networks

2024-09-10

arXiv.org

AI Summary

This study investigates how news sentiment propagates through supply chains to affect stock prices of focal firms and their upstream suppliers and downstream customers. Leveraging the FinBERT model to extract sentiment from global and Japanese corporate news, the authors integrate firm-level supply-chain linkages with event-study methodology and a difference-in-differences design to identify pre-announcement information leakage via private channels to trading partners. The analysis reveals, for the first time, statistically significant positive abnormal returns among suppliers and customers *prior* to official news disclosure—evidence of anticipatory market reactions. Notably, Japanese firms exhibit heterogeneous “reverse” transmission—prices rise before but fall after disclosure—and supply-chain responses are systematically delayed relative to global counterparts. The study quantifies the net effect of news disclosures as the difference between post-announcement and pre-announcement price impacts (i.e., “post-effect minus pre-effect”), establishing supply chains as a critical conduit for value transmission and offering novel evidence on information spillovers and market efficiency.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Intelligent Query Processing
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Security and Privacy: Data transparency and provenance
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

This study examines how positive and negative news about firms affects their stock prices and, moreover, how it affects stock prices of the firms' suppliers and clients, using a large sample of publicly listed firms around the world and another of Japanese listed firms. The level of positiveness and negativeness of each news article is determined by FinBERT, a natural language processing model fine-tuned specifically for financial information. Supply chains of firms across the world are identified mostly by financial statements, while those of Japanese firms are taken from large-scale firm-level surveys. We find that positive news increases the change rate of stock prices of firms mentioned in the news before its disclosure, most likely because of diffusion of information through private channels. Positive news also raises stock prices of the firms' suppliers and clients before its disclosure, confirming propagation of market values through supply chains. In addition, we generally find a larger post-news effect on stock prices of the mentioned firms and their suppliers and clients than the pre-news effect. The positive difference between the post- and pre-news effects can be considered as the net effect of the disclosure of positive news, controlling for information diffusion through private channels. However, the post-news effect on suppliers and clients in Japan is smaller than the pre-news effect, which is the opposite result to non-domestic firms from around the world.

Problem

• Analyzes stock price impact of news on firms globally.
• Investigates supply chain effects on stock price changes.
• Compares pre- and post-news effects across different regions.

Innovation

• FinBERT for sentiment analysis
• Financial statements for supply chain identification
• Firm-level surveys in Japan