I can do this, but I need to lock the scope first because “tech IPOs in the last 10 years” can mean hundreds of listings depending on geography, SPAC inclusion, and whether we include recent IPOs with little trading history.

Plan: I’ll build a reproducible dataset of US-listed traditional technology IPOs over the last 10 years, fetch post-IPO price paths, compute 1M/6M/1Y returns from IPO price, then add revenue evolution where public-company fundamentals are available. I’ll exclude SPACs/direct listings and benchmark charts against QQQ. First I’m going to discover/validate IPO source fields, then run the analysis.

I found the IPO calendar table source works well from 2019 onward, but 2016–2018 failed on the first pass due to an HTML parser issue. I’ll fix the ingestion and then classify/filter technology IPOs.

Next I’ll classify current/available tickers using sector/industry metadata and remove obvious SPAC/direct-listing cases. For “tech,” I’ll include Technology-sector names plus internet/platform/software/data names that Yahoo classifies as Communication Services or Consumer Cyclical, and exclude biotech/healthcare and blank-check/SPAC vehicles.

The Yahoo sector pass identifies live tickers reliably but misses many delisted/renamed tickers. I’ll proceed with an “available-current-metadata” universe, plus keyword capture for tech-adjacent internet/software/platform companies, and I’ll explicitly report coverage limits. Next I’ll compute IPO-price-relative 1M/6M/1Y returns.

The first return pass exposed a classic IPO-analysis issue: split/reverse-split distortion. A few microcap technology IPOs have undergone large reverse splits, so comparing an unadjusted IPO offer price to split-adjusted historical bars creates absurd returns. I’ll correct this by adjusting each IPO offer price for subsequent stock splits before computing returns.

The split adjustment is still producing implausible outliers for microcaps, which suggests mixed adjusted/unadjusted handling between the IPO-price source, OHLCV source, and later corporate actions. I’ll inspect the worst cases and then apply robust outlier controls / focus on economically interpretable operating-company results.

Query execution was stopped by user.

I’ll empirically verify using known stock splits by comparing getOHLCV prices before/after splits against known split-adjusted behavior.

Yes — getOHLCV appears to be split-adjusted, based on the empirical test.

I checked known splits:

If the OHLCV data were not split-adjusted, the pre/post close ratio would have been roughly the split ratio — e.g. about 4x for AAPL or 5x for TSLA. Instead, the prices are continuous across the split dates, so the OHLCV data is adjusted.

The issue in my prior run was likely this:

• getOHLCV prices are split-adjusted.
• The IPO offer prices from the IPO calendar source are nominal IPO prices, not retroactively split-adjusted.
• Therefore, to compare today’s or later split-adjusted OHLCV prices to the IPO price, we do need to adjust the IPO price onto the same split-adjusted basis.
• However, my first adjustment pass probably over-adjusted or got polluted by microcap reverse split / ticker-change edge cases, especially for tiny foreign issuers and low-float IPOs.

So the right approach is:

1. Use getOHLCV prices as split-adjusted.
2. Adjust IPO offer price for post-IPO splits.
3. Exclude or separately bucket microcap reverse-split distortions, delistings, ticker changes, and questionable operating-company classifications.
4. Present both:
   • full universe, and
   • clean investable operating-tech IPO universe.

That should produce a much more reliable analysis.

I’ll continue with a cleaner methodology: keep the full available tech-IPO set, but separate “extreme/microcap/corporate-action distorted” names from the core distribution, and add revenue-at-IPO vs later-revenue analysis where FactSet revenue data exists.

Now I’ll add current price performance and revenue evolution: revenue near the IPO, latest available revenue, revenue multiple since IPO, and how those relate to 1Y and current price returns.

I’ll generate the requested charts: return distributions, cohort medians, revenue-growth vs price-return scatter, and bucketed revenue/return relationships.

I’ll pull the final compact tables so the written summary can use exact medians, win rates, and revenue relationships.

I’ll print the full compact return table without column truncation so the final write-up has the exact 1M/6M/1Y figures.

IPO analysis continued — US-listed traditional tech IPOs

I completed the analysis using the available IPO calendar + market/fundamental data in the notebook.

Important data caveat

The IPO source successfully loaded 2019–2026 IPOs, not the full 2016–2018 portion of the 10-year window. So the analysis below is best described as:

US-listed traditional technology IPOs available from 2019 through June 13, 2026, excluding obvious SPACs/direct listings, with price and revenue data where available.

I also separated the dataset into:

1. Full available universe: 118 tech IPO candidates
2. Core clean universe: 97 names, excluding extreme >1,000% absolute return cases that are often microcap / reverse-split / ticker-change / corporate-action-distorted names.

The core clean universe is the more useful one for investment interpretation.

1. Post-IPO price performance

Core clean universe: 97 IPOs

Interpretation

The IPO pattern is very clear:

• First month: generally strong. Median IPO gained about 25% after one month.
• Six months: enthusiasm faded. Median return was basically flat at -0.09%.
• One year: the median tech IPO was down 18.4% from IPO price.
• The mean 1Y return was positive because a small number of major winners skewed the average upward.
• The median and win-rate are more informative than the mean.

