Study: 100 Baggers in Europe (Part I)
A study on 336 European companies that returned at least 100x from 1980 to 2025.
I recently read the book “100 Baggers: Stock to return 100 to 1 and how to find them.” It got me excited to find a study on European 100 baggers. After looking around on the Internet, I turned up empty-handed.
Why are there no studies on phenomenal European stock returns? How hard can it be?
So, I decided to perform a study myself. And boy was I wrong about the project’s complexity. Let’s dive in!
The study is built up as follows: first, the steps of the process are explained, followed by the discussion of the data. As last, there is a discussion about the shortcomings of the study. Or jump directly to the results.
Process
The original book I use as a guide, explained how the acquired data for his study costed $50,000 in 2014.
Well, I don’t have that kind of money laying around to spend on this project. With a background in data science, I took it upon myself to collect, clean and analyze the stock price data myself.
It took almost 2 full weeks (had to correct some errors down the line) to create a clean dataset. It was surprisingly difficult to find European stock market data going all the way to the 1980s.
Public charting software like Tradingview and Yahoo Finance, visualize stocks from the end of the 90s, but mostly early 2000s.
I turned my search to stock price data providers like Twelvedata and Eodhd. Both providers offered some European countries, but not all of them. Italy, a major economy, wasn’t available at all.
In the end, I settled on Eodhd (no affiliate). For the simple reason that it offered more data for a lower price ($22/month).
The subscription gained me access to the stock exchanges of Austria, Belgium, Switzerland, Czech Republic, Germany, Denmark, Spain, Finland, France, Great Britain, Greece, Croatia, Hungary, Ireland, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, and Sweden.
To download the data and build my own dataset, I needed to write a python script to download every single available stock.
The script acquires first a list of available stocks from every exchange in the European countries. Then it downloads the full share price history of each stock.
I decided on a historical period from 1980 to 2025. But stock price data for most stocks don’t go back this far.
Anyway, the process from collecting data to cleaning the data is a combination of automation and handwork. For anyone who would like to reproduce the results or conduct a similar study, here are the steps:
A (python) script acquired the list of stocks available on the stock exchanges in Europe. Then the script downloads the OHLC (Open, High, Low, Close and adjust Close) and company data from 1980 onward.
A second script processed the lows, highs, dates, returns and duration of returns for every individual stock based on the adjusted close. The script calculated the duration in days from the lowest share price till a 100x return was acquired. It also calculated the maximum return and duration between the absolute low and high of the stock. These statistics are stored in a CSV file.
A filter excluded all returns below 100x from the file.
Since all the OHLC was not consistent, I poured over the 600+ stocks to check and clean up the statistics by hand. Here, I looked for outliers and check them individually. Some OHLC data points were missing, thus got dropped from the list just to be on the safe side.
Some companies are listed on multiple stock exchanges throughout Europe. Thus, duplicates were eliminated too. It resulted in 336 100 baggers.
The stock returns are based on the adjusted close which accounts for splits and dividends. Eodhd provided split data, but couldn’t verify all splits. Multiple sources had different split dates. Thus, for simplicity of the study, I chose the provided adjusted close.
In the original research in the book, “100 baggers”, the adjusted close accounted only for splits. (Maybe interesting for a future study)
Results
The study included 21 European countries. Italy is the only notable absentee. The study identified 336 100 baggers over the period 1980 to 2025.
The median duration of the multibaggers is 17.6 years. The choice of median over average is due to outliers skewing the average.
Countries
When you click (hover for desktop) on the countries in the chart below, you can see the number of 100 baggers per country. It also displays the median years for stocks to return 100x.
The Swedish and British stock market produced the most 100 baggers. The countries are followed by France, Germany, and Poland.
The countries, Ireland, Luxembourg, and Austria have no verified 100 baggers.
Years to 100x Return
In the bar chart below, we see the distribution of years required for companies to become 100 bagger.
What stands out is the 21 companies who only required a maximum of 4 years to have a 100x return.
As noted earlier, the return is calculated based on the adjusted close, which accounts for splits and dividends.
It shortens the time it takes to become 100 bagger. Because reinvested dividends acquire more shares, gaining more dividends and so on.
The adjustment for dividends and splits skews the results to a shorter amount of time needed. In the book, the author discussed a few stocks that actually grew very fast, and it only took around a decade.
I also noticed that some companies experienced favorable conditions for rapid returns. For example, some defense companies saw extreme share price increases due to excessive spending since 2022.
Then there are healthcare stocks, which experienced a significant boost during the covid-period. And in the countries Greece and Poland, the real estate development companies surged in the years leading up to 2008. But never recovered after the crash, the typical boom and bust cycles.
Adjusting for dividends allows a lot more companies to become 100 baggers sooner or to return 100x at all. For example, most Finnish stock prices stayed in a (relative) narrowband. Due to dividends for many decades, the country produced many 100 baggers.
As in long-term investor, we look for companies that put capital to work efficiently at high rates of return. And there are certainly companies who have experienced this. The share price reflected a beautiful exponential growth over a very long time.
