Every investor dreams of finding companies that multiply by 5, by 10, or by 100. It is the philosopher’s stone of investing, the holy grail, the elixir of life for people obsessed with looking at charts and reading fundamentals.

When I started investing, one of the books that fascinated me the most was 100 Baggers: Stocks That Return 100-to-1 and How to Find Them, by Chris Mayer. It was incredible. The promise was that instead of finding stocks that would make me rich at 67, they could let me retire at 35.

Since then, I have read other “studies” on the topic with the same enthusiasm. Unfortunately, they all have one fatal flaw: anecdotes, qualitative analysis, and little evidence of causality. My engineer soul was missing something more rigorous.

Luckily, I recently came across a paper that tries to go one step further: The Alchemy of Multibagger Stocks, by Anna Yartseva.

Although the paper is not perfect, far from it, it brings a more methodological and scientific approach to the subject. It does several things I like.

It starts with a review of what has traditionally been said about multibaggers, which is perfect for anyone who has never read anything on the topic. Then it tries to study what characteristics these companies shared, starting from the Fama-French five-factor model, and later adapting the model to multibaggers. In the process, it uncovers a few things that had rarely been discussed before.

Today’s post is about this paper and some of its most interesting conclusions. I have published the full article on my website, with a more detailed analysis, interactive widgets, and a more rigorous critique for anyone who wants to read it. In this article, I am only going to comment briefly on some interesting conclusions.

In the original post, I also go through the “anatomy of a classic multibagger”, which summarizes what was commonly known about multibaggers and is also very useful for anyone interested in the topic.

Experiment

The study analyzes companies listed on the NYSE and NASDAQ, including ADRs, between 2009 and 2024.

The window starts just after the financial crisis and covers 15 very eventful years: bull and bear markets, COVID, inflation, interest rates, the banking crisis, wars, and commodity shocks.

It identifies more than 500 stocks that reached a 10x return, but only keeps those that maintained that level until the end and removes those with incomplete data. The final sample consists of 464 multibaggers.

What is interesting is that it does not only look at the 2009-2024 increase, but also at the companies’ prior history from the year 2000 onward. The idea is not simply to celebrate winners after the fact, but to look for signals that were already present before the big move.

Starting point: the Fama-French five-factor model

The analysis starts with the Fama-French five-factor model, one of the most widely used frameworks to explain why some stocks earn higher returns than others.

The idea, simplifying a lot, is that a stock’s return can be explained by its exposure to several factors: market, size, valuation, profitability, and investment.

https://preview.redd.it/jgkmio6f922h1.png?width=1506&format=png&auto=webp&s=1d84979d401232a544e01622879d338094fa725b

In other words, the model tries to explain how much a stock has earned by comparing it with what a risk-free asset would have earned and by seeing how much of that return comes from different known factors.

https://preview.redd.it/mo49unng922h1.png?width=1628&format=png&auto=webp&s=de5edd773aded66713c88165063f4990e045f8a7

The appeal of the model is that it lets you ask a very useful question: did multibaggers earn so much simply because they were exposed to known factors such as size, value, or profitability, or was there something else?

And that “something else” is exactly what the study tries to find.

https://preview.redd.it/4919o6si922h1.png?width=1640&format=png&auto=webp&s=7d07657d452c642931f6929a9b00153c6ec37ef3

https://preview.redd.it/3fititlj922h1.png?width=1508&format=png&auto=webp&s=6cd28ebdf673fd874d87f0e4bcba470d797e0e03

Alpha and beta

In a factor regression, beta measures how much a stock moves relative to the market. A beta of 1 means it moves more or less like the market; above 1, it is more sensitive; below 1, less so.

Alpha is what remains after explaining the return using the model’s factors: market, size, value, profitability, and investment. Put simply, it is the part of the return that the model cannot explain.

But be careful: alpha is not an explanation. It is a clue. It may reflect a real company advantage, a missing factor in the model, or simple statistical noise. That is why it should be treated as an interesting signal, not definitive proof.

The study uses the Fama-French five-factor model to see whether it can explain the historical returns of multibaggers.

The basic idea of the model is that, over the long term, small, cheap, profitable companies with prudent investment tend to do better. To test whether this also holds here, the study sorts the companies in the sample, between 2000 and 2024, into different groups:

• Size: small, medium, and large.
• Valuation: low, medium, and high, using book-to-market.
• Profitability: robust or weak.
• Investment: conservative or aggressive, based on asset growth.

When all of these are crossed, the result is 36 different portfolios.

The objective is twofold:

1. To check whether the classic factors also work within the multibagger universe.
2. To measure how much unexplained alpha remains. If a lot of return remains outside the model, it means these companies have something that the five factors do not capture well.

And that is where things start to get interesting: looking for more specific variables to understand where that extraordinary return really came from.

The results

The table groups the companies by size, valuation, profitability, and investment, and colors the return of each combination to quickly show what works best.

https://preview.redd.it/7glxh1lq922h1.png?width=1782&format=png&auto=webp&s=4963488717f91ac9e60849064829ca741f0db2d9

The best portfolio appears among small, cheap, profitable companies with aggressive investment. In other words: small caps, with high book-to-market, good operating profitability, and strong asset growth.

The main conclusions are quite clear:

• Size helps: small companies beat medium-sized companies on average, and medium-sized companies beat large ones. But the median is not as clean, so simply buying small caps is not magic either.
• Valuation matters: even within multibaggers, cheaper companies tend to do better.
• Profitability also matters: companies with weak profitability deliver worse results than profitable ones.

And the big surprise is investment. According to Fama and French, companies that invest aggressively should do worse. But here, almost the opposite happens: companies with higher asset growth achieve better returns. It makes sense. A company that wants to multiply cannot stand still. It needs to reinvest, grow, and build something much bigger.

Then, the study runs a regression to see how much the five factors explain. And here is the important part: operating profitability contributes little, these stocks have high beta, and alpha remains too high.

Translation: the five-factor model does not explain multibaggers very well. It captures part of the story, but it misses something important. And that is exactly where the interesting part begins.

Improving the model

Because the classic Fama-French model leaves too much alpha unexplained, the study tries to adapt it better to the case of multibaggers.

To do this, it tests different metrics for size, valuation, profitability, and investment: market capitalization, enterprise value, sales, book-to-market, P/E, price-to-sales, margins, ROE, return on capital, asset growth, EBITDA, and free cash flow.

In an intermediate version, the study changes some variables: it uses TEV for size, P/E for valuation, and EBITDA margin for profitability. But P/E ends up losing weight because it adds too much noise: it does not work for loss-making companies and explodes when earnings are very low. That is why the most useful valuation metrics end up being B/M and FCF/P, meaning how much free cash flow the company generates relative to the price paid.

The most interesting part is investment.

The study introduces a variable that detects when assets grow faster than EBITDA. And the result is strong: when a company expands assets faster than EBITDA growth, the following year’s return falls by around 22.8 percentage points.

The interpretation is quite clear: multibaggers need to invest, grow, and expand capacity. But that investment has to be accompanied by real EBITDA growth. If assets grow and EBITDA does not follow, the company is probably buying bad growth, inflating its balance sheet, or reinvesting at mediocre returns.

In short: the best multibaggers are not only small, cheap, and profitable. They also know how to invest aggressively without destroying returns. It is not about growing for the sake of growing, but about growing with profits behind it.

Static and dynamic return models

Here the objective changes: the author is no longer trying to see whether multibaggers fit into Fama-French, but to build a more complete model to explain their future returns.

To do this, she tests more than 150 variables: growth, valuation, profitability, quality, debt, solvency, momentum, interest rates, analysts, investment, R&D, marketing, and sector comparisons. Much more than the classic “small, cheap, and profitable”.

To separate signal from noise, she uses Hendry’s general-to-specific methodology: you start with a huge model and gradually remove what does not add value until you are left with something cleaner and more robust. First, you throw everything into the pot. Then you remove ingredients until the thing finally tastes like something.

The interesting part of the analysis is here: it moves from describing what multibaggers looked like after the fact to trying to identify which variables best explained their returns before they happened. It is not perfect, but this is where the most useful conclusions for investors appear.

Main results

The model works reasonably well: almost all coefficients have the expected sign. The market matters, size penalizes returns, valuation matters a lot, and investment only works if it is accompanied by real EBITDA growth.

The most important conclusions are:

• Multibaggers also depend on the market. When the S&P 500 helps, it helps them too; when the environment gets difficult, they also suffer.
• Size remains key: the larger the company, the lower its future return tends to be. Multiplying by 10 from a small base is much easier than doing so from a gigantic base.
• Profitability matters, but less than expected. In the dynamic models, EBITDA margin loses strength and ROA works better. Even so, FCF/P ends up carrying more weight than many classic profitability metrics.
• Accounting growth disappoints. Variables such as revenue growth, EBITDA growth, EPS growth, or free cash flow growth are not especially significant. This does not mean growth does not matter. It means that within a sample of companies that already became multibaggers, the price paid, FCF yield, and quality of investment explain future returns better.
• Investment is useful, but with one condition: if assets grow faster than EBITDA, future returns fall. In other words, growing for the sake of growing is not enough. If the company invests heavily but EBITDA does not follow, it may be buying bad growth or reinvesting at mediocre returns.
• Interest rates also matter. In periods of rising rates, future multibagger returns fall significantly. This makes sense: the more a company depends on future cash flows, the more it suffers from a higher discount rate.
• Valuation is the main protagonist. Book-to-market and FCF/P are the most powerful variables in the model. Even the best growth stocks need to be bought at reasonable prices. It is not enough to grow a lot; what you pay matters enormously.
• P/E does not work well because it breaks with loss-making companies or companies with very small earnings. That is why the study prefers B/M and FCF/P.
• Momentum behaves strangely: the effect seems very short-lived and quickly reverses. Buying right after a big move can be expensive.

There are also variables that surprisingly add little: debt, debt coverage, Altman Z-score, buybacks, dividends, share issuance, and R&D. But be careful not to misinterpret this: because the analysis only studies companies that survived and ended up being winners, there is selection bias. The fact that debt does not explain much within the survivors does not mean it does not matter when trying to avoid dying along the way.

In other words, the best multibaggers are not simply companies that grow a lot. They tend to be small, reasonably cheap, profitable companies that can invest without destroying capital and that are bought before the market has discounted too much future growth.

Conclusions

The study challenges some dogmas about multibaggers. Not because growth does not matter, but because isolated accounting growth explains less than expected. Valuation, free cash flow yield, size, interest rates, and investment quality matter more.

• The best multibaggers tend to be small, cheap, profitable companies capable of investing aggressively without destroying capital. The key is that asset growth must be accompanied by real EBITDA growth. If assets grow but EBITDA does not, that is a bad sign.
• Free cash flow yield appears as one of the most important variables. It is not enough to grow a lot: the company also has to generate cash and trade at a reasonable price.
• Interest rates also matter. In rising-rate environments, multibaggers suffer much more than many would assume. They are not immune to the cost of money.
• And momentum works in a counterintuitive way: buying near 12-month highs does not seem to help. In fact, the best opportunities usually appear when the stock is closer to its lows and after meaningful declines. That may be where the market has not yet discounted too much future growth.

In short: a multibagger is not simply “a company that grows a lot”. According to this study, the most attractive combination would look more like this: a small, cheap, profitable company, with good free cash flow yield, capable of investing without destroying capital, and bought at a moment when the market is not yet too excited.

So yeah, it was never going to be easy.

---

I have left a lot out of this article, so here is the link to my original post, where I explain everything with much more detail and nuance.

The original post includes “the anatomy of a classic multibagger”, all sections explained in greater detail, and 3 additional appendices:

• “Past studies”: a brief history of what has been done before.
• “Limitations”: this section is essential if you are thinking of using this information in your investment process.
• “Descriptive statistics of the sample”: a short section describing the growth, returns, size, and other characteristics of these multibaggers. It is very illustrative of what these companies looked like before and during the process of multiplying by 10.

Link here: https://www.jeravalue.com/en/blog/10x-stocks-the-dna-of-multibaggers

(It is completely free without paywall)

Every investor dreams of finding companies that multiply by 5, by 10, or by 100. It is the philosopher’s stone of investing, the holy grail, the elixir of life for people obsessed with looking at charts and reading fundamentals.

When I started investing, one of the books that fascinated me the most was 100 Baggers: Stocks That Return 100-to-1 and How to Find Them, by Chris Mayer. It was incredible. The promise was that instead of finding stocks that would make me rich at 67, they could let me retire at 35.

Since then, I have read other “studies” on the topic with the same enthusiasm. Unfortunately, they all have one fatal flaw: anecdotes, qualitative analysis, and little evidence of causality. My engineer soul was missing something more rigorous.

Luckily, I recently came across a paper that tries to go one step further: The Alchemy of Multibagger Stocks, by Anna Yartseva.

Although the paper is not perfect, far from it, it brings a more methodological and scientific approach to the subject. It does several things I like.

It starts with a review of what has traditionally been said about multibaggers, which is perfect for anyone who has never read anything on the topic. Then it tries to study what characteristics these companies shared, starting from the Fama-French five-factor model, and later adapting the model to multibaggers. In the process, it uncovers a few things that had rarely been discussed before.

Today’s post is about this paper and some of its most interesting conclusions. I have published the full article on my website, with a more detailed analysis, interactive widgets, and a more rigorous critique for anyone who wants to read it. In this article, I am only going to comment briefly on some interesting conclusions.

In the original post, I also go through the “anatomy of a classic multibagger”, which summarizes what was commonly known about multibaggers and is also very useful for anyone interested in the topic.

Experiment

The study analyzes companies listed on the NYSE and NASDAQ, including ADRs, between 2009 and 2024.

The window starts just after the financial crisis and covers 15 very eventful years: bull and bear markets, COVID, inflation, interest rates, the banking crisis, wars, and commodity shocks.

It identifies more than 500 stocks that reached a 10x return, but only keeps those that maintained that level until the end and removes those with incomplete data. The final sample consists of 464 multibaggers.

What is interesting is that it does not only look at the 2009-2024 increase, but also at the companies’ prior history from the year 2000 onward. The idea is not simply to celebrate winners after the fact, but to look for signals that were already present before the big move.

