The Blueprint for 1,000%+ Returns (Part 1)

A Groundbreaking Study Analysed 464 Stocks That Returned Ten Times Their Value. Here's What They All Had in Common.

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Today I’m not sharing a stock pick. Instead, I’m kicking off a three-part series breaking down what might be the most rigorous academic study ever conducted on multibagger stocks.

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Back in January, I wrote about the Alta Fox Capital study - a brilliant piece of practitioner research that screened for every stock returning more than 350% over five years.

That post resonated with a lot of you.

But there was always a limitation. Alta Fox studied 104 companies. Their methodology was largely descriptive -they looked at averages, noted patterns, and drew conclusions. Useful, but not statistically rigorous.

Now someone has done the rigorous version.

In February 2025, Anna Yartseva at Birmingham City University published a working paper through the Centre for Applied Finance and Economics (CAFÉ) titled “The Alchemy of Multibagger Stocks.”

She didn’t study 104 stocks that returned 350%.

She studied 464 stocks that returned 1,000%+ - true ten-baggers - listed on major U.S. exchanges between 2009 and 2024.

And she didn’t just describe them. She built dynamic panel data models, ran Fama-French factor analysis, tested over 150 variables, and identified which factors actually cause multibagger outperformance -not just correlate with it.

This is the first serious academic attempt to crack the multibagger code with real econometric rigour.

Here’s what she found...

The Dataset

Let me explain why this study is different from everything that came before it.

Previous multibagger research - Phelps (1972), Mayer (2018), Oswal (2014) - relied on case studies, anecdotal examples, and descriptive statistics. They’d look at past winners and say “these companies had strong earnings growth” or “they had great management.”

That’s interesting. But it’s not evidence.

Yartseva’s approach was fundamentally different.

She pulled every stock listed on the NYSE and NASDAQ that increased in value by at least tenfold between January 2009 and January 2024. Not stocks that briefly touched 10x and fell back -only companies that sustained that level of appreciation.

464 companies made the cut.

She then built a panel dataset covering 25 years of data (2000-2024) for each company -meaning she could study what these businesses looked like before they became ten -baggers, not just after.

11,600 company-year observations. Over 150 variables tested. Dynamic panel data models with Granger causality testing.

This isn’t a blog post or an investor letter. It’s real research. And the findings are fascinating.

The Fama-French Foundation

Yartseva started with the standard five-factor model that dominates academic finance - the Fama-French framework that says stock returns are driven by market risk, size, value, profitability, and investment patterns.

She sorted her 464 multibaggers into 36 portfolios based on combinations of these factors and measured their excess returns above the S&P 500.

The traditional factors all showed up. But some were far more powerful than others.

Factor 1: Size - Small Caps Dominate

This one confirmed what most of us already suspected.

When controlling for value, profitability, and investment factors, small-cap stocks outperformed medium and large companies in 11 out of 12 portfolio comparisons.

The numbers are stark:

• Large firms (average market cap ~$32 billion): outperformed the S&P 500 by 9.7% annually

• Mid-cap firms (average market cap ~$2 billion): outperformed by 14.5% annually

• Small-cap firms (market cap below $250 million): outperformed by 37.7% annually

Small companies delivered nearly four times the excess return of large companies.

But here’s the nuance -and this is why Yartseva’s work is so valuable. When she looked at median returns instead of averages, several small-cap portfolios actually showed negative returns. Specifically, small companies with low book-to-market values and weak profitability were the worst performers in the entire dataset.

Small size alone isn’t enough. It has to be combined with the right characteristics.

This is a critical insight that gets lost in the simplified version of “just buy small caps.”

Factor 2: Value - The Most Powerful Driver

This finding might surprise growth investors.

Yartseva sorted companies by their book-to-market ratios. High book-to-market means the company’s book value exceeds its market price - in other words, the stock is cheap relative to its assets.

The value effect was the strongest and most consistent factor across the entire study.

• Low book-to-market (overvalued) multibaggers: outperformed the S&P 500 by 12.8% annually

• Medium book-to-market: outperformed by 14.5% annually

• High book-to-market (undervalued) multibaggers: outperformed by 34.7% annually

The undervalued stocks delivered nearly triple the excess return of overvalued ones.

And this held consistently across every size group, every profitability level, and every investment pattern. When she looked at median values, the pattern became perfectly linear - every high-value portfolio outperformed every medium-value portfolio, which outperformed every low-value one.

This is worth sitting with for a moment.

These are all stocks that went on to deliver 1,000%+ returns. Every single one is a ten-bagger. And still, the ones bought at cheap valuations dramatically outperformed the ones bought at expensive valuations.