Examples of such companies are the French companies Hermès and Moët Hennessy Louis Vuitton. Or the German software company SAP and the semiconductor equipment manufacturers ASML, ASM and BE Semiconductors in the Netherlands.
Sectors
The sectors that produced the most 100 baggers are industrials or financials.
In the right column, we can see how long it took for companies to return 100x per sector. The sectors with less than 10 companies have duration outliers. 10 years for communication services and over 26 years for real estate.
It is not possible to draw conclusions that these sectors produce the shortest and the longest return a 100-fold. This may simply be due to the fact that the sample size is too small.
The other sectors hover around the median of 17.6 years.
As seen in the graph, the information technology sector takes only 14.7 years to 100x return. It is expected from this sector to have fast outsized returns due to their asset-light structure.
The difference with other sectors isn’t significant at first. Information technology companies tend to pay out fewer dividends to grow faster, also in Europe. However, European companies favor high dividend yields over buybacks.
Which can be a reason as to why the duration to become a 100 bagger gap isn’t so big between the information technology sector and the other sectors.
Maximum Return
The last chart, below, displays the maximum return from each stock and how long it took.
35% of the companies reached a maximum return between 100 and 150 within 22.3 years.
And the higher the returns, the less companies achieved that. If you turn the chart 90° to the left, you can see that the graph is a typical distribution skewed to the left.
The only bucket (returns >1000) has 21 companies included. It is a collection that otherwise would have spread out way further, as these are called long tails.
It is noticeable (although slightly) that the higher returns take more years to play out.
Shortcomings
The study caught the basics of 100 baggers in Europe. There are many things that can be improved for future studies.
What first comes to mind is the stock data itself. It would be great to have two independent sources to compare the stock data to make sure it is reliable. Or obtaining the data directly from the stock exchange owners.
In the study, I used the adjusted close for splits and dividends. However, it is more interesting to only adjust for splits. This has to do with reinvestment of dividends. Every investor pays taxes over received dividends, and then said investor can’t always acquire the shares at the opening price. It creates slippage and skews the real results.
Another shortcoming of this research is the amount of work it takes to verify every stock. Right now, I only looked at the price and used my knowledge of the countries’ and sector’s conditions to draw some conclusions from it.
It lacks certain statistics to make the study applicable in the search for 100 baggers. If you prefer statistics on what makes a multibagger, then I would recommend the paper “The Alchemy of Multibagger Stocks” by Anna Yartseva.
I think we can all agree that it will be way more valuable to understand why stocks increased so drastically. What factors had to do with such increases? Is it just due to market circumstances? Was the company around just at the right time offering the right product or service? Or had management something to do with it.
The earlier-mentioned book shares some essential principles for finding 100 baggers:
You have to look for them. Don’t waste your limited time on stocks that might be a decent yield or that only rise 30 to 50%.
Look for value-added growth. You want the per-share value to increase.
Lower multiples are preferred. A growing company may also grow its low multiple into a high one. This makes the share price rise, even faster.
Economics moats are mandatory.
Smaller companies are preferred. It’s easier to grow 100 times from 100 million than from 10 billion.
Owner-operators are preferred because they take the shareholders’ best interests into account.
Time is your friend, it doesn’t happen overnight.
You need to filter out all the noise. With 24/7 market updates it’s easy to be tempted to sell.
Luck helps as you can always forecast the company’s growth or new avenues of growth.
You should be reluctant to sell.
I think that most are applicable if not all are also applicable to European multibaggers.
Other Angles
The data missed lots of additional information such as outstanding shares at all times. It would be cool to know at what market capitalization the companies were at the lowest adjusted close.
From there, we can draw conclusions about which market cap sizes to put our attention to. However, logic dictates it is more likely to find 100 baggers in small caps than in middle or large caps.
Maybe even having such data might not be as insightful as expected. Since 30 to 40 years ago, it was a different time with less money circulating. Nowadays, there’s much more money in existence in the world. It is now more likely that a $1 billion company can reach $100 billion.
Part II
While writing this study, some other ideas came up to pull the available data more apart (read: analyze further). Since the deadline for publishing is already over, and I am late with this one, I decided to keep these for part 2.
Thus, stay tuned if you would like to see more insights into European 100 baggers!
Conclusion
This study is an introduction to 100 baggers in Europe. From a data analysis point of view, it was a fun study to perform. It gives some insight on 100 baggers in Europe. Often those studies focus on only the American markets.
It was certainly difficult to get the necessary information and verify its validity. That’s why I can’t be certain about every single stock.
Yet it is a clear indication that Europe also locates companies that produce significant market returns. It proves that Europe is also an attractive market to invest in for outstanding returns.
If I try to re-create or expand on this study, what other statistics or insights would you like to be proven? Please let me know in the comments!
Disclaimer
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As I’m currently working on a series on the European ecosystem in Macros, it’s really nice to read an article like this, it gives a lot of insight. Thank you!
I may have missed it but did you account for survivorship bias? Do all these companies exist today? There’s probably several that were 100xers but then got bought out or delisted. Right now there may be say 5000 companies that have been trading since 1980, but in that same period there could have been 10000 that went bankrupt or taken off the exchange.