It’s IPO season! Despite the pandemic-induced IPO lull early in 2020, we’re now seeing momentum from the IPO train, particularly in the tech space. Snowflake’s IPO (SNOW) this past week made headlines when the stock shot out of the gate on Wednesday with its shares up 130% within a few minutes. Two other tech names, Unity (U) and JFrog (FROG) also debuted this past week with major price jumps on their first day of trading. With popular companies like Airbnb, Palantir, Robinhood and plenty of others expected to IPO later in 2020, there is no shortage of exciting IPO activity despite the challenging economic environment.
Risk “Guess-timation” for IPOs
Since there is insufficient returns history and public information to accurately model every single potential factor exposure for IPOs, risk model providers generally use statistical methods to create a synthetic returns history. They then approximate the IPO’s factor exposures using weighted averages within a comparable universe defined by market cap, sector, and other characteristics. Over time, as the asset builds a true daily return history and fundamentals become publicly available, the asset’s actual characteristics become the driver in the factor exposure and specific risk calculations. Understandably, it can require some time before the exposures and specific risk can be reliably estimated using only the asset’s characteristics alone, so extra caution should be taken when evaluating risk on a security in the initial time period after trading.
The Risk Profile of an IPO
With this caveat in mind, we’ll take a look at some of the top IPOs from 2010-2019 based on IPO proceeds, focusing on the tech/tech-related and health care names: Alibaba (BABA), Facebook (FB), Uber (UBER), HCA Health Care (HCA), and Snap (SNAP).
The risk estimates for BABA and HCA as of their IPO dates are very much in line with the actual 1-year realized risk for these securities. However, for FB, UBER, and SNAP, there’s an apparent spread between the actual realized risk and the estimated risk. It is important to note that the COVID-19 market downturn from March 2020 is included in the 1-year period after UBER’s IPO, so that could contribute to UBER’s estimated vs. realized risk spread.
If we re-run the analysis 3 months after the IPO date, we see that the estimated vs. realized risk spread tightens for certain names and deviates further for others. Once we move 6 months following the IPO date, the estimated risk is much more in line with the realized risk across the board (note: UBER is excluded in the 6 month variant of the analysis due to a lack of return history).
Looking Forward for IPOs
While there is no clear conclusion on when risk estimates for IPOs become the most effective, the analysis gives us the sense that the risk estimates unquestionably improve the further you get from the IPO date.
|US & Global Market Summary|
US Market: 9/14/20 - 9/18/20
Normalized Factor Returns: Axioma Worldwide Equity Risk Model (AXWW4-MH)
Please don’t hesitate to reach out if you’d like to better understand IPOs from a risk modeling perspective and/or discuss factor trends that may be impacting your portfolio.