Wednesday, September 14, 2016

Fairness Opinions: Fix them or Flush them!

My post on the Tesla/SCTY deal about the ineptitude and laziness that Lazard and Evercore brought to the valuation process did not win me any friends in the banking M&A world. Not surprisingly, it drew some pushback, not so much from bankers, but from journalists and lawyers, taking me to task for not understanding the context for these valuations. As Matt Levine notes in his Bloomberg column, where he cites my post, "a fairness opinion is not a real valuation, not a pure effort to estimate the value of a company from first principles and independent research" (Trust me. No one is setting the bar that high. I was looking for biased efforts using flawed principles and haphazard research and these valuation could not even pass that standard)  and that "they (Lazard and Evercore) are just bankers; their expertise is in pitching and sourcing and negotiating and executing deals -- and in plugging in discount rates into preset spreadsheets -- not in knowing the future". (Bingo! So why are they doing these fairness opinions and charging millions of dollars for doing something that they are not good at doing? And there is a difference between knowing the future, which no one does, and estimating the future, which is the essence of valuation.) If Matt is right, the problems run deeper than the bankers in this deal, raising questions about what the purpose of a   "fairness opinion" is and whether it has outlived its usefulness (assuming that it was useful at some point).

Fairness Opinions: The Rationale
What is a fairness opinion? I am not a lawyer and I don't play intend to play one here, but it is perhaps best to revert back to the legal definition of the term. In an excellent article on the topic, Steven Davidoff defines a fairness opinion as an "opinion provided by an outsider that a transaction meets a threshold level of fairness from a financial perspective". Implicit in this definition are the assumptions that the outsider is qualified to pass this judgment and that there is some reasonable standard for fairness.  In corporate control transactions (acquisition, leveraged buyout etc.), as practiced today, the fairness opinion is delivered (orally) to the board at the time of the transaction, and that presentation is usually followed by a written letter that summarizes the transaction terms and the appraiser's assumptions and attests that the price paid is "fair from a financial point of view". That certainly sounds like something we should all favor, especially in deals that have obvious conflicts of interest, such as management-led leveraged buyouts or transactions like the Tesla/Solar City deal, where the interests of Elon Musk and the rest of Tesla 's stockholders may diverge.

Note that while fairness opinions have become part and parcel of most corporate control transactions, they are not required either by regulation or law. As with so much of business law, especially relating to acquisitions, the basis for fairness opinions and their surge in usage can be traced back to Delaware Court judgments. In Smith vs Van Gorkom, a 1985 case, the court ruled against the board of directors of Trans Union Corporation, who voted for a leveraged buyout, and specifically took them to task for the absence of a fairness opinion from an independent appraiser. In effect, the case carved out a safe harbor for the companies by noting that “the liability could have been avoided had the directors elicited a fairness opinion from anyone in a position to know the firm’s value”.  I am sure that the judges who wrote these words did so with the best of intentions, expecting fairness opinions to become the bulwark against self-dealing in mergers and acquisitions. In the decades since, through a combination of bad banking practices, the nature of the legal process and confusion about the word "fairness", fairness opinions, in my view, have not just lost their power to protect those that they were intended to but have become a shield used by managers and boards of directors against serious questions being raised about deals. 

Fairness Opinions: Current Practice?
There are appraisers who take their mission seriously and evaluate the fairness of transactions in their opinions, but the Tesla/Solar City valuations reflect not only how far we have strayed from the original idea of fairness but also how much bankers have lowered the bar on what constitutes acceptable practice.  Consider the process that Lazard and Evercore used by  to arrive at their fairness opinions in the Tesla/Solar City deal, and if Matt is right, they are not alone:

What about this process is fair, if bankers are allowed to concoct discount rates, and how is it an opinion, if the numbers are supplied by management? And who exactly is protected if the end result is a range of values so large that any price that is paid can be justified?  And finally, if the contention is that the bankers were just using professional judgment, in what way is it professional to argue that Tesla will become the global economy (as Evercore is doing in its valuation)? 

To the extent that what you see in the Tesla/Solar City deal is more the rule than the exception, I would argue that fairness opinions are doing more harm than good. By checking off a legally required box, they have become a way in which a board of directors buy immunization against legal consequences. By providing the illusion of oversight and an independent assessment, they are making shareholders too sanguine that their rights are being protected. Finally, this is a process where the worst (and least) scrupulous appraisers, over time, will drive out the best (and most principled) ones, because managers (and boards that do their bidding) will shop around until they find someone who will attest to the fairness of their deal, no matter how unfair it is. My interest in the process is therefore as much professional, as it is personal. I believe the valuation practices that we see in many fairness opinions are horrendous and are spilling over into the other valuation practices.

It is true that there are cases, where courts have been willing to challenge the "fairness" of fairness opinions, but they have been infrequent and  reserved for situations where there is an egregious conflict of interest. In an unusual twist, in a recent case involving the management buyout of Dell at $13.75 by Michael Dell and Silver Lake, Delaware Vice Chancellor Travis Lester ruled that the company should have been priced at $17.62, effectively throwing out the fairness opinion backing the deal. While the good news in Chancellor Lester's ruling is that he was willing to take on fairness opinions, the bad news is that he might have picked the wrong case to make his stand and the wrong basis (that markets are short term and under price companies after they have made big investments) for challenging fairness opinions.

Fish or Cut Bait?
Given that the fairness opinion, as practiced now, is more travesty than protection and an expensive one at that, the first option is to remove it from the acquisition valuation process. That will put the onus back on judges to decide whether shareholder interests are being protected in transactions. Given how difficult it is to change established legal practice, I don't think that this will happen. The second is to keep the fairness opinion and give it teeth. This will require two ingredients to work, judges that are willing to put fairness opinions to the test and punishment for those who consistently violate those fairness principles.

A Judicial Check
Many judges have allowed bankers to browbeat them into accepting the unacceptable in valuation, using the argument that what they are doing is standard practice and somehow professional valuation.  As someone who wanders across multiple valuation terrain, I am convinced that the valuation practices in fairness opinions are not just beyond the pale, they are unprofessional. To those judges, who would argue that they don't have the training or the tools to detect bad practices, I will make my pro bono contribution in the form of a questionnaire with flags (ranging from red for danger to green for acceptable) that may help them separate the good valuations from the bad ones.

Who is paying you to do this valuation and how much? Is any of the payment contingent on the deal happening? (FINRA rule 2290 mandates disclosure on these)
Payment reflects reasonable payment for valuation services rendered and none of the payment is contingent on outcome
Payment is disproportionately large, relative to valuation services provided, and/or a large portion of it is contingent on deal occurring.
Where are you getting the cash flows that you are using in this valuation?
Appraiser estimates revenues, operating margins and cash flows, with input from management on investment and growth plans.
Cash flows supplied by management/ board of company.
Are the cash flows internally consistent?
1.     Currency: Cash flows & discount rate are in same currency, with same inflation assumptions.
2.     Claim holders: Cash flows are to equity (firm) and discount rate is cost of equity (capital).
3.     Operations: Reinvestment, growth and risk assumptions matched up.
No internal consistency tests run and/or DCF littered with inconsistencies, in currency and/or assumptions.
-       High growth + Low reinvestment
-       Low growth + High reinvestment
-       High inflation in cash flows + Low inflation in discount rate
What discount rate are you using in your valuation?
A cost of equity (capital) that starts with a sector average and is within the bounds of what is reasonable for the sector and the market.
A cost of equity (capital) that falls outside the normal range for a sector, with no credible explanation for difference.
How are you applying closure in your valuation?
A terminal value that is estimated with a perpetual growth rate < growth rate of the economy and reinvestment & risk to match.
A terminal value based upon a perpetual growth rate > economy or a multiple (of earnings or revenues) that is not consistent with a healthy, mature firm.
What valuation garnishes have you applied?
A large dose of premiums (control, synergy etc.) pushing up value or a mess of discounts (illiquidity, small size etc.) pushing down value.
What does your final judgment in value look like?
A distribution of values, with a base case value and distributional statistics.
A range of values so large that any price can be justified.

