Research on 464 10-Baggers

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In her February 2025 study "The Alchemy of Multibagger Stocks," Birmingham City University researcher Anna Yartseva analyzed 464 companies that delivered at least 10-fold returns between 2009 and 2024.

While investment professionals have long offered theories about what creates these exceptional winners, Yartseva's work represents one of the first complete academic studies to test these assumptions using actual market data.

Her empirical investigation challenges several widely held beliefs about what drives exceptional stock performance while confirming others through rigorous statistical analysis, as we’ll cover in this comprehensive post.

Rather than relying on anecdotes or case studies, Yartseva built a large dataset tracking every company on the NYSE and NASDAQ that achieved 10-fold returns during the 15-year period starting January 2009, right after the financial crisis market bottom:

The study’s timing and scope were carefully chosen for methodological reasons:

Yartseva also excluded "transitory" multibaggers, companies that temporarily achieved 10-fold returns but later dropped below the 900% return threshold. This ensured the study focused on enduring winners rather than temporary market darlings.

The final sample included 24 companies that became 100-baggers, returning over 100 times the initial investment.

To identify what drives multibagger returns, Yartseva tested over 150 different variables across 11,600 company-year observations.

These included traditional metrics like P/E ratios and profit margins, technical indicators like momentum and price patterns, and macroeconomic factors like interest rates. She examined growth rates over multiple time periods, from year-over-year changes to five-year compound annual growth rates.

Building on the Fama-French five-factor model—which identifies size, value, profitability, investment, and market risk as key return drivers—Yartseva created 36 different portfolios by sorting companies into groups based on size (small, medium, large), value (low, medium, high), profitability (weak, robust), and investment approach (conservative, aggressive).

This 3×3×2×2 sorting allowed her to test whether the factors that explain general stock returns also apply to multibaggers, while isolating each factor's impact by controlling for others.

Moreover, the approach specifically focused on predictive power rather than mere correlation. While the multibaggers were identified from 2009-2024 performance, Yartseva collected financial data going back to 2000 to understand these companies' characteristics before they became winners.

She used this extended 2000-2022 dataset for model building, then tested predictions against actual 2023 and 2024 returns. This out-of-sample testing proved the model could genuinely forecast future performance, not just explain past results.

Despite decades of investment literature insisting that earnings growth drives multibagger returns, Yartseva found that earnings growth, in all its forms, was statistically insignificant in predicting future multibagger performance:

Therefore, companies with spectacular earnings growth were no more likely to deliver exceptional returns than those with modest growth.

This lack of predictive power extended beyond just earnings metrics. Sales growth, gross profit growth, operating profit growth, net profit growth, and free cash flow (FCF) growth all failed to predict which stocks would become multibaggers. This held true whether examining short-term year-over-year rates or longer-term five-year compound annual growth rates.

This finding directly contradicts conventional wisdom from well-known investors:

Yet when subjected to statistical scrutiny across the 464 multibaggers in Yartseva's study, earnings growth showed no predictive value.

What Else Doesn’t Matter

It’s also worth noting that several widely cited factors showed no predictive power:

Yartseva also tested whether R&D spending predicted multibagger returns. Despite the common belief that innovation drives growth, R&D expense relative to free cash flow showed no correlation with becoming a multibagger.

Another distinctive finding concerns how companies invest for growth.

Traditional academic models, including the Fama-French five-factor model, suggest that aggressive investment reduces future returns. The theory holds that companies overspending on expansion often destroy value through poor capital allocation.

Yartseva found the opposite for multibaggers, but with an important caveat. Companies that aggressively expanded their assets achieved superior returns in 100% of cases when compared to similar companies with conservative investment approaches. However, this only held when expansion was supported by corresponding EBITDA growth:

The study introduced an "investment dummy" variable that equaled 1 when asset growth exceeded EBITDA growth. This variable showed strong negative coefficients between -4.7 and -22.8 across different model specifications. In practical terms, when a company's assets grew faster than its EBITDA, next year's returns dropped by -5% to -23%.

Those with conservative investment approaches, where assets actually shrank year-over-year, generated the worst returns.

Average asset growth for these underperformers was negative 6.8%, compared to positive 40% for aggressive investors. These shrinking companies also had negative operating profitability averaging -17.9%. But aggressive investment without profitability to support it proved equally destructive.

If earnings growth doesn't predict multibagger returns, what does? Yartseva's analysis identified several factors with strong predictive power, and they paint a different picture than most growth investing literature suggests.

FCF Yield

The most powerful predictor was free cash flow (FCF) yield (FCF / stock price). This metric combines both profitability and valuation, measuring how much actual cash a company generates relative to what investors pay for it.

In the regression models, FCF yield coefficients ranged from 46 to 82, meaning a company with 1% higher FCF yield would have annual stock returns 46% to 82% higher. This massive impact dwarfed all other variables.

Small Market Cap

Size emerged as another strong predictor, confirming the small-cap advantage documented by Fama and French.

In Yartseva's sample, the average market cap for companies that became multibaggers was $348M at the start, with median revenue of $702M. Small-cap stocks generated average excess returns of 37.7% annually, compared to 14.5% for mid-caps and 9.7% for large-caps.