Starting point: the Fama-French five-factor model

The analysis starts with the Fama-French five-factor model, one of the most widely used frameworks to explain why some stocks earn higher returns than others.

The idea, simplifying a lot, is that a stock’s return can be explained by its exposure to several factors: market, size, valuation, profitability, and investment.

https://preview.redd.it/jgkmio6f922h1.png?width=1506&format=png&auto=webp&s=1d84979d401232a544e01622879d338094fa725b

In other words, the model tries to explain how much a stock has earned by comparing it with what a risk-free asset would have earned and by seeing how much of that return comes from different known factors.

https://preview.redd.it/mo49unng922h1.png?width=1628&format=png&auto=webp&s=de5edd773aded66713c88165063f4990e045f8a7

The appeal of the model is that it lets you ask a very useful question: did multibaggers earn so much simply because they were exposed to known factors such as size, value, or profitability, or was there something else?

And that “something else” is exactly what the study tries to find.

https://preview.redd.it/4919o6si922h1.png?width=1640&format=png&auto=webp&s=7d07657d452c642931f6929a9b00153c6ec37ef3

https://preview.redd.it/3fititlj922h1.png?width=1508&format=png&auto=webp&s=6cd28ebdf673fd874d87f0e4bcba470d797e0e03

Alpha and beta

In a factor regression, beta measures how much a stock moves relative to the market. A beta of 1 means it moves more or less like the market; above 1, it is more sensitive; below 1, less so.

Alpha is what remains after explaining the return using the model’s factors: market, size, value, profitability, and investment. Put simply, it is the part of the return that the model cannot explain.

But be careful: alpha is not an explanation. It is a clue. It may reflect a real company advantage, a missing factor in the model, or simple statistical noise. That is why it should be treated as an interesting signal, not definitive proof.

The study uses the Fama-French five-factor model to see whether it can explain the historical returns of multibaggers.

The basic idea of the model is that, over the long term, small, cheap, profitable companies with prudent investment tend to do better. To test whether this also holds here, the study sorts the companies in the sample, between 2000 and 2024, into different groups:

• Size: small, medium, and large.
• Valuation: low, medium, and high, using book-to-market.
• Profitability: robust or weak.
• Investment: conservative or aggressive, based on asset growth.

When all of these are crossed, the result is 36 different portfolios.

The objective is twofold:

1. To check whether the classic factors also work within the multibagger universe.
2. To measure how much unexplained alpha remains. If a lot of return remains outside the model, it means these companies have something that the five factors do not capture well.

And that is where things start to get interesting: looking for more specific variables to understand where that extraordinary return really came from.

The results

The table groups the companies by size, valuation, profitability, and investment, and colors the return of each combination to quickly show what works best.

https://preview.redd.it/7glxh1lq922h1.png?width=1782&format=png&auto=webp&s=4963488717f91ac9e60849064829ca741f0db2d9

The best portfolio appears among small, cheap, profitable companies with aggressive investment. In other words: small caps, with high book-to-market, good operating profitability, and strong asset growth.

The main conclusions are quite clear:

• Size helps: small companies beat medium-sized companies on average, and medium-sized companies beat large ones. But the median is not as clean, so simply buying small caps is not magic either.
• Valuation matters: even within multibaggers, cheaper companies tend to do better.
• Profitability also matters: companies with weak profitability deliver worse results than profitable ones.

And the big surprise is investment. According to Fama and French, companies that invest aggressively should do worse. But here, almost the opposite happens: companies with higher asset growth achieve better returns. It makes sense. A company that wants to multiply cannot stand still. It needs to reinvest, grow, and build something much bigger.

Then, the study runs a regression to see how much the five factors explain. And here is the important part: operating profitability contributes little, these stocks have high beta, and alpha remains too high.

Translation: the five-factor model does not explain multibaggers very well. It captures part of the story, but it misses something important. And that is exactly where the interesting part begins.

Improving the model

Because the classic Fama-French model leaves too much alpha unexplained, the study tries to adapt it better to the case of multibaggers.

To do this, it tests different metrics for size, valuation, profitability, and investment: market capitalization, enterprise value, sales, book-to-market, P/E, price-to-sales, margins, ROE, return on capital, asset growth, EBITDA, and free cash flow.

In an intermediate version, the study changes some variables: it uses TEV for size, P/E for valuation, and EBITDA margin for profitability. But P/E ends up losing weight because it adds too much noise: it does not work for loss-making companies and explodes when earnings are very low. That is why the most useful valuation metrics end up being B/M and FCF/P, meaning how much free cash flow the company generates relative to the price paid.

The most interesting part is investment.

The study introduces a variable that detects when assets grow faster than EBITDA. And the result is strong: when a company expands assets faster than EBITDA growth, the following year’s return falls by around 22.8 percentage points.

The interpretation is quite clear: multibaggers need to invest, grow, and expand capacity. But that investment has to be accompanied by real EBITDA growth. If assets grow and EBITDA does not follow, the company is probably buying bad growth, inflating its balance sheet, or reinvesting at mediocre returns.

In short: the best multibaggers are not only small, cheap, and profitable. They also know how to invest aggressively without destroying returns. It is not about growing for the sake of growing, but about growing with profits behind it.

Static and dynamic return models

Here the objective changes: the author is no longer trying to see whether multibaggers fit into Fama-French, but to build a more complete model to explain their future returns.

To do this, she tests more than 150 variables: growth, valuation, profitability, quality, debt, solvency, momentum, interest rates, analysts, investment, R&D, marketing, and sector comparisons. Much more than the classic “small, cheap, and profitable”.

To separate signal from noise, she uses Hendry’s general-to-specific methodology: you start with a huge model and gradually remove what does not add value until you are left with something cleaner and more robust. First, you throw everything into the pot. Then you remove ingredients until the thing finally tastes like something.

The interesting part of the analysis is here: it moves from describing what multibaggers looked like after the fact to trying to identify which variables best explained their returns before they happened. It is not perfect, but this is where the most useful conclusions for investors appear.

Main results

The model works reasonably well: almost all coefficients have the expected sign. The market matters, size penalizes returns, valuation matters a lot, and investment only works if it is accompanied by real EBITDA growth.

The most important conclusions are:

• Multibaggers also depend on the market. When the S&P 500 helps, it helps them too; when the environment gets difficult, they also suffer.
• Size remains key: the larger the company, the lower its future return tends to be. Multiplying by 10 from a small base is much easier than doing so from a gigantic base.
• Profitability matters, but less than expected. In the dynamic models, EBITDA margin loses strength and ROA works better. Even so, FCF/P ends up carrying more weight than many classic profitability metrics.
• Accounting growth disappoints. Variables such as revenue growth, EBITDA growth, EPS growth, or free cash flow growth are not especially significant. This does not mean growth does not matter. It means that within a sample of companies that already became multibaggers, the price paid, FCF yield, and quality of investment explain future returns better.
• Investment is useful, but with one condition: if assets grow faster than EBITDA, future returns fall. In other words, growing for the sake of growing is not enough. If the company invests heavily but EBITDA does not follow, it may be buying bad growth or reinvesting at mediocre returns.
• Interest rates also matter. In periods of rising rates, future multibagger returns fall significantly. This makes sense: the more a company depends on future cash flows, the more it suffers from a higher discount rate.
• Valuation is the main protagonist. Book-to-market and FCF/P are the most powerful variables in the model. Even the best growth stocks need to be bought at reasonable prices. It is not enough to grow a lot; what you pay matters enormously.
• P/E does not work well because it breaks with loss-making companies or companies with very small earnings. That is why the study prefers B/M and FCF/P.
• Momentum behaves strangely: the effect seems very short-lived and quickly reverses. Buying right after a big move can be expensive.

There are also variables that surprisingly add little: debt, debt coverage, Altman Z-score, buybacks, dividends, share issuance, and R&D. But be careful not to misinterpret this: because the analysis only studies companies that survived and ended up being winners, there is selection bias. The fact that debt does not explain much within the survivors does not mean it does not matter when trying to avoid dying along the way.

In other words, the best multibaggers are not simply companies that grow a lot. They tend to be small, reasonably cheap, profitable companies that can invest without destroying capital and that are bought before the market has discounted too much future growth.

Conclusions

The study challenges some dogmas about multibaggers. Not because growth does not matter, but because isolated accounting growth explains less than expected. Valuation, free cash flow yield, size, interest rates, and investment quality matter more.

• The best multibaggers tend to be small, cheap, profitable companies capable of investing aggressively without destroying capital. The key is that asset growth must be accompanied by real EBITDA growth. If assets grow but EBITDA does not, that is a bad sign.
• Free cash flow yield appears as one of the most important variables. It is not enough to grow a lot: the company also has to generate cash and trade at a reasonable price.
• Interest rates also matter. In rising-rate environments, multibaggers suffer much more than many would assume. They are not immune to the cost of money.
• And momentum works in a counterintuitive way: buying near 12-month highs does not seem to help. In fact, the best opportunities usually appear when the stock is closer to its lows and after meaningful declines. That may be where the market has not yet discounted too much future growth.

In short: a multibagger is not simply “a company that grows a lot”. According to this study, the most attractive combination would look more like this: a small, cheap, profitable company, with good free cash flow yield, capable of investing without destroying capital, and bought at a moment when the market is not yet too excited.

So yeah, it was never going to be easy.

---

I have left a lot out of this article, so here is the link to my original post, where I explain everything with much more detail and nuance.

The original post includes “the anatomy of a classic multibagger”, all sections explained in greater detail, and 3 additional appendices:

• “Past studies”: a brief history of what has been done before.
• “Limitations”: this section is essential if you are thinking of using this information in your investment process.
• “Descriptive statistics of the sample”: a short section describing the growth, returns, size, and other characteristics of these multibaggers. It is very illustrative of what these companies looked like before and during the process of multiplying by 10.

Link here: https://www.jeravalue.com/en/blog/10x-stocks-the-dna-of-multibaggers

(It is completely free without paywall)

Every investor dreams of finding companies that multiply by 5, by 10, or by 100. It is the philosopher’s stone of investing, the holy grail, the elixir of life for people obsessed with looking at charts and reading fundamentals.

When I started investing, one of the books that fascinated me the most was 100 Baggers: Stocks That Return 100-to-1 and How to Find Them, by Chris Mayer. It was incredible. The promise was that instead of finding stocks that would make me rich at 67, they could let me retire at 35.

Since then, I have read other “studies” on the topic with the same enthusiasm. Unfortunately, they all have one fatal flaw: anecdotes, qualitative analysis, and little evidence of causality. My engineer soul was missing something more rigorous.

Luckily, I recently came across a paper that tries to go one step further: The Alchemy of Multibagger Stocks, by Anna Yartseva.

Although the paper is not perfect, far from it, it brings a more methodological and scientific approach to the subject. It does several things I like.

It starts with a review of what has traditionally been said about multibaggers, which is perfect for anyone who has never read anything on the topic. Then it tries to study what characteristics these companies shared, starting from the Fama-French five-factor model, and later adapting the model to multibaggers. In the process, it uncovers a few things that had rarely been discussed before.

Today’s post is about this paper and some of its most interesting conclusions. I have published the full article on my website, with a more detailed analysis, interactive widgets, and a more rigorous critique for anyone who wants to read it. In this article, I am only going to comment briefly on some interesting conclusions.

In the original post, I also go through the “anatomy of a classic multibagger”, which summarizes what was commonly known about multibaggers and is also very useful for anyone interested in the topic.

Experiment

The study analyzes companies listed on the NYSE and NASDAQ, including ADRs, between 2009 and 2024.

The window starts just after the financial crisis and covers 15 very eventful years: bull and bear markets, COVID, inflation, interest rates, the banking crisis, wars, and commodity shocks.

It identifies more than 500 stocks that reached a 10x return, but only keeps those that maintained that level until the end and removes those with incomplete data. The final sample consists of 464 multibaggers.

What is interesting is that it does not only look at the 2009-2024 increase, but also at the companies’ prior history from the year 2000 onward. The idea is not simply to celebrate winners after the fact, but to look for signals that were already present before the big move.

Starting point: the Fama-French five-factor model

The analysis starts with the Fama-French five-factor model, one of the most widely used frameworks to explain why some stocks earn higher returns than others.

The idea, simplifying a lot, is that a stock’s return can be explained by its exposure to several factors: market, size, valuation, profitability, and investment.

https://preview.redd.it/jgkmio6f922h1.png?width=1506&format=png&auto=webp&s=1d84979d401232a544e01622879d338094fa725b

In other words, the model tries to explain how much a stock has earned by comparing it with what a risk-free asset would have earned and by seeing how much of that return comes from different known factors.

https://preview.redd.it/mo49unng922h1.png?width=1628&format=png&auto=webp&s=de5edd773aded66713c88165063f4990e045f8a7

The appeal of the model is that it lets you ask a very useful question: did multibaggers earn so much simply because they were exposed to known factors such as size, value, or profitability, or was there something else?

And that “something else” is exactly what the study tries to find.

https://preview.redd.it/4919o6si922h1.png?width=1640&format=png&auto=webp&s=7d07657d452c642931f6929a9b00153c6ec37ef3

https://preview.redd.it/3fititlj922h1.png?width=1508&format=png&auto=webp&s=6cd28ebdf673fd874d87f0e4bcba470d797e0e03

Alpha and beta

In a factor regression, beta measures how much a stock moves relative to the market. A beta of 1 means it moves more or less like the market; above 1, it is more sensitive; below 1, less so.

Alpha is what remains after explaining the return using the model’s factors: market, size, value, profitability, and investment. Put simply, it is the part of the return that the model cannot explain.

But be careful: alpha is not an explanation. It is a clue. It may reflect a real company advantage, a missing factor in the model, or simple statistical noise. That is why it should be treated as an interesting signal, not definitive proof.

The study uses the Fama-French five-factor model to see whether it can explain the historical returns of multibaggers.

The basic idea of the model is that, over the long term, small, cheap, profitable companies with prudent investment tend to do better. To test whether this also holds here, the study sorts the companies in the sample, between 2000 and 2024, into different groups:

• Size: small, medium, and large.
• Valuation: low, medium, and high, using book-to-market.
• Profitability: robust or weak.
• Investment: conservative or aggressive, based on asset growth.

