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Why Aren't Founders and Boards Pissed About Day 1 IPO Pops?
Here’s a brain teaser for you.
Today, you are worth $500,000. Tomorrow you are worth $50 million
(Footnote: You could have been worth $57 million)
Are you mad?

The official name for this in cognitive psychology is Prospect Theory. It was made famous by psychologists Daniel Kahneman and Amos Tversky in 1979. Kahneman eventually won a Nobel Prize for the work. Tversky almost certainly would have been standing next to him had he not died six years earlier.

Prospect Theory turns out to be very important when thinking about Day 1 IPO pops. It helps explain why boards and founders don't get nearly as pissed as the public when there’s money left on the table.
Jay Ritter, Director of the IPO Institute at the University of Florida, and his colleague Tim Loughran have spent decades studying this phenomenon. Ritter’s IPO database covers U.S. IPOs going all the way back to 1980. I asked him about this phenomenon on the podcast.
To set the scene, they define money left on the table as:
shares sold × (first-day closing price − IPO offer price)
It's effectively the value transferred from the pre-IPO shareholders to the investors lucky enough to receive shares at the IPO price.
Here are the metrics:
Average first-day pop: 19.0%
Median first-day pop: 7.0% because there are some absolute whoppers pulling up the average
Proceeds-weighted average pop: 20.6%
Total money left on the table: $250.1 billion
Money left on the table equals roughly 21% of all the capital actually raised over the period: $250B / $1.19T
That's enormous.
So what gives?
For context, an IPO process typically takes around six months. At the beginning, the company hires underwriters and starts talking valuation. The bankers put some numbers on the board and execs immediately start doing mental math against however many shares they own.
Say you’re the CTO and co-founder. You own 2 million shares.
The bankers initially tell you the company could be worth about $15 per share.
You start thinking:
I’m worth $30 million bucks. Word.
Then the IPO process trudges into months three and four. Investors are eating up the story. The roadshow is approaching and demand looks swell.
Now the bankers think investors might value the shares at $25!?!
But they want to make sure the IPO is oversubscribed, so instead of going all the way to $25, they recommend pricing it at $18.
Your paper wealth just went from $30 million to $36 million.
Are you pissed?
Of course not.
You just “made” $6 million in a matter of weeks.
Never mind that if the shares immediately trade to $25, your stake is really worth $50 million and the company arguably could have sold those IPO shares for substantially more and put that extra capital on its balance sheet to, you know, do more business stuff.
But remember… $50 million was never the number you had in your head…$30 million was your anchor.
I think it’s fair to surmise that underwriters benefit enormously from this psychological setup. Management teams experience the IPO pricing process as a series of upward revisions to their wealth, even while the company may ultimately be selling its shares substantially below where the market is willing to value them.
Another crazy stat: The average IPO leaves roughly twice as much money on the table as the direct investment banking fees paid by the issuer.
That’s another way of thinking of the banker’s effective “fee”.
And from the outside, we experience the IPO completely differently.
We see two numbers:
IPO price: $18
Day 1 close: $25
From our vantage point, that looks insane. A rational person would immediately ask why they didn’t just sell the shares for more.
But we weren’t there for the six months leading up to it. We didn’t hear $12, then $15, then $17, then finally $18, while mentally marking up the value of our own shares each time.
The founder most def did.
Who’s Leaving Money on the Table?

