An Investor Demand-Oriented Approach for Financial Market Segmentation Based on Network Embedding Techniques

Financial market segmentation has predominantly relied on supply-based methods that emphasize the corporate business market, while paying scarce attention to demandoriented schemes that highlight shared investor preferences and behaviors. This study narrows this gap by proposing an investor demand-oriented approach for financial market segmentation, which consists of three fundamental phases: financial network construction based on investor co-attention, network representation learning using network embedding techniques, and clustering over network embeddings. By implementing the approach in the U.S. stock market and cross-validating its usefulness in the Chinese stock market, we first show that it can reflect pairwise stock similarity, as network embedding similarity is significantly and positively associated with excess comovement between stocks. We then partition the stock market into different peer groups that demonstrate significant within-group comovement. The utility and efficacy of our approach are comprehensively evaluated by comparing it with traditional industry classifications, geographical location-based classification, text-based classification, and search cluster-based investment habitats. The results demonstrate that our approach not only achieves better performance in capturing greater withingroup homogeneity and between-group heterogeneity, but also outperforms standard classification schemes in predicting excess comovement and stock returns. This study adds to design science research on financial market segmentation by extending the important yet underexplored domain of demand-oriented methods. By systematically decoding latent investor preferences and behavioral commonalities, our approach can be integrated into automated decision support systems to improve the accuracy and efficiency of financial market analytics pragmatically.