When all of these are crossed, the result is 36 different portfolios.

The objective is twofold:

1. To check whether the classic factors also work within the multibagger universe.
2. To measure how much unexplained alpha remains. If a lot of return remains outside the model, it means these companies have something that the five factors do not capture well.

And that is where things start to get interesting: looking for more specific variables to understand where that extraordinary return really came from.

The results

The table groups the companies by size, valuation, profitability, and investment, and colors the return of each combination to quickly show what works best.

https://preview.redd.it/7glxh1lq922h1.png?width=1782&format=png&auto=webp&s=4963488717f91ac9e60849064829ca741f0db2d9

The best portfolio appears among small, cheap, profitable companies with aggressive investment. In other words: small caps, with high book-to-market, good operating profitability, and strong asset growth.

The main conclusions are quite clear:

• Size helps: small companies beat medium-sized companies on average, and medium-sized companies beat large ones. But the median is not as clean, so simply buying small caps is not magic either.
• Valuation matters: even within multibaggers, cheaper companies tend to do better.
• Profitability also matters: companies with weak profitability deliver worse results than profitable ones.

And the big surprise is investment. According to Fama and French, companies that invest aggressively should do worse. But here, almost the opposite happens: companies with higher asset growth achieve better returns. It makes sense. A company that wants to multiply cannot stand still. It needs to reinvest, grow, and build something much bigger.

Then, the study runs a regression to see how much the five factors explain. And here is the important part: operating profitability contributes little, these stocks have high beta, and alpha remains too high.

Translation: the five-factor model does not explain multibaggers very well. It captures part of the story, but it misses something important. And that is exactly where the interesting part begins.

Improving the model

Because the classic Fama-French model leaves too much alpha unexplained, the study tries to adapt it better to the case of multibaggers.

To do this, it tests different metrics for size, valuation, profitability, and investment: market capitalization, enterprise value, sales, book-to-market, P/E, price-to-sales, margins, ROE, return on capital, asset growth, EBITDA, and free cash flow.

In an intermediate version, the study changes some variables: it uses TEV for size, P/E for valuation, and EBITDA margin for profitability. But P/E ends up losing weight because it adds too much noise: it does not work for loss-making companies and explodes when earnings are very low. That is why the most useful valuation metrics end up being B/M and FCF/P, meaning how much free cash flow the company generates relative to the price paid.

The most interesting part is investment.

The study introduces a variable that detects when assets grow faster than EBITDA. And the result is strong: when a company expands assets faster than EBITDA growth, the following year’s return falls by around 22.8 percentage points.

The interpretation is quite clear: multibaggers need to invest, grow, and expand capacity. But that investment has to be accompanied by real EBITDA growth. If assets grow and EBITDA does not follow, the company is probably buying bad growth, inflating its balance sheet, or reinvesting at mediocre returns.

In short: the best multibaggers are not only small, cheap, and profitable. They also know how to invest aggressively without destroying returns. It is not about growing for the sake of growing, but about growing with profits behind it.

Static and dynamic return models

Here the objective changes: the author is no longer trying to see whether multibaggers fit into Fama-French, but to build a more complete model to explain their future returns.

To do this, she tests more than 150 variables: growth, valuation, profitability, quality, debt, solvency, momentum, interest rates, analysts, investment, R&D, marketing, and sector comparisons. Much more than the classic “small, cheap, and profitable”.

To separate signal from noise, she uses Hendry’s general-to-specific methodology: you start with a huge model and gradually remove what does not add value until you are left with something cleaner and more robust. First, you throw everything into the pot. Then you remove ingredients until the thing finally tastes like something.

The interesting part of the analysis is here: it moves from describing what multibaggers looked like after the fact to trying to identify which variables best explained their returns before they happened. It is not perfect, but this is where the most useful conclusions for investors appear.

Main results

The model works reasonably well: almost all coefficients have the expected sign. The market matters, size penalizes returns, valuation matters a lot, and investment only works if it is accompanied by real EBITDA growth.

The most important conclusions are:

• Multibaggers also depend on the market. When the S&P 500 helps, it helps them too; when the environment gets difficult, they also suffer.
• Size remains key: the larger the company, the lower its future return tends to be. Multiplying by 10 from a small base is much easier than doing so from a gigantic base.
• Profitability matters, but less than expected. In the dynamic models, EBITDA margin loses strength and ROA works better. Even so, FCF/P ends up carrying more weight than many classic profitability metrics.
• Accounting growth disappoints. Variables such as revenue growth, EBITDA growth, EPS growth, or free cash flow growth are not especially significant. This does not mean growth does not matter. It means that within a sample of companies that already became multibaggers, the price paid, FCF yield, and quality of investment explain future returns better.
• Investment is useful, but with one condition: if assets grow faster than EBITDA, future returns fall. In other words, growing for the sake of growing is not enough. If the company invests heavily but EBITDA does not follow, it may be buying bad growth or reinvesting at mediocre returns.
• Interest rates also matter. In periods of rising rates, future multibagger returns fall significantly. This makes sense: the more a company depends on future cash flows, the more it suffers from a higher discount rate.
• Valuation is the main protagonist. Book-to-market and FCF/P are the most powerful variables in the model. Even the best growth stocks need to be bought at reasonable prices. It is not enough to grow a lot; what you pay matters enormously.
• P/E does not work well because it breaks with loss-making companies or companies with very small earnings. That is why the study prefers B/M and FCF/P.
• Momentum behaves strangely: the effect seems very short-lived and quickly reverses. Buying right after a big move can be expensive.

There are also variables that surprisingly add little: debt, debt coverage, Altman Z-score, buybacks, dividends, share issuance, and R&D. But be careful not to misinterpret this: because the analysis only studies companies that survived and ended up being winners, there is selection bias. The fact that debt does not explain much within the survivors does not mean it does not matter when trying to avoid dying along the way.

In other words, the best multibaggers are not simply companies that grow a lot. They tend to be small, reasonably cheap, profitable companies that can invest without destroying capital and that are bought before the market has discounted too much future growth.

Conclusions

The study challenges some dogmas about multibaggers. Not because growth does not matter, but because isolated accounting growth explains less than expected. Valuation, free cash flow yield, size, interest rates, and investment quality matter more.

• The best multibaggers tend to be small, cheap, profitable companies capable of investing aggressively without destroying capital. The key is that asset growth must be accompanied by real EBITDA growth. If assets grow but EBITDA does not, that is a bad sign.
• Free cash flow yield appears as one of the most important variables. It is not enough to grow a lot: the company also has to generate cash and trade at a reasonable price.
• Interest rates also matter. In rising-rate environments, multibaggers suffer much more than many would assume. They are not immune to the cost of money.
• And momentum works in a counterintuitive way: buying near 12-month highs does not seem to help. In fact, the best opportunities usually appear when the stock is closer to its lows and after meaningful declines. That may be where the market has not yet discounted too much future growth.

In short: a multibagger is not simply “a company that grows a lot”. According to this study, the most attractive combination would look more like this: a small, cheap, profitable company, with good free cash flow yield, capable of investing without destroying capital, and bought at a moment when the market is not yet too excited.

So yeah, it was never going to be easy.

Every investor dreams of finding companies that multiply by 5, by 10, or by 100. It is the philosopher’s stone of investing, the holy grail, the elixir of life for people obsessed with looking at charts and reading fundamentals.

When I started investing, one of the books that fascinated me the most was 100 Baggers: Stocks That Return 100-to-1 and How to Find Them, by Chris Mayer. It was incredible. The promise was that instead of finding stocks that would make me rich at 67, they could let me retire at 35.

Since then, I have read other “studies” on the topic with the same enthusiasm. Unfortunately, they all have one fatal flaw: anecdotes, qualitative analysis, and little evidence of causality. My engineer soul was missing something more rigorous.

Luckily, I recently came across a paper that tries to go one step further: The Alchemy of Multibagger Stocks, by Anna Yartseva.

Although the paper is not perfect, far from it, it brings a more methodological and scientific approach to the subject. It does several things I like.

It starts with a review of what has traditionally been said about multibaggers, which is perfect for anyone who has never read anything on the topic. Then it tries to study what characteristics these companies shared, starting from the Fama-French five-factor model, and later adapting the model to multibaggers. In the process, it uncovers a few things that had rarely been discussed before.

Today’s post is about this paper and some of its most interesting conclusions. I have published the full article on my website, with a more detailed analysis, interactive widgets, and a more rigorous critique for anyone who wants to read it. In this article, I am only going to comment briefly on some interesting conclusions.

In the original post, I also go through the “anatomy of a classic multibagger”, which summarizes what was commonly known about multibaggers and is also very useful for anyone interested in the topic.

Experiment

The study analyzes companies listed on the NYSE and NASDAQ, including ADRs, between 2009 and 2024.

The window starts just after the financial crisis and covers 15 very eventful years: bull and bear markets, COVID, inflation, interest rates, the banking crisis, wars, and commodity shocks.

It identifies more than 500 stocks that reached a 10x return, but only keeps those that maintained that level until the end and removes those with incomplete data. The final sample consists of 464 multibaggers.

What is interesting is that it does not only look at the 2009-2024 increase, but also at the companies’ prior history from the year 2000 onward. The idea is not simply to celebrate winners after the fact, but to look for signals that were already present before the big move.

Starting point: the Fama-French five-factor model

The analysis starts with the Fama-French five-factor model, one of the most widely used frameworks to explain why some stocks earn higher returns than others.

The idea, simplifying a lot, is that a stock’s return can be explained by its exposure to several factors: market, size, valuation, profitability, and investment.

https://preview.redd.it/jgkmio6f922h1.png?width=1506&format=png&auto=webp&s=1d84979d401232a544e01622879d338094fa725b

In other words, the model tries to explain how much a stock has earned by comparing it with what a risk-free asset would have earned and by seeing how much of that return comes from different known factors.

https://preview.redd.it/mo49unng922h1.png?width=1628&format=png&auto=webp&s=de5edd773aded66713c88165063f4990e045f8a7

The appeal of the model is that it lets you ask a very useful question: did multibaggers earn so much simply because they were exposed to known factors such as size, value, or profitability, or was there something else?

And that “something else” is exactly what the study tries to find.

https://preview.redd.it/4919o6si922h1.png?width=1640&format=png&auto=webp&s=7d07657d452c642931f6929a9b00153c6ec37ef3

https://preview.redd.it/3fititlj922h1.png?width=1508&format=png&auto=webp&s=6cd28ebdf673fd874d87f0e4bcba470d797e0e03

Alpha and beta

In a factor regression, beta measures how much a stock moves relative to the market. A beta of 1 means it moves more or less like the market; above 1, it is more sensitive; below 1, less so.

Alpha is what remains after explaining the return using the model’s factors: market, size, value, profitability, and investment. Put simply, it is the part of the return that the model cannot explain.

But be careful: alpha is not an explanation. It is a clue. It may reflect a real company advantage, a missing factor in the model, or simple statistical noise. That is why it should be treated as an interesting signal, not definitive proof.

The study uses the Fama-French five-factor model to see whether it can explain the historical returns of multibaggers.

The basic idea of the model is that, over the long term, small, cheap, profitable companies with prudent investment tend to do better. To test whether this also holds here, the study sorts the companies in the sample, between 2000 and 2024, into different groups:

• Size: small, medium, and large.
• Valuation: low, medium, and high, using book-to-market.
• Profitability: robust or weak.
• Investment: conservative or aggressive, based on asset growth.

When all of these are crossed, the result is 36 different portfolios.

The objective is twofold:

1. To check whether the classic factors also work within the multibagger universe.
2. To measure how much unexplained alpha remains. If a lot of return remains outside the model, it means these companies have something that the five factors do not capture well.

And that is where things start to get interesting: looking for more specific variables to understand where that extraordinary return really came from.

The results

The table groups the companies by size, valuation, profitability, and investment, and colors the return of each combination to quickly show what works best.

https://preview.redd.it/7glxh1lq922h1.png?width=1782&format=png&auto=webp&s=4963488717f91ac9e60849064829ca741f0db2d9

The best portfolio appears among small, cheap, profitable companies with aggressive investment. In other words: small caps, with high book-to-market, good operating profitability, and strong asset growth.

The main conclusions are quite clear:

• Size helps: small companies beat medium-sized companies on average, and medium-sized companies beat large ones. But the median is not as clean, so simply buying small caps is not magic either.
• Valuation matters: even within multibaggers, cheaper companies tend to do better.
• Profitability also matters: companies with weak profitability deliver worse results than profitable ones.

And the big surprise is investment. According to Fama and French, companies that invest aggressively should do worse. But here, almost the opposite happens: companies with higher asset growth achieve better returns. It makes sense. A company that wants to multiply cannot stand still. It needs to reinvest, grow, and build something much bigger.

Then, the study runs a regression to see how much the five factors explain. And here is the important part: operating profitability contributes little, these stocks have high beta, and alpha remains too high.

Translation: the five-factor model does not explain multibaggers very well. It captures part of the story, but it misses something important. And that is exactly where the interesting part begins.

Improving the model

Because the classic Fama-French model leaves too much alpha unexplained, the study tries to adapt it better to the case of multibaggers.

To do this, it tests different metrics for size, valuation, profitability, and investment: market capitalization, enterprise value, sales, book-to-market, P/E, price-to-sales, margins, ROE, return on capital, asset growth, EBITDA, and free cash flow.

In an intermediate version, the study changes some variables: it uses TEV for size, P/E for valuation, and EBITDA margin for profitability. But P/E ends up losing weight because it adds too much noise: it does not work for loss-making companies and explodes when earnings are very low. That is why the most useful valuation metrics end up being B/M and FCF/P, meaning how much free cash flow the company generates relative to the price paid.

The most interesting part is investment.

The study introduces a variable that detects when assets grow faster than EBITDA. And the result is strong: when a company expands assets faster than EBITDA growth, the following year’s return falls by around 22.8 percentage points.

The interpretation is quite clear: multibaggers need to invest, grow, and expand capacity. But that investment has to be accompanied by real EBITDA growth. If assets grow and EBITDA does not follow, the company is probably buying bad growth, inflating its balance sheet, or reinvesting at mediocre returns.