In plain English:

Tech IPOs often get an early pop, but after 6–12 months the typical outcome becomes much weaker. The distribution is highly asymmetric: a few winners dominate, while the median IPO loses money by year one.

2. IPO cohort performance by year

Core universe, median returns:

Interpretation

The market regime mattered enormously.

2019 and 2020 were excellent IPO vintages.
Many SaaS, cybersecurity, cloud, and fintech IPOs benefited from strong growth expectations and COVID-era multiple expansion.

2021 was the peak-bubble cohort.
The 1M median was still positive, but by 1Y the median return was -52.7%, with only 8.8% of names positive after one year.

2022 onward was harsh.
Higher rates, lower software multiples, and a weaker IPO market punished recent issues. The available 2022–2025 cohorts had deeply negative median 1Y outcomes.

3. Best and worst 1Y IPO outcomes

Top 1Y winners in the clean universe

Bottom 1Y performers in the clean universe

4. Current-price performance from IPO price

For the core clean universe:

Interpretation

This is the most important result.

Even though the mean current return is positive, the median IPO is down more than 50% from IPO price. Only about 31% of names are currently positive.

That means the average-looking return is misleading. The IPO market is dominated by a small number of exceptional winners such as CrowdStrike, Cloudflare, Datadog, SiTime, etc.

5. Revenue evolution analysis

Revenue data was available for 89 of 97 core-clean names.

I measured:

• Revenue near IPO
• Latest available revenue
• Revenue growth from IPO-time revenue to latest revenue
• Relationship between revenue and stock-price outcome

Correlation results

Interpretation

The strongest relationship was:

Revenue growth after IPO had a meaningful positive relationship with current stock return.

The Spearman correlation between revenue growth to latest and current return was +0.582, which is fairly strong for cross-sectional equity data.

But revenue size at IPO itself was much less predictive. Larger companies at IPO did somewhat better on 1Y median returns, but simply being large at IPO did not guarantee strong long-term stock performance.

6. Revenue-at-IPO buckets

Interpretation

Companies with larger revenue bases at IPO tended to have better 1Y outcomes, but current returns were still often negative.

The biggest lesson is:

Revenue scale at IPO helped reduce risk, but it was not enough by itself. Valuation and continued growth mattered more.

7. Revenue-growth buckets

Interpretation

This is the cleanest fundamental result:

The highest revenue-growth IPOs were the only group with positive median current stock performance.

The top revenue-growth quartile had:

• Median latest revenue growth: +467%
• Median current stock return: +51.9%
• Current win rate: 72.7%

By contrast, the lowest revenue-growth quartile had:

• Median current return: -74.1%
• Current win rate: only 4.8%

So revenue growth did matter — strongly — but mostly over the longer run, not necessarily within the first few months after IPO.

8. Examples: strong revenue growth and strong stock outcomes

These are the archetypal successful tech IPOs: revenue compounded rapidly enough to justify or exceed the IPO valuation.

9. Examples: revenue growth but poor stock outcomes

This group is very important.

It shows that:

Revenue growth alone was not sufficient if the IPO valuation was too high, margins were weak, competition was intense, or the market multiple compressed.

Many 2020–2021 software IPOs grew revenue meaningfully but still produced poor stock returns because they IPOed at peak software multiples.

10. Bottom-line conclusions

Main findings

1. The average tech IPO outcome is misleading.
   A few huge winners skew the mean upward, while the median IPO loses money over time.

2. The first month is usually the best window.
   Median 1M return in the clean universe was +24.8%, with a 70.3% win rate.

3. Performance decays sharply by one year.
   Median 1Y return was -18.4%, with only 40.7% of names positive.

4. The 2019–2020 vintages were much stronger than 2021–2025.
   2019 and 2020 IPOs benefited from growth-stock multiple expansion. 2021 IPOs were badly hurt by valuation compression.

5. Revenue growth mattered more than revenue size at IPO.
   IPO-time revenue size had only weak predictive power. Revenue growth after IPO had a much stronger relationship with current stock performance.

6. High revenue growth was the key differentiator.
   The highest revenue-growth quartile had a +51.9% median current return and 72.7% win rate.

7. Growth was necessary but not always sufficient.
   Several companies doubled or tripled revenue after IPO but still lost 50–80% of market value because they IPOed at inflated valuations.

My practical takeaway

For tech IPOs, the best-performing companies generally had some combination of:

• strong post-IPO revenue compounding,
• large and expanding addressable market,
• durable product category,
• improving margins,
• reasonable IPO valuation relative to forward growth,
• and IPO timing outside of extreme valuation bubbles.

The worst outcomes were often:

• 2021-vintage high-multiple software names,
• low-float / microcap tech IPOs,
• companies with poor post-IPO execution,
• companies that grew revenue but failed to justify IPO valuations,
• and names exposed to severe multiple compression.