The growth vs. value debate? For multibaggers, it’s a false choice. The best ten-baggers were growth and value stocks simultaneously.

Factor 3: Profitability - But Not How You Think

The profitability effect also showed up clearly. Controlling for other factors, companies with robust operating profitability consistently outperformed those with weak profitability.

The comparison:

• Low-value stocks with weak profitability: 9.6% excess returns

• Low-value stocks with robust profitability: 16.0% excess returns

• High-value stocks with weak profitability: 28.5% excess returns

• High-value stocks with robust profitability: 40.9% excess returns

The profitability advantage appeared in 82% of individual portfolio comparisons.

But here’s where it gets interesting. When Yartseva moved from the sorting exercise into her full regression models, the specific metric for profitability mattered enormously.

The standard Fama-French measure - operating profitability - had a coefficient close to zero in the regression. Almost no predictive power.

When she replaced it with EBITDA margin, the coefficient jumped dramatically. And in the dynamic models, even EBITDA margin was replaced by ROA as the significant profitability variable.

The lesson: profitability matters, but which measure of profitability you use completely changes the result. The conventional academic metrics understate how much profitability drives ten-bagger returns.

Factor 4: Investment - A Unique Multibagger Pattern

This is where Yartseva’s findings diverge most sharply from standard academic finance.

The Fama-French model says that companies investing aggressively in asset growth should underperform. Conservative investors do better. That’s the theory.

For multibaggers, the opposite is true.

In every single one of the 24 pairwise portfolio comparisons, companies with aggressive asset growth outperformed companies with conservative investment - a 100% hit rate.

Multibaggers invest aggressively. They expand. They build.

But there’s a catch - and this is one of the study’s most original findings.

Yartseva created a dummy variable that flagged years when a company’s asset growth exceeded its EBITDA growth. In other words, years when the company was investing faster than its earnings were growing.

That variable was strongly negative. When asset growth outpaced EBITDA growth, next-year returns dropped by 4-11 percentage points.

The message: multibaggers invest aggressively, but the investment must be affordable. When companies spend beyond what their earnings can support, the market punishes them. When they invest heavily and grow EBITDA to match, returns soar.

This is a pattern you won’t find in any textbook.

The Red Flags to Avoid

Yartseva’s data also clearly identifies what not to buy.

Every portfolio that generated actual negative returns -meaning investors lost money -shared the same characteristics:

• Low book-to-market values (overvalued stocks)

• Weak operating profitability (loss-making or barely profitable)

• Conservative investment (shrinking asset base)

These companies had assets declining at -6.8% per year on average. They weren’t investing enough to maintain their existing capabilities, let alone grow.

The smaller the company with these characteristics, the worse the losses. Small-cap stocks with low value and weak profitability lost 18.1% annually. Mid-caps lost 9.4%. Large-caps lost 7.6%.

Yartseva suggests these stocks could even be candidates for short selling.

For me, the takeaway is simpler: avoid overvalued, unprofitable companies that aren’t investing in their future. That sounds obvious, but in a market obsessed with narrative-driven momentum stocks, it’s a filter that would save many portfolios from disaster.

What This Means So Far

Let me summarise the key findings from Part 1:

1. Small size matters. Ten-baggers were overwhelmingly small-cap at the start. But small size alone isn’t enough - it must be paired with value and profitability.

2. Value is the most powerful factor. Even among stocks that all returned 1,000%+, the cheap ones massively outperformed the expensive ones. Growth and value aren’t opposites - the best multibaggers are both.

3. Profitability drives returns - but the right metric matters. EBITDA margin and ROA are better predictors than the standard academic measures.

4. Aggressive investment is essential - but only when supported by growing earnings. The companies that invest heavily while growing EBITDA to match are the ones that deliver.

5. Know what to avoid. Overvalued, unprofitable companies with shrinking assets are the worst performers in the dataset - even when they happen to be future ten-baggers.

These findings alone are enough to refine any stock screening process. But the real surprises come in Part 2.

In Part 2, I’ll cover Yartseva’s most counterintuitive findings - including why earnings growth doesn’t predict multibagger returns (yes, really), why momentum is a trap, and why the entry point matters more than almost anything else.

Part 2 will come out next Monday.

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This is Part 1 of a three-part series based on Anna Yartseva’s working paper “The Alchemy of Multibagger Stocks” (CAFÉ Working Paper No. 33, Centre for Applied Finance and Economics, Birmingham City University, February 2025). The original paper is published under a Creative Commons BY-NC-SA license. Full credit to Anna Yartseva for this exceptional research - all findings and data referenced in this series are her work. Any errors in interpretation are my own.

Thanks for reading,

Nico

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