Sunday, February 1, 2015

Discounted Cashflow Valuations (DCF): Academic Exercise, Sales Pitch or Investor Tool?

In my last post, I noted that I will be teaching my valuation class, starting tomorrow (February 2, 2015). While the class looks at the whole range of valuation approaches, it is built around intrinsic valuation, reflecting my biases and investment philosophy. I have already received a few emails, asking me whether this is an academic or a practical valuation class, a question that leaves me befuddled, since I am not sure what an academic value is.  As some of you who have read this blog for awhile know, I do try to value companies, but I do so not because I am intellectually curious (I don't lie awake at night wondering what Twitter is worth!) but because I need investments for my portfolio. In the context of these valuations, I have been accused of being a valuation theorist, and I cringe because I know how little theory there is in valuation or at least my version of it. In fact, my entire class is built around one simple equation:


Put in non-mathematical terms, the equation posits that the value of an asset is the value of the expected cash flows over its lifetime, adjusted for risk and the time value of money. If that sounds familiar, it should, because it is the starting point for every Finance 101 class, a rite of passage that in conjunction with buying a financial calculator sets you on the pathway to being a Financial Yoda! 

That is the only theory that you need for valuation! The rest of the class is about the practice of valuation: defining and estimating expected cash flows for different types of assets and businesses at different stages in the life cycle and estimating and adjusting the discount rate for risk and time value. Note that there is nothing in this fundamental equation that has not been known to investors and business people through the ages, i.e., the value of a business has always been a function of its cash flows, growth potential and risk and that you certainly don’t need to be mathematically inclined to be able to do valuation. So, if you don’t remember how to take first differentials or solve algebraic equations, never fear. You can still value companies.

DCF : Neither Magic Bullet nor Bogeyman
If DCF valuation is simple as its core, why does it intimidate so many? The fault lies both with its proponents and its critics. The proponents, and I would include myself on the list, have undercut the approach's usage and acceptance by:
  1. Over complicating DCF: It is undeniable that most discounted cash flow models suffer from bloat, with layers of detail that we not only don't need, but also make no difference to the ultimate value. These details and complexities are sometimes added with the best of intentions (to get better estimates of cash flows and risk) and sometimes with the worst (to intimidate and to hide the big assumptions). No matter what the intentions are, they make people on the receiving end suspicious.
  2. Over selling DCF: In the hands of bankers, analysts, consultants and managers, DCF models are less analytical devices and more sales tools, backing up a recommendation to buy, sell or change the way we do things.  While that is neither surprising nor newsworthy, it does make those who are the targets of these sales pitches cynical about the process, and who can blame them?
  3. Over sanitizing DCF: I don't know whether DCF's proponents feel that it cannot be defended on its merits or that it is too weak to stand up to scrutiny, but they seem to want to cover up the uncertainties that are embedded into every valuation and play down any hint of story telling that may underlie the numbers or uncertainty in their estimates.
Like anyone who has ever used a DCF, I have been guilty of these practices and therefore understand the motivation. At the core, it is because we are insecure both about our understanding of DCF and our capacity to explain in intuitive terms why we do what we do. If paid to do valuation, we over compensate and believe that we will be more credible if we churn out overcomplicated, number-driven models and that our clients would not pay us, if they realized how simple the process actually was.

Those who critique discounted cash flow models (and I certainly agree that there is often to disagree with), are driven by their own share of sins, where they conflate disagreements that they have with input estimation techniques, the model-builder and model output with disagreements with the DCF process itself.
  1. The Baby/Bathwater syndrome: While it is an analogy that makes me cringe each time I use it, with visions of babies flying out of bathroom windows, it is apt in its description of those who take issue with how an input is estimated in a DCF and then extrapolate to conclude that the entire process is flawed. The input that creates the most angst, of course, is the risk measure used in the valuation, with even a mention of beta generating the gag reflex among old-time value investors. 
  2. Dislike you, dislike your model: The line between a DCF model and its builder must be a gray one, since many critics seem to have trouble finding it. Not surprisingly, dislike of a user because of his or her investment philosophy, personality or style of presentation can very quickly translate into disdain about the process by which he or she values companies.
  3. Don’t like your answer: It is human nature but investors tend to like DCF models that deliver answers that they like and dislike models that do not. Even in my limited blog posting experiences, I have been lauded for using sound intrinsic value models, by Apple Bulls, when my valuations have suggested that Apple is cheap. I have also been blasted by often the same investors for using a flawed DCF model, when my valuations suggest otherwise.
As with the proponents, I think I understand where critics are coming from. After all, if you were constantly the target for sales pitches by analysts who use complicated DCF models to sell snake oil, you would be suspicious too.