We’ll call it the Mostly Metrics IPO index
To make this more concrete, I pulled 15 of the most recent tech IPOs and their Day 1 pops.
$23.18B left on the table / $93.09B raised = 24.9%
Compared with Ritter's long-term figure of roughly 21 cents left on the table for every $1 raised, this recent group left about 25 cents on the table for every $1 of IPO proceeds.
And that's despite SpaceX massively weighting the denominator. SpaceX alone represents about 81% of all the capital raised in this sample and had a relatively modest 19.2% pop.
Take SpaceX out and the result gets nuts:
$8.78B left / $18.09B raised = 48.6%
The non-SpaceX companies in this cohort effectively transferred almost 49 cents to IPO investors for every $1 of stock they sold.
To be fair…
Now, it’s all too common for investment bankers to write back to me and tell me I’m an idiot. I've had more than one of them on the podcast and we are now good friends.
So, in their defense, there are legitimate reasons for IPOs to pop.
Bankers actually grew up calling this the IPO discount, rather than the IPO pop (which is telling in and of itself). Investors can always put their money into companies with years of public trading history, so you throw them a discount bone to compensate them for taking the risk on something new.
You also want the deal oversubscribed. If you price an IPO to perfection, you leave yourself zero room for error, and if it immediately trades down, you've created a pretty terrible first impression.
Price discovery is imperfect too. Everyone gets to call the IPO mispriced after seeing the closing price. I also like to criticize Drake Maye for throwing three picks after he’s already played Seattle.
And Day 1 is a pretty weird market. There’s only so much stock available, insiders are still locked up, and everyone who wanted more shares in the IPO is now fighting with thirsty retail buyers and momentum traders to get them in the open market.
A banker friend of mine argues you should wait until roughly the third earnings report before deciding how big the IPO discount actually was. By then, management has reported a few times, the typical 180-day lockup has expired, and more shares have made their way into the market.
Figma closing at $115 after pricing at $33 tells us there was WAY more demand for Figma stock than there was stock available on July 31st. It doesn't necessarily tell us Figma was worth $115 per share…. Which has taken me like a year to concede.
Especially if the closest public comp trades at 15x revenue and the newly public company suddenly trades at 50x. Good luck convincing long-term institutional investors to buy hundreds of millions of dollars of stock at that valuation just because momentum traders eventually did.
Most importantly, you aren't selling the entire company. If you float 10% slightly cheap but the offering creates liquidity and supports the value of the other 90%, whoopdie do.
So I'm not arguing that every Day 1 IPO pop represents incompetence. Some level of underpricing is perfectly rational.
Because the IPO price isn’t supposed to be the absolute highest price you could squeeze out of investors. If that were the goal, we’d just run a Dutch Auction, which is a topic for another post.
The better question is: What was the company actually worth, and when should we measure it?
But at some point, a successful offering becomes a very, very expensive form of price discovery.
If you price at $33 and close at $36, fine.
If you price at $33 and close at $115, I have some Q’s.
And perhaps the most interesting part is that the people theoretically getting screwed are psychologically the least likely to feel screwed.
Which totally put me in a mental pretzel…
Because they’re too busy getting rich.

Run the Numbers Podcast
In this episode of Run the Numbers, I sit down with Jay Ritter, “Mr. IPO” who has studied public offerings for more than 40 years. He breaks down what Apple, Nvidia, the dot-com boom, and today’s AI giants can teach us about IPOs, why companies leave money on the table on day one, why firms stay private longer, and what the data actually says about long-term IPO performance.
Weekly Valuation and Efficiency Metrics