In short: the best multibaggers are not only small, cheap, and profitable. They also know how to invest aggressively without destroying returns. It is not about growing for the sake of growing, but about growing with profits behind it.

Static and dynamic return models

Here the objective changes: the author is no longer trying to see whether multibaggers fit into Fama-French, but to build a more complete model to explain their future returns.

To do this, she tests more than 150 variables: growth, valuation, profitability, quality, debt, solvency, momentum, interest rates, analysts, investment, R&D, marketing, and sector comparisons. Much more than the classic “small, cheap, and profitable”.

To separate signal from noise, she uses Hendry’s general-to-specific methodology: you start with a huge model and gradually remove what does not add value until you are left with something cleaner and more robust. First, you throw everything into the pot. Then you remove ingredients until the thing finally tastes like something.

The interesting part of the analysis is here: it moves from describing what multibaggers looked like after the fact to trying to identify which variables best explained their returns before they happened. It is not perfect, but this is where the most useful conclusions for investors appear.

Main results

The model works reasonably well: almost all coefficients have the expected sign. The market matters, size penalizes returns, valuation matters a lot, and investment only works if it is accompanied by real EBITDA growth.

The most important conclusions are:

• Multibaggers also depend on the market. When the S&P 500 helps, it helps them too; when the environment gets difficult, they also suffer.
• Size remains key: the larger the company, the lower its future return tends to be. Multiplying by 10 from a small base is much easier than doing so from a gigantic base.
• Profitability matters, but less than expected. In the dynamic models, EBITDA margin loses strength and ROA works better. Even so, FCF/P ends up carrying more weight than many classic profitability metrics.
• Accounting growth disappoints. Variables such as revenue growth, EBITDA growth, EPS growth, or free cash flow growth are not especially significant. This does not mean growth does not matter. It means that within a sample of companies that already became multibaggers, the price paid, FCF yield, and quality of investment explain future returns better.
• Investment is useful, but with one condition: if assets grow faster than EBITDA, future returns fall. In other words, growing for the sake of growing is not enough. If the company invests heavily but EBITDA does not follow, it may be buying bad growth or reinvesting at mediocre returns.
• Interest rates also matter. In periods of rising rates, future multibagger returns fall significantly. This makes sense: the more a company depends on future cash flows, the more it suffers from a higher discount rate.
• Valuation is the main protagonist. Book-to-market and FCF/P are the most powerful variables in the model. Even the best growth stocks need to be bought at reasonable prices. It is not enough to grow a lot; what you pay matters enormously.
• P/E does not work well because it breaks with loss-making companies or companies with very small earnings. That is why the study prefers B/M and FCF/P.
• Momentum behaves strangely: the effect seems very short-lived and quickly reverses. Buying right after a big move can be expensive.

There are also variables that surprisingly add little: debt, debt coverage, Altman Z-score, buybacks, dividends, share issuance, and R&D. But be careful not to misinterpret this: because the analysis only studies companies that survived and ended up being winners, there is selection bias. The fact that debt does not explain much within the survivors does not mean it does not matter when trying to avoid dying along the way.

In other words, the best multibaggers are not simply companies that grow a lot. They tend to be small, reasonably cheap, profitable companies that can invest without destroying capital and that are bought before the market has discounted too much future growth.

Conclusions

The study challenges some dogmas about multibaggers. Not because growth does not matter, but because isolated accounting growth explains less than expected. Valuation, free cash flow yield, size, interest rates, and investment quality matter more.

• The best multibaggers tend to be small, cheap, profitable companies capable of investing aggressively without destroying capital. The key is that asset growth must be accompanied by real EBITDA growth. If assets grow but EBITDA does not, that is a bad sign.
• Free cash flow yield appears as one of the most important variables. It is not enough to grow a lot: the company also has to generate cash and trade at a reasonable price.
• Interest rates also matter. In rising-rate environments, multibaggers suffer much more than many would assume. They are not immune to the cost of money.
• And momentum works in a counterintuitive way: buying near 12-month highs does not seem to help. In fact, the best opportunities usually appear when the stock is closer to its lows and after meaningful declines. That may be where the market has not yet discounted too much future growth.

In short: a multibagger is not simply “a company that grows a lot”. According to this study, the most attractive combination would look more like this: a small, cheap, profitable company, with good free cash flow yield, capable of investing without destroying capital, and bought at a moment when the market is not yet too excited.

So yeah, it was never going to be easy.

---

I have left a lot out of this article, so here is the link to my original post, where I explain everything with much more detail and nuance.

The original post includes “the anatomy of a classic multibagger”, all sections explained in greater detail, and 3 additional appendices:

• “Past studies”: a brief history of what has been done before.
• “Limitations”: this section is essential if you are thinking of using this information in your investment process.
• “Descriptive statistics of the sample”: a short section describing the growth, returns, size, and other characteristics of these multibaggers. It is very illustrative of what these companies looked like before and during the process of multiplying by 10.

Link here: https://www.jeravalue.com/en/blog/10x-stocks-the-dna-of-multibaggers

(It is completely free without paywall)

Every investor dreams of finding companies that multiply by 5, by 10, or by 100. It is the philosopher’s stone of investing, the holy grail, the elixir of life for people obsessed with looking at charts and reading fundamentals.

When I started investing, one of the books that fascinated me the most was 100 Baggers: Stocks That Return 100-to-1 and How to Find Them, by Chris Mayer. It was incredible. The promise was that instead of finding stocks that would make me rich at 67, they could let me retire at 35.

Since then, I have read other “studies” on the topic with the same enthusiasm. Unfortunately, they all have one fatal flaw: anecdotes, qualitative analysis, and little evidence of causality. My engineer soul was missing something more rigorous.

Luckily, I recently came across a paper that tries to go one step further: The Alchemy of Multibagger Stocks, by Anna Yartseva.

Although the paper is not perfect, far from it, it brings a more methodological and scientific approach to the subject. It does several things I like.

It starts with a review of what has traditionally been said about multibaggers, which is perfect for anyone who has never read anything on the topic. Then it tries to study what characteristics these companies shared, starting from the Fama-French five-factor model, and later adapting the model to multibaggers. In the process, it uncovers a few things that had rarely been discussed before.

Today’s post is about this paper and some of its most interesting conclusions. I have published the full article on my website, with a more detailed analysis, interactive widgets, and a more rigorous critique for anyone who wants to read it. In this article, I am only going to comment briefly on some interesting conclusions.

In the original post, I also go through the “anatomy of a classic multibagger”, which summarizes what was commonly known about multibaggers and is also very useful for anyone interested in the topic.

Experiment

The study analyzes companies listed on the NYSE and NASDAQ, including ADRs, between 2009 and 2024.

The window starts just after the financial crisis and covers 15 very eventful years: bull and bear markets, COVID, inflation, interest rates, the banking crisis, wars, and commodity shocks.

It identifies more than 500 stocks that reached a 10x return, but only keeps those that maintained that level until the end and removes those with incomplete data. The final sample consists of 464 multibaggers.

What is interesting is that it does not only look at the 2009-2024 increase, but also at the companies’ prior history from the year 2000 onward. The idea is not simply to celebrate winners after the fact, but to look for signals that were already present before the big move.

Starting point: the Fama-French five-factor model

The analysis starts with the Fama-French five-factor model, one of the most widely used frameworks to explain why some stocks earn higher returns than others.

The idea, simplifying a lot, is that a stock’s return can be explained by its exposure to several factors: market, size, valuation, profitability, and investment.

https://preview.redd.it/jgkmio6f922h1.png?width=1506&format=png&auto=webp&s=1d84979d401232a544e01622879d338094fa725b

In other words, the model tries to explain how much a stock has earned by comparing it with what a risk-free asset would have earned and by seeing how much of that return comes from different known factors.

https://preview.redd.it/mo49unng922h1.png?width=1628&format=png&auto=webp&s=de5edd773aded66713c88165063f4990e045f8a7

The appeal of the model is that it lets you ask a very useful question: did multibaggers earn so much simply because they were exposed to known factors such as size, value, or profitability, or was there something else?

And that “something else” is exactly what the study tries to find.

https://preview.redd.it/4919o6si922h1.png?width=1640&format=png&auto=webp&s=7d07657d452c642931f6929a9b00153c6ec37ef3

https://preview.redd.it/3fititlj922h1.png?width=1508&format=png&auto=webp&s=6cd28ebdf673fd874d87f0e4bcba470d797e0e03

Alpha and beta

In a factor regression, beta measures how much a stock moves relative to the market. A beta of 1 means it moves more or less like the market; above 1, it is more sensitive; below 1, less so.

Alpha is what remains after explaining the return using the model’s factors: market, size, value, profitability, and investment. Put simply, it is the part of the return that the model cannot explain.

But be careful: alpha is not an explanation. It is a clue. It may reflect a real company advantage, a missing factor in the model, or simple statistical noise. That is why it should be treated as an interesting signal, not definitive proof.

The study uses the Fama-French five-factor model to see whether it can explain the historical returns of multibaggers.

The basic idea of the model is that, over the long term, small, cheap, profitable companies with prudent investment tend to do better. To test whether this also holds here, the study sorts the companies in the sample, between 2000 and 2024, into different groups:

• Size: small, medium, and large.
• Valuation: low, medium, and high, using book-to-market.
• Profitability: robust or weak.
• Investment: conservative or aggressive, based on asset growth.

When all of these are crossed, the result is 36 different portfolios.

The objective is twofold:

1. To check whether the classic factors also work within the multibagger universe.
2. To measure how much unexplained alpha remains. If a lot of return remains outside the model, it means these companies have something that the five factors do not capture well.

And that is where things start to get interesting: looking for more specific variables to understand where that extraordinary return really came from.

The results

The table groups the companies by size, valuation, profitability, and investment, and colors the return of each combination to quickly show what works best.

https://preview.redd.it/7glxh1lq922h1.png?width=1782&format=png&auto=webp&s=4963488717f91ac9e60849064829ca741f0db2d9

The best portfolio appears among small, cheap, profitable companies with aggressive investment. In other words: small caps, with high book-to-market, good operating profitability, and strong asset growth.

The main conclusions are quite clear:

• Size helps: small companies beat medium-sized companies on average, and medium-sized companies beat large ones. But the median is not as clean, so simply buying small caps is not magic either.
• Valuation matters: even within multibaggers, cheaper companies tend to do better.
• Profitability also matters: companies with weak profitability deliver worse results than profitable ones.

And the big surprise is investment. According to Fama and French, companies that invest aggressively should do worse. But here, almost the opposite happens: companies with higher asset growth achieve better returns. It makes sense. A company that wants to multiply cannot stand still. It needs to reinvest, grow, and build something much bigger.

Then, the study runs a regression to see how much the five factors explain. And here is the important part: operating profitability contributes little, these stocks have high beta, and alpha remains too high.

Translation: the five-factor model does not explain multibaggers very well. It captures part of the story, but it misses something important. And that is exactly where the interesting part begins.

Improving the model

Because the classic Fama-French model leaves too much alpha unexplained, the study tries to adapt it better to the case of multibaggers.

To do this, it tests different metrics for size, valuation, profitability, and investment: market capitalization, enterprise value, sales, book-to-market, P/E, price-to-sales, margins, ROE, return on capital, asset growth, EBITDA, and free cash flow.

In an intermediate version, the study changes some variables: it uses TEV for size, P/E for valuation, and EBITDA margin for profitability. But P/E ends up losing weight because it adds too much noise: it does not work for loss-making companies and explodes when earnings are very low. That is why the most useful valuation metrics end up being B/M and FCF/P, meaning how much free cash flow the company generates relative to the price paid.

The most interesting part is investment.

The study introduces a variable that detects when assets grow faster than EBITDA. And the result is strong: when a company expands assets faster than EBITDA growth, the following year’s return falls by around 22.8 percentage points.

The interpretation is quite clear: multibaggers need to invest, grow, and expand capacity. But that investment has to be accompanied by real EBITDA growth. If assets grow and EBITDA does not follow, the company is probably buying bad growth, inflating its balance sheet, or reinvesting at mediocre returns.

In short: the best multibaggers are not only small, cheap, and profitable. They also know how to invest aggressively without destroying returns. It is not about growing for the sake of growing, but about growing with profits behind it.

Static and dynamic return models

Here the objective changes: the author is no longer trying to see whether multibaggers fit into Fama-French, but to build a more complete model to explain their future returns.

To do this, she tests more than 150 variables: growth, valuation, profitability, quality, debt, solvency, momentum, interest rates, analysts, investment, R&D, marketing, and sector comparisons. Much more than the classic “small, cheap, and profitable”.

To separate signal from noise, she uses Hendry’s general-to-specific methodology: you start with a huge model and gradually remove what does not add value until you are left with something cleaner and more robust. First, you throw everything into the pot. Then you remove ingredients until the thing finally tastes like something.

The interesting part of the analysis is here: it moves from describing what multibaggers looked like after the fact to trying to identify which variables best explained their returns before they happened. It is not perfect, but this is where the most useful conclusions for investors appear.

Main results

The model works reasonably well: almost all coefficients have the expected sign. The market matters, size penalizes returns, valuation matters a lot, and investment only works if it is accompanied by real EBITDA growth.

The most important conclusions are:

• Multibaggers also depend on the market. When the S&P 500 helps, it helps them too; when the environment gets difficult, they also suffer.
• Size remains key: the larger the company, the lower its future return tends to be. Multiplying by 10 from a small base is much easier than doing so from a gigantic base.
• Profitability matters, but less than expected. In the dynamic models, EBITDA margin loses strength and ROA works better. Even so, FCF/P ends up carrying more weight than many classic profitability metrics.
• Accounting growth disappoints. Variables such as revenue growth, EBITDA growth, EPS growth, or free cash flow growth are not especially significant. This does not mean growth does not matter. It means that within a sample of companies that already became multibaggers, the price paid, FCF yield, and quality of investment explain future returns better.
• Investment is useful, but with one condition: if assets grow faster than EBITDA, future returns fall. In other words, growing for the sake of growing is not enough. If the company invests heavily but EBITDA does not follow, it may be buying bad growth or reinvesting at mediocre returns.
• Interest rates also matter. In periods of rising rates, future multibagger returns fall significantly. This makes sense: the more a company depends on future cash flows, the more it suffers from a higher discount rate.
• Valuation is the main protagonist. Book-to-market and FCF/P are the most powerful variables in the model. Even the best growth stocks need to be bought at reasonable prices. It is not enough to grow a lot; what you pay matters enormously.
• P/E does not work well because it breaks with loss-making companies or companies with very small earnings. That is why the study prefers B/M and FCF/P.
• Momentum behaves strangely: the effect seems very short-lived and quickly reverses. Buying right after a big move can be expensive.

