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The small IPO and the investing preferences of mutual funds
We examine how liquidity and return concerns at large mutual funds explain their diminished participation in small IPOs since the late 1990s. Using 5825 IPOs and portfolio-level information for 37,052 funds, we exploit Russia's 1998 debt default as an exogenous shock to funds' liquidity concerns. After 1998, large funds invested in fewer small/illiquid IPOs and more large/liquid IPOs than smaller funds and received higher returns for small IPO investments. Given increased fund sizes since 1990, these results are consistent with funds' liquidity concerns and their demand for greater compensation when investing in transactions representing a trivial fraction of fund assets.
The determinants of buyout returns: Does transaction strategy matter?
Using an original dataset of fully monetized LBOs initiated from 1990 to 2006, we examine the emergence of an entrepreneurial transaction strategy focused on revenue growth and its incidence relative to more “classic” strategies focused on operating efficiencies. We additionally show how the conventional focus on returns measured at an IPO or acquisition frequently overstates actual realized returns to sponsors. Using this return data, we evaluate how “classic” and “entrepreneurial” strategies are associated with sponsors' equity returns. Among successful LBOs, LBOs that enhance operating efficiencies produce the highest “exit” returns; however, LBO sponsors commonly fail to monetize these returns due to delays associated with liquidating portfolio positions. In contrast, LBOs that focus on growing revenues are associated with higher fully realized equity returns, suggesting more sustainable value-creation for sponsors and their investors.
The Market Inside the Market: Odd-Lot Quotes
We show current market practices relating to odd-lot quotes create a large “inside” market where better prices routinely exist relative to the National Best Bid or Offer. We show that odd-lot quotes play a price discovery role, and these quotes provide valuable information to traders with access to proprietary data feeds. Using a XGBoost machine learning algorithm that uses odd-lot data to predict future prices, we demonstrate a simple and profitable trading strategy. We argue the SEC’s proposed round-lot redefinition reduces—but does not eliminate—the high incidence of superior odd-lot quotes within the NBBO.