If this sounds like too much work, there are four changes that courts can incorporate into the practice of fairness opinions that will make an immediate difference:
  1. Deal makers should not be deal analysts: It should go without saying that a deal making banker cannot be trusted to opine on the fairness of the deal, but the reason that I am saying it is that it does happen. I would go further and argue that deal makers should get entirely out of the fairness opinion business, since the banker who is asked to opine on the fairness of someone else's deal today will have to worry about his or her future deals being opined on by others.
  2. No deal-contingent fees: If bias is the biggest enemy of good valuation, there is no simpler way to introduce bias into fairness opinions than to tie appraisal fees to whether the deal goes through. I cannot think of a single good reason for this practice and lots of bad consequences. It should be banished.
  3. Valuing and Pricing: I think that appraisers should spend more time on pricing and less on valuation, since their focus is on whether the "price is fair" rather than on whether the transaction makes sense. That will require that appraisers be forced to justify their use of multiples (both in terms of the specific multiple used, as well as the value for that multiple) and their choice of comparable firms. If appraisers decide to go the valuation route, they should take ownership of the cash flows, use reasonable discount rates and not muddy up the waters with arbitrary premiums and discounts. And please, no more terminal values estimated from EBITDA multiples!
  4. Distributions, not ranges: In my experience, using a range of value for a publicly traded stock to determine whether a price is fair is useless. It is analogous to asking, "Is it possible that this price is fair?", a question not worth asking, since the answer is almost always "yes". Instead, the question that should be asked and answered is "Is it plausible that this price is a fair one?"  To answer this question, the appraiser has to replace the range of values with a distribution, where rather than treat all possible prices as equally likely, the appraiser specifies a probability distribution. To illustrate, I valued Apple in May 2016 and derived a distribution of its values:

Let's assume that I had been asked to opine on whether a $160 stock price is a fair one for Apple. If I had presented this valuation as a range for Apple's value from $80.81 to $415.63, my answer would have to be yes, since it falls within the range. With a distribution, though, you can see that a $160 price falls at the 92nd percentile, possible, but neither plausible, nor probable.  To those who argue that this is too complex and requires more work, I would assume that this is at the minimum what you should be delivering, if you are being paid millions of dollars for an appraisal.

The most disquieting aspect of the acquisition business is the absence of consequences for bad behavior, for any of the parties involved, as I noted in the aftermath of the disastrous HP/Autonomy merger. Thus, managers who overpay for a target are allowed to use the excuse of "we could not have seen that coming" and the deal makers who aided and abetted them in the process certainly don't return the advisory fees, for even the most abysmal advice. I think while mistakes are certainly part of business, bias and tilting the scales of fairness are not and there have to be consequences:
  1. For the appraisers: If the fairness opinion is to have any heft, the courts should reject fairness opinions that don't meet the fairness test and remove the bankers involved  from the transaction, forcing them to return all fees paid. I would go further and create a Hall of Shame for those who are repeat offenders, with perhaps even a public listing of their most extreme offenses. 
  2. For directors and managers: The boards of directors and the top management of the firms involved should also face sanctions, with any resulting fines or fees coming out of the pockets of directors and managers, rather than the shareholders involved.
I know that your reaction to these punitive suggestions is that they will have a chilling effect on deal making. Good! I believe that much as strategists, managers and bankers like to tell us otherwise, there are more bad deals than good ones and that shareholders in companies collectively will only gain from crimping the process.

YouTube Video

  1. The Fairness Questionnaire (as a word file, which you are free to add to or adapt)

Tuesday, September 6, 2016

Keystone Kop Valuations: Lazard, Evercore and the TSLA/SCTY Deal

It is get easy to get outraged by events around you, but I have learned, through hard experience, that writing when outraged is dangerous. After all, once you have climbed onto your high horse, it is easy to find fault with others and wallow in self-righteousness. It is for that reason that I have deliberately avoided taking issue with investment banking valuations of specific companies, much as I may disagree with the practices used in many of them. I understand that bankers make money on transactions and that their valuations are more sales tools than assessments of fair value and that asking them to pay attention to valuation first principles may be asking too much. Once in a while, though, I do come across a valuation so egregiously bad that I cannot restrain myself and reading through the prospectus filed by Tesla for their Solar City acquisition/merger was such an occasion. My first reaction as I read through the descriptions of how the bankers in this deal (Evercore for Tesla and Lazard for Solar City) valued the two companies was "You must be kidding me!".

The Tesla/Solar City Deal
In June 2016, Tesla announced that it intended to acquire Solar City in a stock swap, a surprise to almost everyone involved, except for Elon Musk. By August 1, the specifics of the deal had been ironed out and the broad contours of the deal are captured in the picture below:

At the time of the deal, Mr. Musk contended that the deal made sense for stockholders in both companies, arguing that it was a "no-brainer" that would allow Tesla to expand its reach and become a clean energy company. While Mr. Musk has a history of big claims and perhaps the smarts and charisma to deliver on them, this deal attracted attention because of its optics. Mr. Musk was the lead stockholder in both companies and CEO of Tesla and his cousin, Lyndon Rive, was the CEO of Solar City. Even Mr. Musk's strongest supporters could not contest the notion that he was in effective control at both companies, creating, at the very least. the potential for conflicts of interests. Those questions have not gone away in the months since and the market concerns have been reflected in the trend lines in the stock prices of the two companies, with Solar City down about 24% and Tesla's stock price dropping about 8%.

The board of directors at Tesla has recognized the potential for a legal backlash and as this New York Times article suggests, they have been careful to create at least the appearance of an open process, with Tesla's board hiring Evercore Partners, an investment bank, to review the deal and Solar City's board calling in Lazard as their deal assessor. Conspicuously missing is Goldman Sachs, the investment banker on Tesla's recent stock offering, but more about that later.

The Banking Challenge in a Friendly Merger
In any friendly merger, the bankers on the two sides of the deal face, what at first sight, looks like an impossible challenge. The banker for the acquiring company has to convince the stockholders of the acquiring company that they are getting a good deal, i.e., that they are acquiring the target company at a price, which while higher that the prevailing market price, is lower than the fair value for the company. At the same time, the banker for the target company has to convince the stockholders of the target company that they too are getting a good deal, i.e., that they are being acquired at is higher than their fair value. If you are a reasonably clever banking team, you discover very quickly that the only way you can straddle this divide is by bringing in what I call the two magic merger words, synergy and control. Synergy in particular is magical because it allows both sides to declare victory and control adds to the allure because it comes with the promise of unspecified changes that will be made at the target company and a 20% premium:

In the Tesla/Solar City deal, the bankers faced a particularly difficult challenge. Finding synergy in this merger of an electric car company and a solar cell company, one of which (Tesla) has brand name draw and potentially high margins and the other of which is a commodity business (Solar City) with pencil thin margins) is tough to do. Arguing that the companies will be better managed as one company is tricky when both companies have effectively been controlled by the same person(Musk) before the merger. In fact, it is far easier to make the case for reverse synergy here, since adding a debt-laden company with a questionable operating business (Solar City) to one that has promise but will need cash to deliver seems to be asking for trouble. The bankers could of course have come back and told the management of both companies (or just Elon Musk) that the deal does not make sense and especially so for the stockholders of Tesla but who can blame them for not doing so? After all, they are paid based upon whether the deal gets done and if asked to justify themselves, they would argue that Musk would have found other bankers who would have gone along. Consequently, I am not surprised that both banks found value in the deal and managed to justify it.

