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Firm connection and equity return predictability – Graph-based machine learning methods
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DOI：10.1016/j.bar.2024.101436.png)
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We develop a unified measure of firm linkage from popular linkage indicators in the literature via graph-based machine learning methods and investigate its asset pricing implication. Using all A-shares listed in the Chinese stock market from 2003 to 2022, we reveal a widespread momentum spillover in the cross section of stock returns based on our linkage measure. In particular, a long-short trading strategy for portfolios sorted by our measure generates significant risk-adjusted returns of 0.83% on a monthly basis. We show that our measure contains incremental information on firm fundamental connections relative to well-documented alternative measures, and its predictive ability can be rationalized by the investor inattention hypothesis. Our study contributes to the literature which explores economic linkages that generate lead-lag predictability and sheds new light on the interconnected nature of companies in an economy via advanced machine learning techniques.
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