A Return to Basics
The first step in spanning the divide is to strip away the layers of complexity that we have built into valuation over the decades and return to the equation that I started this post. At the risk of stating the obvious, I would like to draw on four simple and self-evident propositions that get overlooked or ignored frequently in the discussion of discounted cashflow valuation (DCF).
  1. The Duh Proposition: For an asset to have value, its expected cash flows have to be positive at some point in time, but that does not imply that the cash flow has to be positive every single year and it is quite clear that you can have a valuable business (asset) with negative cash flows in the first year, the first three years or even the first seven or eight, if it can deliver disproportionately large positive cash flows later in their lives. It is true that those whose DCF toolbox has only one model in it, usually the Gordon Growth Model (a stable growth dividend discount model), have trouble with such companies, but using the Gordon Growth Model to value most equities is the equivalent of doing surgery with a  hammer: painful, ineffective and designed to come to a bloody end.
  2. You can hate beta (or modern portfolio theory or all of academic finance), but still love DCF: This may come as news to its worst critics but the DCF model does not come prepackaged with modern portfolio theory and its most famous handmaiden, the beta. In fact, while the discount rate in the discounted cash flow model is usually risk-adjusted and reflects the time value of money, the model itself is completely agnostic about how you adjust for risk (you can come up with your own creative ways of making the adjustment) or even whether you adjust for risk. The DCF model is a descriptive equation of a cash-flow generating asset or business, not a theory or a hypothesis.
  3. It is the asset's life, not your time horizon: A DCF model is designed to value an asset over it's life, and is really not malleable to what you (as the investor looking at the asset) believe your time horizon to be. If the value of an asset is the present value of cash flows over its life, what is that life? It clearly depends on the asset. If you are valuing a machine whose functioning life is only one year, all you need is one year's cash flows, but if estimating a value for a rental building with a 20-year life, it would be twenty years. With public companies that at least in theory can last forever, we do stop estimating cash flows at a point in time and assume that cash flows beyond that point continue in perpetuity, but this is an assumption of convenience, not necessity. In fact, there is nothing that stops you from replacing that perpetuity assumption with one that assumes that cash flows will continue for only 20 or 30 years after your closure year.
  4. You will be wrong, and it is not your fault: If you take expected cash flows (where the expectations are across a wide spectrum of outcomes) and discount those expected cash flows at a risk-adjusted discount rate, it should go without saying (but I am going to say it anyway) that the present value that you get is an estimate of value. Thus, you are almost guaranteed to be wrong when valuing assets with any uncertainty about the future, and more wrong when there is more uncertainty. So what? The market price is just as affected by uncertainty, and you are judged not by how wrong you are in absolute terms but how wrong you are, relative to other people valuing the stock.
Ten Myths about the DCF Model
While the architecture of the DCF model is simple and the truths that emerge from it are universal, there is a great deal of mythology around DCF valuation, some of it promoted by model-users and some by model-haters.
  • Myth 1: If you have a D(discount rate) and a CF (cash flow), you have a DCF. As a DCF-observer, I see a lot of pseudo DCF, DCFs in drag and other fake DCFs being pushed as discounted cash flow valuations. 
  • Myth 2: A DCF is an exercise in modeling & number crunching. There is no room for creativity or qualitative factors.
  • Myth 3: You cannot do a DCF when there is too much uncertainty, thus making it useless as a tool in valuing start-ups, companies in emerging markets or during macroeconomic crises.
  • Myth 4: The most critical input in a DCF is the discount rate and if you don’t believe in modern portfolio theory (or beta), you cannot use a DCF.
  • Myth 5: If most of your value in a DCF comes from the terminal value, there is something wrong with your DCF, since the value rests almost entirely on what you assume in that terminal value.
  • Myth 6: A DCF requires too many assumptions and can be manipulated to yield any value you want.
  • Myth 7: A DCF cannot value brand name or other intangibles. 
  • Myth 8: A DCF yields a conservative estimate of value. It is better to under estimate value than over estimate it.
  • Myth 9: If your DCF value changes significantly over time, there is either something wrong with your valuation (since intrinsic value should not change over time) or it is pointless (since you cannot make money on a shifting value)
  • Myth 10: A DCF is an academic exercise, making it useless for investors, managers or others who inhabit the real world.
Each of these myths deserves its own post and I plan to cover all of them in the next year (one myth a month). Stay tuned!

A Trial Run
I know that some of you are skeptical about my pitch but if you are, at least give the process a try. If you feel a little rusty on the basics or have questions about details, you are welcome to take my class in real time or the online version of it (which is less trying and has shorter webcasts).

