Perhaps you’ve seen this chart, or some variant of it.  It encapsulates the pain felt by value investors since the financial crisis:  

It is a great chart given the range of historical context it provides, and the depth and length of underperformance conveyed.  Investing in “value” stocks through a quantitative definition of value (more on that later) has been a loser’s proposition for the last 10 years, the most extended period since 1974.  The reasons for this are hotly debated, ranging from distortive global monetary policy to technological paradigm shifts. 

Those that hold dear to value for values sake are often dogged in what has become a contrarian posture.  They point to the first principles of value investing, dating back to Ben Graham.  Graham identified value as a superior investment philosophy as it aim’s the investor toward buying assets that trade below their intrinsic value.  This takes away a lot of the guess-work and timing often associated with more speculative investing (“growth”), which at its heart discounts future cash flows in a potentially riskier fashion. 

A Quick History Lesson in Value 

Over the last 100 years, value-investing in the mold of Benjamin Graham has been proselytized by two major forces: Buffett on the qualitative end, and Fama on the quantitative.  The two are rarely thought of together, but I think they should, as they exemplify the two major schools of thought surrounding value.  Both spawned generations of like-minded investors.  Under Buffett, many fundamental practitioners of value – from Seth Klarman to Lou Simpson – have developed new strains of value investing.  Under Fama’s quantitative framework sprung asset management giants such as DFA and LSV.  While both groups have faced headwinds in this recent period, we’ll take a dive into the later camp as our sphere of interest is more closely aligned with quantitative investing. 

Quantitative Value Investing 

Fama’s seminal work on EMH (Efficient Market Hypothesis) led to a 3-factor model which attempted to quantify how abnormal returns could be tested scientifically.  Unlike Buffett’s school which naturally gravitated towards (and incorporated) the behavioral mechanisms which underlie value investing, quant research in the 1980s and 1990s by Fama and French, as well as Shleifer, Vishny, Lakonishok and others tested hypotheses to explain the abnormal return patterns they empirically studied.  What they uncovered was a premium that could be arbitraged through a systematic investment approach.  Various papers using variant methodologies during this period demonstrated abnormal returns of the order of 5 – 10% per annum through extended historical examination.  This spawned an industry of factor-based practitioners. 

Essentially, they took a systematic approach to implementing Graham’s core thesis: own assets at lower market value than intrinsic value.  But market practitioners who look at often funky names in these portfolios know that statistical approximation is just that: approximation.  We have come to learn that in order to properly ascertain the intrinsic value of a company, simple statistical methods can only get you so far.  First, intrinsic value is not an absolute truth; it is subjective and at the mercy of a complex adaptive system.  One of the key input variables for its calculation (financial disclosures), while standardized, cannot be simply processed in a truly scientific fashion; they often need interpretation and pattern recognition by trained practitioners to suss out “true” intrinsic value. 

Because there is no rules-based methodology for finding intrinsic value, quantitative practitioners tested various fundamental statistics that aimed to approximate a value quotient.  We have written on this blog about how approximation can look absolutely correct until it is no-longer right, and this may be one reason why “value” has failed to work in the last 10 years.

The Death of Value Investing 

In that prior blog post, we chronicled Ptolemy’s folly in searching for a model to validate an assumed truth.  He found a mathematical approximation to validate his hypothesis that the earth is the center of the universe.  While clearly not as extreme, there are parallels here to quantitative value.  As the second chart above shows, the quantitative value approach epitomized by Fama-French HML Value has hit a rough patch.  It is too soon to tell if this is a persistent deterioration or a transitory phase.  Remember, at the height of the dotcom bubble, pundits were calling for the end of value investing and for a new model of valuation (eyeballs instead of dollars?).  How can we determine if recent data is transitory or permanent?  We can start with the foundations for how statistical value is calculated and question why metrics such as price to book (P/B) or price to earnings (P/E) could fail to capture intrinsic value in the last 10 years. 

Problems with Statistical Approximation 

The problem with statistical approximation is its validity can change as the world changes.  Thoughtful value investment shops have been sounding this alarm for some time.  P/B or P/E are the tools quants used to approximate value. But these metrics can vary with changing macroeconomic conditions.  A tangible example: domestic energy stocks trade at attractive free-cash-flow yields, a metric of value.  They also trade at some of the worst price/earning ratios, another metric of value.  Which is correct?  It really depends on a subjective discounting of the viability of that cash-flow yield over time, something not easily approximated through quant methods.

