I Tracked Every Stock That 10x'd in 5 Years
I Tracked Every Stock That 10x’d in 5 Years
ELI5/TLDR
Someone crunched 20 years of data on 15,000 stocks, looking for the unicorns — the stocks that multiplied ten times in value within five years. The surprise: these mega-winners weren’t usually profitable, weren’t growing fast, and didn’t look “good” by any normal measure. What they did share was almost no debt, a strong tilt toward biotech, and — this is the weird part — they mostly started their runs during bear markets, right when everyone else was panicking.
The Full Story
The setup, and a clever trick to keep it fair
The guy behind the channel did the kind of thing you’d do on a rainy weekend if your idea of fun involved a spreadsheet with 15,000 rows. He pulled twenty years of price data on roughly every public stock he could find, and hunted for the ones that multiplied by ten — “ten-baggers,” as Peter Lynch called them back in the 80s — inside a five-year window.
Here’s the clever part. If you just ask “when was the earliest you could have bought Nvidia and made 10x?” you’ll always end up back at the tiny-startup phase. Everything starts small, so the answer is boring and predetermined. Instead, he flipped the question around. He asked: what’s the latest moment you could have jumped in and still walked away with a 10x? Think of it like asking how far behind in a race you can start and still win. That reframing lets you see the real shape of the winners right before they took off, not at their accidental-origin-story stage.
So how big were they, actually?
Small. Stubbornly, consistently small.
Seventy percent of these stocks were worth under $500 million when their last-chance window closed. Another thirteen percent were between half a billion and a billion. That means eighty-three percent of the would-be unicorns were still under a billion dollars even at the latest moment you could’ve bought in.
For reference, a billion dollars sounds huge — it’s not, by stock market standards. The S&P 500 these days requires roughly $10 billion just to be considered. Only 2% of the 10x stocks were already at that size. A handful of names in that tiny 2% club you’ll recognize: Nvidia, Palantir, Shopify, Moderna. The rest were obscure.
So yes, big companies can 10x, but they usually need more time. Microsoft pulled it off between 2011 and 2021 — which is impressive, but took ten years, twice our window.
The speed-versus-survival tradeoff
Within five years, how fast did they actually rise? The median was 35 months — call it just under three years. Some were much faster. Sixteen percent hit 10x in under a year, which sounds like the dream until you see what happens next.
Stocks that rocketed up within twelve months dropped a median of 90% from their peak just three years later. Ninety percent. These are the lottery tickets that cash in, get photographed at the podium, and then quietly self-destruct in the parking lot.
“These get-rich-quick types of stocks, they do exist, but they’re extremely rare.”
Inovio is the cautionary tale. A biotech firm, they 10x’d in a matter of months in 2020 on the hope of producing a COVID vaccine. The vaccine failed in trials. The stock has since lost more than 99% of its value. One of those stories where the whole arc — the hype, the crash, the obscurity — happens inside a single business cycle.
And the pattern was pretty linear. The slower a stock climbed, the better its odds of holding its gains. Even the ones that took the full five years still dropped a median of 30% afterward. Nothing stays perfectly up. But the faster it went, the harder it fell.
The traits that didn’t matter
Now here’s where things get interesting. If you asked a room full of investors to sketch the profile of a stock about to 10x, they’d say something like: profitable, fast-growing revenue, strong return on equity — basically, a healthy business firing on all cylinders. This is the reasonable answer. It is, in fact, what the standard stock-screening checklists use to find “good” companies.
None of those things predicted the 10x stocks.
- Only 33% were even profitable when their run started.
- The median return on equity — basically, how efficiently they turned shareholder money into profit — was negative 9.6%.
- The median revenue growth over the prior year was just 4%. That’s barely keeping pace with inflation.
These were, by the conventional rulebook, mediocre-looking companies. Many were losing money. Most weren’t growing. And yet they were about to go vertical.
The one trait that did matter: low debt
One thing showed up consistently. Seventy-two percent of the 10x stocks had a debt-to-equity ratio below one. That just means they owed less than the value of the shareholder money they held — they weren’t drowning in loans.
Think of debt as a financial straitjacket. When you owe a lot, every strategic move has to serve the next interest payment. You can’t pivot, can’t take a risk, can’t run an experiment that might fail. A lender is basically peering over your shoulder asking when they’re getting paid. Low-debt companies don’t have that voice in their ear. They can reinvent themselves. They can pour everything into a new product line. They can wait out a bad quarter.
“History tells us that leverage often produces zeros even when it’s employed by very smart people.” — Warren Buffett
It’s the same story at every level of stock analysis. Debt is the killer. Low debt doesn’t make a company great, but it gives one a chance to become great.
Where the unicorns come from
Which industries produced all these 10x winners? Healthcare topped the sector list at 25%. Tech came second at 18% — which sounds low, because if you squinted at the last twenty years you’d swear tech owned the whole list.