The Valuations
It is with this perspective in mind that I opened up the prospectus, expecting to see two bankers doing what I call Kabuki valuations, elaborately constructed DCFs where the final result is never in doubt, but you play with the numbers to make it look like you were valuing the company. Put differently, I was willing to cut a lot of slack on specifics, but what I found failed even the minimal tests of adequacy in valuation. Summarizing what the banks did, at least based upon the prospectus (lest I am accused of making up stuff):
Tesla Prospectus
Conveniently, these number provide backing for the Musk acquisition story, with Evercore reassuring Tesla stockholders that they are getting a good deal and Lazard doing the same with Solar City stockholders, while shamelessly setting value ranges so wide that they get legal cover, in case they get sued.  Note also not only how much money paid to these bankers for their skills at plugging in discount rates into spreadsheets but that both bankers get an additional payoff, if the merger goes through, with Evercore pocketing an extra $5.25 million and Lazard getting 0.4% of the equity value of Solar City.  There are many parts of these valuations that I can take issue with, but in the interests of fairness, I will start with what I term run-of-the-mill banking malpractice, i.e., bad practices that many bankers are guilty of.
  1. No internal checks for consistency: There is almost a cavalier disregard for the connection between growth, risk and reinvestment. Thus, when both banks use ranges of growth for their perpetual value estimates, it looks like neither adjusts the cash flows as growth rates change. (Thus, when Lazard moves its perpetual growth rate for Solar City from 1.5% to 3%, it looks like the cash flow stays unchanged, a version of magical growth that can happen only on a spreadsheet).
  2. Discount Rates: Both companies pay lip service to standard estimation technology (with talk of the CAPM and cost of capital), and I will give both bankers the benefit of the doubt and attribute the differences in their costs of capital to estimation differences, rather than to bias.  The bigger question, though, is why the discount rates don't change as you move through time to 2021, where both Tesla and Solar City are described as slower growth, money making companies.
  3. Pricing and Valuation: I have posted extensively on the difference between pricing an asset/business and valuing it and how mixing the two can yield a incoherent mishmash. Both investment banks move back and forth between intrinsic valuation (in their use of cash flows from 2016-2020) and pricing, with Lazard estimating the terminal value of Tesla using a multiple of EBITDA. (See my post on dysfunctional DCFS, in general, and Trojan Horse DCFs, in particular).
There are two aspects of these valuations that are the over-the-top, even by banking valuation standards:
  1. Outsourcing of cash flows: It looks like both bankers used cash flow forecasts provided to them by the management. In the case of Tesla, the expected cash flows for 2016-2020 were generated by Goldman Sachs Equity Research (GSER, See Page 99 of prospectus) and for Solar City, the cash flows for that same period were provided by Solar City, conveniently under two scenarios, one with a liquidity crunch and one without. Perhaps, Lazard and Evercore need reminders that if the CF in a DCF is supplied to you by someone else,  you are not valuing the company, and charging millions for plugging in discount rates into preset spreadsheets is outlandish. 
  2. Terminal Value Hijinks: The terminal value is, by far, the biggest single number in a DCF and it is also the number where the most mischief is done in valuation. While some evade these mistakes by using pricing, there is only one consistent way to get terminal value in a DCF and that is to assume perpetual growth. While there are a multitude of estimation issues that plague perpetual growth based terminal value, from not adjusting the cost of capital to reflect mature company status to not modifying the reinvestment to reflect stable growth, there is one mistake that is deadly, and that is assuming a growth rate that is higher than that of the economy forever. With that context, consider these clippings from the prospectus on the assumptions about growth forever made by Evercore in their terminal value calculations:
    Tesla Prospectus
    I follow a rule of keeping the growth rate at or below the risk free rate but I am willing to accept the Lazard growth range of 1.5-3% as within the realm of possibility, but my reaction to the Evercore assumption of 6-8% growth forever in the Tesla valuation or even the 3-5% growth forever with the Solar City valuation cannot be repeated in polite company. 
Not content with creating one set of questionable valuations, both banks doubled down with a number of  of other pricing/valuations, including sum-of-the-parts valuations, pricing and transaction premiums, using a "throw everything at the fan and hope something sticks" strategy.

Now what? 
I don't think that Tesla's Solar City acquisition passes neither the smell test (for conflict of interest) nor the common sense test (of creating value), but I am not a shareholder in either Tesla or Solar City and I don't get a vote. When Tesla shareholders vote, given that owning the stock is by itself an admission that they buy into the Musk vision, I would not be surprised if they go along with his recommendations. Tesla shareholders and Elon Musk are a match made in market heaven and I wish them the best of luck in their life together.

As for the bankers involved in this deal, Lazard's primary sin is laziness, accepting an assignment where they are reduced to plugging in discount rates into someone else's cash flow forecasts and getting paid $2 million plus for that service. In fact, that laziness may also explain the $400 million debt double counting error made by Lazard on this valuation,. Evercore's problems go deeper. The Evercore valuation section of the prospectus is a horror story of bad assumptions piled on impossible ones, painting a picture of ignorance and incompetence. Finally, there is a third investment bank (Goldman Sachs), mentioned only in passing (in the cash flow forecasts provided by their equity research team), whose absence on this deal is a story by itself. Goldman's behavior all through this year, relating to Tesla, has been rife with conflicts of interest, highlighted perhaps by the Goldman equity research report touting Tesla as a buy, just before the Tesla stock offering. It is possible that they decided that their involvement on this deal would be the kiss of death for it, but I am curious about (a) whether Goldman had any input into the choice of Evercore and Lazard as deal bankers, (b) whether Goldman had any role in the estimation of Solar City cash flows, with and without liquidity constraints, and (c) how the Goldman Sachs Equity Research forecast became the basis for the Tesla valuations. Suspicious minds want to know! As investors, the good news is that you have a choice of investment bankers but the bad news is that you are choosing between the lazy, the incompetent and the ethically challenged.

If there were any justice in the world, you would like to see retribution against these banks in the form of legal sanctions and loss of business, but I will not hold my breath waiting for that to happen. The courts have tended to give too much respect for precedence and expert witnesses, even when the precedent or expert testimony fails common sense tests and it is possible that these valuations, while abysmal, will pass the legally defensible test. As for loss of business, my experience in valuation is that rather than being punished for doing bad valuations, bankers are rewarded for their deal-making prowess. So, for the many companies that do bad deals and need an investment banking sign-off on that deal (in the form of a fairness opinion), you will have no trouble finding a banker who will accommodate you.

If this post comes across as a diatribe against investment banking, I am sorry and I am not part of the "Blame the Banks for all our problems" school. In fact, I have long argued that bankers are the lubricants of a market economy, working through kinks in the system and filling in capital market needs and defended banking against its most virulent critics. That said, the banking work done on deals like the this one vindicate everyone's worst perceptions of bankers as a hired guns who cannot shoot straight, more Keystone Kops than Wyatt Earps!

YouTube Video

  1. Tesla Prospectus for Solar City Deal

Thursday, September 1, 2016

The School Bell Rings! It's Time for Class!

As most teachers do, I mark time in academic rather than in calendar years and as September dawns, it is New Year's eve for me and a new class is set to begin. In just under a week, on September 7, 2016, I will walk into a classroom and face up to a roomful of students, not quite ready for summer to end, and start teaching, as I have every year since 1984. This semester, I will be back to teaching Valuation to MBAs at Stern, and as I have in semesters past, I invite you to join me on this journey, as we look at the mix of art, science and magic that makes valuation such a fascinating discipline.

Class Philosophy
I have always believe that to teach a class well, you have to start with a story and that the class is an extended serialization of the story. I also believe that to teach well, you have to, at least over time, make that story your own and mold the class to reflect it. In fact, the valuation class that I will be teaching this Fall has its seeds in the very first valuation class that I taught in 1986, but the differences reflect not only how much the world has changed since then, but also how my own thinking on valuation has evolved. The class remains a work in progress, where each time I teach it, I learn something new as well as recognize how much I have left to learn.

