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So, there is actually a statistically sound study on Multibaggers (stocks that did 5x, 10x, or even 100x in stock price). I spent the last days going through **Anna Yartseva’s paper “Alchemy of Multibagger Stocks”**, which looked at **464 NYSE/Nasdaq stocks** that went on to deliver big multi‑year returns from **2009 to 2024**. It’s one of the few studies that actually uses **panel regression models**, so there is some actual data behind it... Let's break it down - I found it very insightful:
# 1. What didn’t predict the big winners
A few things most people assume matter… didn’t:
* **Earnings growth** (EPS, EBIT, EBITDA, net income, gross profit) over *1‑year or 5‑year windows* didn’t show predictive power.
* **Sector bias** was pointless. Winners were spread across IT, industrials, consumer, healthcare… even a few utilities.
* **Dividends**, **buybacks**, **analyst coverage**, **R&D intensity**, **Altman Z‑score**, **debt ratios:** none of these had a consistent statistical link to future outperformance.
Basically: screening for “fast growers,” “undiscovered stocks,” or “tech only” would have filtered out plenty of actual multibaggers.
# 2. The strongest signal by far: FCF/Price
This was the standout result.
* **Free cash flow to price (FCF/P)** had the *largest coefficients* in the regressions.
* **Book‑to‑Market (B/M)** (which is Book Value per Share / Market Price per Share) helped explain which stocks became long‑term winners better than models without it.
* When both improved together, the annual return impact was substantial.
That means:
**Starting cheap on free cash flow** mattered more than almost anything else.
But watch out:
**Firms with negative equity** massively underperformed across all size buckets.
# 3. Size: small companies dominated
Median “starting point” for winners:
* **$348M market cap**
* **$702M revenue**
Small caps outperformed mid‑caps and large‑caps by a wide margin.
The size effect was one of the cleanest patterns in the study.
# 4. Profitability: modest, but improving
The typical winner didn’t start off with extraordinary profitability:
* **ROE \~9%**
* **EBIT margin \~3.9%**
* **ROC \~6.5%**
* **Gross margin \~35%**
The important part was the trend: Winners tended to *improve* these metrics over time.
Earnings growth happened later, but wasn’t a reliable predictor upfront.
# 5. Revenue growth was fine, but not the “edge”
Median long‑term revenue growth was around **11%**, but again:
it wasn’t the variable that separated future multibaggers from the pack.
The “engine” wasn’t rapid revenue growth, but **cash generation (positive FCF) + valuation (aka multiple expansion) + improving margins** (aka margin expansion).
# 6. Reinvestment quality
An interesting result:
If **asset growth exceeded EBITDA growth**, future returns dropped noticeably.
Companies that aggressively expanded the balance sheet without equivalent earnings progress tended to disappoint.
Why? Because it usually means one of three things:
* a. The company is spending a lot… but not producing much
* b. The business isn’t earning good returns on new investments
* c. Management is chasing scale to hide weak economics
# 7. Entry points and price behavior
Some practical points:
* Stocks trading **near 12‑month lows** at the buy point had better outcomes(!) They started their run from bottoms.
* **1‑month momentum** was slightly positive. Meaning: if the stock was up last month, it tended to continue a bit.
* **3–6 month momentum** was negative (mean reversion). Meaning: over the medium term, strong recent performance was actually a red flag.
* Many winners had choppy, non‑linear price paths -> Multibaggers almost never look like multibaggers in real time.
Nothing “smooth” about the journey... Keep holding, if the foundamentals stay in tact...
# 8. Macro conditions
Rising interest rates reduced next‑year returns by roughly **8–12%** for potential winners.
Smaller companies are more rate‑sensitive, so this fits:
higher discount rates → lower valuations → tougher conditions. Meaning: if you expect interest rates to fall, it's a better time to invest.
# A simple screen based on her findings
If you wanted to build a starting list based strictly on what her models highlighted, it would look like:
* High **FCF/Price** (5% FCF yield or more)
* **B/M > 0.40** and **positive operating profitability**
* **Market cap < $2B**
* Profitability **improving** (margins and returns trending up)
* **Asset growth ≤ EBITDA growth**
* Trading **near 12‑month lows**
* No **negative equity**
# TL;DR
Yartseva’s study in one message:
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Let me know if you are surprised by some of these metrics
Do you screen for some of those metrics when you research?