Revenue Multiples
Revenue multiples are a shortcut to compare valuations across the technology landscape, where companies may not yet be profitable. The most standard timeframe for revenue multiple comparison is on a “Next Twelve Months” (NTM Revenue) basis.
NTM is a generous cut, as it gives a company “credit” for a full “rolling” future year. It also puts all companies on equal footing, regardless of their fiscal year end and quarterly seasonality.
However, not all technology sectors or monetization strategies receive the same “credit” on their forward revenue, which operators should be aware of when they create comp sets for their own companies. That is why I break them out as separate “indexes”.
Reasons may include:
Recurring mix of revenue
Stickiness of revenue
Average contract size
Cost of revenue delivery
Criticality of solution
Total Addressable Market potential
From a macro perspective, multiples trend higher in low interest environments, and vice versa.
Multiples shown are calculated by taking the Enterprise Value / NTM revenue.
Enterprise Value is calculated as: Market Capitalization + Total Debt - Cash
Market Cap fluctuates with share price day to day, while Total Debt and Cash are taken from the most recent quarterly financial statements available. That’s why we share this report each week - to keep up with changes in the stock market, and to update for quarterly earnings reports when they drop.
Historically, a 10x NTM Revenue multiple has been viewed as a “premium” valuation reserved for the best of the best companies.
Efficiency
Companies that can do more with less tend to earn higher valuations.
Three of the most common and consistently publicly available metrics to measure efficiency include:
CAC Payback Period: How many months does it take to recoup the cost of acquiring a customer?
CAC Payback Period is measured as Sales and Marketing costs divided by Revenue Additions, and adjusted by Gross Margin.
Here’s how I do it:
Sales and Marketing costs are measured on a TTM basis, but lagged by one quarter (so you skip a quarter, then sum the trailing four quarters of costs). This timeframe smooths for seasonality and recognizes the lead time required to generate pipeline.
Revenue is measured as the year-on-year change in the most recent quarter’s sales (so for Q2 of 2024 you’d subtract out Q2 of 2023’s revenue to get the increase), and then multiplied by four to arrive at an annualized revenue increase (e.g., ARR Additions).
Gross margin is taken as a % from the most recent quarter (e.g., 82%) to represent the current cost to serve a customer
Revenue per Employee: On a per head basis, how much in sales does the company generate each year? The rule of thumb is public companies should be doing north of $450k per employee at scale. This is simple division. And I believe it cuts through all the noise - there’s nowhere to hide.
Revenue per Employee is calculated as: (TTM Revenue / Total Current Employees)
Rule of 40: How does a company balance topline growth with bottom line efficiency? It’s the sum of the company’s revenue growth rate and EBITDA Margin. Netting the two should get you above 40 to pass the test.
Rule of 40 is calculated as: TTM Revenue Growth % + TTM Adjusted EBITDA Margin %
A few other notes on efficiency metrics:
Net Dollar Retention is another great measure of efficiency, but many companies have stopped quoting it as an exact number, choosing instead to disclose if it’s above or below a threshold once a year. It’s also uncommon for some types of companies, like marketplaces, to report it at all.
Most public companies don’t report net new ARR, and not all revenue is “recurring”, so I’m doing my best to approximate using changes in reported GAAP revenue. I admit this is a “stricter” view, as it is measuring change in net revenue.
OPEX
Decreasing your OPEX relative to revenue demonstrates Operating Leverage, and leaves more dollars to drop to the bottom line, as companies strive to achieve +25% profitability at scale.
The most common buckets companies put their operating costs into are:
Cost of Goods Sold: Customer Support employees, infrastructure to host your business in the cloud, API tolls, and banking fees if you are a FinTech.
Sales & Marketing: Sales and Marketing employees, advertising spend, demand gen spend, events, conferences, tools.
Research & Development: Product and Engineering employees, development expenses, tools.
General & Administrative: Finance, HR, and IT employees… and everything else. Or as I like to call myself “Strategic Backoffice Overhead.”
All of these are taken on a Gaap basis and therefore INCLUDE stock based comp, a non cash expense.
Companies Included
1. Security & Identity (16 companies) Endpoint, network, IAM, security operations. The CISO budget.
CrowdStrike, Palo Alto Networks, Fortinet, Cloudflare, Zscaler, Okta, SentinelOne, SailPoint, Check Point, Qualys, Tenable, Rapid7, Varonis, Rubrik, Mitek, OneSpan
2. Data & AI Infrastructure (12 companies) Modern data stack, AI/ML platforms, vector and analytics infra, GPU compute. Software-native by design.
Snowflake, Arista Networks, Equinix, CoreWeave, MongoDB, DigitalOcean, Elastic, Akamai, Fastly, Teradata, C3.ai, Cerebras
3. Dev Tools & Observability (10 companies) Anything bought out of the engineering budget.
Datadog, Atlassian, Figma, Dynatrace, Nutanix, GitLab, UiPath, JFrog, AvePoint, PagerDuty
4. Horizontal SaaS & Back Office (18 companies) Software sold across industries to ops, HR, finance, and collaboration teams. Not vertical-specific.
Oracle, ServiceNow, Workday, ADP, Paychex, Paycom, Paylocity, Zoom, DocuSign, Navan, monday.com, Asana, Workiva, BlackLine, RingCentral, 8x8, Box, Dropbox
5. GTM (MarTech & SalesTech) (16 companies) Anything bought out of the revenue org. Marketing automation, sales engagement, CRM, ad tech, customer experience.
Salesforce, Adobe, HubSpot, The Trade Desk, Twilio, Klaviyo, Braze, ZoomInfo, Freshworks, Amplitude, Five9, Zeta Global, Wix, Sprout Social, Yext, Criteo
6. Vertical SaaS (15 companies) Software built for a specific industry without take-rate or transaction economics.
Palantir, Autodesk, Veeva, Samsara, ServiceTitan, Guidewire, Tyler Technologies, Doximity, Procore, AppFolio, CCC Intelligent Solutions, Blackbaud, nCino, CareCloud, CS Disco
7. Take-Rate Platforms (18 companies) Marketplaces and commerce platforms that earn money on transaction volume.
Uber, Airbnb, Shopify, MercadoLibre, DoorDash, eBay, Zillow, CarGurus, Instacart, Etsy, Toast, Lyft, Opendoor, StubHub, Upwork, Coursera, Ethos, Fiverr
8. Payments & Money Movement (10 companies) The rails. Payment processors, payment infrastructure, B2B payments, treasury. Volume game, utility margins.
Intuit, Fiserv, Adyen, PayPal, Block, Shift4, BILL, Flywire, Marqeta, Lightspeed
9. Consumer Fintech, Lending & Crypto (15 companies) The front-end. Consumer-facing financial apps, BNPL, lending platforms, crypto exchanges. CAC-driven, marketing-heavy, totally different unit economics from #8.
Coinbase, Robinhood, SoFi, Chime, Affirm, Upstart, Circle, Bullish, Figure, Klarna, Sezzle, Gemini, Blend, Remitly, LendingClub
Please check out our data partner, Koyfin. It’s dope.
Wishing you trade at a high revenue and EBITDA multiple,
CJ