There are also variables that surprisingly add little: debt, debt coverage, Altman Z-score, buybacks, dividends, share issuance, and R&D. But be careful not to misinterpret this: because the analysis only studies companies that survived and ended up being winners, there is selection bias. The fact that debt does not explain much within the survivors does not mean it does not matter when trying to avoid dying along the way.

In other words, the best multibaggers are not simply companies that grow a lot. They tend to be small, reasonably cheap, profitable companies that can invest without destroying capital and that are bought before the market has discounted too much future growth.

Conclusions

The study challenges some dogmas about multibaggers. Not because growth does not matter, but because isolated accounting growth explains less than expected. Valuation, free cash flow yield, size, interest rates, and investment quality matter more.

• The best multibaggers tend to be small, cheap, profitable companies capable of investing aggressively without destroying capital. The key is that asset growth must be accompanied by real EBITDA growth. If assets grow but EBITDA does not, that is a bad sign.
• Free cash flow yield appears as one of the most important variables. It is not enough to grow a lot: the company also has to generate cash and trade at a reasonable price.
• Interest rates also matter. In rising-rate environments, multibaggers suffer much more than many would assume. They are not immune to the cost of money.
• And momentum works in a counterintuitive way: buying near 12-month highs does not seem to help. In fact, the best opportunities usually appear when the stock is closer to its lows and after meaningful declines. That may be where the market has not yet discounted too much future growth.

In short: a multibagger is not simply “a company that grows a lot”. According to this study, the most attractive combination would look more like this: a small, cheap, profitable company, with good free cash flow yield, capable of investing without destroying capital, and bought at a moment when the market is not yet too excited.

So yeah, it was never going to be easy.

---

I have left a lot out of this article, so here is the link to my original post, where I explain everything with much more detail and nuance.

The original post includes “the anatomy of a classic multibagger”, all sections explained in greater detail, and 3 additional appendices:

• “Past studies”: a brief history of what has been done before.
• “Limitations”: this section is essential if you are thinking of using this information in your investment process.
• “Descriptive statistics of the sample”: a short section describing the growth, returns, size, and other characteristics of these multibaggers. It is very illustrative of what these companies looked like before and during the process of multiplying by 10.

Link here: https://www.jeravalue.com/en/blog/10x-stocks-the-dna-of-multibaggers

(It is completely free without paywall)

Todo inversor sueña con encontrar empresas que multipliquen por 5, por 10 o por 100. Es la piedra filosofal de la inversión, el santo grial, el elixir de la vida para obsesos de mirar gráficas y leer fundamentales.

Cuando empecé a invertir, uno de los libros que más me fascinó fue «100 Baggers: Acciones que se multiplican por 100 y cómo encontrarlas», de Chris Mayer. Fue increíble. La promesa era que en vez de encontrar acciones que me hicieran millonario los 67, conseguirían retirarme a los 35. Desde entonces he leído otros "estudios" sobre el tema con igual entusiasmo. Para mi desgracia, todos tienen un "error fatal". Anécdotas, análisis cualitativo y poca prueba de causalidad. Mi alma de ingeniero echaba en falta algo más riguroso. Por suerte, hace poco me topé con un paper que pretende ir un pasó más allá: "The Alchemy of Multibagger Stocks" de Anna Yartseva.

Aunque el paper no es perfecto (ni mucho menos) aporta un enfoque más metodológico y científico al asunto. Este paper hace varias cosas que me gustan.

Empieza por un repaso de lo que tradicionalmente se ha dicho sobre las Multibaggers (perfecto para quien nunca haya leído nada sobre el tema), y luego intenta estudiar qué carácterísticas compartían estas empresas partiendo desde el modelo de 5 factores de Fama y French, para posteriormente adaptar el modelo a las múltibaggers. Descubriendo por el proceso algunas cosas que jamás se habían dicho.

El post de hoy va sobre este paper y algunas de sus conclusiones más interesantes. He publicado en mi web el post completo con una análisis más detallado, widgets interactivos y una crítica más rigurosa para quien quiera leerlo. En este artículo, me voy a dedicar a comentar superficialmente algunas conclusiones interesantes.

En el post completo también repaso la "anatomía de una multibaggers clásica", que hace un resumen de lo que comúnmente se sabía sobre las multibaggers y es también muy útil para todo aquel interesado en el tema.

Experimento

El estudio analiza empresas cotizadas en NYSE y NASDAQ, incluyendo ADRs, entre 2009 y 2024.

La ventana empieza justo después de la crisis financiera y cubre 15 años bastante movidos: mercados alcistas y bajistas, COVID, inflación, tipos, crisis bancaria, guerras y shocks de materias primas.

Identifica más de 500 acciones que llegaron a multiplicar por 10, pero solo conserva las que mantuvieron ese nivel hasta el final y elimina las que tienen datos incompletos. La muestra final queda en 464 multibaggers.

Lo interesante es que no mira solo la subida de 2009-2024, sino también la historia previa desde el año 2000. La idea no es celebrar ganadoras a posteriori, sino buscar qué señales ya estaban presentes antes del gran movimiento.

Punto de partida: el modelo de 5 factores de Fama y French

El análisis empieza con el modelo de cinco factores de Fama y French (2015), uno de los marcos más usados para explicar por qué unas acciones tienen más rentabilidad que otras.

La idea, simplificando mucho, es que la rentabilidad de una acción se puede explicar por su exposición a varios factores: mercado, tamaño, valoración, rentabilidad e inversión.

https://preview.redd.it/8uz7sn9a822h1.png?width=1080&format=png&auto=webp&s=9ed44f89eab2a6dac8764394399ec0db767a28d6

Es decir, el modelo intenta explicar cuánto ha ganado una acción comparándola con lo que habría ganado un activo sin riesgo y viendo cuánto de esa rentabilidad viene de distintos factores conocidos.

https://preview.redd.it/x0y2nw9b822h1.png?width=1080&format=png&auto=webp&s=0ed15621bb8be0027fbbb820f014742d6630c7f9

La gracia del modelo es que permite hacer una pregunta muy útil: ¿las multibaggers ganaron tanto porque simplemente estaban expuestas a factores conocidos, como tamaño, valor o rentabilidad, o porque había algo más?

Y ese “algo más” es justo lo que intenta encontrar el estudio.

https://preview.redd.it/u2bwhudc822h1.png?width=1080&format=png&auto=webp&s=9f746832dc24b3524d27c4befa4c754b39e1cebd

https://preview.redd.it/4pocj4zc822h1.png?width=1080&format=png&auto=webp&s=fd4522bb987645af2e317fdb766df65bce707db8

Alpha y beta

En una regresión factorial, la beta mide cuánto se mueve una acción respecto al mercado. Una beta de 1 implica que se mueve más o menos como el mercado; por encima de 1, es más sensible; por debajo, menos.

El alpha es lo que queda después de explicar la rentabilidad por los factores del modelo: mercado, tamaño, valor, rentabilidad e inversión. Dicho simple: es la parte de la rentabilidad que el modelo no consigue explicar.

Pero ojo: el alpha no es una explicación, es una pista. Puede reflejar una ventaja real de la empresa, un factor que falta en el modelo o simple ruido estadístico. Por eso conviene tratarlo como una señal interesante, no como una prueba definitiva.

---

El estudio usa el modelo de cinco factores de Fama y French para ver si puede explicar la rentabilidad histórica de las multibaggers.

La idea base del modelo es que, a largo plazo, suelen hacerlo mejor las empresas pequeñas, baratas, rentables y con inversión prudente. Para comprobar si esto también se cumple aquí, el estudio ordena las compañías de la muestra, entre 2000 y 2024, en distintos grupos:

• Tamaño: pequeñas, medianas y grandes.
• Valoración: baja, media y alta, usando book-to-market.
• Rentabilidad: robusta o débil.
• Inversión: conservadora o agresiva, según crecimiento de activos.

Al cruzar todo, salen 36 carteras distintas.

El objetivo es doble:

1. comprobar si los factores clásicos también funcionan dentro del universo de multibaggers
2. Medir cuánto alpha queda sin explicar. Si queda mucha rentabilidad fuera del modelo, significa que estas empresas tienen algo que los cinco factores no capturan bien.

Y ahí empieza lo interesante: buscar variables más específicas para entender de dónde salió realmente esa rentabilidad extraordinaria.

Los resultados

La tabla agrupa las empresas por tamaño, valoración, rentabilidad e inversión, y colorea la rentabilidad de cada combinación para ver rápido qué funciona mejor.

https://preview.redd.it/gmf5su9e822h1.png?width=1080&format=png&auto=webp&s=b23acdca102db10c83fcdad55250f48e8dbe5fc6

La mejor cartera aparece en empresas pequeñas, baratas, rentables y con inversión agresiva. Es decir: small caps, con book-to-market alto, buena rentabilidad operativa y fuerte crecimiento de activos.

Las conclusiones principales son bastante claras:

• El tamaño ayuda: las empresas pequeñas baten de media a las medianas, y las medianas a las grandes. Pero la mediana no es tan limpia, así que comprar small caps sin más tampoco es magia.
• La valoración importa: incluso dentro de las multibaggers, las empresas más baratas tienden a hacerlo mejor.
• La rentabilidad también importa: las compañías con rentabilidad débil obtienen peores resultados que las rentables.

Y la gran sorpresa está en la inversión. Según Fama y French, las empresas que invierten de forma agresiva deberían hacerlo peor. Pero aquí ocurre casi lo contrario: las compañías con mayor crecimiento de activos obtienen mejores retornos. Tiene sentido. Una empresa que quiere multiplicarse no puede quedarse quieta; necesita reinvertir, crecer y construir algo bastante más grande.

Después, el estudio hace una regresión para ver cuánto explican los cinco factores. Y aquí está lo importante: la rentabilidad operativa aporta poco, la beta de estas acciones es elevada y el alpha sigue siendo demasiado alto.

Traducción: el modelo de cinco factores no explica bien las multibaggers. Captura parte de la historia, pero se le escapa algo importante. Y justo ahí empieza lo interesante.

Mejorando el modelo

Como el modelo clásico de Fama-French deja demasiado alpha sin explicar, el estudio intenta adaptarlo mejor al caso de las multibaggers.

Para ello prueba distintas métricas de tamaño, valoración, rentabilidad e inversión: capitalización, enterprise value, ventas, book-to-market, PER, precio ventas, márgenes, ROE, retorno sobre capital, crecimiento de activos, EBITDA y free cash flow.

En una versión intermedia, el estudio cambia algunas variables: usa TEV para tamaño, PER para valoración y margen EBITDA para rentabilidad. Pero el PER acaba perdiendo peso porque mete demasiado ruido: no sirve con empresas en pérdidas y se dispara cuando el beneficio es muy bajo. Por eso, las métricas de valoración más útiles terminan siendo B/M y FCF/P, es decir, cuánto free cash flow genera la empresa frente al precio pagado.

La parte más interesante está en la inversión.

El estudio introduce una variable que detecta cuándo los activos crecen más rápido que el EBITDA. Y el resultado es fuerte: cuando una empresa expande activos por encima del crecimiento de su EBITDA, la rentabilidad del año siguiente cae unos 22,8 puntos porcentuales.

La interpretación es bastante clara: las multibaggers necesitan invertir, crecer y ampliar capacidad. Pero esa inversión tiene que venir acompañada de crecimiento real del EBITDA. Si los activos crecen y el EBITDA no acompaña, probablemente la empresa está comprando crecimiento malo, inflando balance o reinvirtiendo a retornos mediocres.

En resumen: las mejores multibaggers no solo son pequeñas, baratas y rentables. También saben invertir agresivamente sin destruir retornos. No se trata de crecer por crecer, sino de crecer con beneficios detrás.

Modelos estáticos y dinámicos de retornos

Aquí el objetivo cambia: la autora ya no intenta ver si las multibaggers encajan en Fama-French, sino construir un modelo más completo para explicar sus retornos futuros.

Para ello prueba más de 150 variables: crecimiento, valoración, rentabilidad, calidad, deuda, solvencia, momentum, tipos de interés, analistas, inversión, I+D, marketing y comparaciones sectoriales. Bastante más que el clásico “pequeña, barata y rentable”.

Para separar señal de ruido, usa la metodología general-to-specific de Hendry: empiezas con un modelo enorme y vas eliminando lo que no aporta hasta quedarte con algo más limpio y robusto. Primero metes de todo en la olla. Luego quitas ingredientes hasta que aquello sabe a algo.

La gracia del análisis está aquí: pasa de describir cómo eran las multibaggers a posteriori a intentar identificar qué variables explicaban mejor sus retornos antes de que ocurrieran. No es perfecto, pero es donde aparecen las conclusiones más útiles para el inversor.

Resultados principales

El modelo funciona razonablemente bien: casi todos los coeficientes tienen el signo esperado. El mercado importa, el tamaño penaliza, la valoración pesa mucho y la inversión solo funciona si viene acompañada de crecimiento real del EBITDA.