I could give you an extended essay on what this class is about, but I would repeating what I said at the start of the Fall 2015 semester in this post. In short, I said this class is not an extended accounting class (where you forecast entire financial statements for extended periods), or a modeling class (where you become an Excel Ninja) or a theory class (since there is so little of it in  valuation to begin with). Instead, here are the broad themes that underlie this class, all captured in the picture below:

If you find this picture a little daunting, I did do a Google talk that encapsulated these themes into about an hour-long session. 

In particular, this class is less about the tools and techniques of valuation and more about developing a foundation that you can use to build your own investment philosophy. I know that faith is a word that is seldom used and often viewed with suspicion by many in the valuation community, but it is at the heart of this class, both in terms of how you build up faith in your own capacity to value assets and businesses and how you hold on to that faith when the market price moves away from your value.  Since I still struggle on both of these fronts, I cannot give you a template for success but I will be open about my own insecurities both about my own valuations and about markets.

Class Structure
Since my objective in the class is that by the end of it, you should be able to attach a number to just about any asset, I will roam the spectrum. I will start with the basics of intrinsic value, partly because it is where I am most comfortable and partly because it provides me with ways of dealing with other approach. The mechanics of estimating discount rates, cash flows, growth and terminal value are not just simple, but easily mechanized. It is the specifics that we will wrestle with in this class:

  • On risk free rates, usually the least troublesome and more easily obtained input in valuation, we will talk about why risk free rates vary across currencies, what to do about currencies that have negative risk free rates and whether normalizing risk free rates (as many practitioners have taken to doing) is a good idea or a bad one.
  • On risk premiums and discount rates, we will wrestle with questions of what risks should and should not be incorporated into discount rates and the different methods of bringing them in. In the process, we will examine how best to estimate equity risk premiums and default spreads, and why even if you don't like betas or portfolio theory, you should should still be able to estimate discount rates and do intrinsic valuation.  
  • On cash flows, we will focus on why accounting inconsistencies (on dealing with R&D, leases and other items) can lead to misstated earnings and how to fix those inconsistencies, examine what should and should not be included in reinvestment (capital expenditures and working capital) and what to do about stock based compensation.
  • On growth, we will start with the easy cases (where historical earnings growth is a good predictor of future growth) but quickly move on to more difficult cases (of companies in transition) and to what some view as impossible cases (like estimating growth in a start-up)>
  • On terminal value, the big number in every DCF,  that can very quickly hijack otherwise well-done valuations, we will develop simple rules for keeping the number in check and put to sleep many myths surrounding it.
We will apply intrinsic valuation to value companies across the life cycle, in different sectors and across different markets. We will value small and large companies, private and public, developed and emerging and discuss how to value movie franchises (like Star Wars), phenomena (Pokemon Go) and sports teams. We will talk about why start ups can and should be valued in the face of daunting uncertainty and how probabilistic tools (simulations and decision trees) can help.

About half way through the class, we will turn our attention to pricing assets/businesses, where rather than build up to a value from a company's fundamentals, we price it, based on how the market is pricing similar companies. Put simply, we will shine a light on the practice of using pricing multiples (PE, EV/EBITDA, EV/Sales) and comparable companies not with the intent of improving how it is done. We will also talk about why, even when you are careful and take care of the details, your pricing of a company can be very different form its value.

In the last segment of the class, we will stretch our valuation muscles by talking about how option pricing models can sometime be used to estimate the additional value in a business, such as undeveloped reserves for a natural resource company or expansion potential for a young growth firm, and sometimes to value equity in deeply distressed companies. We will close by looking at acquisition valuation, where good sense seems to be in short supply, and how understanding value can be critical to corporate managers.

Want to sit in?
If you are intrigued or interested, you are welcome to sit in on the class (online and unofficially). While my immediate attention will be reserved for the Stern MBAs who will be registered in this class, you will have access to all of the resources that they do, starting with the lectures but also extending to lecture notes, quizzes/exams and even emails. The bad news is that I will be unable to grade your work or give you a certificate of completion. The good news is that the price is right. There are three ways in which you can join the class:

  1. My website: The most comprehensive and most updated center of all things related to this class at this link. You will find the webcasts, lecture notes, past exams, reading and even the emails I send on this class here.
  2. iTunes U: Just as I am not an Excel Ninja, my capacity to deal with html is primitive and my website's design reflects that lack of sophistication. If you prefer more polish, you can try the iTunes U app in the Apple app store. It is a free app that you can download and install on your Apple device. Once you have it installed, click on the add course and enter the enroll code FER-SFJ-AKA. Like magic, the class should pop up on your shelf. If you don't have an Apple device, you can get to the course on your computer using this link. If you have an Android device, you can use a workaround by downloading this app first. Like all things Apple, the set up is amazing and easy to work with.
  3. YouTube: The problem with the first two choices is that they presuppose that you don't have a broadband constraint, perhaps a phone internet connection or worse. My suggestion is that you use the YouTube playlist that I have created for this class at this link. The nice thing about YouTube is that it adjusts the image quality to your connection speed. So, it should work in almost any setting.
Since I have made this offer for almost 20 years now, predating the MOOC boom and bust, I can offer some suggestions. First, it is a lot of work to watch two 80-minute lectures a week, try your hand out at working through actual valuations and finish the class in fifteen weeks, if you have other things going on in your life (and who does not?). My suggestion is that you cut yourself some slack and take more time, since the materials will stay up for at least a year after the class ends. Second, watching a lecture online for almost an hour and a half can be painful and for those of you who find the pain unbearable, I do have an alternative. A couple of years ago, I created an online version of this class, shrinking each 80-minute session into 10-15 minute sessions and this class is also available on my website at this link, on iTunes U at this link and on YouTube. Third, whichever version of the class you take will stick more if you pick a company and value it and even more, if you keep doing it. 

The End Game
I would love to tell you that I live a life of serenity and that I am sharing for noble reasons, but that would not be true. I am sharing my class for the most selfish of all reasons. I am a performer (and every teacher is) and what performer does not wish for a bigger audience? If I am going to prepare and deliver a class, would I not rather have thirty thousand people watch the class than three hundred. If you get something of value from this class, and you feel the urge to repay me, I will make the same suggestion that I did last year. Learning is one of those rare resources that is never diminished by sharing. So, please pass it on to someone else! See you in class!

  1. Entry Page for the Valuation Spring 2016 (on my website)
  2. Webpage for the Valuation Spring 2016 class webcasts (on my website)
  3. iTunes U for the Valuation Spring 2016 class (Enroll code on device: FER-SFJ-AKA)
  4. YouTube Playlist for the Valuation Spring 2016 class
  5. Webpage for Valuation Online class (short sessions)
  6. iTunes U for the Valuation Online class (short sessions)
  7. YouTube for the Valuation Online class (short sessions)

Wednesday, August 31, 2016

Mean Reversion: Gravitational Super Force or Dangerous Delusion?

In my last post on the danger of using  single market metrics to time markets, I made the case that though the Shiller CAPE was high, relative to history, it was not a sufficient condition to conclude that US equities were over valued. In the comments that followed, many disagreed. While some took issue with measurement questions, noting that I should have looked at ten-year correlations, not five and one-year numbers, others argued that this metric was never meant for market timing and that the real message was that the expected returns on stocks over the next decade are likely to be low. I was surprised at how few brought up what I think is the central question, which is the assumption that the CAPE or any other market metric will move back to historic norms. This unstated belief that things revert back to the way they used to be is both deeply set, and at the heart of much of value investing, especially of the contrarian stripe. Thus, when you buy low PE stocks and or sell a stock because it has a high PE, you are implicitly assuming that the PE ratios for both will converge on an industry or market average. I am just as prone to this practice as anyone else, when I do intrinsic valuation, when I assume that operating margins and costs of capital for companies tend to converge on industry norms. That said, I continue to worry about how many of my valuation mistakes occur because I don’t question my assumptions about mean reversion enough. So, you should view this post as an attempt to be honest with myself, though I will use CAPE data as an illustrative example of both the allure and the dangers of assuming mean reversion.