The charts displayed in the notebook show the return distributions, cohort differences, and the revenue-growth / price-return relationship visually.

I’ll break the clean IPO universe into revenue-at-IPO quartiles and compare 1M, 6M, 1Y, and current performance for small-revenue vs high-revenue IPOs.

The table output was truncated, so I’ll output a focused JSON/table with only the high-vs-small metrics.

Yes — here is the clean comparison of high-revenue vs small-revenue tech IPOs.

Universe: core clean US-listed traditional tech IPOs, 2019–2026 available sample, with revenue-at-IPO data.

Direct comparison: smallest vs highest revenue IPOs

Win rates

Key conclusion

High-revenue tech IPOs did materially better than small-revenue tech IPOs.

The difference became more obvious as time passed:

• At 1 month, both groups often traded well.
• At 6 months, high-revenue IPOs were still positive on median, while small-revenue IPOs had already turned negative.
• At 1 year, high-revenue IPOs had a positive median return, while small-revenue IPOs were down heavily.
• On a current basis, both groups are negative on median, but high-revenue IPOs lost much less.

Full quartile breakdown

Interpretation

1. Small-revenue IPOs had a sharper post-IPO fade

Small-revenue IPOs looked fine in the first month:

• 1M median return: +15.3%
• 1M win rate: 72.7%

But by one year:

• 1Y median return: -38.4%
• current median return: -57.3%
• current win rate: only 18.2%

So the typical small-revenue tech IPO produced an early pop, then badly underperformed.

2. High-revenue IPOs were more durable

Highest-revenue IPOs had:

• 6M median return: +27.4%
• 1Y median return: +12.8%
• 1Y win rate: 54.6%
• current win rate: 36.4%

They were not immune to losses, but their outcomes were clearly better.

3. Revenue size helped, but did not guarantee long-term success

Even the highest-revenue group had a current median return of -39.4%.

That means revenue scale reduced risk, but IPO valuation and market regime still mattered a lot.

A high-revenue IPO in 2021 at an excessive valuation could still perform poorly.

Bottom line

If you bought tech IPOs at the IPO price:

• Small-revenue IPOs were much riskier.
• High-revenue IPOs had better 6M and 1Y performance.
• But over longer periods, even high-revenue IPOs often lost money unless revenue growth remained strong and valuation was reasonable.

In short:

Revenue size at IPO improved the odds, but revenue growth after IPO was the stronger driver of long-term stock performance.

I’ll add plots specifically comparing revenue-size groups and emphasize the top 10% highest-revenue-at-IPO companies versus the rest.

The first top-10% table was truncated, so I’ll output the exact top-10% summary as JSON.

Done — I added three plots:

1. Top 10% revenue-at-IPO vs bottom 90% median return by horizon
2. Revenue at IPO vs current return scatter, with top 10% highlighted
3. Company-level current-return chart for top 10% revenue-at-IPO names

Top 10% threshold

The top 10% by revenue at IPO started at approximately:

$656.7M revenue at IPO

There were 9 companies in the top-revenue group.

Top 10% revenue-at-IPO vs bottom 90%

Win rates

Interpretation

The top 10% revenue companies did not have the strongest first-month pop.

Actually:

• Bottom 90% median 1M return: +31.0%
• Top 10% median 1M return: +20.0%

But the top 10% revenue companies held up much better over time.

By the current date:

• Bottom 90% median return: -50.9%
• Top 10% median return: +17.7%

So the high-revenue IPOs were less explosive early, but more durable.

Top 10% revenue-at-IPO names

Main takeaway

For the top 10% highest-revenue IPOs, the story was mixed but meaningfully better than the rest:

• They had less early IPO pop than smaller-revenue companies.
• They had better durability after 6–12 months.
• They were the only revenue-size group with a positive median current return.
• But there was still huge dispersion: PSN, GFS, UBER, DASH did well; LOGC, LYFT, DIDIY did very poorly.

So the conclusion is:

High revenue at IPO improved survival and long-term odds, but did not guarantee success. The top 10% revenue group performed better mainly because the winners compounded and the group avoided the extreme median collapse seen in smaller IPOs.

I’ll create a single scatter visualization with revenue at IPO on the x-axis and returns on the y-axis, faceted/colored by return horizon: 1M, 6M, 1Y, and current.

Done — I created the plot:

Revenue at IPO vs Post-IPO Returns by Horizon

It shows:

• X-axis: revenue at IPO in $M, on a log scale
• Y-axis: return from IPO price
• Separate panels for:
  • 1M return
  • 6M return
  • 1Y return
  • Current return
• Points are symbol-highlighted for:
  • Top 10% revenue-at-IPO companies
  • Bottom 90%
• Each panel includes a trendline.

The top 10% revenue threshold used was:

$656.66M revenue at IPO

Main visual takeaway:

Revenue size at IPO does not strongly explain the very short-term IPO pop, but the relationship becomes more favorable at longer horizons, especially for current returns. The high-revenue companies are less concentrated in the deepest-loss region than the smaller-revenue IPOs.