The Aging of the Tech Sector: The Pricing Divergence of Young and Old Tech Companies

As the NASDAQ approaches historic highs, Apple’s market cap exceeds that of the Bovespa (the Brazilian equity index) and young social media companies like Snapchat have nosebleed valuations, there is talk of a tech bubble again. It is human nature to group or classify individuals or entities and assign common characteristics to the group and we tend to do the same, when investing. Specifically, we categorize stocks into sectors or groups and assume that many or most stocks in each group share commonalities. Thus, we assume that utility stocks have little growth and pay large dividends and commodity and cyclical stocks have volatile earnings largely because of macroeconomic factors. With “tech” stocks, the common characteristics that come to mind for many investors are high growth, high risk and low cash payout. While that would have been true for the typical tech stock in the 1980s, is it still true? More specifically, what does the typical tech company look like, how is it priced and is its pricing put it in a bubble? As I hope to argue in the section below, the answers depend upon which segment of the tech sector you look at.

A Short History of Tech Stocks

My first foray into investing was in the early 1980s, as the market started its long bull market run that lasted for almost two decades. In 1981, the technology stocks in the market were mainframe computer manufacturers, led by IBM and a group of smaller companies lumped together as the seven dwarves (Burroughs, Univac, NCR, Honeywell etc.). Not only were they collectively a small proportion of the entire market, but of the list of top ten companies, in market capitalization terms, in 1981, only one (IBM) could have been categorized as a technology stock (though GE had a small stake in computer-related businesses then):

During the 1980s, the personal computer revolution created a new wave of technology companies and while IBM fell from grace, companies catering to the PC business such as Microsoft, Compaq and Dell rose up the market cap ranks. By 1991, the top ten stocks still included only one technology company, IBM, and it had slipped in the rankings. However even in 1991, technology stocks remained a small portion of the market, comprising less than 7% of the S&P 500. During the 1990s, the dot-com boom created a surge in technology companies and their valuations, and while the busting of that boom in 2000 caused a reassessment, technology has become a larger piece of the overall market, as evidenced by this graph that describes the breakdown, by sector, for the S&P 500 from 1991 to 2014:

Market Capitalization at the end of each year (S&P Capital IQ)

There are two things to note in this graph. 

1. The first is that technology as a percentage of the market has remained stable since 2009, which calls into question the notion that technology stocks have powered the bull market of the last five years. 
2. The second is that technology is now the largest single slice of the equity market in the United States and close to the second largest in the global market. So what? Just as growth becomes more difficult for a company as it gets larger and becomes a larger part of the economy, technology collectively is running into a scaling problem, where its growth rate is converging on the growth rate for the economy. While this convergence is sometimes obscured by the focus on earnings per share growth, the growth rate in revenues at technology companies collectively has been moving towards the growth rate of the economy.

The Diversity of Technology

As technology ages and becomes a larger part of the economy, a second phenomenon is occurring. Companies within the sector are becoming much more heterogeneous not only in the businesses that they operate in, but also in their growth and operating characteristics. To see these differences, let’s start by looking at the sector and its composition in terms of age at the start of 2015. In February 2015, there were 2816 firms that were classified as technology companies, just in the United States, accounting for 31.7% for all publicly traded companies in the US market. Some of these companies have been listed for only a few years but others have been around for decades. Using the year of their founding as the birth year, I estimated the age for each company and came up with the following breakdown of tech stocks, by age:

Age: Number of years from founding of company to 2015

Note that 341 technology companies have been in existence for more than 35 years and an additional 427 firms have been in existence between 25 and 35 years, and they collectively comprise about 41% of the firms that we had founding years available in the database. While being in existence more than 25 years may sound unexceptional, given that there are manufacturing and consumer product companies that have been around a century or longer, tech companies age in dog years, as the life cycles tend to be more intense and compressed. Put differently, IBM may not be as old as Coca Cola in calendar time but it is a corporate Methuselah, in tech years.

