Supervised Similarity for Firm Linkages

Prior literature has explored the use of fundamental information as a proxy for firm linkages. If investors have limited attention, then news impacting the price of a firm may only slowly be incorporated into prices of related firms, leading to return predictability across firms. Indeed, for many such firm linkages it has been shown that lagged returns of a firm are predictive of future returns for firms which are more similar to it. This effect is sometimes referred to as a momentum spillover effect, or a lead-lag strategy. Momentum spillover has been documented for similarities formed from a variety of fundamental information including industry [24], supply chain [12], analyst coverage [1], and geography [32], among others. Unrelated literature explores the application of machine learning techniques to the learning of similarity relations between securities, often with the goal of clustering securities for risk management, signal generation, or portfolio construction. See e.g. the literature review in [37] for examples of classification and clustering techniques, [44] for a demonstration of how embeddings from Large Language Models can be used to extract company similarity relations, or [6] for a more general review of machine learning applications in finance. More recent work has begun to explore the use of supervised learning techniques to extract similarity relationships.