Wednesday, January 28, 2015

The Dance of the Disrupted: Observations from the front lines

Teaching is my passion, writing gives me joy and finance is my playground. While I am blessed in being able to immerse myself in all three, my activities put me in three businesses, education, publishing and financial services, that are begging to be disrupted. In fact, as disruption starts to challenge the status quo in all three businesses, I have a front row seat to observe how they react to these changes and perhaps add to their discomfort. 


The targets of disruption

As technology and globalization disrupt one business after another, it is useful to start with a simple question. Why do some businesses get targeted for disruption and others left alone? As I see it, there are three characteristics that businesses that get disrupted seem to share:
  1. Sizable economic footprint: The probability of a business being disrupted increases proportionately with the amount of money that is spent on that business. Using this template, it is easy to see why financial services (active money management, financial advisory services, corporate finance) and education are attracting so many disruptors and why publishing offers a smaller target.
  2. Inefficient production and delivery mechanisms: A common characteristic that disrupted businesses share is that they are inefficiently run, and neither producers nor consumers seem happy. Consumers are unhappy because producers are non-responsive to their needs and deliver sub-standard products at premium prices, but producers seem to have little to show in surplus. In education, for instance, students (especially under graduates at research universities) complain that they get a bad deal for the money they spend but colleges collectively seem to have trouble balancing their budgets, as is evidenced by their frequent and frantic attempts to raise money from alumni to cover their unmet needs. Publishers claim that their business models are being threatened by Amazon, while textbooks still cost outlandish amounts of money. Even in finance, where there are a few big winners every period, it is becoming increasingly difficult to find entities that win big consistently, and consumers of financial services are not exactly happy campers.
  3. Outdated competitive barriers and inertia: If these businesses are so big and inefficiently run, you may wonder what has allowed them to continue in existence for as long they have. The strongest force that they have going for them is inertia, where consumers have been programmed to accept the status quo: that it should take four years to get an undergraduate degree, that you need professional (paid) help to invest and that it makes sense to pay outlandish amounts for new editions of textbooks (on accounting, economics or mathematics) that are little changed from the old editions. Adding to the protections are regulatory or licensing requirements that have long outlived their original purpose and serve to protect incumbents from insurgencies. I have posted previously on how universities have bundled together screening, classes, networking and entertainment into packages that students have to take whole or leave and publishers and financial service companies have their own bundling variants.
The Dance of the Disrupted: The Five Stages
One of the enduring challenges that we face is explaining why disrupted businesses take so long to respond to disruption. Why did retailers not react faster to online retailing, in general, and Amazon, in particular? In a more updated version, what is it that is stopping traditional cab service companies from responding better to the car-sharing services like Uber and Lyft? I will let corporate strategists hash out the answers to those questions, but watching the education business respond to disruption has given me some perspective. With apologies to Elizabeth Kubler-Ross, I see disruption working its way through disrupted businesses in five stages, starting with denial and ending with acceptance.

Stage 1: Denial and Delusion
The first reaction to a disruptive challenge at most established businesses is delusion and denial, the delusion coming from the belief on the part of the existing players that the established way of doing business is the only (and best) way, notwithstanding widespread dissatisfaction on the part of both producers and consumers, followed by denial that others can do it better. I see this clearly in the education business, where I hear university overseers, administrators and faculty all express shock that anyone would question the Rube Goldberg contraption that forms the modern university education and conviction that no one outside the hierarchy understands education like they do.

Stage 2: Failure and False Hope
In most businesses, the initial wave of disruption usually fails, both because the disruptors do not understand the businesses that they are trying to disrupt and/or ran foul of the rules of the game (written by the establishment). Thus, Napster’s initial foray into the music business ended in it being shut down and the online retailing challenge was derailed (at least temporarily) by the tech market collapse in 2000. In the education business, the MOOC phenomenon was the shooting star that challenged the education establishment five years ago but it looks like it has fizzled out, partly because its providers mistook a university degree for a collection of courses. That initial failure was a moment of relief for the education establishment, since it reinforced its sense of superiority, and has created the hope among some in it that the disruption has passed.

Stage 3: Imitation and Institutional Inertia
The threat of disruption scares the establishment, though it moves in conventional ways to counter the disruption: by mapping out long-term plans and trying to borrow ideas from the disruptors. Those moves, while initiated with fanfare and backed up by resources, are generally undermined by an unwillingness on the part of those who benefit from the status quo to give up or compromise any of their existing privileges. In the education business, this “me too” phase is in full force, as universities create online course and some even offer online degrees, with a few faculty contributing willingly and a large majority going along either grudgingly, or not at all. If the only way that traditional colleges can compete with online education is by forcing professors to be accountable for the classes they teach (tying hiring, pay and tenure to teaching quality more than to research output) and firing those who do not measure up (no matter how productive they have been in their research), do you think that proposal has any chance of succeeding at a modern research university? I do not!

