Franchise Quality Score: A metric for the intangible

High valuations of Amazon, Facebook, Alphabet, Netflix and other tech stocks can be misleading – especially for price investors.

Despite the already exceptionally high valuations based on traditional metrics such as price-to-earnings (P / E) and price-to-book (P / B) ratios, the price of these stocks has skyrocketed.

So what is the explanation?

One argument we have been making for a long time is that these companies have invested substantially in valuable volatile assets, but need accounting rules that these investments should be spent instead of capital. This reduces the current income and book quality and as a result increases the P / E and P / B ratio.

As we observed in “Intangibles: The Next Frontier in Stock Valuation” in 2002:

“Current accounting standards were created in the early industrial age and were primarily designed for manufacturing companies. These standards do not keep pace with a growing service-oriented economy characterized by rapid technological and financial innovation. , It is important for investment professionals to come up with a logical approach to include ambiguities in their decision making; otherwise they run the risk of collapsing in the information age. “

Feng Gu and Baruch Lev make this argument more credible. They present evidence that increasing investment by firms in obscure assets is obsolete of old stock valuation models and necessitates a new valuation framework.

We have designed a structure that captures the underlying value of opacity. We call this proprietary metric the Franchise Quality Score.

So what is it, what is its rationale and how can it inform stock valuation?

Background of elusive resources

Obscure resources come in many forms, including patents and brands, among the more obvious varieties.

Intangibles example

Intangibles example

As we noted in 2002, the growing importance of ambiguity is a function of the transition of the economy from a manufacturing to a more service-oriented focus. Service-oriented businesses require less investment in physical resources than their competitors in production.

The transition of the US economy from industrial to information age

The transition of the US economy from industrial to information age

Source: US Bureau of Economic Analysis

At a more granular level, Gu and Lev see that corporate investment in fuzzy assets increased from 9% of the national gross value added (GVA) in 1977 to 14% in 2014. In contrast, investment in real estate fell from 15% to 9%. This trend has exacerbated the impact of inconsistent accounting treatment that accepts vague and unrealistic investments.

This means that accounting data is accurately capturing increasingly irrelevant information while missing the key. So what is being captured by accounting data and what is being missed?

What Accounting Data Shows vs. What Really Matters

Include ambiguous assets in stock selection

Three factors make it particularly difficult to integrate obscure assets into a coherent stock-selection structure:

  • Revelations about obscure resources are not strong or standard.
  • The technique of evaluating obscure things is primitive.
  • Intangibles vary by industry, which makes it difficult to compare two stocks with two different types of opacity.

In overcoming these obstacles, we focus on the “advantages” that provide ambiguity without obscuring things. For example, patents create high barriers to entry, and although they are not common across all industries, the high barriers to entry can be assessed for all sectors. Similarly, valuable brands can give brand owners the power to set prices. But again, although brands are not critical across all sectors, we can measure the extent to which specific companies enjoy strong pricing potential due to their brand or other relevant factors.

We’ve created franchise quality scores based on our evaluations that offer the fuzzy. We define franchise quality as the ability of a firm to consistently and repeatedly earn additional returns – that is, a return on capital in excess of its capital cost – without inviting competition that would eliminate the additional return.

We calculate scores by determining a value of eight component factors on a scale of 1 to 5. Of course, while scoring for a variety of reasons on such a scale may seem arbitrary and subjective, we find that applying certain criteria helps to make the scores reasonably objective. These eight elements are designed to answer two important questions:

  • How attractive is the business?
  • How well is the business going for long term success?

How attractive is the business?

How attractive is the business?

How well is it being managed for long term success?

How well is it being managed for long term success?

We derive the Composite Franchise Quality Score from this framework and apply it as an independent variable in a regression model that uses valuation, quality and growth factors to identify undervalued stocks. Our vision bears some resemblance to Michael Porter’s Five Forces Framework for competition within an industry. Both try to differentiate good business from bad.

