AgPa #11: Concentrated Stock Markets (2/7)

Long-Term Shareholder Returns: Evidence from 64,000 Global Stocks (2021)
Hendrik Bessembinder, Ta-Feng Chen, Goeun Choi, K.C. John Wei
SSRN Working Paper, URL

This is the second of seven AGNOSTIC Papers about the extreme concentration within stock markets. As announced last week, this one goes beyond the US and the authors do the same analyses for global stock markets. Although the paper is full of interesting statistics, I will focus on the same key results as last week. For the full details, I recommend to look at the original charts and tables in the paper. To (hopefully) make it clearer and more readable, I will refer to last week‘s papers as “US-papers”.

Since this is a follow-up, I skip the part on the Setup and Idea of the paper. I discussed this extensively in last week‘s post and I recommend reading this one first. But feel free to use the following list to navigate for yourself if this better suits you.

  • Week 1: Concentration in the US Stock Market between 1926 and 2019
  • Week 2: Concentration in Global Stock Markets between 1990 and 2019
  • Week 3: Dominance of the Tech-Industry?
  • Week 4: Characteristics of Big Winners?
  • Week 5: Identifying Big Winners Upfront?
  • Week 6: Even Big Winners had Bad Drawdowns
  • Week 7: The Same Pattern for US Mutual Funds

Everything that follows is only my summary of the original paper. So unless indicated otherwise, all tables and charts belong to the authors of the paper and I am just quoting them. The authors deserve full credit for creating this material, so please always cite the original source.

Data and Methodology

Except for the data, the methodology is essentially the same as for the original US-papers. The key concept is again Shareholder Wealth Creation (SWC). As a reminder, SWC measures the total wealth that a company creates over its lifetime in excess of risk-free treasuries. So it is something like a realized equity risk premium in absolute dollars instead of percentages.

In addition to their existing data for the US stock market from CRSP, the authors source information on global companies from Compustat. Both services are state of the art for finance-research and provide high quality data. The final sample includes 64,738 stocks from 26 developed- and 16 emerging countries. The sample period “only” ranges from January 1990 to December 2020 because data on global stocks is hardly available before. This is of course much shorter than the >90 years for the original US-papers, but I think the authors did the best they could. Obtaining a long history for more than 64,000 stocks without data errors is very hard. In my opinion, 30 years are therefore actually quite good.

Important Results and Takeaways

Longer investment-horizons lead to extremer return distributions – also outside the US

Similar to the US-papers, the authors start with simple frequency distributions of compounded buy-and-hold returns over different time frames. As a reminder, the key result was that high volatility and compounding of returns lead to much extremer return distributions over longer time periods. Those results are almost identical for the global non-US sample.

The first chart on the right shows the distribution of annual returns. The blue line denotes the US sample, the red line corresponds to the rest of the world. In general, the distributions look very similar. For both samples, annual returns are fairly symmetrically distributed between -100% and 100%. But there are substantial outliers on the right tail that lead to positive skewness in both samples.

The distribution changes dramatically for decade returns. For both samples, the most common return is no longer close to 0% but around -100%. There are also some more pronounced differences between the US- and non-US sample. Although the US had more observations around -100%, there are generally more negative decade returns for the non-US sample.1The red line is above the blue line for most of the interval between -100% and 0%. Due to compounding, the impact of outliers is much stronger over this longer time horizon for both samples.

Finally, the lifetime returns of companies are even more concentrated (note the different scaling of the y-axis). Most companies in the US and globally had negative lifetime returns, in many cases even a total loss of -100%. Over the long-term, very few big winners have driven the entire market while most other stocks underperformed.

Figure 2, 3 and 4 of Bessembinder et al. (2021).

Just 2.4% of all companies created the entire net wealth in global stock markets

Similar to last week, I will continue with the concentration curve of shareholder wealth creation (SWC) in the following chart. In this follow-up, the authors further distinguish net- and gross SWC. Gross SWC only counts firms that actually created wealth while net SWC also includes wealth-destroyers. In my opinion, net SWC is the more relevant number because this is what investors ultimately receive. So I will stick with it for the remaining post.

Figure 5 of Bessembinder et al. (2021).