Las conclusiones más importantes son:

• Las multibaggers también dependen del mercado. Cuando el S&P 500 acompaña, ayuda; cuando el entorno se complica, también les pesa.
• El tamaño sigue siendo clave: cuanto más grande es la empresa, menor tiende a ser su rentabilidad futura. Multiplicar por 10 desde una base pequeña es mucho más fácil que hacerlo desde una base gigantesca.
• La rentabilidad importa, pero menos de lo esperado. En los modelos dinámicos, el margen EBITDA pierde fuerza y el ROA funciona mejor. Aun así, el FCF/P acaba teniendo más peso que muchas métricas clásicas de rentabilidad.
• El crecimiento contable decepciona. Variables como crecimiento de ingresos, EBITDA, EPS o free cash flow no salen especialmente significativas. No significa que el crecimiento no importe, sino que, dentro de una muestra de empresas que ya fueron multibaggers, el precio pagado, el FCF yield y la calidad de la inversión explican mejor los retornos futuros.
• La inversión es útil, pero con una condición: si los activos crecen más rápido que el EBITDA, la rentabilidad futura cae. Es decir, crecer por crecer no vale. Si la empresa invierte mucho pero el EBITDA no acompaña, puede estar comprando crecimiento malo o reinvirtiendo a retornos mediocres.
• Los tipos de interés también pesan. En entornos de subidas de tipos, las rentabilidades futuras de las multibaggers caen de forma significativa. Tiene sentido: cuanto más dependen de flujos futuros, más les duele una tasa de descuento más alta.
• La valoración es el gran protagonista. Book-to-market y FCF/P son las variables más potentes del modelo. Las mejores acciones de crecimiento también tienen que comprarse a precios razonables. No basta con crecer mucho; importa muchísimo cuánto pagas.
• El PER no funciona bien porque se rompe con empresas en pérdidas o beneficios muy pequeños. Por eso el estudio prefiere B/M y FCF/P.
• El momentum sale raro: el efecto parece muy corto y con reversión rápida. Comprar justo después de una gran subida puede salir caro.

También hay variables que sorprenden por aportar poco: deuda, cobertura de deuda, Altman Z-score, recompras, dividendos, emisión de acciones e I+D. Pero ojo con interpretar esto mal: al analizar solo empresas que sobrevivieron y acabaron siendo ganadoras, hay sesgo de selección. Que la deuda no explique mucho dentro de las supervivientes no significa que no importe para evitar morirse por el camino.

Es decir, las mejores multibaggers no son simplemente empresas que crecen mucho. Suelen ser compañías pequeñas, razonablemente baratas, rentables, capaces de invertir sin destruir capital y compradas antes de que el mercado descuente demasiado futuro.

Conclusiones

El estudio cuestiona algunos dogmas sobre las multibaggers. No porque el crecimiento no importe, sino porque el crecimiento contable aislado explica menos de lo esperado. Pesan más la valoración, el free cash flow yield, el tamaño, los tipos de interés y la calidad de la inversión.

Las mejores multibaggers tienden a ser empresas pequeñas, baratas, rentables y capaces de invertir agresivamente sin destruir capital. La clave está en que el crecimiento de activos venga acompañado de crecimiento real del EBITDA. Si los activos crecen pero el EBITDA no, mala señal.

El free cash flow yield aparece como una de las variables más importantes. No basta con crecer mucho: también hay que generar caja y pagar un precio razonable.

Los tipos de interés también importan. En entornos de tipos al alza, las multibaggers sufren bastante más de lo que muchos asumirían. No son inmunes al coste del dinero.

Y el momentum funciona de forma poco intuitiva: comprar cerca de máximos de 12 meses no parece ayudar. De hecho, las mejores oportunidades suelen aparecer cuando la acción está más cerca de mínimos y después de caídas relevantes. Ahí, quizá, es donde el mercado todavía no ha descontado demasiado futuro.

En resumen: una multibagger no es simplemente “una empresa que crece mucho”. Según este estudio, la combinación más atractiva sería algo más parecido a esto: empresa pequeña, barata, rentable, con buen free cash flow yield, capaz de invertir sin destruir capital y comprada en un momento en el que el mercado todavía no está demasiado emocionado.

O sea, fácil no era.

---

En este artículo me he dejado mucho en el tintero, así que te dejo el link a mi post original dónde explico todo con mucho más detalle y matices.

El post original incluye "la anatomía de una multibaggers clásica", todas las secciones explicadas más en detalle y 3 apéndices adicionales:

• "Estudios pasados": Una breve historia sobre lo que se ha hecho antes
• "Limitaciones": Este apartado es fundamental si piensas usar esta información en tu proceso de inversión
• "Estadísticas descriptivas de la muestra": Pequeño apartado donde se describen qué crecimientos han tenido estas multibaggers, qué retornos, qué tamaños, etc... muy ilustrativo sobre como estas empresas eran antes y durante el proceso de multiplicarse por 10.

Link aquí: https://www.jeravalue.com/es/blog/10x-stocks-the-dna-of-multibaggers

(es completamente gratis y no hace falta registrarse)

I’ve been digging into share buybacks recently, and I wanted to test something simple:

>Do companies that buy back shares actually perform better, or are buybacks mostly financial tricks?

More specifically: Which type of buyback works best?

Because “the company is buying back stock” is not enough. A buyback can be great, neutral, or actively stupid depending on the context.

A good buyback should probably do a few things:

1. Actually reduce the share count.
2. Be funded by real cash flow, not desperation leverage.
3. Happen at a reasonable valuation.
4. Not exist purely to offset stock-based compensation.
5. Not just make EPS look better while shareholders own the same percentage of the business.

So I ran a step-by-step backtest to see whether historical data supports that idea.

>This is not an academic paper, and I’m not claiming causality. It is more of a practical signal test: can buyback-related variables rank stocks by future returns?

Setup

I used Portfolio123 with FactSet data.

• Universe: Easy To Trade USA, basically US stocks with some liquidity and quality-of-data filters.
• Period: 2006 to 2026
• Method: Every 4 weeks, rank the universe into 10 equal-weight deciles based on different buyback-related factors. Then compare future performance by decile.

Returns include dividends, use point-in-time data, include delisted stocks, and are before taxes, transaction costs, and slippage.

Important: this is signal research, not a production-ready strategy!

Test 1: Net buyback yield

First, I tested the obvious metric:

>

The idea is simple. If a company is buying back a lot of stock relative to its size, maybe that contains useful information.

Result:

Factor	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	Decile 9

So yes, there is signal.

The high buyback-yield companies did much better than the low buyback-yield companies.

But the result was messy.

https://preview.redd.it/tn0s1cd0jo0h1.png?width=1516&format=png&auto=webp&s=14c0e4898f9a5cdb6efac461b8b4c74d0a8aa294

The best decile was not decile 10, it was decile 9. The middle deciles were also not especially clean. That suggests that simply buying the companies with the most aggressive buybacks is not enough.

And that makes sense.

A company can spend a lot on buybacks but still fail to reduce the diluted share count because of stock-based compensation, acquisitions paid with shares, options, RSUs, convertibles, or other forms of dilution.

So net buyback yield is useful, but noisy.

>Finding 1: Buyback yield contains signal, but “buying back a lot” is not the same as “buying back well.”

Test 2: Add real diluted share count reduction

Next, I added a measure of whether the buyback actually reduced the fully diluted share count over three years.

The logic: If a company buys back stock but diluted shares do not go down, the buyback may be more narrative than economics.

Metric:

>

Why diluted shares?

Because diluted shares better capture options, RSUs, convertibles, and other instruments that can dilute shareholders.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10

This improved the signal.

The top decile became the best decile, and the spread between the best and worst groups widened.

https://preview.redd.it/5nqp0r92jo0h1.png?width=1514&format=png&auto=webp&s=e8b098e70ad2300d36d9cb1b594a4479a7884a1a

This is probably the most intuitive result of the whole test:

Buybacks work better as a signal when they actually reduce the number of diluted shares.

That sounds obvious, but a lot of companies announce buybacks that do not meaningfully change shareholder ownership.

>Finding 2: The market seems to reward “real” buybacks more than cosmetic buybacks.

A real buyback is not just cash spent. It is a buyback that leaves remaining shareholders owning a larger percentage of the business.

Test 3: Add free cash flow yield

Then I added free cash flow yield.

Why?

Because FCF yield helps with two things at once:

1. It tells us whether the company generates enough cash to plausibly fund buybacks.
2. It adds a valuation component.

Buying back stock with abundant FCF is not the same as buying back stock with weak FCF.

Buying back stock at a low valuation is not the same as buying back stock at a crazy valuation.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10

This was the biggest improvement.

https://preview.redd.it/gb6zb604jo0h1.png?width=1506&format=png&auto=webp&s=e8fae4e14c0ad33f0a2ca0e590f137dc95556542

The top 20% improved, but the bottom 20% got much worse. That is useful because a good factor should not only help identify winners, it should also help avoid disasters.

The interpretation:

A buyback is more attractive when it is backed by actual cash generation and reasonable valuation.

>Finding 3: The best buyback signal was not “high buyback yield.” It was high buyback yield plus real share reduction plus FCF yield.

Look for companies that are buying back shares, actually reducing diluted share count, and generating enough cash to justify it.

Test 4: Add debt control

Finally, I added a balance sheet guardrail:

>

If a company is already highly levered, spending cash on buybacks may be a bad capital allocation decision.

Buybacks are great when the company has excess capital.

They are less great when management is borrowing heavily just to shrink the share count or support EPS.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10
Buyback yield + diluted share reduction + FCF yield + debt	11.59%	-1.80%	+13.39 pp	10

This was interesting.

Adding debt control did not improve the top deciles much. In fact, the top 20% CAGR was slightly lower than the previous version.

https://preview.redd.it/8nm80p36jo0h1.png?width=1512&format=png&auto=webp&s=fed8820da015e1cab06df3b0275d27b5bbaf7d69

But it did improve the separation at the bottom.

So debt was not really a return engine. It was more of a mistake filter.

>Finding 4: Debt control helps identify bad buybacks more than it helps identify great buybacks.

This makes sense.

A strong company with high FCF and real buybacks can still have some debt.

But companies buying back stock while financially stretched are often playing a more dangerous game.

Main takeaway

The simple version:

Don’t look for companies that buy back a lot. Look for companies that buy back well.

The full version:

Buybacks become more interesting when:

1. The company has positive net buyback activity.
2. Diluted shares are actually going down.
3. The company generates strong free cash flow.
4. The stock is not obviously expensive, proxied here by FCF yield.
5. The balance sheet is not being abused to fund buybacks.

What each variable corrected

Added signal	What it fixes	Result
Net buyback yield	Measures buyback flow	Has signal, but noisy
Diluted share reduction	Filters cosmetic buybacks	Improves ranking quality
FCF yield	Adds cash generation and valuation	Stronger separation between good and bad deciles
Net debt / FCF	Avoids stretched balance sheets	Helps identify the bottom

Important!

This is not a complete investment strategy.

The best deciles beat SPY, but not by enough to declare victory and call it done.

There are still many things to test:

• transaction costs
• slippage
• taxes
• turnover
• volatility
• drawdowns
• sector exposure
• and waaaay more

So the conclusion is not “here is a strategy, go buy decile 10.” but something more like "buybacks contain useful information, but only when interpreted properly."

I’ve been digging into share buybacks recently, and I wanted to test something simple:

>Do companies that buy back shares actually perform better, or are buybacks mostly financial tricks?

More specifically: Which type of buyback works best?

Because “the company is buying back stock” is not enough. A buyback can be great, neutral, or actively stupid depending on the context.

A good buyback should probably do a few things:

1. Actually reduce the share count.
2. Be funded by real cash flow, not desperation leverage.
3. Happen at a reasonable valuation.
4. Not exist purely to offset stock-based compensation.
5. Not just make EPS look better while shareholders own the same percentage of the business.

So I ran a step-by-step backtest to see whether historical data supports that idea.

>This is not an academic paper, and I’m not claiming causality. It is more of a practical signal test: can buyback-related variables rank stocks by future returns?

Setup

I used Portfolio123 with FactSet data.

• Universe: Easy To Trade USA, basically US stocks with some liquidity and quality-of-data filters.
• Period: 2006 to 2026
• Method: Every 4 weeks, rank the universe into 10 equal-weight deciles based on different buyback-related factors. Then compare future performance by decile.

Returns include dividends, use point-in-time data, include delisted stocks, and are before taxes, transaction costs, and slippage.

Important: this is signal research, not a production-ready strategy!

Test 1: Net buyback yield

First, I tested the obvious metric:

>

The idea is simple. If a company is buying back a lot of stock relative to its size, maybe that contains useful information.

Result:

Factor	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	Decile 9

So yes, there is signal.

The high buyback-yield companies did much better than the low buyback-yield companies.

But the result was messy.

https://preview.redd.it/tn0s1cd0jo0h1.png?width=1516&format=png&auto=webp&s=14c0e4898f9a5cdb6efac461b8b4c74d0a8aa294

The best decile was not decile 10, it was decile 9. The middle deciles were also not especially clean. That suggests that simply buying the companies with the most aggressive buybacks is not enough.

And that makes sense.

A company can spend a lot on buybacks but still fail to reduce the diluted share count because of stock-based compensation, acquisitions paid with shares, options, RSUs, convertibles, or other forms of dilution.

So net buyback yield is useful, but noisy.

>Finding 1: Buyback yield contains signal, but “buying back a lot” is not the same as “buying back well.”

Test 2: Add real diluted share count reduction

Next, I added a measure of whether the buyback actually reduced the fully diluted share count over three years.

The logic: If a company buys back stock but diluted shares do not go down, the buyback may be more narrative than economics.

Metric:

>

Why diluted shares?

Because diluted shares better capture options, RSUs, convertibles, and other instruments that can dilute shareholders.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10

This improved the signal.

https://preview.redd.it/5nqp0r92jo0h1.png?width=1514&format=png&auto=webp&s=e8b098e70ad2300d36d9cb1b594a4479a7884a1a

The top decile became the best decile, and the spread between the best and worst groups widened.

This is probably the most intuitive result of the whole test:

Buybacks work better as a signal when they actually reduce the number of diluted shares.

That sounds obvious, but a lot of companies announce buybacks that do not meaningfully change shareholder ownership.

>Finding 2: The market seems to reward “real” buybacks more than cosmetic buybacks.

A real buyback is not just cash spent. It is a buyback that leaves remaining shareholders owning a larger percentage of the business.

Test 3: Add free cash flow yield

Then I added free cash flow yield.

Why?

Because FCF yield helps with two things at once:

1. It tells us whether the company generates enough cash to plausibly fund buybacks.
2. It adds a valuation component.

Buying back stock with abundant FCF is not the same as buying back stock with weak FCF.

