Long-Term Relationships Between Investors And Underwriters Influence Pricing And Trading Behaviors.
- Institutional underwriting networks create followings of loyal investors that affect asset prices and trading behavior.
- Investors who partner with the same underwriters for repeated investment rounds form long-term, influential relationships.
- The personal network of an institution underwriting a stock can have a greater impact on its price – and who trades it – than the actual leadership of the institution.
Personal relationships matter, even in the data-driven world of investment banking. Despite the cascade of information about companies, stocks and leaders from news sources such as Google Finance, recent research shows that access to high quality, filtered information from individuals still shapes investor decisions.
Imagine preparing for the initial public offering (IPO) of your startup, one as exciting as Twitter. Your firm goes with the underwriter that has had the best record with promising tech companies. An investment bank network led by Goldman Sachs has a strong reputation for knowing how to put out the right information to generate demand for your new stocks.
Or put yourself in the shoes of a high-powered money manager. You’re going to have $24 million to invest next quarter, and you’re trying to place one or two aggressive positions in your portfolio. You pick up the phone and check with your friend from college who consistently has an ear to the pavement. His word checks out with an insightful colleague and tracks with what the Wall Street Journal has been saying over the past few weeks. When the stock goes public, you want to buy.
Gustavo Grullon and James Weston, professors at Rice Business, tested how institutional underwriting networks create followings of loyal corporate clients and investors that affect asset prices and trading behavior. They created two samples of all equity offerings between 1980 and 2008 from the Securities Data Corporation Platinum database. They analyzed stock price movement around IPOs and secondary equity offering (SEO) events, noting the underwriter or change in underwriter.
The researchers found that investors who partner with the same underwriters for repeated investment rounds form long-term, influential relationships. The information these underwriters offer is a key factor in equity trading, on par with geography and index inclusion and price.
For example, if Credit Suisse covers a firm’s SEO, but JP Morgan Chase does not, then their affiliated investors networks have different information at different times on which to base their buying decisions. This difference has the potential to segment the market and influence a firm’s stock price.
In fact, investors’ affiliations with banking networks shape trading behaviors so much that Grullon and Weston found a correlation between firm stock prices during an IPO and SEO when the same underwriter was used for both. A coincidence?
Probably not. Grullon and Weston also found that when firms switched underwriters between IPO and SEO offerings, their pricing behaviors look more like those linked with the new bank versus the old. The change in stock movement patterns was actually greater for stocks completing a first SEO than for those whose parent firms were undergoing large changes in ownership.
This means the personal network of the institution underwriting the stock had a greater impact on its price – and who traded it – than the actual leadership of the institution.
Personal bonds, Grullon and Weston confirmed, matter in investing. Biologically, it makes sense: Before we relied on computers and the internet to learn, we based decisions on our fellow primates’ actions, narratives and visual cues. When the decisions are big, it seems our brains are still programmed this way, choosing to chew data over a business lunch in addition to compiling, analyzing and synthesizing it in two dimensions.
To learn more, please see: Grullon, G., Weston, J. P., & Underwood, S. (2014). Comovement and investment banking networks. Journal of Financial Economics, 113(1), 73-89.