Assessing the quality of a stock in the light of its valuation, quality and growth characteristics is not only logical, but also mathematically consistent with the Discounted Cash Flow (DCF) method of stock valuation.

Reunion of franchise quality assessment model with traditional model

Below is a simple example of a stock valuation model based on linear regression that includes franchise quality scores.

P / E = α + β1 (franchise quality) + β2 (growth rate) + e

In its functional form, this model – Equation 1 – can be rewritten as follows:

P / E = fn(Franchise quality, growth rate)

Based on this model, the P / E multiple that we should be willing to pay for a firm depends on two key considerations: the quality of the franchise and the growth rate. It is worth comparing this model with the gold standard of evaluation, the DCF model. An example of such a model is the dividend discount model.

Dividend discount model

P = D / (kg)

By rewriting dividends as earnings multiplied by the payout ratio, we get:

P = (E * payout ratio) / (k – g)

Dividing both sides of the equation by E we get the following equation.

P / E = payout ratio / (k – g)

Assuming the simplification that the payout ratio is constant, the above equation can be written in its functional form, which we will call Equation 2:

P / E = fn(Discount rate, growth rate)

Compare Equation 1 and Equation 2. Both models show that the P / E ratio of a stock should depend on two factors, one of which is growth rate. Where the difference between the two models is the second factor. Should it be a discount rate or a franchise quality score?

Think about it: both the discount rate factor and the franchise quality score try to capture the level of risk. The discount rate is an estimate of the risk. Franchise Quality Score measures risk with intuitive factors derived from the basic building blocks of the business.

Companies with high franchise quality scores are less risky than their low-scoring competitors because they are more likely to maintain and increase their earnings regardless of the economic environment.


Capturing obscure assets in stock valuation is a difficult task. Complete accuracy is impossible. But perfect good should not be the enemy. This elusive asset cannot be excluded from valuation. Even a basic, logical effort to incorporate elusive resources is no better than any.

This is the only way we can expect to see a complete mosaic of stock valuations.

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All post author opinions. As such, they should not be construed as investment advice, or the opinions expressed do not reflect the views of the CFA Institute or the author’s employer.

Image Credit: © Getty Images / FrankRamspott

Gautam Dhingra, PhD, CFA

Gautam Dhingra, PhD, Founder and CEO of CFA, High Point Capital Management, LLC. He has developed the firm’s forward-looking investment approach based on the concept of franchise quality and, under his leadership, has created an enviable investment performance record at high points. Dhingra has been a faculty member at Northwestern University’s Kellogg School of Management for two years. In this role, he designed and taught The Business of Investing course in the school’s MBA curriculum. His research interests include ESG investments and valuation of intangible assets. He holds a PhD in Finance from the University of Florida, Warrington College of Business, specializing in investment and economics. At Warrington, he taught two courses, Securities Analysis and Derivatives.

Christopher J. Olson, CFA

Christopher J. Olson, CFA, is a Head of High Point Capital Management and Portfolio Manager. Prior to High Point, he was a portfolio manager at Columbia Wanger Asset Management in Chicago for 15 years where he managed both equity and equity mutual funds. He began his investment management career at Yasuda Kasai Brinson in Tokyo in 1991 and later joined the parent company, Brinson Partners, to help launch the firm’s emerging market investment strategy. He has lived and worked in Sweden, Japan and Taiwan. He is fluent in Mandarin Chinese and has studied five other foreign languages. Olson holds an MBA with distinction from the Wharton School of Business and an MA in International Studies from the University of Pennsylvania’s School of Arts and Sciences. He graduated from Middlebury College with a BA in Political Science, Suma Kam Loud. He obtained his CFA Charter in 1998 and is a member of CFA Chicago. His civic responsibilities include being chairman of the board of the Swedish Covenant Hospital in Chicago and trustee of the Lincoln Academy in Maine.

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