The surprise clears up when you zoom in. Sectors are broad — the U.S. market has only about ten of them. Industries are narrower; there are over a hundred. And once you split healthcare into its actual industries, one specific slice jumps out: biotech. Biotechnology alone accounts for 15% of every 10x stock in the dataset. The only industry even close is software.
Biotech is the land of the 10-bagger for a reason that’s almost mechanical. Regular pharma mostly treats symptoms — take the pill, the pain fades. Biotech tries to go upstream and fix the underlying cause. Imagine the difference between swallowing paracetamol when you get a migraine versus actually rewiring whatever in your brain is causing the migraine in the first place. One is a band-aid; the other is a cure.
These treatments have to go through years of R&D and clinical trials and then, at the end, an FDA approval. That approval is the moment everything changes. A tiny lab-coat company with a good molecule and a patent suddenly has a legal monopoly on treating a real disease for a huge population. The share price doesn’t crawl up — it teleports. Inovio’s almost-10x on a COVID-vaccine hope is the same mechanism, just with a sad ending.
The counterintuitive part: these runs start in bear markets
Now for the finding that surprised even the analyst himself. He took the number of 10x stocks that started their runs each year, and laid it on top of a chart of the S&P 500. Then he marked the bear markets — the years the market fell 20% or more from its peak.
The 10x stocks cluster in the bad years.
2008 and 2009 — the financial crisis, the year your uncle stopped talking about his portfolio at Thanksgiving — kicked off mega-runs for Under Armour, Domino’s Pizza, and Lululemon. 2016’s correction (not quite a bear market, but a 13-15% drop) seeded Nvidia, Shopify, and Etsy. 2020’s COVID crash lit the fuse on Broadcom, Nio, and Arista Networks. The only year that looks “missing” a spike is 2022 — and 2022 just isn’t five years old yet, so its 10x stocks are still cooking.
Even more telling: during these bear-market years, the median future 10x stock wasn’t just dipping. It was 30 to 40 percent below its own 52-week high. The broader panic was dragging it down, and the deeper it got dragged, the more runway there was to explode upward later. It’s a sort of compressed spring — the harder the market steps on it, the further it bounces back.
“Be fearful when others are greedy, and greedy when others are fearful.” — Warren Buffett, again
The guy running the numbers says he didn’t expect that saying to extend this far into the weirdest corner of the market. The everyday wisdom about buying in downturns — it turns out to apply even to the most extreme outliers.
What he actually takes away from this
He’s clear that he’s not running off to hunt for nano-cap biotechs now. If anything, the data convinced him how ugly that strategy is — most tiny stocks don’t 10x, most fast climbers collapse, and catching one is closer to luck than skill. What the analysis did confirm, for him, is the simpler and older lesson: the best moments to buy anything are the moments when everyone else is running the other way.
Claude’s Take
This is a well-constructed piece of amateur data journalism, and the “latest possible entry point” reframe is genuinely smart — it dodges the obvious survivorship trap most people fall into when they talk about ten-baggers. The headline findings are mostly defensible. Low debt correlating with survivability and flexibility is well-established across decades of corporate finance research. The bear-market-is-the-best-entry-point observation is real and echoed in lots of other studies (e.g., Research Affiliates, AQR papers on crisis-period returns). Biotech being a disproportionately high-variance industry is also not news — options traders have known this for ages.
What I’d flag: the sample is US-listed stocks over a 20-year window that is overwhelmingly a bull market with a few sharp interruptions. Twenty years isn’t actually that many business cycles. “83% under $1 billion” sounds strong until you remember that under-a-billion stocks vastly outnumber big ones in the first place — if you’re going to fish, that’s where most of the fish are regardless of whether they’re 10x candidates. He doesn’t show the base rate. How many all stocks under $500M are there compared to stocks over $10B? Without that denominator, the “70%” figure is less informative than it sounds.
The “only 33% were profitable” finding is the one I’d be most careful with. It could genuinely mean profitability doesn’t predict mega-winners. It could also mean the dataset is heavily dragged by biotechs and pre-revenue software companies that are losing money for structural reasons — which would make the stat a description of those specific industries rather than a universal truth. And if you believed the claim literally, you’d conclude that losing money is better for 10x-ing, which is clearly wrong.
The bear-market timing claim is probably the most useful takeaway, and it’s also the one most at risk of looking great in hindsight. Of course the stocks that started their runs in 2009 did well — 2009 was the bottom of a generational crash. Anything that survived had runway. The harder question, the one investors actually face, is: how do you tell, while the market is falling, whether it’s a bottom or just a waypoint on the way further down? His data doesn’t answer that, because it only looks backward from known winners. Still, as a rough principle — own cash in good years, spend it in bad ones — it’s as solid as any rule in investing.
Last note: the mid-video ad for his free screener is soft-pedaled, but worth clocking. The video’s framing nudges you toward his service. That doesn’t make the analysis wrong, just tells you who’s paying for it.