AgPa #9: The Age of Intangible Assets

Equity Investing in the Age of Intangibles (2021)
Amitabh Dugar & Jacob Pozharny
Financial Analysts Journal, 77(2), 21-42, URL

I am late to the party with this one but I think it is still relevant. This week’s AGNOSTIC Paper examines the role of intangible assets for equity investors. As the world got eaten by software and technology, such assets became increasingly important. And of course, we just had this tremendous boom of technology-driven, asset-light companies over the last decade.

The underlying idea is quite simple. Most business models adapted to a world of digital services, research and development (R&D), and data analytics. In contrast, accountants apparantly still live in the tangible, brick-and-mortar-factory world of the last century.1That’s of course somewhat exaggerated and some accountants work on the weaknesses of the current system. But in general, most accounting rules work better for old-fashioned, capital-intense companies. As a consequence, many investors now question the relevance of traditional metrics like book value of equity and see a need for adjustments. The issue is heavily important because it deals with one of the most important sources of information in stock markets – financial statements of companies.

The authors develop a transparent methodology to classify industries into High Intangible Intensity and Low Intangible Intensity. In the second step, they examine the relevance of Book Value per Share (BPS) and Earnings per Share (EPS) within those two groups. Although the paper doesn’t include actionable adjustments for intangibles, I still like it. The authors present a testable framework for a discussion that is often story-based. In my opinion, this is exactly how we should approach such issues. As always, I divided this post into the following parts.

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.

Setup and Idea

To illustrate the impact of intangibles, let’s have a look at Microsoft. As of June 30, 2021 Microsoft’s book value of equity was $141,998M or about $18.88 per share. The stock price at this date was $268.71, meaning Microsoft traded at a fairly high Price-to-Book ratio of 14.23.2Data from TIKR* and Yahoo Finance. The large gap between book- and market value is of course not surprising. Accounting numbers document the past whereas market prices (should) reflect investor’s collective expectations about the future.

So most of Microsoft’s value is not visible on its balance sheet. The brainpower and creativity of its employees, the billions of people who use the products every day, the potential breakthroughs that are currently under development, and much more. In fact, most of these things are intangible assets. Unfortunately, accountants have a problem with those.3Again, this is exaggerated and only true on a big picture level. In practice, some intangible assets are treated similar to tangibles under certain conditions. Depending on the country, the accounting rules involve a lot of details. They tend to be conservative and don’t want to have assets with uncertain values on companies’ balance sheets. Firms shouldn’t appear richer than they are.

As a consequence, much of the interesting (and valuable) stuff doesn’t show up on the balance sheet. For example, Microsoft invested $69,749M in tangible assets between 2017 and 2021. This is a lot, but they invested even more in R&D ($84,624M). The $70B of tangible assets show up on the balance sheet but the $85B of research efforts (and results) do not. Research may lead to nothing productive, so accountants force firms to treat it as an immediate cost. But R&D could also lead to the next Microsoft Office that may be worth billions. Again, you won’t find it on the balance sheet. And this is just one example how book- and market values detach themselves from eachother.4There is a lot of work on adjusting assets for R&D expenses. One of the best resources for this is Aswath Damodaran who explains it in great detail.

I picked this example to show the relevance of the topic. Large companies like Microsoft now invest much more in intangible assets than in traditional tangibles. But only a fraction of it ends up on the balance sheet. That’s an unfortunate deviation between accounting and reality. As a consequence, it became more difficult to value companies for investors. At least in some industries, the available accounting figures just became less useful. The paper shows that this is not just a reasonable story but also a robust empirical fact.

Data and Methodology

The authors collect financial- and market data for publicly traded companies in the world’s 15 largest economies between 1994 and 2018. Specifically, they use Standard & Poor’s Xpressfeed database and apply various filters to ensure data availability and consistency. Xpressfeed is widely used for finance research, so data-quality should be very high. I also like the fact that the authors not only include the US but also international companies. Overall, the sample consists of several thousand companies in each year.

The authors explicitly retain very small- and loss-making companies. Researchers often exclude them because they are hardly investable for larger investors. However, the authors argue that those companies are often the most intangible-intense of all. Think for example about a small biotech start-up that has nothing but an idea for a revolutionary drug.

An evident problem with such international studies of financial statements are different accounting rules. An intangible asset in the US is not necessarily an intangible asset in Europe and vice versa. Data providers try to harmonize such differences but in the end, it remains somewhat inconsistent. The authors address this issue with a bunch of statistical tests. All of those suggest that their results are internationally consistent.

Finally, the authors use 21 Global Industry Classification Standard (GICS) Industry Groups for their analysis.5More information about the GICS classification here. Due to their specific reporting reporting practices, they exclude Banks, Insurance, and Diversified Financials. This is common practice in research on corporate fundamentals and the GICS classification is widely-used in practice. For brevity, I will just refer to “industries” in the remaining post.

Important Results and Takeaways

A measure for intangible-intensity of industries

Based on a comprehensive literature review, the authors use a composite of the following three accounting metrics to measure the intangible-intensity of industries:

  • Intangible Assets (excl. Goodwill) / Total Assets
  • Research and Development Expenses / Total Revenues
  • Sales, General & Administrative Expenses / Total Revenues

The first two are quite intuitive, so let me comment on the last one. Sales, General & Administrative (SG&A) Expenses include for example marketing campaigns or employer training. Depending on the business model, brand value and human capital of employees are important assets. For example, the Super Bowl Halftime Show is clearly part of the Pepsi brand. But again, you won’t find it on the balance sheet. Although imprecise, SG&A expenses are therefore a reasonable indicator for intangible-intensity.