Mean Reversion: Basis and Push Back
The notion of mean reversion is widely held and deeply adhered to not just in many disciplines but in every day life. In sports, whether it be baseball, basketball, football or soccer, we use mean reversion to explain why hot (and cold) streaks end. In investments, it is an even stronger force explaining why funds and investors that fly high come back to earth and why strategies that deliver above-average returns are  unable to sustain that momentum.

In statistics, mean reversion is the term used to describe the phenomenon that if you get an extreme value (relative to the average) in a draw of a variable, the second draw from the same distribution is likely to be closer to the average. It was a British statistician, Francis Galton, who first made official note of this process when studying the height of children, noted that extreme characteristics on the part of parent (a really tall or short parent) were not passed on. Instead, he found that the heights reverted back to what he called a mediocre point, a value-laden word that he used to describe the average. In the process, he laid the foundations for linear regressions in statistics.

In markets and in investing, mean reversion has not only taken on a much bigger role but has arguably had a greater impact than in any other discipline. Thus, Jeremy Siegel's argument for why "stocks win in the long term" is based upon his observation that over a very long time period (more than 200 years), stocks have earned higher returns than other asset classes and that there is no 20-year time period in his history where stocks have not outperformed the competition. Before we embark on on examination of the big questions in mean reversion, let's start by laying out two different versions of mean reversion that co-exist in markets.
  • In time series mean reversion, you assume that the value of a variable reverts back to a historical average. This, in a sense, is what you are using when looking at the CAPE today at 27.27 (in August 2016) and argue that stocks are over priced because the average CAPE between 1871 and 2016 is closer to 16.
  • In cross sectional mean reversion, you assume that the value of a variable reverts back to a cross sectional average. This is the basis for concluding that an oil stock with a  PE ratio of 30 is over priced, because the average PE across oil stocks is closer to 15. 
At the risk of over generalization, much of market timing is built on time series mean reversion, whereas the bulk of stock selection is on the basis of cross sectional mean reversion. While both may draw their inspiration from the same intuition, they do make different underlying assumptions and may pose different dangers for investors.

The nature of markets, though, is that every point of view has a counter, and it should come as no surprise that just as there are a plethora of strategies built around mean reversion, there are almost as many built on the presumption that it will not happen, at least during a specified time horizon. Many momentum-based strategies, such as buying stocks with high relative strength (that have gone up the most over a recent time period) or have had the highest earnings growth in the last few years, are effectively strategies that are betting against mean reversion in the near term. While it is easy to be an absolutist on this issue, the irony is that not only can both sides be right, even though their beliefs seem fundamentally opposed, but worse, both sides can be and often are wrong.

Mean Reversion: The Questions
You can critique mean reversion at two levels. At the level at which it is usually done, it is more about measurement than about process, with arguments centered around both how to compute the mean and the timing and form of the reversion process. There is a fundamental and perhaps more significant critique of the very basis of mean reversion, which is based on structural changes in the process being analyzed.

The Measurement Critique
Let’s say that both you and I both believe in mean reversion. Will we respond to data in the same way and behave the same way? I don't think so and that is because there are layers of judgments that lie under the words “mean” and “reversion”, where we can disagree. 
  • On the mean, the numbers that you arrive at can be different, depending upon the time period you look at (if it time series mean reversion) or the cross sectional sample (if it is a cross sectional mean reversion), and you can get very different values with the arithmetic average as opposed to the median. With cross sectional data, for instance, the oil company analysis may be altered depending on whether your sample is of all oil companies, just larger integrated oil companies or smaller, emerging market oil companies. For time series variations, consider the historical time series of CAPE and how different the "mean" looks depending on the time period used and how it was computed.
  • On the reversion part, there can be differences in judgment as well. First, even if we both agree that there is mean reversion, we can disagree on how quickly it will happen. That has profound consequences for investing, because there may be a time horizon threshold at which we may not be to devise an investment strategy to take advantage of the reversion. Second, we can disagree over how the metric in question will adjust. To illustrate, assume that the mean reversion metric is CAPE and that we both agree that  the CAPE of 27 should drop to the historic norm of 16 over the next decade. This can be accomplished by a drop in stock prices (a market crash) or by a surge in earnings (if you can make an argument that earnings are depressed and are due for recovery). The implications for investing can be very different.
In summary, there is a lot more nuance to mean reversion than its strongest proponents let on. One reason that they try to make their case look stronger than it is may be because they are selling others on their investment thesis and hoping that if they can convince enough people to make it self fulfilling. The other, and perhaps more dangerous reason, is to convince themselves that they are right, as a precursor to action. 

The Fundamental Critique
The process of mean reversion is built on the presumption that the underlying distribution (whether it be a time series or cross sectional) is stationary and that while there may be big swings from year to year (or from company to company), the numbers revert back to a norm. That is the elephant in the room, the really big assumption, that drives all mean reversion and it is its weakest link. If there are structural changes that alter the underlying distribution, there is no quicker way to ruin that trusting in mean reversion. The types of structural changes that can cause distribution to go awry range the spectrum, and the following is a list, albeit not comprehensive, of why these changes in the context of mean reversion over time.
  • The first is aging, with the argument easiest to make with individual companies and more difficult with entire markets. As companies move through the life cycle, you will generally see the numbers for the company reflect that aging, rather moving to historic norms. That is especially true for growth rates, with growth rates decreasing as a company scales up and becomes more mature, but it is also true of both other operating numbers (margins, costs of capital) as well as pricing metrics (price earnings ratios and EV multiples). While markets, composed of portfolios of companies, are less susceptible to aging, you could argue that aging equity markets (the US, Japan and Europe) will exhibit different characteristics than they did when were younger and more vibrant. 
  • The second is technology and industry structure, shaking up both the product market structure and creating challenges for accountants. This is true clearly at the company level, as is the case with retailing, where Amazon's entry and subsequent growth has laid waste to historic norms for this sector, bringing down operating margins and changing reinvestment patterns. It is also true at the market level, where an increasing proportion of the equity market (say, the S&P 500) are service and technology stocks and the accounting for expenses in these sectors (with many capital expenses being treated as operating expenses) creating questions about whether the E in the PE for the S&P 500 is even comparable over time.
  • The third is changes in consumer and investor preferences, with the first affecting the numbers in product markets and the latter in financial markets. For instance, there is an argument to be made that the surge in index funds has altered how stocks are priced today, as opposed to two or three decades ago.
In the context of CAPE, again, and using Shiller's entire database, which goes back to 1871, let's take a quick look at how much both the US economy has grown and changed since 1871 and how those changes have affected the composition of US stocks.

In 1871, coming out of the civil war, the US was more emerging than developed market, with the growth and risk that goes with that characterization. In 1900, the US equity market had become the largest in the world, but 63% of its value came from railroad stocks, reflecting both their importance to the US economy then and their need for equity capital. For most of the next few decades, the US continued on its path as a growth market and economy, though the growth trend was brought to a stop by the great depression.  The Second World War firmly established the US as the center of the global economy and the period between 1945 and 2000 represents the golden age of mean reversion, a period where at least in the US, mean reversion worked like a charm not just across stocks but across time. It is worth noting that many of the now-accepted standard practices in both corporate finance and valuation, from using historical risk premiums for stocks to attaching premiums for expected returns to small-cap stocks to believing that value stocks beat growth stocks (with low PBV or low PE as a proxy for value) came from researchers poring over this abnormally mean-reverting financial history. I trace my awakening to the dangers of mean reversion to the 2008 crisis but I believe that the signs of structural change were around me for at least a decade prior. After all, the shift from a US-centric global economy to one that was more broadly based started occurring in the 1970s and continued, with fits and bounds, in the decades after. Similarly, the US dollar's reign as the global currency was challenged by the introduction of the Euro in 1999 and put under further strain by the growth in emerging market currencies.

