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Industry Concentration Strategy

I came across this recent paper: “Are US Industries Becoming More Concentrated” (see here), which contains some interesting findings. The authors convincingly argue that industries in the US have become significantly more concentrated in the last two decades as fewer companies have gone public and there are more mergers (Credit Suisse has also put out a similar research note documenting the drop in the number of listed securities). The authors show that over the last two decades, over 75% of the industries have become more concentrated, leading to larger companies that face less competition. In fact, there are fewer public companies now than there were in the early 1970’s, when GDP was a fraction of what it is now. They further show that the decline in public companies has not been replaced by private firms or foreign firms.

The main measure they use to gauge industry concentration is called the Herfindahl-Hirschman Index (HHI) (see here), which is the same measure used by regulators to determine whether a potential merger will be anti-competitive. The HHI is defined as the sum of the squared ratios of firm sales to total industry sales. For example, if there were three firms in an industry, one with a market share of 50% and the other two with market shares of 25% each, then the HHI = (0.5)^2 + (0.25)^2 + (0.25)^2 = 0.375. The highest possible HHI, when there is only one firm in a particular industry, is 1.0

Even though the focus of the paper is to examine and explain recent trends in industry concentration, the authors briefly describe a trading strategy. The authors suggest buying the stocks in the ten industries with the largest yearly increase in HHI, shorting the stocks in the ten industries with the largest yearly decrease in HHI, and holding for one year. They form equally weighted portfolios of the ten industries, and use the NAICS industry classification (which is available on Quantopian from Morningstar). They claim to achieve an annual alpha of around 9% from 2001-2014. These results suggest that investors do not fully appreciate the benefits of operating in an environment of less competition.

Unfortunately, when I tried to reproduce the results of their paper, I achieved less than half of the returns they reported, and even that required a little bit of data mining. I tried a second measure of industry concentration, and I also measured HHI using Q500, Q1500, and even the entire universe of stocks in existence (I had to save the sales in the prior quarters in context variables to avoid timeout errors). Counterintuitively, the backtesting results were worse when I measured HHI more accurately using the full universe of stocks. While there are several small differences in methodology, it’s unclear whether that would account for the gap in results.

The attached algorithm is very simple – holding stocks for a full year and making no attempt to pick stocks within an industry – so there are certainly areas for potential enhancement. For example, since firms that complete stock mergers tend to underperform (abstract here), perhaps the subset of stocks that are not involved in mergers benefit the most from consolidation in an industry. Also, it is possible that other signals can be used to sort stocks within long and short industries.

Even though the backtesting results were not spectacular, I thought the paper offered a nice example of using the industry classification data in a novel way. Industry classifications are typically used to reduce the industry exposure of a portfolio, but here it’s used as a signal itself. Incidentally, another paper published recently uses the same industry concentration measure to enhance a separate anomaly (see here). The authors create a signal based on corporate governance, and find that stocks that have strong governance (for example, no poison pills or staggered boards) outperform, but only for firms in industries with a high HHI. They argue that firms in less competitive industries benefit more from good governance, where there is less pressure imposed by the markets.

Clone Algorithm
Backtest from to with initial capital
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 591e19f2ca415d6193d28893
There was a runtime error.

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4 responses

Hi Rob,
Thanks so much for this post! I have learned a lot from it. I want to ask you a question regarding this post. The result of running the program looks like the figure( The figure shows that numbers of unique industries is 65, but the result does not print out whole 65 industries. If I want to see the result of 65 industries, could you tell me how to do? Thank you.

If the theory is right, could it work better cap-weighting the stocks (or buying/selling cap-weighted industry ETFs), in that the top player(s) may benefit more from concentration?

Fantastic post Rob! I just joined this community and already learning so much. Keep up the good work!

Hi Rob,
Thanks for the post. However, there's some question that I encounter running this code. The algorithm doesn't work. I'm wondering how can I fix it.Thank you.
Here is the result