The authors’ composite is then actually quite simple. For each of the three metrics, they calculate the median within the 21 GICS Industry Groups in each year. Subsequently, they rank the industries by the respective medians. Finally, they take the average rank of all three as final measure. The higher the rank, the higher more intangible-intense the industry. The following chart shows the average ranks for each industry over the entire sample period.

Table 1 of Dugar and Pozharny (2021).

The authors define the 10 lowest-ranked industries as Low Intangible Intensity and the remaining 11 as High Intangible Intensity. In my opinion, there are very few surprises. The least intangible-intense industries are businesses that require a lot of physical capital. For example, Utilities, Real Estate, Energy, Materials, or Capital Goods. On the contrary, knowledge-based businesses like Biotechnology, Software, or Health Care appear on the other end of the list. The classification of industries is also remarkable similar for the US and the international sample. Except for Energy and Retailing, classifications are identical. As mentioned above, the authors also support this international consistency with further statistical tests.

Book values became less relevant for intangible-intense industries but still remain important

In the second part of the paper, the authors use their measure to examine the relevance of Earnings per Share (EPS) and Book Value per Share (BPS) for equity investors. Investors use those metrics in popular valuation multiples like Price-to-Earnings- or Price-to-Book-Ratios to evaluate the attractiveness of stocks. But since those metrics do not properly reflect important intangible assets, such approaches may be misleading. At least in intangible-intense industries.

The idea for this analysis is again quite simple. In each year, the authors regress stock prices on EPS and BPS. The higher the R2 of this regression, the more relevant are EPS and BPS for stock prices.6The R2 ranges from 0 to 1 and tells us how much variation the model explains. For example, a value of 0.2 indicates that EPS and BPS explain 20% of the variation in stock prices. Although this model is not very practical, a higher R2 indicates that studying EPS and BPS helps to explain stock prices. The following chart shows the pattern for the two industry-classifications in the US over time.

Figure 1 of Dugar and Pozharny (2021).

In my opinion, the chart looks somewhat messy. However, there are a few insights. First, there are no clear differences before the financial crisis 2008/09. But starting from 2009, the gap opens and traditional metrics became less important for intangible-intense industries. Since 2015, this gap is closing again. Second, despite all changes of business-models, EPS and BPS still remain important for stock prices. Even for intangible-intense industries, the R2 are consistently above 0.15 and increased substantially since 2014. That’s good news for fundamental analysts and investors. At least according to this data, their profession is still not hopeless.

Figure 2 of Dugar and Pozharny (2021).

The chart above shows the same picture for the international sample. The picture looks fairly different, especially the scaling of the y-axis. EPS and BPS were apparently much more relevant in international markets than in the US. Except for the period between 1994 and 2000, the R2 are about twice as high. I couldn’t find a specific explanation for this in the paper, but I suspect that this is because the US is the more efficient stock market.7More efficient market means less predictability and therefore lower R2. Apart from that, there are much clearer differences between high- and low intangible-intensity industries in international markets. In fact, the gap between the two lines continuously widened since 2006.

Overall, the authors conclude that the two traditional accounting metrics became indeed less relevant for companies in intangible-intense industries. They also find this in further regression analyses. I think the methodology is quite good and I mostly trust the results. But when I read the paper, one question came to my mind. Why even look at the R2? We could also just run a regression of stock prices on EPS, BPS, and interactions with the intangible-intensity indicator. A statistically significant coefficient for the interaction should also tell us something about the different role of EPS and BPS in intangible-intense industries. But note that this is not a critique. It’s just a different idea and I don’t know if its actually better.

Conclusions and Further Ideas

This paper offers surprisingly few actionable insights. However, I think there are two important results with clear implications.

First, intangible assets are important and here to stay. Most of today’s business strategies are based on brand, network effects, R&D, smart employees and other intangible factors. I don’t believe this will reverse any time soon. Much less do I believe that we will see a revolution of accounting rules to correctly reflect those issues. So it is important to deal with intangibles and having a classification of industries is clearly helpful. The authors show impressively that even a simple methodology can work very well.

Second, traditional ways of analyzing stocks remain important but probably require adjustments. Especially those in intangible-intense industries. The paper offers a natural starting point and there are many related approaches. For example, re-capitalizing R&D expenses or analyzing patent data. In any case, the issue is at the heart of fundamental analysis and corporate valuation.

For me, the main takeaway is that investors must include enough details in their processes. Just looking at aggregated earnings or book values is not sufficient. Notably, this applies to both discretionary and systematic investors. For example, Arnott et al. (2021) show that the current drawdown of the academic value factor can be partly explained by – what else could it be – unconsidered intangible assets.

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1 That’s of course somewhat exaggerated and some accountants work on the weaknesses of the current system. But in general, most accounting rules work better for old-fashioned, capital-intense companies.
2 Data from TIKR* and Yahoo Finance.
3 Again, this is exaggerated and only true on a big picture level. In practice, some intangible assets are treated similar to tangibles under certain conditions. Depending on the country, the accounting rules involve a lot of details.
4 There is a lot of work on adjusting assets for R&D expenses. One of the best resources for this is Aswath Damodaran who explains it in great detail.
5 More information about the GICS classification here.
6 The R2 ranges from 0 to 1 and tells us how much variation the model explains. For example, a value of 0.2 indicates that EPS and BPS explain 20% of the variation in stock prices.
7 More efficient market means less predictability and therefore lower R2.