So, how did 2008 change my thinking about markets, investing and valuation? First, globalization is here to stay and while it has brought pluses, it has already brought some minuses. As I noted in my post on country risk, no investor or company can afford to stay localized any more, since not only do market crisis in one country quickly become global epidemics, but a company that depends on just its domestic market for operations (revenues and production) is now more the exception than the rule. Second, the fact that financial service firms were at the center of the crisis, has had long term consequences. Not only has it led to a loss of faith in banks as well-regulated entities, run by sensible (and risk averse) people, but it has increased the role of central bankers in economies, with perverse consequences. In their zeal to be saviors of the economy, central bankers (in my view) have contributed to an environment of low economic growth and higher risk premiums. Third, the low economic growth and low inflation has resulted in interest rates lower than they have been historically in most currencies and negative interest rates in some. I know that there are many who believe that I am over reacting and that it only a question of time before we revert back to more normal interest rates, higher economic growth and typical inflation but I am not convinced. 

From Statistical Significance to Investment Return Payoff
The standard approach to showing mean reversion is start with historical data and establish mean reversion with statistics. I will start with that basis, again using CAPE as my illustrative example, but will then build on it to show why, even if you believe in mean reversion and you base it on sound statistics, it is so difficult to convert statistical significance into market-beating returns.

The statistics
If you were looking at a data series, how would you go about showing mean reversion? There are three simple statistical devices that you can draw. The first is graphical, a scatter plot of the data that shows the mean reversion over time. In the context of CAPE, for instance, this is the graph that you saw in my last post:
Historical data on Shiller CAPE
The problem with this plot is that it is weak evidence for investing, since you don't make money from buying or selling PE but from buying and selling stocks. In fact, even in this plot, you can see that the CAPE case that stocks are over priced is weakened because I have used a 25-year median for comparison. A stronger graphical backing for mean reversion would then graph stock returns in subsequent time periods as  a function of the CAPE today, with a higher CAPE (relative to history) translating into lower returns in a future period. 

Looking at this data, at least, the evidence seems strong that a high CAPE today goes with lower stock returns in future periods, with the mean reversion becoming stronger for longer time periods.

The relationship between the market timing metric and returns can be quantified in one of two ways. You could compute the correlation between the metric and returns, with a more negative correlation indicating stronger mean reversion. Updating my CAPE/ returns correlation metric, with 10-year returns added to the mix, you can see again the basis for the market timing argument:

You an build on these correlations and run regressions (linear or otherwise) where you regress returns in future periods against the value of the metric today. The results of those regressions, with CAPE as the market metric, are summarized below:
What does this mean? If you buy into mean reversion and can live with the noise or error in your estimate (captured in the R-squared), these regressions back up the correlation findings, insofar as your CAPE-based predictions get more precise for longer time period returns. In fact, if you are one of those who lives and dies by statistics, using today's CAPE of 27.27 in this regression will yield a predicted annualized return of 4.30% on stocks for the next 10 years:
Expected annualized return in next 10 years = 16.24% - 0.0044 (27.27) = 4.30%
Scary, right? But before you over react, first recognize that this prediction comes with a standard error and range and second, please read on.

The Investment Action
If you have sat through a statistics class, you have probably heard the oft-repeated caution that "correlation is not causation", a good warning if you are a researcher trying to explain a phenomenon but not particularly relevant, if you are an investor. After all, if you can consistently make a lot of money from a strategy, do you really need to know why? The biggest challenge in investing is whether you can convert statistical significance ( a high correlation or a regression with impressive predictive power) into investment strategy. It is at this level that market timing metrics run into trouble, and using CAPE again, here are the two ways in which you can use the results from the data to change the way you invest.

If you are willing to buy into the notion that the structural changes in the economy and markets have not changed the historical mean reversion tendencies in the CAPE, the most benign and defensible use of the data is to reset expectations. In other words, if you are an investor in stocks today, you should expect to make lower returns for the next 10 years than you have historically. This has consequences for how much investors should save for future retirement or how much states should set aside to cover future contractual obligations, with both set asides increasing because your expected returns are lower. 

It is when you decide to use the CAPE findings to do market timing that the tests become more arduous and difficult to meet. To understand what this means, let's go back to the basic asset allocation decision that all investment begins with. Given your risk aversion (a function of both your psychological make-up and the environment you are in) and liquidity needs (a function of your age, wealth and dependents), there is a certain mix of stocks, bonds and cash that is right for you. With market timing, you will alter this mix to reflect your views on desirable (or under priced) markets and undesirable ones. Thus, your natural mix is 60% stocks, 30% bonds and 10% cash, and you believe (using whatever market timing metric you choose) that stocks are over priced, you would lower your allocation to stocks and increase your allocation to either bonds or cash. You could further refine this market timing algorithm for domestic stocks versus foreign stocks or bring in other asset classes such as collectibles and real estate. The test of a market timing strategy therefore requires more structure than the statistical analysis of checking for correlation or regression:
  1. Timing threshold: If you decide that you will time markets using a metric, you have to follow through with specifics. For instance, with CAPE as your market metric, and a high (low) CAPE being used as an indicator of an over valued (under valued) market, you have to indicate the trigger  that will initiate action. In other words, does the CAPE have to be 10% higher, 25% higher or 50% higher than the historic average for you to start moving money out of stocks?
  2. Asset class alternatives: If you decide to move money out of stocks, you have to also specify where the money will go and you have four choices. 
  3. Holding period: You will have to specify how long you plan to stay with the "market timed" allocation mix, with the answers ranging from a pre-specified time horizon (1 year, 2 years or 5 years) to until the market timing metric returns to safe territory. 
  4. Allocation Constraints (if any): The allocation that you have for an asset class can be floored at zero, if you are a long only investor, but can be negative, if you are willing to go short. The cap on what you can allocate to an asset class is 100%, if you cannot or choose not to borrow money, but can be greater than 100%, if you can. 
Put simply, the lower your threshold, the more alternatives you have to investing in stocks, the shorter your holding period and the fewer your constraints, the more active you are as a market timer. It is in this context that I tried out different market timing strategies built around CAPE. The table below lists out the returns from a buy and hold strategy with a fixed mix of stocks, bonds and bills (60%, 30% and 10%) and contrasts it with returns over the same period from using a CAPE timing strategy of reducing the equity allocation to 40% if the CAPE is 25% higher than a 50-year median value and increasing the equity allocation to 80% if the CAPE is 25% lower than a 50-year median value. I report the numbers for the entire time period 1917-2016 and break it down into two fifty-year time periods (1917-1966, 1967-2016):
Download market timing spreadsheet
With this mix of timing choices (50-year median, 25% threshold and the given changes to equity allocation), the Shiller CAPE outperforms the buy and hold strategy for the 1917-2016 time period  but  under performs in the last fifty year time period. I know that your timing choices can be very different from mine and I have created options in this spreadsheet to let you change the choices to reflect your preferences to see if you can deliver better market timing results using CAPE. I did try a few variants and here is what I found.
  1. Time Period: With every variation of timing that I tried, the CAPE delivers a positive market timing payoff in the first half of the entire time period (from 1917 to 1966) and a negative one in the second half (1967-2016). In fact, I could not find a combination of timing devices that delivered positive payoff in the second time period.
  2. Choice of median: Using the lifetime median delivers better results during the "good" period (1917-1966) but worse results during the "bad" period (1967-2016). Using a shorter time periods for the median reduces the outperformance in the first half of the analysis period but improves it in the second half.
  3. Buy and Sell: The CAPE's timing payoff is greater when it is used as a buying metric than as a selling metric. In fact, you make a positive payoff from using a low CAPE as a buying indicator over the entire period but using it is a signal of over priced markets costs you money in both time period. 
  4. Market Timing magnitude: Increasing the degree to which you tilt towards or away from stocks, in reaction to the CAPE, just magnifies the return difference, positive or negative. Thus, in the first half of the century (1917-1966), changing your equity exposure more increases the payoff to market timing. In the second half, it makes the negative payoff worse.
In many ways, this testing is tilted in favor of finding that the Shiller CAPE works. First, while I have been careful not to use ex-post data, I have acted as if I know what the earnings for the year will be, at the end of each year, when my market timing decision is made. In reality, on December 31, 2012, I would know only the earnings for the first three quarters of 2012 and not quite the full year. Second, I am ignoring the transactions costs and taxes due from shifting large amounts in and out of stocks in my timing years. Those will represent a significant drain on my returns as an investor. Finally, I am assuming that there have been no structural shifts large enough to cause the mean reversion to break down. In spite of all of this, I am hard pressed to explain why we are so swayed by arguments based on this metric.

