AgPa #12: Concentrated Stock Markets (3/7)

Extreme Stock Market Performers, Part II: Do Technology Stocks Dominate? (2020)
Hendrik Bessembinder
SSRN Working Paper, URL

This is the third of seven AGNOSTIC Papers about the extreme concentration within stock markets. After establishing the results in both the US and global markets, the next papers examine the few big winners in more detail. For (active) investors, this is of course even more important because their job is to find winners before others. Following the old saying, “If you want to catch fish, fish where the fish are.”, I start with the industry composition of the most successful companies. The performance of the large tech-companies may suggest that technology is the only industry to look for big winners. But as we will see, it is (unfortunately) not that easy.

Similar to last week, I recommend to read the posts of this series chronologically. 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

All following analyses are based on the original US sample. The author uses stock market data from CRSP to construct a sample that includes 26,285 US companies between 1950 and 2019. For fundamental data and SIC industries, the author uses Compustat and Kenneth French’s well-known website.[1]SIC stands for Standard Industry Classification. More information about it here. All of those services are state of the art for finance-research. So even though the paper is not peer-reviewed, data quality shouldn’t be an issue.

The key concepts of the analysis are total-returns in excess of cash[2]Total return means that all dividends are reinvested. For brevity, I will just refer to “returns” or “decade-returns” in the following. and Shareholder Wealth Creation (SWC). Again the 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. Most importantly, it accounts for the fact that dividends cannot be reinvested in aggregate. Therefore, it takes the perspective of a hypothetical investor who owns the entire company.

The author calculates returns and SWC for the seven unique calendar-decades between 1950 and 2019. For each of those decades, he identifies the best and worst 200 companies in terms of total returns and SWC (Top 200 and Bottom 200, respectively). Each top- and bottom-list therefore consists of 1,400 companies.[3]200 firms for seven decades: 200 x 7 = 1,400 Those collections of winners and losers are the basis for the following analysis. The more ordinary companies in between (Non-200) serve as a benchmark.

Important Results and Takeaways

The Tech-Industry is not as dominant as it seems at first glance

Before coming to the overall results, let’s first answer the question about the tech-industry’s dominance. Apple, Microsoft, Amazon, Alphabet/Google, and Meta/Facebook[4]By the way, it’s time that Wall Street invents a new acronym to scream on TV. With two name-changes and Netflix being no longer the darling of investors, FANG/FAANGM is finally obsolete, isn’t it? were without any doubt among the most successful companies in history. However, they are compensating for hundreds of failed tech-companies. So just being a tech-company doesn’t necessarily give you a higher chance to generate outstanding returns or become a large wealth-creator.

Of the Top 200 companies with the highest decade-returns, about 23.27% are tech-companies. This is the highest number for any industry, so a lot of the very successful firms are indeed tech-companies. But, and this is the catch, there are even more tech-companies among the Bottom 200 (30.16%). So if you pick a random tech-company, it is more likely to end up with a big loser than a big winner. At least statistically, the tech-industry is therefore not the best place to search for big winners. For SWC instead of returns, the pattern is slightly better but still not attractive.

There is (unfortunately) not “the one” industry to look at

After the primer on the tech-industry, let’s now look at the remaining industries. The following table shows the industry-composition of Top 200, Bottom 200, and Non-200 firms for decade-returns. For a fair comparison, the author uses over- and under-representation to evaluate the attractiveness of industries. For example, 23.27% of the Top 200 were tech-companies but only 20.38% of the Non 200. The technology industry is thus about 14.17% over-represented among winners.[5](23.27 / 20.38 – 1) x 100 = 14.17%. Deviations due to rounding.

Table 1 of Bessembinder (2020).

The most attractive industries to catch the companies with the highest decade-returns are Telecommunications, Healthcare and Pharmaceuticals, and Energy.[6]Excluding the Missing industry. But again, this is not the full picture. For example, telecommunication-firms are most over-represented among the Top 200 (71.06%), but they are even more over-represented among the Bottom 200 (132.47%). So just like in the tech-industry, the odds are against you and it’s more likely to catch a loser.

From a more balanced “net-perspective”, the Healthcare and Energy industry appear to be the most attractive. Both are substantially over-represented among the Top 200, while being simultaneously under-represented among the Bottom 200 (60.72% vs. -28.09% and 53.22% vs. -28.06%, respectively). Statistically, this offers the best chance to catch winners and avoid losers.

These were the results for cumulative returns. But as discussed in the first post of this series, the companies with the highest returns are not necessarily the biggest wealth-creators. As the next table shows, the industry-composition is indeed quite different for the big winners and losers in terms of SWC.

Table 2 of Bessembinder (2020).

The largest fraction of wealth-creators are still tech-companies, but they are now even under-represented among the Top 200 (-37.05%). In contrast, Utilities and Energy are by far the most over-represented industries among the Top 200. The author doesn’t comment on specific reasons for this. However, I suspect that this is because such companies often pay high dividends which are important for the SWC measure.

Form the again more balanced “net-perspective”, Consumer Nondurables, Utilities, and Finance look (statistically) most attractive to find big wealth-creators. This is entirely different than the most attractive industries for the best decade-returns. A part of this is certainly driven by the different size of companies, but ultimately, the results are somewhat inconclusive.[7]A very small company with outstanding returns will show up among the Top 200 cumulative returns, but not among the Top 200 SWC.

Conclusions and Further Ideas

For me, the main result of this paper is that there is not “the one” industry to look for big winners. This is bad news for active investors because industry-filters don’t really help to narrow the search. However, the good news is that there are great companies in every industry. So if you don’t enjoy analyzing tech-companies, you can also find some big winners elsewhere. Although somewhat inconclusive, the analyses is of course not entirely useless. In the sense of higher probabilities to end up with big losers, some industries are clearly more risky than others.

Finally, I would like to mention one methodological detail. The author calculates returns and SWC for seven non-overlapping decades and aggregates the results over this time period. There is nothing wrong with that but I would have been interested to see the industry-composition of winners and losers for each decade over time. I suspect that it is quite unstable and that the winning industry of one decade is rarely the winning industry of following. Therefore, industries may be even less helpful to identify big winners in practice. But these are just my hypotheses, I haven’t seen the data.

Overall, information about a company’s industry appears not really useful to identify big winners. Next week, I will therefore go one step further and examine observable company-characteristics of the big winners.

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