The global curve looks very similar to last week’s version for the US. Only 1,526 firms created the entire net wealth in global stock markets between 1990 and 2020. These are 2.4% of companies creating 100% of the wealth. This is extreme concentration by any standard. Also similar to the results of last week, the majority of stocks (57.7%) underperformed risk-free treasuries over their lifetime. In fact, just 42.3% of companies created wealth over their lifetime.

To illustrate this more dramatically. The top 1,526 companies (2.4%) created the entire net wealth. The other 25,441 companies (39.9%) with positive wealth-creation just compensated the wealth-destruction of the remaining 36,818 companies (57.7%). The cynic in me says that this is very similar to many other aspects of life. A small fraction is really pushing it while the decent job of many is just sufficient to compensate the damage of the majority.

Overall, the results are very similar to the US-papers or even more extreme. However, we cannot directly compare the numbers with those of last week because of the different sample period. But the authors show in another table that global stock markets were indeed more concentrated than just the US. This is very strong out-of-sample evidence for the pattern. Let’s now do the most interesting part of this post and look who those top wealth creators are.

Table 4 of Bessembinder et al. (2021).

Unsurprisingly, the large and well-known US corporations still dominate the list. But there are also a few international companies. For example, Tencent, Samsung, TSMC, Nestle, and Kweichow Moutai (a Chinese liquor company) are all within the top 20. In total, the top 50 in this table created 30.87% of the wealth in global stock markets. So even within the small group of the top 2.4%, wealth creation is again extremely concentrated.

All stock markets are concentrated but there are regional differences

To examine the degree of concentration around the world, the authors provide a few statistics in the following table. This is just an excerpt of regions and development statuses, but the full version also includes each of the 42 countries. However, I decided to keep it rather brief at this point and refer to the original for those who are more interested.

Excerpt from Table 6 of Bessembinder et al. (2021).

The first interesting result is the composition of wealth between the US and non-US countries. Of the total $75,661B2Just to illustrate the size of this number: 75,661,000,000,000. net wealth, less than half comes from international companies ($30,733B). The tremendous success of the US always surprises me again.

Another interesting result are the differences across regions. Asia-Pacific is by far the region with the most concentrated stock markets, followed by North America and Europe, respectively. This is particularly interesting because there are much more Asian than European companies in the sample. There are also subtle differences between Developed and Emerging countries but I doubt that those are statistically significant.

Conclusions and Further Ideas

The important part of an out-of-sample study is that the original results hold up with different data.3Of course, this still doesn’t proof anything. But for messy environments like stock markets, out-of-sample tests are the best we have. For the extreme concentration within stock markets, this is clearly the case. The pattern is very similar for 41 countries beside the US and in some cases even stronger. So it is fair to say that extreme concentration is a pervasive feature of stock markets and not limited to certain countries.

As a consequence, the implications also remain unchanged. Active investors use the results to argue how much value they can create by selecting the few big winners and/or avoiding the underperforming majority. Passive investors use the same results to argue in favor of broad diversification and market-cap weighting to ensure you don’t miss the big winners. At this point, it is worth mentioning that the authors received (financial) support from Baillie Gifford for this global follow-up. Baillie Gifford is an active asset manager specialized on concentrated long-term investing in (hopefully) big winners. So this fit’s quite well to the implication for active investors and it is nice to see that this research finally arrived in the industry.

For (active) asset managers, it is of course more important to find the few big winners before they become the few big winners. This will be a large topic for the remaining 5 weeks of this series. Next week, I will start with the industries to look at.

This content is for educational and informational purposes only and no substitute for professional or financial advice. The use of any information on this website is solely on your own risk and I do not take responsibility or liability for any damages that may occur. The views expressed on this website are solely my own and do not necessarily reflect the views of any organisation I am associated with. Income- or benefit-generating links are marked with a star (*). All content that is not my intellectual property is marked as such. If you own the intellectual property displayed on this website and do not agree with my use of it, please send me an e-mail and I will remedy the situation immediately. Please also read the Disclaimer.


1 The red line is above the blue line for most of the interval between -100% and 0%.
2 Just to illustrate the size of this number: 75,661,000,000,000.
3 Of course, this still doesn’t proof anything. But for messy environments like stock markets, out-of-sample tests are the best we have.