The Pricing of Technology

The speedy rise of social media companies like Facebook, Twitter and Linkedin from nothing to large market cap companies, priced richly relative to revenues and earnings, has led some to the conclusion that this rich pricing must be across the entire sector. To see if this is true, I look at common pricing metrics across companies in the technology sector, broken down by age.

Pricing as of February 2015, Trailing 12 month values for earnings and book value

To adjust for the fact that cash holdings at some companies are substantial, I computed a non-cash PE, by netting cash out of the market capitalization and the income from cash holdings from the net income. While it is true that the youngest tech companies look highly priced, the pricing becomes more reasonable, as you look across the age scale. For instance, while the youngest companies in the tech sector trade at 4.34 times revenues (based upon enterprise value), the oldest companies trade at 2.44 times revenues. 

How do tech companies measure up against non-tech companies? After all, any story that is built on the presumption that tech companies are the sources of a market bubble has to backed up by data that indicates that tech companies are over priced relative to the rest of the market. To answer this question, I looked at the youngest (<10 and="" companies="" oldest="" tech="" years="">35 years) relative to the  youngest (<10 and="" companies:="" div="" non-tech="" oldest="" years="">

Based on  February 2015 Pricing & Trailing 12 month numbers: 2807 US technology and  6076 non-technology companies.

The assessment depends upon what part of the technology sector you are focused on. While the youngest tech companies trade at much higher multiples of revenues, earnings and book value than the rest of the market, the oldest tech companies actually look under priced (rather than over priced) relative to both the rest of the market and to the oldest non-tech companies. In fact, even focusing just on the youngest companies, it is interesting that while young tech companies trade at higher multiples of earnings (EBITDA, for instance) than young non-tech companies, the difference is negligible if you add back R&D, an expense that accountants mis-categorize as an operating expense.

Does this mean that you should be selling your young tech companies and buying old tech companies? I am not quite ready to make that leap yet, because the differences in these pricing multiples can be partially or fully explained by differences in fundamentals, i.e., young tech companies may be highly priced because they have high growth and old tech companies may trade at lower multiples because they have more risk and tech companies collectively may differ fundamentally from non-tech companies.

The Fundamentals of Tech Companies

There are three key fundamentals that determine value: the cash flows that you generate from your existing assets, the value generated by expected growth in these cash flows and the risk in these cash flows. Again, rather than look at tech stocks collectively, I will break them down by age and compare them to non-tech stocks.

a. Cash Flows and Profitability

To measure profitability, I looked at two statistics, the percentage of money making companies in each group and the aggregate profit margins (using EBITDA, operating income and net income):

Young technology companies are far more likely to be losing money and have lower profit margins that young non-technology companies, even if you capitalize R&D expenses and restate both operating and net income (which I did). At the other end of the spectrum, old technology companies are much more profitable, both in terms of margins and accounting returns, than old non-technology companies, adding to their investment allure, since they are also priced cheaper than non-technology companies.

b. Growth – Level and Quality

To test the conventional wisdom that technology companies have higher growth potential than non-technology companies, I looked at both past and expected future growth in different operating measures starting with revenues and working down the income statement:

The results are surprising and cut against the conventional wisdom, on most measures of growth. Young non-technology companies have grown both revenues and income faster than young technology companies, though analyst estimates of expected growth in earnings per share remains higher for young tech companies. With old tech companies, the contrast is jarring, with historic growth at anemic levels for technology companies but at much healthier levels for non-tech companies, perhaps explaining some of the lower pricing for the former. It is true, again, that the expected growth in earnings per share is higher at tech companies than non-tech companies, reflecting perhaps an optimistic bias on the part of analysts as well as more active share buyback programs at tech companies.

c. Risk – Financial and Market

Are tech companies riskier than non-tech companies? Again, the conventional wisdom would say they are, but I look at two measures of risk in the table below: standard deviation in stock prices and debt ratios across groups:

I get a split verdict, with much higher volatility in stock prices in tech companies, young and old, than non-tech companies, accompanied by much lower financial leverage at tech companies, again across the board, than non-tech companies. As we noted in the earlier table, young tech companies are more likely to be losing money and that may explain why they borrow less, but I think that the high price volatility has less to do with fundamentals and more to do with the fact the investors in young tech companies are too busy playing the price and momentum game to even think about fundamentals. 

d. Cash Return – Dividends, Buybacks and FCFE

In the final comparison, I look at how much cash is being returned in the form of dividends and buybacks by companies in each group, as well as how much cash is being held back in the company as a percent of overall firm value (in market value terms):

FCFE = Cash left over after taxes, debt payments and reinvestment; Firm value = Market Cap + Total Debt; Cash Return = Dividends + Buybacks - Stock Issues

Note that both young tech and young non-tech companies have raised more new equity than they return in the form of dividends and buybacks, giving them a negative cash return yield. Old tech companies return more cash to stockholders both in dividends and collectively, with buybacks, than old non-tech companies. Finally, notwithstanding the attention paid to Apple's cash balance, old tech companies hold less cash than old non-tech companies do. 

In summary, here is what the numbers are saying. Young technology companies are less profitable, have higher growth, higher price risk and are priced more richly than the young non-tech companies. Old technology companies are more profitable, have less top line growth and are priced more reasonably than old non-tech companies. 

Bottom line

The size of the technology sector and the diversity of companies in the sector makes it difficult to categorize the entire sector. In my view, the data suggests that we should be doing the following:

1. Truth in labeling: We are far too casual in our classifications of companies as being in technology. In my book, Tesla is an automobile company, Uber is a car service (or transportation) company and The Lending Club is a financial services company, and none of them should be categorized as technology companies. The fact that these firms use technology innovatively or to their advantage cannot be used as justification for treating them as technology companies, since technology is now part and parcel of even the most mundane businesses. Both companies and investors are complicit in this loose labeling, companies because they like the “technology” label, since it seems to release them from the obligation of explaining how much they need to invest to scale up, and investors, because it allows them to pay multiples of revenues or earnings that would be difficult (if not impossible) to justify in the actual businesses that these firms are in.
2. Age classes: We should start classifying technology companies by age, perhaps in four groups: baby tech (start up), young tech (product/service generating revenues but not profits), middle-aged tech (profits generated on significant revenues) and old tech (low top line growth, though sometimes accompanied by high profitability), without any negative connotations to any of these groupings. If we want to point to mispricing, we should be specific about which group the mispricing is occurring. In this market, for instance, if there is a finger to be pointed towards a group, it is not technology collectively that looks like it is richly priced, but baby and young technology companies. By the same token, if you follow rigid value investing advice, where you are told to stay away from technology on the grounds that it is high growth, high risk and highly priced, that may have been solid advice in 1985 but you will be missing your best “value” opportunities, if you follow it now.
3. Youth or Sector: When we think of start-ups and young firms, we tend to assume that they are technology-based and that presumption, for the most part, is backed up by the numbers. However, there are start-ups in other businesses as well, and it is worth examining when mispricing occurs, whether it is sector or age-driven. It is true that young social media companies have gone public to rapturous responses over the last few years but Shake Shack, which is definitely not a technology company (unless you can have a virtual burger and an online shake) also saw its stock price double on its offering day and biotechnology companies  had their moment in the limelight in 2014, as well. 
4. Life Cycle dynamics: I have talked about the corporate life cycles in prior posts and as I have noted in this one, there is evidence that the life cycle for a technology company may be both shorter and more intense than the life cycle for a non-technology company. That has implications for how we value and price these companies. In valuation, we may have to revisit the assumptions we make about long lives (perpetual) and positive growth that we routinely attach in discounted cash flow models to arrive at terminal value, when valuing technology companies, and perhaps replace them with finite period, negative growth terminal value models for fading technologies. In pricing, we should expect to see a much quicker drop off in the multiples of earnings that we are willing to pay, as tech companies age, relative to non-tech companies. I will save that for a future post.