Stage 4: Regulation and Rule Rigging
The initial disruption may fail but it exposes both the weaknesses of the existing system and ways of getting around its defenses. Just as Napster softened up the music business for the assault of Apple’s iTunes store, the failure of MOOCs has offered valuable clues to disruptors as to what they need to do differently to beat universities at their game. In this post from September 2014, I laid out what I think will characterize the first successful online university: a combination of student screening, top-notch classes, discussion and interaction forums and networking opportunities , and I remain convinced that it will happen sooner rather than later. I predict that the education status quo will respond as other disrupted businesses have in the past, with a combination of complaints about unfairness and bad quality of the disruptors and a demand for protection from regulatory and licensing authorities from competition. Anecdotal evidence about the poor quality of education at some online education portals will be used to tar all online education, as if traditional colleges do not churn out their own share of substandard graduates.

Stage 5: Acceptance and Adjustment
The end game in disruption is painful. There will be jobs lost not only at the disrupted institutions and there will be ripple effects in the communities that serve them. With universities that have tenure-protected older faculty, the pain will be borne disproportionately by younger faculty and doctoral students entering the academic job market, and even tenure-protected faculty will find out that a guaranteed job does not come with guaranteed pay or research support. I am not predicting that universities will cease to exist, but there will be fewer of them, and the ones that survive will do so because they have carved out niches for themselves. IT is unfair, but it will be easier for a Harvard, MIT or Oxford to make it than lesser schools, with less illustrious histories, smaller endowments and less connected alumni.

My Disruption Plans
As I watch the businesses that I am in face the threat of disruption and respond badly, I plan to contribute to the disruption with small (and perhaps futile) acts of my own.
  1. In the publishing business, there is nothing more perverse and irrational than the textbook game, where books are obscenely over priced (even in their e-book versions) and old editions are made obsolete with a few selected edits. Of my ten books, four are textbooks and the way they are priced is the reason that I don’t require them in my own classes. The first editions of these books were written more than 15 years ago, and I had no choice but to use a publisher, but if I were writing these books today, I would do things differently. Then again, I am not done writing and will perhaps get a chance to make amends to those who have read my books.
  2. The finance business is too big for me to even cause a ripple, but I will continue to make the case that investors need to stop paying financial advisors for useless (and often counter productive) investment advice, that businesses should be able to make fundamental corporate finance decisions without calling in consultants and that the valuations that you get from bankers in IPOs and acquisitions are more pricing than value. One reason that Anant Sundaram and I co-developed uValue, a (free) valuation app for the iPhone/iPad is to make it easier for investors/companies to do valuations on their own.
  3. On the education front, anyone who has been reading my blog for a while knows that I put my regular classes online, class webcasts, lectures and exams included. I will be teaching corporate finance and valuation to MBAs at Stern in the spring, with classes starting on February 2, 2015 and continuing through May 11, 2015. The corporate finance class is the first one in the sequence, offered to first year MBAs, and valuation is an elective. You have four forums where you can take these classes:


Corporate Finance
Valuation
My site (Stern NYU)
Apple iTunes U
Yellowdig
YouTube


Each option has its pluses and minuses. My site will include everything I offer my regular class, including emails and announcements but it is an online site without any bells and whistles. The iTunes U site is the most polished in terms of offerings, but there is no forum for interaction and requires more work if you don't have an Apple device. Yellowdig is a new add-on to my menu and it is a site where you will be able to access the classes and material and hopefully interact with others in the class. (You will have to register on Yellowdig and it is restrictive on what email addresses it will accept.) YouTube is the least broadband-intensive forum, since the file size adjusts to your device, but you will be able to get only the class videos (and not the material).

If you are wondering why I would disrupt businesses that I am part of, I have three responses. The first is that, with four children, I am a consumer of the products/services of these businesses and I am sick and tired of paying what I do for textbooks, college tuition and minor financial services. The second is that it is so much more fun being a disruptor than the disrupted and being in a defensive posture for the rest of my life does not appeal to me. The third is that with Asia's awakening, we face a challenge of huge numbers and the systems (education, public and financial services) as we know them don't measure up.