Buying back stock at a low valuation is not the same as buying back stock at a crazy valuation.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10

This was the biggest improvement.

https://preview.redd.it/gb6zb604jo0h1.png?width=1506&format=png&auto=webp&s=e8fae4e14c0ad33f0a2ca0e590f137dc95556542

The top 20% improved, but the bottom 20% got much worse. That is useful because a good factor should not only help identify winners, it should also help avoid disasters.

The interpretation:

A buyback is more attractive when it is backed by actual cash generation and reasonable valuation.

>Finding 3: The best buyback signal was not “high buyback yield.” It was high buyback yield plus real share reduction plus FCF yield.

Look for companies that are buying back shares, actually reducing diluted share count, and generating enough cash to justify it.

Test 4: Add debt control

Finally, I added a balance sheet guardrail:

>

If a company is already highly levered, spending cash on buybacks may be a bad capital allocation decision.

Buybacks are great when the company has excess capital.

They are less great when management is borrowing heavily just to shrink the share count or support EPS.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10
Buyback yield + diluted share reduction + FCF yield + debt	11.59%	-1.80%	+13.39 pp	10

This was interesting.

https://preview.redd.it/8nm80p36jo0h1.png?width=1512&format=png&auto=webp&s=fed8820da015e1cab06df3b0275d27b5bbaf7d69

Adding debt control did not improve the top deciles much. In fact, the top 20% CAGR was slightly lower than the previous version.

But it did improve the separation at the bottom.

So debt was not really a return engine. It was more of a mistake filter.

>Finding 4: Debt control helps identify bad buybacks more than it helps identify great buybacks.

This makes sense.

A strong company with high FCF and real buybacks can still have some debt.

But companies buying back stock while financially stretched are often playing a more dangerous game.

Main takeaway

The simple version:

Don’t look for companies that buy back a lot. Look for companies that buy back well.

The full version:

Buybacks become more interesting when:

1. The company has positive net buyback activity.
2. Diluted shares are actually going down.
3. The company generates strong free cash flow.
4. The stock is not obviously expensive, proxied here by FCF yield.
5. The balance sheet is not being abused to fund buybacks.

What each variable corrected

Added signal	What it fixes	Result
Net buyback yield	Measures buyback flow	Has signal, but noisy
Diluted share reduction	Filters cosmetic buybacks	Improves ranking quality
FCF yield	Adds cash generation and valuation	Stronger separation between good and bad deciles
Net debt / FCF	Avoids stretched balance sheets	Helps identify the bottom

Important!

This is not a complete investment strategy.

The best deciles beat SPY, but not by enough to declare victory and call it done.

There are still many things to test:

• transaction costs
• slippage
• taxes
• turnover
• volatility
• drawdowns
• sector exposure
• and waaaay more

So the conclusion is not “here is a strategy, go buy decile 10.” but something more like "buybacks contain useful information, but only when interpreted properly."

ps: for more details and visuals you can go to the original blogpost at https://www.jeravalue.com/en/blog/buybacks-performance

I’ve been digging into share buybacks recently, and I wanted to test something simple:

>Do companies that buy back shares actually perform better, or are buybacks mostly financial tricks?

More specifically: Which type of buyback works best?

Because “the company is buying back stock” is not enough. A buyback can be great, neutral, or actively stupid depending on the context.

A good buyback should probably do a few things:

1. Actually reduce the share count.
2. Be funded by real cash flow, not desperation leverage.
3. Happen at a reasonable valuation.
4. Not exist purely to offset stock-based compensation.
5. Not just make EPS look better while shareholders own the same percentage of the business.

So I ran a step-by-step backtest to see whether historical data supports that idea.

>This is not an academic paper, and I’m not claiming causality. It is more of a practical signal test: can buyback-related variables rank stocks by future returns?

Setup

I used Portfolio123 with FactSet data.

• Universe: Easy To Trade USA, basically US stocks with some liquidity and quality-of-data filters.
• Period: 2006 to 2026
• Method: Every 4 weeks, rank the universe into 10 equal-weight deciles based on different buyback-related factors. Then compare future performance by decile.

Returns include dividends, use point-in-time data, include delisted stocks, and are before taxes, transaction costs, and slippage.

Important: this is signal research, not a production-ready strategy!

Test 1: Net buyback yield

First, I tested the obvious metric:

>

The idea is simple. If a company is buying back a lot of stock relative to its size, maybe that contains useful information.

Result:

Factor	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	Decile 9

So yes, there is signal.

The high buyback-yield companies did much better than the low buyback-yield companies.

But the result was messy.

https://preview.redd.it/tn0s1cd0jo0h1.png?width=1516&format=png&auto=webp&s=14c0e4898f9a5cdb6efac461b8b4c74d0a8aa294

The best decile was not decile 10, it was decile 9. The middle deciles were also not especially clean. That suggests that simply buying the companies with the most aggressive buybacks is not enough.

And that makes sense.

A company can spend a lot on buybacks but still fail to reduce the diluted share count because of stock-based compensation, acquisitions paid with shares, options, RSUs, convertibles, or other forms of dilution.

So net buyback yield is useful, but noisy.

>Finding 1: Buyback yield contains signal, but “buying back a lot” is not the same as “buying back well.”

Test 2: Add real diluted share count reduction

Next, I added a measure of whether the buyback actually reduced the fully diluted share count over three years.

The logic: If a company buys back stock but diluted shares do not go down, the buyback may be more narrative than economics.

Metric:

>

Why diluted shares?

Because diluted shares better capture options, RSUs, convertibles, and other instruments that can dilute shareholders.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10

This improved the signal.

The top decile became the best decile, and the spread between the best and worst groups widened.

https://preview.redd.it/5nqp0r92jo0h1.png?width=1514&format=png&auto=webp&s=e8b098e70ad2300d36d9cb1b594a4479a7884a1a

This is probably the most intuitive result of the whole test:

Buybacks work better as a signal when they actually reduce the number of diluted shares.

That sounds obvious, but a lot of companies announce buybacks that do not meaningfully change shareholder ownership.

>Finding 2: The market seems to reward “real” buybacks more than cosmetic buybacks.

A real buyback is not just cash spent. It is a buyback that leaves remaining shareholders owning a larger percentage of the business.

Test 3: Add free cash flow yield

Then I added free cash flow yield.

Why?

Because FCF yield helps with two things at once:

1. It tells us whether the company generates enough cash to plausibly fund buybacks.
2. It adds a valuation component.

Buying back stock with abundant FCF is not the same as buying back stock with weak FCF.

Buying back stock at a low valuation is not the same as buying back stock at a crazy valuation.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10

This was the biggest improvement.

https://preview.redd.it/gb6zb604jo0h1.png?width=1506&format=png&auto=webp&s=e8fae4e14c0ad33f0a2ca0e590f137dc95556542

The top 20% improved, but the bottom 20% got much worse. That is useful because a good factor should not only help identify winners, it should also help avoid disasters.

The interpretation:

A buyback is more attractive when it is backed by actual cash generation and reasonable valuation.

>Finding 3: The best buyback signal was not “high buyback yield.” It was high buyback yield plus real share reduction plus FCF yield.

Look for companies that are buying back shares, actually reducing diluted share count, and generating enough cash to justify it.

Test 4: Add debt control

Finally, I added a balance sheet guardrail:

>

If a company is already highly levered, spending cash on buybacks may be a bad capital allocation decision.

Buybacks are great when the company has excess capital.

They are less great when management is borrowing heavily just to shrink the share count or support EPS.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10
Buyback yield + diluted share reduction + FCF yield + debt	11.59%	-1.80%	+13.39 pp	10

This was interesting.

https://preview.redd.it/8nm80p36jo0h1.png?width=1512&format=png&auto=webp&s=fed8820da015e1cab06df3b0275d27b5bbaf7d69

Adding debt control did not improve the top deciles much. In fact, the top 20% CAGR was slightly lower than the previous version.

But it did improve the separation at the bottom.

So debt was not really a return engine. It was more of a mistake filter.

>Finding 4: Debt control helps identify bad buybacks more than it helps identify great buybacks.

This makes sense.

A strong company with high FCF and real buybacks can still have some debt.

But companies buying back stock while financially stretched are often playing a more dangerous game.

Main takeaway

The simple version:

Don’t look for companies that buy back a lot. Look for companies that buy back well.

The full version:

Buybacks become more interesting when:

1. The company has positive net buyback activity.
2. Diluted shares are actually going down.
3. The company generates strong free cash flow.
4. The stock is not obviously expensive, proxied here by FCF yield.
5. The balance sheet is not being abused to fund buybacks.

What each variable corrected

Added signal	What it fixes	Result
Net buyback yield	Measures buyback flow	Has signal, but noisy
Diluted share reduction	Filters cosmetic buybacks	Improves ranking quality
FCF yield	Adds cash generation and valuation	Stronger separation between good and bad deciles
Net debt / FCF	Avoids stretched balance sheets	Helps identify the bottom

Important!

This is not a complete investment strategy.

The best deciles beat SPY, but not by enough to declare victory and call it done.

There are still many things to test:

• transaction costs
• slippage
• taxes
• turnover
• volatility
• drawdowns
• sector exposure
• and waaaay more

So the conclusion is not “here is a strategy, go buy decile 10.” but something more like "buybacks contain useful information, but only when interpreted properly."

I’ve been digging into share buybacks recently, and I wanted to test something simple:

>Do companies that buy back shares actually perform better, or are buybacks mostly financial tricks?

More specifically: Which type of buyback works best?

Because “the company is buying back stock” is not enough. A buyback can be great, neutral, or actively stupid depending on the context.

A good buyback should probably do a few things:

1. Actually reduce the share count.
2. Be funded by real cash flow, not desperation leverage.
3. Happen at a reasonable valuation.
4. Not exist purely to offset stock-based compensation.
5. Not just make EPS look better while shareholders own the same percentage of the business.

So I ran a step-by-step backtest to see whether historical data supports that idea.

>This is not an academic paper, and I’m not claiming causality. It is more of a practical signal test: can buyback-related variables rank stocks by future returns?

Setup

I used Portfolio123 with FactSet data.

• Universe: Easy To Trade USA, basically US stocks with some liquidity and quality-of-data filters.
• Period: 2006 to 2026
• Method: Every 4 weeks, rank the universe into 10 equal-weight deciles based on different buyback-related factors. Then compare future performance by decile.

Returns include dividends, use point-in-time data, include delisted stocks, and are before taxes, transaction costs, and slippage.

Important: this is signal research, not a production-ready strategy!

Test 1: Net buyback yield

First, I tested the obvious metric:

>

The idea is simple. If a company is buying back a lot of stock relative to its size, maybe that contains useful information.

Result:

Factor	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	Decile 9

So yes, there is signal.

The high buyback-yield companies did much better than the low buyback-yield companies.

But the result was messy.

https://preview.redd.it/tn0s1cd0jo0h1.png?width=1516&format=png&auto=webp&s=14c0e4898f9a5cdb6efac461b8b4c74d0a8aa294

The best decile was not decile 10, it was decile 9. The middle deciles were also not especially clean. That suggests that simply buying the companies with the most aggressive buybacks is not enough.

And that makes sense.

A company can spend a lot on buybacks but still fail to reduce the diluted share count because of stock-based compensation, acquisitions paid with shares, options, RSUs, convertibles, or other forms of dilution.

So net buyback yield is useful, but noisy.

>Finding 1: Buyback yield contains signal, but “buying back a lot” is not the same as “buying back well.”

Test 2: Add real diluted share count reduction

Next, I added a measure of whether the buyback actually reduced the fully diluted share count over three years.

The logic: If a company buys back stock but diluted shares do not go down, the buyback may be more narrative than economics.

Metric:

>

Why diluted shares?

Because diluted shares better capture options, RSUs, convertibles, and other instruments that can dilute shareholders.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10

This improved the signal.

https://preview.redd.it/5nqp0r92jo0h1.png?width=1514&format=png&auto=webp&s=e8b098e70ad2300d36d9cb1b594a4479a7884a1a

The top decile became the best decile, and the spread between the best and worst groups widened.

This is probably the most intuitive result of the whole test:

Buybacks work better as a signal when they actually reduce the number of diluted shares.

That sounds obvious, but a lot of companies announce buybacks that do not meaningfully change shareholder ownership.

>Finding 2: The market seems to reward “real” buybacks more than cosmetic buybacks.

A real buyback is not just cash spent. It is a buyback that leaves remaining shareholders owning a larger percentage of the business.

Test 3: Add free cash flow yield

Then I added free cash flow yield.

Why?

Because FCF yield helps with two things at once:

1. It tells us whether the company generates enough cash to plausibly fund buybacks.
2. It adds a valuation component.

Buying back stock with abundant FCF is not the same as buying back stock with weak FCF.

Buying back stock at a low valuation is not the same as buying back stock at a crazy valuation.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10

This was the biggest improvement.

https://preview.redd.it/gb6zb604jo0h1.png?width=1506&format=png&auto=webp&s=e8fae4e14c0ad33f0a2ca0e590f137dc95556542

The top 20% improved, but the bottom 20% got much worse. That is useful because a good factor should not only help identify winners, it should also help avoid disasters.

The interpretation:

A buyback is more attractive when it is backed by actual cash generation and reasonable valuation.

>Finding 3: The best buyback signal was not “high buyback yield.” It was high buyback yield plus real share reduction plus FCF yield.

Look for companies that are buying back shares, actually reducing diluted share count, and generating enough cash to justify it.

Test 4: Add debt control

Finally, I added a balance sheet guardrail:

>

If a company is already highly levered, spending cash on buybacks may be a bad capital allocation decision.

Buybacks are great when the company has excess capital.

They are less great when management is borrowing heavily just to shrink the share count or support EPS.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10
Buyback yield + diluted share reduction + FCF yield + debt	11.59%	-1.80%	+13.39 pp	10

This was interesting.

https://preview.redd.it/8nm80p36jo0h1.png?width=1512&format=png&auto=webp&s=fed8820da015e1cab06df3b0275d27b5bbaf7d69

Adding debt control did not improve the top deciles much. In fact, the top 20% CAGR was slightly lower than the previous version.

But it did improve the separation at the bottom.

So debt was not really a return engine. It was more of a mistake filter.

>Finding 4: Debt control helps identify bad buybacks more than it helps identify great buybacks.

