Decided to compare research from three studies about stocks that return 10-100x+ your money and share my findings here.

Here's what I read through:

• Christopher Mayer: "100 Baggers" (2015); Covered 1962-2014.
• Jenga Investment Partners (Dede Eyesan): "Global Outperformers" (2023); Covered 2012-2022.
• Anna Yartseva: "The Alchemy of Multibagger Stocks" (2025); Covered 2009-2024.

Clearly, these aren't apples-to-apples comparisons. Besides the time period differences, Mayer looked at 100-baggers using case studies. Jenga performed academic research on 446 global 10-baggers (not just US stocks), and Yartseva used statistical models on 464 NYSE and NASDAQ-traded stocks. These studies may suffer from survivorship bias as well.

Regardless, I think it's an interesting comparison to potentially understand recurring themes/patterns and identify any surprising findings.

What Doesn't Matter

Earnings Growth

One of the most surprising findings was on earnings growth and how many investors/books say it's essential, including Mayer.

However, Yartseva's statistical analysis found that earnings growth was NOT a significant predictor of multibagger returns.

Specifically, she tested EPS growth, EBITDA growth, gross profit growth, operating profit (EBIT) growth, and net profit growth over both 1-year and 5-year periods. None were statistically significant in her dynamic panel regression models.

Interestingly, while Yartseva found earnings growth wasn't predictive, her winners still achieved impressive growth rates: 17.3% CAGR in operating profit, 22.9% in net profit, and 20.0% in EPS.

Eyesan found the average profitable company grew operating earnings at 20% CAGR.

Industry

Yartseva's 464 multibaggers came from several sectors, not just tech:

Information Technology (20%), Industrials (19%), Consumer Discretionary (18%), Healthcare (14%), and even traditionally slow-growth sectors like Utilities (1%).

Eyesan found similar sector diversity among his 446 global outperformers: Information Technology led with 25.8%, followed by Industrials (15.2%), Healthcare (14.1%), Materials (13.5%), and Consumer Discretionary (10.5%).

Notably, Information Technology, Healthcare, and Materials outperformed relative to their market representation. Tech represented 25.8% of winners but only 12.7% of the overall market. Semiconductors alone jumped from 1 outperformer in 2002-2012 to 44 in 2012-2022.

This broad distribution suggests screening by sector would eliminate many opportunities.

Other Factors

Yartseva's research also found several widely-tracked metrics showed no predictive power:

• Dividend policies (58% of multibaggers paid dividends at start, 78% by end – no correlation).
• Debt levels (debt-to-equity and debt-to-capital ratios didn't predict returns).
• Share buybacks (no statistical significance).
• Analyst coverage (being followed or ignored didn't matter).
• Altman Z-scores for bankruptcy risk (failed statistical tests).
• R&D spending relative to free cash flow (surprisingly no correlation with becoming a multibagger).

What Actually Matters

Company Size

Every single study found that smaller companies outperform:

• Mayer: Median $500M market cap, with median sales of $170M.
• Eyesan: Found 63% of winners were nano-caps (<$50M market cap) in 2012, with only 7/446 winners (1.6%) being large caps.
• Yartseva: $348M median market cap in 2009, with median revenue of $702M. Notably, Yartseva found that small-cap stocks generated average excess returns of 37.7% annually, compared to 14.5% for mid-caps and 9.7% for large-caps.

This makes logical sense given it's easier to grow from a small base – a $100M company doubling is much easier than a $100B company doubling.

Moats

All three studies agreed on competitive advantages/moats. Companies need something protecting their profits from competition:

• Mayer: Emphasized economic moats as essential for durability. "A 100-bagger requires a high return on capital for a long time. A moat, by definition, is what allows a company to get that return."
• Eyesan: Found that outperformers typically had or developed competitive advantages.
• Yartseva: While acknowledging competitive advantages were important based on prior literature, she didn't isolate this as a specific variable in her models, instead incorporating it into overall business quality metrics.

Patience

They also agreed on patience:

• Mayer: 100-baggers took 26 years on average. Also emphasized the "coffee-can" portfolio philosophy.
• Eyesan: All 446 global outperformers achieved their 10-bagger status within 10 years (2012-2022 study period).
• Yartseva: 10-baggers ranged from 7.5 to 40.5 years, with her 464 stocks averaging 26-fold returns (21.4% CAGR) over 15 years.

Revenue Growth

Revenue growth was discussed across all studies:

• Mayer: Emphasized the need for significant growth but didn't specify a minimum rate.
• Eyesan: Found 15% CAGR average revenue growth in his winners.
• Yartseva: Found 11.1% CAGR median revenue growth in her winners.

FCF Yield & Book Value

Yartseva's statistical model confirmed free cash flow (FCF) to price ratio as the most important driver of multibagger stock outperformance.

In her regression models, FCF/P showed coefficients ranging from 46 to 82, while book-to-market (B/M) showed coefficients from 7 to 42. Together, a 1% increase in FCF/P and B/M ratios was associated with 7-52% higher annual returns.

FCF/P captures both the company's cash generation and what you're paying for it.

B/M ratios above 0.40 combined with positive operating profitability showed higher chances of positive returns in Yartseva's portfolio sorts.

However, Yartseva warns to avoid companies with negative equity (where liabilities exceed assets). Small-cap companies w/negative equity declined 18.1% annually, medium-caps fell 9.4%, and large-caps dropped 7.6%.

Other Valuation Metrics

Yartseva's winners started with median valuations of P/S 0.6, P/B 1.1, forward P/E 11.3, and PEG 0.8, all suggesting they were undervalued at the start.