Monday, January 19, 2015

The X Factor in Value: Excess Returns in Theory and Practice

There are lots of reasons why we try to start and run businesses. Some of them are emotional but the financial rationale for starting and staying in business is a simple one. It is to not just to make money, but to make more than what you would have made elsewhere with the capital (human and financial) invested in the business. Of course, your competitors, the government and sometimes the entire world seems to conspire against you (or at least it seems that way) to prevent you from making these “excess” returns. 

The Search for and Scarcity of Excess Returns
In corporate finance, decision-making tools are constructed with the objective of earning and maximizing excess returns. Thus, the notion of net present value in capital budgeting is built on the presumption that an investment should earn more than what you would have generated as a return on an investment of equivalent risk.  In investing, the search for excess returns or alpha is just as intense, with traders, value investors and growth investors playing their own versions of the game.

While you can plan, hope and pray for excess returns, to earn them consistently, you have to bring something unique that cannot be easily replicated to the game. In the case of businesses, that something is a competitive advantage or a barrier to entry that allows them to continue generating returns that exceed their costs of capital, without competition driving down profitability to more "normal" levels. These competitive advantages can range from economies of scale (Walmart), to brand name (Coca Cola) to patents (Amgen), and while they are have to be earned, they are not uncommon. In the case of investors, those competitive advantages are not only rarer but also more difficult to defend, perhaps explaining why so few active investors beat index funds or the market.

The Measurement of Excess Returns
Assume that you have been given the task of measuring whether a company’s past investments have generated returns for that exceed their cost, i.e. excess returns. To measure excess returns generated by companies on their investments collectively, you need two numbers, the expected return on the investments, given their risk and alternative investment choices today, and the actual return earned on those investments.
  1. The first number is the expected return on the investment, given its risk. As I noted in my last post, the cost of capital, computed right, should be an opportunity cost that reflects the expected return that investors in the company can generate by investing elsewhere in investments of equivalent risk. 
  2. The second number is easy to compute for investors in publicly traded securities, since it a function of how much cash the investments returned (in dividends or other forms) and the price change over the year. Measuring the return earned by companies is more problematic, especially for ongoing and evolving investments. The most logical place to start is with the earnings generated by the company on these investments, but that number,  is volatile and may not reflect the true quality of investments.  The actual earnings (and returns) for a company will move a lot from year to year, sometimes because of actions taken by the firm and sometimes because of macroeconomic shifts. In addition, a company’s earnings and investing history is framed by accounting statements. Thus, accounting profits (net income, operating income) become a proxy for true earnings and the book value of capital invested (book value of equity, invested capital) stand in for earnings and investments, and we get two of the most widely used accounting returns: the return on (invested) capital and the return on equity.


While I have no qualms about using either return measure, the dependence on accounting statements for both the numerator and denominator trouble me.  It is not my objective in this post to belabor the definition of return on equity and capital. If you are interested, I have an extended discourse on the technical issues that you may face in computing accounting returns in this paper.

In my last post, I looked at the simplifying assumptions that I made to compute the costs of capital for industries and for individual companies. To measure the excess returns, I do need to compute the return on invested capital, and I do make simplifying assumptions again to prevent getting bogged down.


Note that I am using the effective tax rate to compute after-tax operating income, both at the industry and company level. For return on equity, I use a similar adjustment process:


I am well aware of the weaknesses in these measures. The first is the use of the most recent year's operating income in the numerator. Earnings at companies can vary over time and the most recent year may yield a number that is not representative of the company. (I did also use a ten-year average income to generate returns to try to counter this problem). The second is that the book value of equity is an accounting number and as such, is affected by accounting decisions on capitalization/expensing, depreciation and write offs. The third is that netting out the most recent period's cash balance, especially at technology or growth companies, can result in a negative invested capital. Finally, this measure, even if the earnings and invested capital are measured right, will be biased against young companies and companies investing in long-gestation period investments (infrastructure, toll roads etc.), since it will be low in the early years.

The Evidence on Excess Returns
Notwithstanding the many limitations of the excess return measure that I have described, I do think that there is value in looking at how firms measure up on it, across sectors and across the globe.

a. Across Sectors
To compute the return on capital for a sector, I used aggregated values for the operating income and invested capital across companies in the sector, rather than a simple average of the returns on capital of individual companies. I did this for two reasons. The first is that it allows me to keep all of the firms in my sample, rather than only the ones for which I can measure excess returns. The second is that it prevents outliers (hugely positive or negative excess returns that I may estimate for a firm, usually because of quirky accounting) from affecting the average. The third is to get a measure of weighted performance, where larger firms in a sector count for more than smaller firms.

