Here's one way to think about the Lyft IPO
Many are busy reading the chicken guts of the Lyft IPO and making dire predictions that it's an ill omen for coming tech IPOs. The gods must be angry.
But maybe the hysterics should be more analytical and less superstitious.
Triton has Scored 143 tech IPOs over 6 years and the data say that Lyft’s stumble doesn’t mean anything except that Lyft and its underwriters managed to blow a lay-up IPO. Lyft’s Triton Score of 5.47 (vs 5.91 for Snap at its IPO and 6.30 average overall) was a clear indication the deal as presented would do poorly.
And Lyft’s IPO is one dead chicken – investors buying into the deal and in the aftermarket (except for the very most nimble) all have losses now. So even though Lyft maximized the amount of money it raised in the offering, every buyer who touched the deal is worse off.
The factors leading to this disaster were 1) highly situation-specific, and 2) utterly recognizable in advance.
Three components of Lyft’s overall Triton Score tell the story: Operational Transparency – 3 out of 10 (vs. 5.51 average over the last 6 years). Components of this Score include what we internally call the Obfuscation Index, and the Opacity Index. Lyft Scored poorly on both by 1) muddying up the figures they did disclose by, for example, making discounts and incentives difficult to isolate inside the revenue and sales & marketing figures, and 2) disclosing none of the key model metrics such as acquisition costs, churn rates, usage patterns, etc. on either side of its marketplace. Model Confidence – 4 out of 10 (vs. 5.06 average over the last 6 years). With scant data to feed a detailed model, projections are more high-level and therefore less reliable. What’s going to happen in 2020? Who knows? Valuation Appropriateness – 3 out of 10 (vs. 6.11 average over the last 6 years). Even at the midpoint of the original price range ($65) Lyft was seeking a valuation of $20b and a multiple of trailing revenue that was nearly double the median of its public comps.Lyft put investors in a position of knowing the deal was expensive but lacking any hard metrics to anchor into a high-confidence view of value. Once the trading dynamics wobbled it was totally rational for investors to run for cover, rather than viewing the price reduction as a buying opportunity.
Lyft could have run a process to reveal the right price for its shares but instead seemed to be solving for the short-term highest price for its shares.
Thus you can be a ride-sharing bull and still hate the Lyft IPO. You can even be a Lyft bull and hate the Lyft IPO. For those looking up to the sky asking “what does it mean?!” the answer is that Lyft made obvious, avoidable, and un-forced errors that the other unicorns coming behind will only repeat if they are not paying attention.