These are dangerous times for those who believe in mean reversion, for two reasons. The first is that our access to historical data is getting broader and deeper, with mixed consequences. Having more data allows us to find out more about the underlying fundamentals but since that data goes back so far, much of what we find no longer has relevance. The second is that doing statistical analysis no longer requires either homework or effort, with tools at our fingertips and statistical results are only a click away. Both in academia and in practice, I see more and more use of statistical significance as proof that you can beat markets and my reason devising and testing out market timing strategies with CAPE were not meant to be an assault on CAPE but more a cautionary note that statistical correlation is not cash in the bank. This may also explain why there are so many ways to beat the market, on paper, and so few seem to be able to deliver those magical excess returns, in practice. 

YouTube Video

  1. CAPE: 1881-2016 (Shiller Data)
  2. Stock, Bond and Bill Returns (1881-2016)
  3. Market Timing Spreadsheet

Wednesday, August 24, 2016

Superman and Stocks: It's not the Cape (CAPE), it's the Kryptonite(Cash flow)!

Just about a week ago, I was on a 13-hour plane trip from Tokyo to New York. I know that this will sound strange but I like long flights for two reasons. The first is that they give me extended stretches of time when I can work without interruption, no knocks on the door or email or phone calls. I readied my lecture notes for next semester and reviewed and edited a manuscript for one of my books in the first half on the trip. The second is that I can go on movie binges with my remaining time, watching movies that I would have neither the time nor the patience to watch otherwise. On this trip, however, I made the bad decision of watching Batman versus Superman, Dawn of Justice, a movie so bad that the only way that I was able to get through it was by letting my mind wander, a practice that I indulge in frequently and without apologies or guilt. I pondered whether Superman needed his suit or more importantly, his cape, to fly. After all, his powers come from his origins (that he was born in Krypton) and not from his outfit and the cape seems to be more of an aerodynamic drag than an augmentation. These deep thoughts about Superman's cape then led me to thinking about CAPE, the variant on PE ratios that Robert Shiller developed, and how many articles I have read over the last decade that have used this measure as the basis for warning me that stocks are headed for a fall. Finally, I started thinking about Kryptonite, the substance that renders Superman helpless, and what would be analogous to it in the stock market. I did tell you that I have a wandering mind and so, if you don't like Superman or stocks, consider yourself forewarned!

The Stock Market’s CAPE
As stocks hit one high after another, the stock market looks like Superman, soaring to new highs and possessed of super powers.

There are many who warn us that stocks are overheating and that a fall is imminent. Some of this worrying is natural, given the market's rise over the last few years, but there are a few who seem to have surrendered entirely to the notion that stocks are in a bubble and that there is no rational explanation for why investors would invest in them. In a post from a couple of years ago, I titled these people as  bubblers and classified them into doomsday, knee jerk, conspiratorial, righteous and rational bubblers. The last group (rational bubblers) are generally sensible people, who having fallen in love with a market metric, are unable to distance themselves from it.

One of the primary weapons that rational bubblers use to back up their case is the Cyclically Adjusted Price Earnings (CAPE), a measure developed and popularized by Robert Shiller, Nobel prize winner whose soothsaying credentials were amplified by his calls on the dot com and housing bubbles. For those who don’t quite grasp what the CAPE is, it is the conventional PE ratio for stocks, with two adjustments to the earnings. First, instead of using the most recent year’s earnings, it is computed as the average earnings over the prior ten years. Second, to allow for the effects of inflation, the earnings in prior years is adjusted for inflation.  The CAPE case against stocks is a simple one to make and it is best seen by graphing Shiller’s version of it over time.
Shiller CAPE data (from his site)
The current CAPE of 27.27 is well above the historic average of 16.06 and if you buy into the notion of mean reversion, the case makes itself, right? Not quite! As you can see, even within the CAPE story, there are holes, largely depending upon what time period you use for your averaging. Relative to the fully history, the CAPE looks high today, but relative to the last 20 years, the story is much weaker. Contrary to popular view, mean reversion is very much in the eyes of the beholder.

The CAPE’s Weakest Links
Robert Shiller has been a force in finance, forcing us to look at the consequences of investor behavior and chronicling the consequences of “irrational exuberance”. His work with Karl Case in developing a real estate index that is now widely followed has introduced discipline and accountability into real estate investing and his historical data series on stocks, which he so generously shares with us, is invaluable. You can almost see the “but” coming and I will not disappoint you. Of all of his creations, I find CAPE to be not only the least compelling but also potentially the most dangerous, in terms of how often it can lead investors astray. So, at the risk of angering those of you who are CAPE followers, here is my case against putting too much faith in this measure, with much of it representing updates of my post from two years ago.
1. The CAPE is not that informative
The notion that CAPE is a significant improvement on conventional PE is based on the two adjustments that it makes, first by replacing earnings in the most recent period with average earnings over ten years and the second by adjusting past earnings for inflation to make them comparable to current earnings. Both adjustments make intuitive sense but at least in the context of the overall market, I am not sure that either adjustment makes much of a difference. In the graph below, I show the trailing PE, normalized PE (using the average earnings over the last ten years) and CAPE for the S&P 500 from 1969 to 2016 (last twelve months). I also show Shiller's CAPE, which is based on a broader group of US stocks in the same graph.
Download spreadsheet with PE ratios
First, it is true that especially after boom periods (where earnings peak) or economic crises (where trailing earnings collapse), the CAPEs (both mine and Shiller's) yield different numbers than PE.  Second, and more important, the four measures move together most of the time, with the correlation matrix shown in the figure. Note that the correlation is close to one between the normalized PE and the CAPE, suggesting that the inflation adjustment does little or nothing in markets like the US and even the normalization makes only a marginal difference with a correlation of 0.86 between the unadjusted PE and the Shiller PE.

2. The CAPE is not that predictive
The question then becomes whether using the CAPE as a valuation metric yields judgments about stocks that are superior to those based upon just PE or normalized PE. To test this proposition, I looked at the correlation between the values of different metrics, including trailing PE, CAPE, the inverse of the dividend yield, earnings yield and the ratio of Shiller PE to the Bond PE) today and stock returns in the following year and the following five years:
There is both good news and bad news for those who use the Shiller CAPE as their stock valuation metric. The good news is that the fundamental proposition that stocks are more likely to go down in future periods, if the Shiller CAPE is high today, seems to be backed up. The bad news is two fold. First, the relationship is noisy or in investment parlance, the predictive power is low, especially with one-year returns. Second, the trailing PE actually does a better job of predicting one-year returns than the CAPE and while CAPE becomes the better predictor than trailing PE over a five-year period, it is barely better than using a dividend yield indicator.  While I have not included these in the table, I will wager that any multiple (such as EV to EBITDA) would do as good (or as bad, depending on your perspective) a job as market timing.