Eyesan found that 48.9% of outperformers started trading below 10x EV/EBIT and 50.7% below 1x EV/Revenue, suggesting most winners begin at low valuations rather than high growth premiums.

Yartseva found EV/Revenue and EV/EBITDA were significant in some model specifications but lost significance in her more robust models, suggesting they matter but aren't as reliable as FCF/P.

Profitability Threshold

Profitability metrics appeared in all three studies but with different focuses:

• Mayer: Preferred 20%+ ROE.
• Eyesan: Focused on return on capital (ROC) and required it to be above industry average.
• Yartseva: Found just 9% median ROE but noted it was growing. Her winners started with modest profitability – gross margins averaged 34.8%, EBIT margins just 3.9%, ROC 6.5%.

Overall, profitability seemed to matter but nothing spectacular to start. Based on these studies, companies should ideally be profitable when you pick them, but you don't need amazing numbers – even 9% ROE may work if it's improving.

Other Profitability Metrics

Beyond ROE, several metrics are worth mentioning:

• Return on assets (ROA): Yartseva found coefficients of 0.4 to 1.9, meaning for every 1% increase in ROA, stock returns increased by 0.4% to 1.9% (which is small).
• Return on capital (ROC): Mayer called it critical, Eyesan required above-industry average, and Yartseva found 6.5% median in her winners.
• Operating (EBIT) margins: 82% of Eyesan's winners were profitable at the start, with median EBIT margin of 12%. Among profitable companies, those with >10% margins grew from 48% in 2012 to 85% by 2021; those with >20% margins grew from 17% to 47%.
• EBITDA margins: 30-60% for winners (Eyesan), confirmed significant by Yartseva whose models showed EBITDA margin as a statistically significant predictor with positive coefficients in her initial models.

Notably, according to Eyesan, 74% of winners grew earnings faster than revenue. This means companies were becoming more profitable over time, not just growing sales.

Multiple Expansion

Multiple expansion means the market paying more for each dollar of earnings over time (e.g., P/E going from 10x to 20x):

• Mayer: Described "twin engines" of earnings growth plus multiple expansion, showing examples of P/E expanding from 3.5x to 26x, which when combined with earnings growth created 100x returns.
• Eyesan: Found 91% of winners had EV/Revenue expansion and 72% had EV/EBITDA expansion.
• Yartseva: While Yartseva didn't isolate multiple expansion as a single variable, her findings strongly suggest valuation changes rather than earnings growth drive multibagger returns.

Reinvestments

All studies emphasized reinvestment capability, but with nuance:

• Mayer: Emphasized reinvestment as the most important factor – specifically companies that can reinvest profits at 20%+ returns consistently.
• Eyesan: Discussed how successful M&A strategies and aggressive expansion drove returns for many outperformers.
• Yartseva: Found that if a company's asset growth exceeds its EBITDA growth, returns drop 4-11%. This means companies must invest aggressively BUT only if their earnings are growing fast enough to support that investment.

Ownership

Mayer found 7% annual outperformance among owner-operators and quoted Martin Sosnoff's rule that management should own at least 10-20% of the company.

Yartseva noted owner-operators in her sample had significant vested interests (though she didn't test ownership as a specific variable).

Eyesan noted that 67% of outperformers had insider ownership above 5% (vs. 49% in the broader market), but didn't treat this as a defining factor. Instead, he emphasized qualitative signs of management-shareholder alignment like maintaining focus through acquisitions, proper capital allocation, and consistent execution of core strategy.

Entry Timing

For timing, buy beaten-down stocks:

• Yartseva: Stock should be near 12-month low at time of purchase.
• Mayer: Highlighted beaten-down, forgotten stocks returning to profitability as prime 100-bagger candidates.
• Eyesan: Found turnarounds deliver strong returns when problems are solvable (like fixing marketing inefficiencies or distribution issues, rather than fundamental product failures).

Yartseva also tested price momentum over various periods and found one-month momentum slightly positive, meaning stocks that rose last month tend to continue rising.

However, 3-6 month momentum was negative – stocks that performed well over the previous quarter or half-year tend to reverse, suggesting multibaggers are volatile and don't follow smooth upward trends.

Macro Environment

Interest rates matter. Yartseva quantified that rising Fed rates knock 8-12% off multibagger returns the following year.

This makes sense because multibaggers tend to be smaller companies that likely rely more on external financing and whose future cash flows are worth less when discount rates rise.

Geographic Shift

Lastly, Eyesan's data showed that 59% of recent 10-baggers came from Asia:

• India: 91 companies.
• USA: 60 companies.
• Japan: 49 companies.
• China: 34 companies.

This suggests that if you're only looking at US stocks, you're missing a lot of opportunity.

Moreover, this is striking given Asia represents only 10% of global mutual fund portfolios, suggesting massive underallocation to the region.

Eyesan also noted important regional differences in how earnings translate to returns. Markets like India, Japan, and the Nordics show good earnings-to-returns conversion efficiency, while markets like China and Latin America often see earnings growth that doesn't translate well to stock returns.

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Think I was able to cover the key findings from these books/papers, but lmk if I missed anything!

Read the books/papers if you want a deeper understanding of their findings and for company-specific examples. I've also written about Mayer, Eyesan, and Yartseva's work in more detail (see my newsletter archive).

Would particularly recommend reading Eyesan's 10 lessons (starting page 256) on what it takes to achieve global outperformance (or you can read my summary).