News-Implied Linkages and Local Dependency in the Equity Market

Abstract

This paper studies a heterogeneous coefficient spatial factor model that separately addresses both common factor risks (strong cross-sectional dependence) and local dependency (weak cross-sectional dependence) in equity returns. From the asset pricing perspective, we derive the theoretical implications of no asymptotic arbitrage for the heterogeneous spatial factor model, generalizing the work of Kou et al. (2018). We also provide the associated Wald tests for the APT restrictions in the general case when there are both traded and non-traded factors. On the empirical side, it is challenging to measure granular firm-to-firm connectivity for a high-dimensional panel of equity returns. We use extensive business news to construct firms’ links through which local shocks transmit, and we use those news-implied linkages as a proxy for the connectivity among firms. Empirically, we document a considerable degree of local dependency among S&P500 stocks, and the spatial component does a great job in capturing the remaining correlations in the de-factored returns. We find that adding spatial interaction terms to factor models reduces mispricing and boosts model fitting. By comparing the performance of the model estimated using different networks, we show that the news-implied linkages provide a comprehensive and integrated proxy for firm-to-firm connectivity

Keywords: Spatial asset pricing model, weak and strong cross-sectional dependence, local dependency, networks, textual analysis, big data, large heterogeneous panel

JEL Classification: C33, C58, G10, G12