As a follow-up, I ran a simple test of the payoff to market timing, using the Shiller CAPE and actual stock returns from 1927 to 2016. At the start of every year, I first computed the median value of the Shiller CAPE over the previous fifty years and assumed an over priced threshold at 25% above the median (which you can change). If the actual CAPE was higher than the threshold, I assumed that you put all your money in treasury bills for the following year and that if the CAPE was lower than the threshold, that you invested all your money in equities. (You can alter these values as well). I computed how much $100 invested in the market in 1927 would have been worth in August of 2016, with and without the market timing based on the CAPE:

Download spreadsheet and change parameters
Note that as you trust CAPE more and more (using lower thresholds and adjusting your equity allocation more), you do more and more damage to the end-value of your portfolio. The bottom line is that it is tough to get a payoff from market timing, even when the pricing metric that you are using comes with impeccable credentials. 

3. Investing is relative, not absolute
Notwithstanding its weak spots, let’s take the CAPE as your measure of stock market valuation. Is a CAPE of 27.27 too high, especially when the historic norm is closer to 16? The answer to you may sound obvious, but before you do answer, you have to consider where you would put your money instead. If you choose not to buy stocks, your immediate option is to put your money in bonds and the base rate that drives the bond market is the yield on a riskless (or close to riskless) investment. Using the US treasury bond as a proxy for this riskless rate in the United States, I construct a bond PE ratio using that rate:
Bond PE = 1/ Treasury Bond Rate
Thus, if you invest in a treasury bond on August 22, with a yield of 1.54%, you are effectively paying 64.94 (1/.0154) times your earnings. In the graph below, I graph Shiller’s measures of the CAPE against this T.Bond PE from 1960 to 2016:
Download T Bond Rate PE data
I also compute a ratio of stock PE to T.Bond PE that will use as a measure of relative stock market pricing, with a low value indicating that stocks are cheap (relative to T.Bonds) and a high value suggesting the opposite. As you can see, bringing in the low treasury bond rates of the last decade into the analysis dramatically shifts the story line from stocks being over valued to stocks being under valued. The ratio is as 0.42 right now, well below the historical average over any of the time periods listed, and nowhere near the 1.91 that you saw in 2000, just before the dot com bust or  even the 1.04 just before the 2008 crisis. 

4. Its cash flow, not earnings that drives stocks
The old adage that it is cash flows, not earnings, that drives stocks is clearly being ignored when you look at any variant of PE ratios. To provide a sense of what stock prices look like, relative to cash flows, I computed a multiple of total cash returned to stockholders by companies (including buybacks) and compared these multiples to Shiller’s CAPE in the graph below:
S&P 500 Earnings and Cash Payout
Here again, there seems to be a disconnect. While the CAPE has risen for the market, from 20.52 in 2009 to 27.27 in 2016, as stocks soared during that period, the Price to CF ratio has remained stable over that period (at about 20), reflecting the rise in cash returned by US companies, primarily in buybacks over the period.

Am I making the case that stocks are under valued? If I did, I would be just as guilty as those who use CAPE to make the opposite case. I am not a market timer, by nature, and any single pricing metric, no matter how well reasoned it may be, is too weak to capture the complexity of the market. Absolutism in market timing is a sign of either hubris or ignorance.

The Market’s Kryptonite
At this point, if you think that I am sanguine about stocks, you would be wrong, since the essence of investing in equities is that worry goes with it. If it’s not the high CAPE that is worrying me, what is? Here are my biggest concerns, the kryptonite that could drain the market of its strength and vitality.
  1. The Treasury Alternative (or how much are you afraid of your central bank?)  If the reason that you are in stocks is because the payoff for being in bonds is low, that equation could change if the bond payoff improves. If you are Fed-watcher, convinced that central banks are all-powerful arbiters of interest rates, your nightmares almost always will be related to a meeting of the Federal Open Market Committee (FOMC), and in those nightmares, the Fed will raise rates from 1.50% to 4% on a whim, destroying your entire basis for investing in stocks. As I have noted in these earlier posts, where I have characterized the Fed as the Wizard of Oz and argued that low rates are more a reflection of low inflation and anemic growth than the result of quantitative easing, I believe that any substantial rate rises will have to come from shifts in fundamentals, either an increase in inflation or a surge in real growth. Both of these fundamentals will play out in earnings as well, pushing up earnings growth and making the stock market effect ambiguous. In fact, I can see a scenario where strong economic growth pushes T. bond rates up to 3% or higher and stock markets actually increase as rates go up.
  2. The Earnings Hangover It is true that we saw a long stint of earnings improvement after the 2008 crisis and that the stronger dollar and a weaker global economy are starting to crimp earnings levels and growth. Earnings on the S&P 500 dropped in 2015 by 11.08% and are on a pathway to decline again this year and if the rate of decline accelerates, this could put stocks at risk. That said, you could make the case that the earnings decline has been surprisingly muted, given multiple crises, and that there is no reason to fear a fall off the cliff. No matter what your views, though, this will be more likely to be a slow-motion correction, offering chances for investors to get off the stock market ride, if they so desire.
  3. Cash flow Sustainability: My biggest concern, which I voiced at the start of the year, and continue to worry about is the sustainability of cash flows. Put bluntly, US companies cannot keep returning cash at the rate at which they are today and the table below provides the reason why:

YearEarningsDividendsDividends + BuybacksDividend PayoutCash Payout
2016 (LTM)98.6143.88110.6244.50%112.18%
In 2015, companies in the S&P 500 collectively returned 105.59% of their earnings as cash flows. While this would not be surprising in a recession year, where earnings are depressed, it is strikingly high in a good earnings year. Through the first two quarters of 2016, companies have continued the torrid pace of buybacks, with the percent of cash returned rising to 112.18%. The debate about whether these buybacks make sense or not will have to be reserved for another post, but what is not debatable is this. Unless earnings show a dramatic growth (and there is no reason to believe that they will), companies will start revving down (or be forced to) their buyback engines and that will put the market under pressure. (For those of you who track my implied equity risk premium estimates, it was this concern about cash flow sustainability that led me to add the option of allowing cash flow payouts to adjust to sustainable levels in the long term).

So, how do these worries play out in my portfolio? They don’t explicitly but they do implicitly affect my investment choices. I cannot do much about interest rates, other than react, and I will stay ready, especially if inflation pressures push up rates and the fixed income market offers me a better payoff. With earnings and cash flows, there may be concerns at the market level, but I bet on individual companies, not markets. With those companies, I can do my due diligence to make sure that they have the operating cash flows (not just dividends or buybacks) to justify their valuations. If that sounds like a pitch for intrinsic valuation, are you surprised?

The Market Timing Mirage
Will there be a market correction? Of course! When it does happen, don't be surprised to see a wave of “I told you so” coming from the bubblers. A clock that is stuck at 12 o'clock will be right twice every day and I would urge you to judge these market timers, not on their correction calls, which will look prescient, but on their overall record. Many of them, after all, have been suggesting that you stay out of stocks for the last five years or longer and it would have to be a large correction for you to make back what you lost from staying on the sidelines. Some of these pundits will be crowned as great market timers by the financial press and they will acquire followers. I hope that I don’t sound like a Cassandra but this much I know, from studying past history. Most of these great market timers usually get it right once, let that success get to their heads and proceed to let their hubris drive them to more and more extreme predictions in the next cycle. As an investor, my suggestion is that you save your money and your sanity by staying far away from market prognosticators.


  1. PE ratios from 1960-2016
  2. Shiller CAPE and T.Bond PE (1960-2016)
  3. S&P 500: Earnings, Dividends and Buybacks (2000-2016)
  4. CAPE Market Timing Test