Legendary value investor Bill Nygren provided a master’s insight into this phenomenon and what has perhaps changed structurally in the economy in the last 10 years. In his 3Q17 letter, he writes about the folly of blindly adhering to P/E levels in evaluating the “value” of currently highly influential companies (emphasis added in bold): 

A less obvious factor that is producing higher P/E ratios today is how accounting practices penalize certain growth investments. When a company builds a new plant, GAAP accounting spreads that cost over its useful life—often 40 years—so the cost gets expensed through 40 years of depreciation as opposed to just flowing through the current income statement. 

But when Amazon hires engineers and programmers to help it prepare for sales that could double over the next four years, those costs get immediately charged to the income statement. When Facebook decides to limit the ad load on WhatsApp to allow it to quickly gain market share, the forgone revenue immediately penalizes the income statement. And when Alphabet invests venture capital in autonomous vehicles for rewards that are years and years away, the costs are expensed now and current earnings are reduced. 

The media is obsessed with supposedly bubble-like valuations of the FANG stocks—Facebook, Amazon, Netflix and Google (Alphabet). The FANG companies account for over 7% of the S&P 500 and sell at a weighted average P/E of 39 times consensus 2017 earnings. In our opinion, the P/E ratio is a very poor indicator of the value of these companies. Alphabet is one of our largest holdings, and our valuation estimate is certainly not based on its search division being worth 40 times earnings. If one removed the FANG stocks from the S&P multiple calculation—not because their multiples are high, but because they misrepresent value—the market P/E would fall by nearly a full point. And, clearly, more companies than these four are affected by income statement growth spending. 


As the durability of a business’ competitive moat has morphed from capital expenditure to “growth spending”, the accounting metrics which translate these business properties have failed to adequately measure relative value.  A key ingredient to discounting a company’s future cash flows is how defensible those cash flows are into the future.  Historically, purchasing plants and equipment was a tangible method of entrenching a profitable enterprise.  Today, dominant companies spend on generating platform scale and cultivating talent feedback loops rather than buying equipment.  This reality is in concert with the core principles of value investing, demonstrating the missed approximation of metrics like P/B.  Of course, not all “growth spending” yields future profitability; some of it will invariably be wasted.  Today, the market is perhaps failing to discount these risks in certain companies.  Nonetheless, missing this evolving reality has led many value investors to miss the boat on the most important drivers of alpha in domestic equities. 

Flows Affect Factors 

The other elephant in the room has been the AUM growth in products which employ statistical approximation to deliver these factor exposures.  The cornerstone firms we alluded to were early to this trend and profited handsomely.  But their success invited imitation, and that imitation led to easier access points for assets chasing the same anomalies.  With that, abnormal returns shrink, and second and third-derivative players enter the space.  These players may not have learned the foundational lessons that the early practitioners struggled through, but cut straight to the ease of implementing statistical approximation.  This leads to a dilution of cause and effect, or in investing returns, sub-par performance. 

Not All is Lost 

This is not to say that we are witnessing a paradigm shift surrounding value versus growth.  Many of these value investors who are lagging-behind market (let alone “growth”) returns have reasons to be cautious that extend beyond simple valuation metrics of equities.  They look at other macroeconomic indicators of froth and fiscal disrepair and triangulate many warning signals that confirm a need for paucity.  If there is a meaningful correction, the rug may be pulled from under some of these frothy companies, validating the contrarian theses of value investors and ultimately rewarding their patience. Growth companies eventually must demonstrate true ROI on “growth spending,” else the market will fail to reward hearty multiples on potential.  Domestic E&P companies are a great recent example of this. 

But in all complex adaptive systems, there is no black and white to what has true value and what does not.  The exception may be the classic Ben Graham “cigar butt” securities which is another story in and of itself.  It takes a truly flexible and open-minded approach to navigate this challenging time.  Great investors adapt, which brings me back to Buffett, who articulates his supreme mental flexibility on this very matter: 

Growth is always a component in the calculation of value, constituting a variable whose importance can range from negligible to enormous and whose impact can be negative as well as positive. 

Buffett demonstrates second-order thinking that attempts to circle the seeming contradiction behind value vs growth.  In truth, they are both inputs in valuation, the goal of all investors.  As the scales change, investors must change too, and they can’t be caught up in metrics which stray from truth. 

Further Reading 

  • The blog title was inspired by a new book by Jerry Muller (“The Tyranny of Metrics) 
  • Lakonishov J., Shleifer A., Vishny R..  Contrarian Investment, Extrapolation, and Risk.  The Journal of Finance.  Vol 49, No 5. Dec 1994. 


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