I report the industry averages in this data in this dataset. In the table below, I report on the five industries, in the US and globally, that report the highest return spreads (a return on capital that most exceeds the cost of capital) and the five that had the lowest return spreads.
Return spreads based on trailing 12 month returns: January 2015
As with any measure, the rankings reveal as much about the quality of the measure as they do about the quality of the sectors. Tobacco companies are at the top of the list partly because repeated stock buybacks have depleted the book values of equity and invested capital, at last in the United States. Aerospace and defense is a volatile business and the high positive excess returns in 2014 can turn negative, if the airline business is troubled. 

b. Across Countries
To look at excess returns across countries, I consolidated companies into five groups: US, emerging markets, Europe, Japan and Australia/NZ/Canada. I then looked at the individual companies within each group and how much they earned, relative to their costs of capital. The table below summarizes the distribution of companies, in terms of excess returns, in each region:

The most striking feature of the data, to me, is that the proportion of companies that earn less than their cost of capital, 65.36% of all companies and 53.99% of companies with market capitalizations that exceed $50 billion. That indicates either that competition is a lot more intense in more businesses than we think and/or that management at many of these companies are either unaware or indifferent that their businesses are not generating sufficient profits, given the risk. 

What next?
This may reflect my biases but everyone should care about these excess returns. Investors should be valuing companies, based on their expectations of future expected returns, and pushing for change in companies that don't deliver them. Anti-trust regulators can use them as proxies for determining whether competition is adequate in markets and lawmakers should consider excess returns rather than absolute profits, in making public policy.

Dataset attachments
  1. Excess Returns by sector (USEmerging MarketsEuropeJapan, Australia/CanadaGlobal)

Putting the D in the DCF: The Cost of Capital

If there were a contest for the most measured number in finance, the winner would be the cost of capital. Corporate finance departments around the world compute it as an integral part of investment analysis. Appraisers estimate it as a step towards estimating intrinsic or discounted cash flow value. Analysts spend disproportionate amounts of their time working on it, though not always for the right reasons or with the right inputs. Since I have spent a significant portion of my life, writing and talking about cost of capital, it stands to reason that it is one of the numbers that I compute for all the companies in my data base at the start of every year.

Defining the cost of capital
There are three different ways to frame the cost of capital and each has its use. Much of the confusion about measuring and using cost of capital stem from mixing up the different definitions:
  1. For businesses, the cost of capital is a cost of raising financing: The first is to read the cost of capital literally as the cost of raising funding to run a business and thus build up to it by estimating the costs of raising different types of financing and the proportions used of each. This is what we do when we estimate a cost of equity, based on a beta, betas or some other risk proxy, a cost of debt, based upon what the business can borrow money at and adjusting for any tax advantages that might accrue from borrowing.
  2. For businesses, the cost of capital is an opportunity cost for investing in projects: The cost of capital is also an opportunity cost, i.e., the rate of return that the business can expect to make on other investments, of equivalent risk. The logic is simple. If you are considering investing in a new asset or security, you have to earn more than you could make by investing the money elsewhere. There are two subparts to this statement. The first is that it is the choices that you have today that should determine this opportunity cost, not choices that you might have had in the past. The second is that it has to be on investments of equivalent risk. Thus, the cost of capital should be higher for riskier investments than safe ones.
  3. For investors, the cost of capital is a discount rate to value a business: Investors looking at buying into a business are effectively buying a portfolio of investments, current and future, and to value the business, they have to make an assessment of the collective risk in the portfolio and how it may change over time. 
A good measure of cost of capital will find a way to bridge the differences between the three definitions and I believe that we can do so, with a little common sense and some data.


For this process to yield a number to meet all three requirements for cost of capital, i.e., that it be a cost of raising funding, an opportunity cost and a required return for investors, here are the requirements:
  1. Investors price companies based upon a reasonable assessment of the company’s business mix (and country risk exposure) and what they can generate as expected returns on alternative choices of equivalent risk. The former requires companies to provide information on their business mixes and the latter generally is easier to do in a liquid, public market.
  2. A company that operates in multiple businesses and many countries cannot use a single, “company-wide” cost of capital as its hurdle rate in investments. It has to adjust the cost of capital for both the riskiness of the business in which the investment is being planned and the part of the world that it is going to be located in.
  3. The overall company’s cost of capital has to be a weighted average of the costs of capitals of the businesses that it operates in, and as the business mix changes, the cost of capital will, as well.
Estimating the Cost of Capital
Having laid the groundwork, let’s get down to specifics. If you, as an investor, are given the task of estimating the cost of capital for a company, here is the sequence of steps. First, you have to estimate the business risk in the company by taking a weighted average of the risks of the businesses that the company operates. Second, you have to adjust that risk measure for the effects of debt, which effectively magnifies your business risk exposure, and use the consolidated risk measure to estimate a cost of equity. Third, you have to bring in the cost of borrowing, net of any tax benefit, which will reflect the default risk in the company. Finally, taking a weighted average of the cost of equity and after-tax cost of debt yields a cost of capital. If you are approaching the same task as a CFO, you have to follow the same sequence to get a cost of capital for the company but you have to go further and estimate the costs of capital for the individual businesses that the company is invested in.