This makes sense.

A strong company with high FCF and real buybacks can still have some debt.

But companies buying back stock while financially stretched are often playing a more dangerous game.

Main takeaway

The simple version:

Don’t look for companies that buy back a lot. Look for companies that buy back well.

The full version:

Buybacks become more interesting when:

1. The company has positive net buyback activity.
2. Diluted shares are actually going down.
3. The company generates strong free cash flow.
4. The stock is not obviously expensive, proxied here by FCF yield.
5. The balance sheet is not being abused to fund buybacks.

What each variable corrected

Added signal	What it fixes	Result
Net buyback yield	Measures buyback flow	Has signal, but noisy
Diluted share reduction	Filters cosmetic buybacks	Improves ranking quality
FCF yield	Adds cash generation and valuation	Stronger separation between good and bad deciles
Net debt / FCF	Avoids stretched balance sheets	Helps identify the bottom

Important!

This is not a complete investment strategy.

The best deciles beat SPY, but not by enough to declare victory and call it done.

There are still many things to test:

• transaction costs
• slippage
• taxes
• turnover
• volatility
• drawdowns
• sector exposure
• and waaaay more

So the conclusion is not “here is a strategy, go buy decile 10.” but something more like "buybacks contain useful information, but only when interpreted properly."

I’ve been digging into share buybacks recently, and I wanted to test something simple:

>Do companies that buy back shares actually perform better, or are buybacks mostly financial tricks?

More specifically: Which type of buyback works best?

Because “the company is buying back stock” is not enough. A buyback can be great, neutral, or actively stupid depending on the context.

A good buyback should probably do a few things:

1. Actually reduce the share count.
2. Be funded by real cash flow, not desperation leverage.
3. Happen at a reasonable valuation.
4. Not exist purely to offset stock-based compensation.
5. Not just make EPS look better while shareholders own the same percentage of the business.

So I ran a step-by-step backtest to see whether historical data supports that idea.

>This is not an academic paper, and I’m not claiming causality. It is more of a practical signal test: can buyback-related variables rank stocks by future returns?

Setup

I used Portfolio123 with FactSet data.

• Universe: Easy To Trade USA, basically US stocks with some liquidity and quality-of-data filters.
• Period: 2006 to 2026
• Method: Every 4 weeks, rank the universe into 10 equal-weight deciles based on different buyback-related factors. Then compare future performance by decile.

Returns include dividends, use point-in-time data, include delisted stocks, and are before taxes, transaction costs, and slippage.

Important: this is signal research, not a production-ready strategy!

Test 1: Net buyback yield

First, I tested the obvious metric:

>

The idea is simple. If a company is buying back a lot of stock relative to its size, maybe that contains useful information.

Result:

Factor	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	Decile 9

So yes, there is signal.

The high buyback-yield companies did much better than the low buyback-yield companies.

But the result was messy.

https://preview.redd.it/tn0s1cd0jo0h1.png?width=1516&format=png&auto=webp&s=14c0e4898f9a5cdb6efac461b8b4c74d0a8aa294

The best decile was not decile 10, it was decile 9. The middle deciles were also not especially clean. That suggests that simply buying the companies with the most aggressive buybacks is not enough.

And that makes sense.

A company can spend a lot on buybacks but still fail to reduce the diluted share count because of stock-based compensation, acquisitions paid with shares, options, RSUs, convertibles, or other forms of dilution.

So net buyback yield is useful, but noisy.

>Finding 1: Buyback yield contains signal, but “buying back a lot” is not the same as “buying back well.”

Test 2: Add real diluted share count reduction

Next, I added a measure of whether the buyback actually reduced the fully diluted share count over three years.

The logic: If a company buys back stock but diluted shares do not go down, the buyback may be more narrative than economics.

Metric:

>

Why diluted shares?

Because diluted shares better capture options, RSUs, convertibles, and other instruments that can dilute shareholders.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10

This improved the signal.

https://preview.redd.it/5nqp0r92jo0h1.png?width=1514&format=png&auto=webp&s=e8b098e70ad2300d36d9cb1b594a4479a7884a1a

The top decile became the best decile, and the spread between the best and worst groups widened.

This is probably the most intuitive result of the whole test:

Buybacks work better as a signal when they actually reduce the number of diluted shares.

That sounds obvious, but a lot of companies announce buybacks that do not meaningfully change shareholder ownership.

>Finding 2: The market seems to reward “real” buybacks more than cosmetic buybacks.

A real buyback is not just cash spent. It is a buyback that leaves remaining shareholders owning a larger percentage of the business.

Test 3: Add free cash flow yield

Then I added free cash flow yield.

Why?

Because FCF yield helps with two things at once:

1. It tells us whether the company generates enough cash to plausibly fund buybacks.
2. It adds a valuation component.

Buying back stock with abundant FCF is not the same as buying back stock with weak FCF.

Buying back stock at a low valuation is not the same as buying back stock at a crazy valuation.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10

This was the biggest improvement.

The top 20% improved, but the bottom 20% got much worse. That is useful because a good factor should not only help identify winners, it should also help avoid disasters.

https://preview.redd.it/gb6zb604jo0h1.png?width=1506&format=png&auto=webp&s=e8fae4e14c0ad33f0a2ca0e590f137dc95556542

The interpretation:

A buyback is more attractive when it is backed by actual cash generation and reasonable valuation.

>Finding 3: The best buyback signal was not “high buyback yield.” It was high buyback yield plus real share reduction plus FCF yield.

Look for companies that are buying back shares, actually reducing diluted share count, and generating enough cash to justify it.

Test 4: Add debt control

Finally, I added a balance sheet guardrail:

>

If a company is already highly levered, spending cash on buybacks may be a bad capital allocation decision.

Buybacks are great when the company has excess capital.

They are less great when management is borrowing heavily just to shrink the share count or support EPS.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10
Buyback yield + diluted share reduction + FCF yield + debt	11.59%	-1.80%	+13.39 pp	10

This was interesting.

Adding debt control did not improve the top deciles much. In fact, the top 20% CAGR was slightly lower than the previous version.

https://preview.redd.it/8nm80p36jo0h1.png?width=1512&format=png&auto=webp&s=fed8820da015e1cab06df3b0275d27b5bbaf7d69

But it did improve the separation at the bottom.

So debt was not really a return engine. It was more of a mistake filter.

>Finding 4: Debt control helps identify bad buybacks more than it helps identify great buybacks.

This makes sense.

A strong company with high FCF and real buybacks can still have some debt.

But companies buying back stock while financially stretched are often playing a more dangerous game.

Main takeaway

The simple version:

Don’t look for companies that buy back a lot. Look for companies that buy back well.

The full version:

Buybacks become more interesting when:

1. The company has positive net buyback activity.
2. Diluted shares are actually going down.
3. The company generates strong free cash flow.
4. The stock is not obviously expensive, proxied here by FCF yield.
5. The balance sheet is not being abused to fund buybacks.

What each variable corrected

Added signal	What it fixes	Result
Net buyback yield	Measures buyback flow	Has signal, but noisy
Diluted share reduction	Filters cosmetic buybacks	Improves ranking quality
FCF yield	Adds cash generation and valuation	Stronger separation between good and bad deciles
Net debt / FCF	Avoids stretched balance sheets	Helps identify the bottom

Important!

This is not a complete investment strategy.

The best deciles beat SPY, but not by enough to declare victory and call it done.

There are still many things to test:

• transaction costs
• slippage
• taxes
• turnover
• volatility
• drawdowns
• sector exposure
• and waaaay more

So the conclusion is not “here is a strategy, go buy decile 10.” but something more like "buybacks contain useful information, but only when interpreted properly."

I’ve been digging into share buybacks recently, and I wanted to test something simple:

>Do companies that buy back shares actually perform better, or are buybacks mostly financial tricks?

More specifically: Which type of buyback works best?

Because “the company is buying back stock” is not enough. A buyback can be great, neutral, or actively stupid depending on the context.

A good buyback should probably do a few things:

1. Actually reduce the share count.
2. Be funded by real cash flow, not desperation leverage.
3. Happen at a reasonable valuation.
4. Not exist purely to offset stock-based compensation.
5. Not just make EPS look better while shareholders own the same percentage of the business.

So I ran a step-by-step backtest to see whether historical data supports that idea.

>This is not an academic paper, and I’m not claiming causality. It is more of a practical signal test: can buyback-related variables rank stocks by future returns?

Setup

I used Portfolio123 with FactSet data.

• Universe: Easy To Trade USA, basically US stocks with some liquidity and quality-of-data filters.
• Period: 2006 to 2026
• Method: Every 4 weeks, rank the universe into 10 equal-weight deciles based on different buyback-related factors. Then compare future performance by decile.

Returns include dividends, use point-in-time data, include delisted stocks, and are before taxes, transaction costs, and slippage.

Important: this is signal research, not a production-ready strategy!

Test 1: Net buyback yield

First, I tested the obvious metric:

>

The idea is simple. If a company is buying back a lot of stock relative to its size, maybe that contains useful information.

Result:

Factor	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	Decile 9

So yes, there is signal.

The high buyback-yield companies did much better than the low buyback-yield companies.

But the result was messy.

https://preview.redd.it/r1sieensjo0h1.png?width=1508&format=png&auto=webp&s=78cf12041ecc2dec47eb8605f6e581059444dca2

The best decile was not decile 10, it was decile 9. The middle deciles were also not especially clean. That suggests that simply buying the companies with the most aggressive buybacks is not enough.

And that makes sense.

A company can spend a lot on buybacks but still fail to reduce the diluted share count because of stock-based compensation, acquisitions paid with shares, options, RSUs, convertibles, or other forms of dilution.

So net buyback yield is useful, but noisy.

>Finding 1: Buyback yield contains signal, but “buying back a lot” is not the same as “buying back well.”

Test 2: Add real diluted share count reduction

Next, I added a measure of whether the buyback actually reduced the fully diluted share count over three years.

The logic: If a company buys back stock but diluted shares do not go down, the buyback may be more narrative than economics.

Metric:

>

Why diluted shares?

Because diluted shares better capture options, RSUs, convertibles, and other instruments that can dilute shareholders.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10

This improved the signal.

https://preview.redd.it/7ll800sqjo0h1.png?width=1522&format=png&auto=webp&s=1af76dc0038a7ca0e26f388fea6ff159fedff9de

The top decile became the best decile, and the spread between the best and worst groups widened.

This is probably the most intuitive result of the whole test:

Buybacks work better as a signal when they actually reduce the number of diluted shares.

That sounds obvious, but a lot of companies announce buybacks that do not meaningfully change shareholder ownership.

>Finding 2: The market seems to reward “real” buybacks more than cosmetic buybacks.

A real buyback is not just cash spent. It is a buyback that leaves remaining shareholders owning a larger percentage of the business.

Test 3: Add free cash flow yield

Then I added free cash flow yield.

Why?

Because FCF yield helps with two things at once:

1. It tells us whether the company generates enough cash to plausibly fund buybacks.
2. It adds a valuation component.

Buying back stock with abundant FCF is not the same as buying back stock with weak FCF.

Buying back stock at a low valuation is not the same as buying back stock at a crazy valuation.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10

This was the biggest improvement.

https://preview.redd.it/3pfot76ojo0h1.png?width=1504&format=png&auto=webp&s=d4528d234b71a68d13fd2a65f170489479e48da1

The top 20% improved, but the bottom 20% got much worse. That is useful because a good factor should not only help identify winners, it should also help avoid disasters.

The interpretation:

A buyback is more attractive when it is backed by actual cash generation and reasonable valuation.

>Finding 3: The best buyback signal was not “high buyback yield.” It was high buyback yield plus real share reduction plus FCF yield.

Look for companies that are buying back shares, actually reducing diluted share count, and generating enough cash to justify it.

Test 4: Add debt control

Finally, I added a balance sheet guardrail:

>

If a company is already highly levered, spending cash on buybacks may be a bad capital allocation decision.

Buybacks are great when the company has excess capital.

They are less great when management is borrowing heavily just to shrink the share count or support EPS.

Result:

Ranking	Top 20% CAGR	Bottom 20% CAGR	Spread	Best decile
Net buyback yield	10.35%	1.51%	+8.84 pp	9
Buyback yield + diluted share reduction	10.93%	0.54%	+10.40 pp	10
Buyback yield + diluted share reduction + FCF yield	11.66%	-1.47%	+13.13 pp	10
Buyback yield + diluted share reduction + FCF yield + debt	11.59%	-1.80%	+13.39 pp	10

This was interesting.

https://preview.redd.it/oxocmpxmjo0h1.png?width=1516&format=png&auto=webp&s=7bf85de43d9d4992f89cb9e05a674a90aea481bc

Adding debt control did not improve the top deciles much. In fact, the top 20% CAGR was slightly lower than the previous version.

But it did improve the separation at the bottom.

So debt was not really a return engine. It was more of a mistake filter.

>Finding 4: Debt control helps identify bad buybacks more than it helps identify great buybacks.

This makes sense.

A strong company with high FCF and real buybacks can still have some debt.

But companies buying back stock while financially stretched are often playing a more dangerous game.

Main takeaway

The simple version:

Don’t look for companies that buy back a lot. Look for companies that buy back well.

The full version:

Buybacks become more interesting when:

1. The company has positive net buyback activity.
2. Diluted shares are actually going down.
3. The company generates strong free cash flow.
4. The stock is not obviously expensive, proxied here by FCF yield.
5. The balance sheet is not being abused to fund buybacks.

What each variable corrected

Added signal	What it fixes	Result
Net buyback yield	Measures buyback flow	Has signal, but noisy
Diluted share reduction	Filters cosmetic buybacks	Improves ranking quality
FCF yield	Adds cash generation and valuation	Stronger separation between good and bad deciles
Net debt / FCF	Avoids stretched balance sheets	Helps identify the bottom

Important!

This is not a complete investment strategy.

The best deciles beat SPY, but not by enough to declare victory and call it done.

There are still many things to test:

• transaction costs
• slippage
• taxes
• turnover
• volatility
• drawdowns
• sector exposure
• and waaaay more

So the conclusion is not “here is a strategy, go buy decile 10.” but something more like "buybacks contain useful information, but only when interpreted properly."