The size effect appeared consistently across all the study’s portfolio groupings. In 11/12 cases, small companies outperformed when controlling for other factors.

The math is simple, as a company worth $300M needs to add $2.7B in value to become a 10-bagger, while a company starting at $30B needs to add $270B, requiring huge market share gains or entirely new market creation.

Valuation

Valuation proved equally important, though not in the way many growth investors expect.

Companies trading at high book-to-market (B/M) ratios, indicating undervaluation (because the market undervalues the company’s assets), consistently outperformed expensive growth stocks.

The portfolios of high B/M companies generated 34.7% excess returns annually, compared to 12.8% for low-value stocks and 14.5% for medium-value stocks. The pattern held across every portfolio comparison when examining median returns.

Very Low Multiples

The median multibagger started with remarkable undervaluation:

These companies traded at discounts typically reserved for value stocks. Yet they delivered growth stock returns, reinforcing Yartseva's point that the growth versus value distinction may be meaningless.

Avoid Negative Equity

Notably, some low-value portfolios included companies with negative equity, where total liabilities exceeded assets.

These portfolios generated negative returns, with small-cap companies having negative equity declining 18.1% annually, medium-caps falling 9.4%, and large-caps dropping 7.6%. The pattern was consistent—the smaller the company with negative equity, the worse the losses.

These multibaggers weren’t explosive growth stories from the start. Profitability metrics were average, not exceptional. Gross margins averaged 34.8%, operating margins just 3.9%, return on equity 9%, and return on capital 6.5%.

Over the 15-year period, these modest businesses grew steadily. Revenue expanded at 11.1% annually, operating profit at 17.3%, net profit at 22.9%, and earnings per share at 20%.

Yartseva's research suggests most multibaggers aren't overnight sensations. They're steady performers that gradually improve their operations while the market continues to undervalue them, creating the dual engine of fundamental improvement and multiple expansion.

The 464 multibaggers came from diverse sectors, challenging the notion that exceptional returns concentrate in hot industries.

Information Technology represented 20%, but within that, software companies were only 4.8% while semiconductors accounted for 7.8%. Industrials followed at 19%, Consumer Discretionary at 18%, Healthcare at 14%, and Financials at 9%.

Even traditionally slow-growth sectors produced multibaggers. Consumer Staples contributed 6%, Materials 5%, Communication Services 4%, Real Estate 2%, Energy 2%, and Utilities 1%.

Overall, the broad distribution suggests screening by sector would eliminate many opportunities.

Beyond fundamental factors, the research reveals that entry timing significantly impacts returns. Multibagger stocks exhibit what Yartseva calls "complex momentum effects with quick trend reversals" that contradict simple momentum strategies.

Three-Month and Six-Month Momentum

To begin, while one-month momentum showed a slight positive effect in one model specification, three-month and six-month momentum consistently showed negative coefficients.

In other words, stocks that performed well over the previous three to six months were more likely to decline in the following year. This pattern appeared across all model specifications with coefficients ranging from -0.24 to -0.81.

“Price Range” Variable

The "price range" variable provided the clearest timing signal. This metric calculates how close the current price sits relative to the 52-week range, from 0% at the yearly low to 100% at the yearly high.

Every regression model tested showed negative coefficients between -0.67 and -0.92. For every 1% closer to its 52-week high, a stock's expected return the following year declined by approximately 0.7% to 0.9%.

This finding aligns with the contrarian overreaction hypothesis (the theory that investors overreact to news, pushing prices too far in either direction and creating opportunities for contrarians) but contradicts momentum strategies that advocate buying strength.

Ultimately, Yartseva found the optimal entry point occurs when stocks trade near 12-month lows after experiencing recent weakness. Investors who bought near yearly highs consistently underperformed those who bought near lows.

Macroeconomic Conditions

Macroeconomic conditions also matter substantially. The study included an interest rate environment dummy variable that equaled 1 when the Federal Reserve raised rates. This variable showed coefficients between -7.9 and -12.1, meaning rising rates reduced next-year returns by approximately -8% to -12%.

To elaborate, during the study period, multibaggers averaged different returns based on rate environments:

This sensitivity makes sense given multibaggers' characteristics. As smaller, growth-oriented companies, they face higher borrowing costs when rates rise. Higher rates also increase the discount rate applied to future cash flows, disproportionately impacting companies whose value depends on distant future earnings.

The model Yartseva developed successfully predicted market direction in both 2023 and 2024 out-of-sample tests. While it tended toward conservative predictions, underestimating returns by an average of 6.63%, it correctly identified every major market turn during the testing period.

The model proved especially accurate during rising interest rate periods and correctly forecast both the magnitude and timing of market declines. Even when it underestimated rallies, it captured the direction of movement, providing valuable signals for portfolio positioning.

Here’s a summary of the key characteristics that actually predict multibagger performance, based on Yartseva's testing of over 150 variables:

Fundamental factors that predict outperformance:

Technical factors for timing entry:

Macro factors for market conditions:

Finding tomorrow's 10-baggers requires patience for attractive entry points, discipline to focus on cash generation over reported earnings, and contrarian courage to buy when others are selling.

The best opportunities come from combining multiple favorable characteristics—strong fundamentals, attractive valuations, and good timing—rather than excelling in just one dimension.