As someone who teaches corporate finance and valuation, I am equally interested in both sides of this estimation process and one of my objectives in providing data is to help both sides. To help companies in investment analysis, I try to estimate costs of capital by sector, in the hope that a multi-business company will be able to find the information here to build up business-specific costs of capital. While investors may also find this information useful in valuation/investment analysis, I also estimate costs of capital for individual companies, and while my data providers no longer allow me to share these company-specific costs of capital, I can still provide information on the distribution of costs of capital across companies that can be useful to investors.

a. Cost of capital by sector
In my data updates each year, I estimate the cost of capital, by sector, for companies both globally and classified by region (US, Europe, Japan, Emerging Markets). In making these estimates, I first begin by breaking my total sample of 41,410 companies down into 96 industry groups, some of which may be far broader than you would like to see. I prefer this broad categorization for two reasons. First, I estimate a beta for each industry group by averaging the betas of the individual companies in that group, and these estimates are more precise with larger sample sizes. Second, from a first principles perspective, I believe that since betas measure risk from a macro risk perspective, you are better served with broader categories than narrow ones. Thus, rather than estimate the beta for shrimp fishing as a business, I would rather estimate the beta for food processing businesses (assuming that the only reason that people buy shrimp is to eat them.). Once I have the industry groups, I estimate the cost of equity for each group (in US dollar terms, by using a US dollar risk free rate and a equity risk premium in US dollar terms, though the magnitude of the premium can vary across countries and regions) by using the average beta across companies in the sector. For the cost of debt, I do have a problem, since all I usually have at the industry level is a book interest rate (obtained by dividing the interest expense by the book value of debt) which is not very useful from a cost of capital perspective. I use the variance in stock prices as an indicator of the risk and use it to estimate a default spread in US dollar terms, which then allows me to compute a cost of debt. As the final step, I use the industry average debt to capital ratios (in market value terms) to compute a cost of capital; in keeping with my view that lease commitments are debt, I convert lease commitments to debt for all companies in my database:


The results from the start of 2015 are captured in the attached spreadsheet, which includes costs of capital by sector not only for global companies, but also includes my regional estimates.

b. Cost of Capital - By company
As part of my data analysis, I also try to estimate the cost of capital for each of the 42,410 companies in my database. Since it is impractical to analyze each company in detail, I do have to make some simplifying assumptions.

  • First, I assign each company to one primary business in estimating business risk and use the unlevered beta for that business as the beta for the company. Optimally, I would compute the unlevered beta for each company, using the mix of businesses it is in, but with my sample size and data access, it is close to impossible to do. 
  • Second, I assume that the company gets all its revenues in the country in which it is incorporated and assign it the equity risk premium of that country. Thus, a Russian company’s cost of equity is computed using the Russian ERP (see my earlier post on country risk) and a German company’s cost of equity is computed based on the German ERP. I know that this violates my earlier point of multinational companies, and I would never make this assumption in building up an individual company’s cost of capital but I am afraid I have no choice with the larger sample. 
  • Third, I estimate a default spread for the company by using the variance in its stock prices. It is true that some of the companies (about 4000 or about 10% of my sample) have bond ratings available on them, but the bulk of my companies do not. In addition, if the company is incorporated in a country with sovereign default risk, I add the default spread for the country on to that of the company. I also use the marginal tax rate of the country that the company is incorporated in to estimate an after-tax cost of debt. 
  • Finally, to keep the numbers comparable, I compute the costs of capital for all companies in US dollars.

While I cannot provide you with the company-level costs of capital, I can provide the cross sectional distribution of my estimates. As you look at companies, I hope that you can use this for perspective, i.e., in making judgments on what comprises a high, low and median cost of capital. With US companies, the cost of capital distribution across all companies is below:

Cost of capital in US dollars: US companies in January 2015

Thus, if you use a cost of capital of 10% in the United States, you would effectively be assuming that your company is in the 98th percentile of US companies, in terms of cost of capital. With global companies, the cost of capital distribution is as follows:
Cost of capital in US dollars: Global companies in January 2015

Note that I have used a larger equity risk premium and incorporated sovereign default spreads into the cost of debt, yielding a larger spread in the cost of capital. A cost of capital of 12.5% for a global company would put it in the 94th percentile of companies.

