The relevance of broker networks for information diffusion in the stock market

Abstract

This paper shows that the network of relationships between brokers and institutional investors shapes the information diffusion in the stock market. We exploit trade-level data to show that central brokers gather information by executing informed trades, which is then leaked to their best clients. We show that after large informed trades, a significantly higher volume of other institutional investors execute similar trades through the same broker, allowing them to capture higher returns in the first few days after the initial trade. In contrast, we find that when the informed asset manager is affiliated with the broker, such imitation does not occur. Similarly, we show that the clients of the broker employed by activist investors to execute their trades tend to buy the same stocks just before the filing of the 13D. This evidence also suggests that an important source of alpha for fund managers is the access to better connections rather than superior skill.

Brokers and Order Flow Leakage: Evidence from Fire Sales

“…Our paper bridges a vast literature on fire sales and a growing literature on the role of networks among market participants in various domains, including Li and Schürhoff (), Di Maggio et al. (), Di Maggio, Kermani, and Song (), Hollifield, Neklyudov, and Spatt (), Afonso, Kovner, and Schoar (), and Hendershott et al. ().…”

“…For instance, Irvine, Lipson, and Puckett () find evidence of such information leakage with respect to future analyst recommendations, McNally, Shkilko, and Smith () show that brokers share information about firm insiders’ order flow, and Di Maggio et al. () document broker dissemination of informed order flow.…”

Clients' Connections: Measuring the Role of Private Information in Decentralized Markets

“…respectively, 14 and X i,t includes controls such as the number of transactions and trading volume. These controls are important for checking that our connections variable is not simply picking up the effect of increased trade size (Easley and O'Hara (1987); Merrick, Naik, and Yadav (2005); Maggio et al (2019)). Throughout the analysis, in computing standard errors, we take a conservative approach and employ two-way clustering at the client and time level.…”

“…To measure trading performance, we follow Maggio et al (2019) and compute the T-day-horizon return on each trade of client i on day t, measured as the percentage difference between the transaction price and the closing price T days after the transaction date. 12 Formally, for each trade j, we construct the measure:…”

“…4 While many empirical works study the trading process in decentralized markets (e.g., Gabrieli and Georg (2014); Hollifield, Neklyudov, and Spatt (2017); Brancaccio, Li, and Schurhoff (2017)), most do not focus on the role of private information. 5 The study most related to this application is Maggio et al (2019). Just as we do, they use the network of transactions across market participants to study the flow of private information among them.…”