A Cost of Capital Computation Template
If you work in finance, you will run into the challenge of estimating the cost of capital for a company sometime during the course of the year. I hope that the datasets that I have created are useful to you in that endeavor and if you decide to use them, here is a simple template for arriving a company's cost of capital in the currency of your choice.


Input
Measure
Comments/ Data sets
1
Risk free rate
Use the prevailing 10-year US T.Bond rate as the risk free rate in US dollars, even if you plan to compute the cost of capital in another currency.
Fight the urge to normalize, tweak or otherwise mess with this rate. It is what you can make today on a risk less investment, no matter what your views on it being too low or high.
2
Business Risk (Unlevered beta)
Break the company down into businesses, using an operating metric (revenues work best) and compute the weighted unlevered beta across the businesses.
Company breakdown: In company’s annual report or financial filings
Beta of businesses: My unlevered betas by business (broad groups) or you can create your own subgroups.
3
Financial Risk (Debt to equity and levered beta)
Lever the beta using the market debt to equity ratio for the company today. (If you prefer to use a target debt to equity ratio, make sure it is based on market values.
Market value of equity: Use the market capitalization as market value of equity. 
Market value of debt: For debt, use book value as your proxy for market value, or better still convert book value to market value.  Add the present value of operating leases to debt.
4
Equity Risk Premium
Obtain the geographical breakdown of the company’s revenues (or other operating metric, if you don’t like revenues). Take a weighted average of the ERP of the countries/regions that the company operates in.
Geographical Breakdown:  The company’s revenues will be in its financial statements, though it is not always as clear and detailed as you would like it to be.
ERP by country: My ERP by country.
5
Cost of debt
If you can find a corporate bond rating for your company, use it to get a default spread and a cost of debt. If you cannot find a bond rating, estimate a bond rating for the company and a default spread on that basis. If you are doing the latter, add a default spread for the country to get the pre-tax cost of debt.
Bond Rating: If available, you should be able to find it at S&P or online.
Synthetic Rating: You can use this spreadsheet to get a synthetic rating for your company.
Rating-based default spread: My lookup table of default spreads for ratings classes.
Country default spreads: My estimates
6
Marginal tax rate
Multiply the pre-tax cost of debt by (1- marginal tax rate) to get the after-tax cost
Marginal tax rate by country: KPMG estimates of country tax rates
7
Debt Ratio
Use the market values of debt and equity (from step 3)
See step 3
8
Currency change
If you want to convert the US dollar cost of capital into another currency, add the differential inflation rate (between that currency and the US dollar) or better still, scale up the  US$ cost of capital for the difference in inflation.
The inflation rate in the US can be estimated as the difference between the US 10-year T.Bond Rate and the US TIPs rate. For other countries, you can use the actual inflation rate last year as a proxy for expected inflation. 

If you are interested, I have a spreadsheet that has these steps incorporated into it. Give it a shot!

Implications
Looking at the costs of capital across sectors and companies, there are lessons that I take away for valuation and corporate finance:
  1. A rising (falling) tide lifts (lowers) all boats: The first reaction that most analysts and CFOs will have to my estimates of the cost of capital is that they look too low, with a median value of 7.40% for US companies and 8.32% for global companies. In fact, the longer that you have been around in markets, the lower today's numbers will look like to you, because what you consider a normal cost of capital will reflect your experiences. The low costs of capital, though, are appropriate, given the level of risk free rates today.
  2. The cost of capital does not (and should not) reflect all risk faced by a business: Even if you accept the proposition that the costs of capital are lower because of low risk free rates, you may still feel that the costs of capital don't look high enough for what you view as the riskiest companies in the market. You are right but that is because the cost of capital captures risk to a diversified investor in a going concern. Consequently, it will not reflect risks that are sector-specific but not market-wide, such as the risk to a biotechnology company that its newest drug will not be approved for production. Those risks are better reflected in the expected cash flows. The cost of capital also does not reflect truncation risk, i.e., that a firm may not survive the early stages of the life cycle or an overwhelming debt burden. That risk is better captured through decision trees and probabilistic approaches.
  3. Don't sweat the small stuff: In my view, analysts spend too much time finessing and tweaking the cost of capital and not enough on the cash flows. After all, the cost of capital, even if you go with the global distribution, varies within a tight range (6% to 12%, if you use the 10th and 90th percentile) and your potential for making mistakes is therefore also restricted. In contrast, profit margins and returns on capital have a much wider distribution across companies and getting those numbers right has a much bigger pay off.
Dataset attachments