I am under no illusions that this post will change the conversation about technology companies, but it will give me an escape hatch the next time I am asked about whether there is a technology bubble. If nothing else, I can point the questioner to this post and save myself the trouble of saying the same thing over and over again. 

DCF Myth 1: If you have a D(discount rate) and a CF (cash flow), you have a DCF!

Earlier this year, I started my series on discounted cash flow valuations (DCF) with a post that listed ten common myths in DCF and promised to do a post on each one over the course of the year. This is the first of that series and I will use it to challenge the widely held misconception that all you need to arrive at a DCF value is a D(iscount rate) and expected C(ash)F(lows). In this post, I will take a tour of what I would term twisted DCFs, where you have the appearance of a discounted cash flow valuation, without any of the consistency or philosophy.

The Consistency Tests for DCF

In my initial post on discounted cash flow valuation, I set up the single equation that underlies all of discounted cash flow valuation:

For this equation to deliver a reasonable estimate of value, it is imperative that it meets three consistency tests:

1. Unit consistency: A DCF first principle is that your cash flows have to defined in the same terms and unit as your discount rate. Specifically, this shows up in four tests:

• Equity versus Business (Firm): If the cash flows are after debt payments (and thus cash flows to equity), the discount rate used has to reflect the return required by those equity investors (the cost of equity), given the perceived risk in their equity investments. If the cash flows are prior to debt payments (cash flows to the business or firm), the discount rate used has to be a weighted average of what your equity investors want and what your lenders (debt holders) demand or a cost of funding the entire business (cost of capital).
• Pre-tax versus Post-tax: If your cash flows are pre-tax (post-tax), your discount rate has to be pre-tax (post-tax). It is worth noting that when valuing companies, we look at cash flows after corporate taxes and prior to personal taxes and discount rates are defined consistently. This gets tricky when valuing pass-through entities, which pay no taxes but are often required to pass through their income to investors who then get taxed at individual tax rates, and I looked at this question in my post on pass-through entities.
• Nominal versus Real: If your cash flows are computed without incorporating inflation expectations, they are real cash flows and have to be discounted at a real discount rate. If your cash flows incorporate an expected inflation rate, your discount rate has to incorporate the same expected inflation rate.
• Currency: If your cash flows are in a specific currency, your discount rate has to be in the same currency. Since currency is primarily a conduit for expected inflation, choosing a high inflation currency (say the Brazilian Reai) will give you a higher discount rate and higher expected growth and should leave value unchanged.

2. Input consistency: The value of a company is a function of three key components, its expected cash flows, the expected growth in these cash flows and the uncertainty you feel about whether these cash flows will be delivered. A discounted cash flow valuation requires assumptions about all three variables but for it to be defensible, the assumptions that you make about these variables have to be consistent with each other. The best way to illustrate this point is what I call the valuation triangle:

I am not suggesting that these relationships always have to hold, but when you do get an exception (high growth with low risk and low reinvestment), you are looking at an unusual company that requires justification and even in that company, there has to be consistency at some point in time.

3. Narrative consistency: In posts last year, I argued that a good valuation connected narrative to numbers. A good DCF valuation has to follow the same principles and the numbers have to be consistent with the story that you are telling about a company’s future and the story that you are telling has to be plausible, given the macroeconomic environment you are predicting, the market or markets that the company operates in and the competition it faces. 

The DCF Hall of Shame

Many of the DCFs that I see passed around in acquisition valuations, appraisal and accounting  don’t pass these consistency tests. In fact, at the risk of being labeled a DCF snob, I have taken to classifying these  defective DCFs into seven groups:

1. The Chimera DCF: In mythology, a chimera is usually depicted as a lion, with the head of a goat arising from his back, and a tail that might end with a snake's head. A DCF valuation that mixes dollar cash flows with peso discount rates, nominal cash flows with real costs of capital and cash flows before debt payments with costs of equity is violating basic consistency rules and qualifies as a Chimera DCF. It is useless, no matter how much work went into estimating the cash flows and discount rates. While it is possible that these inconsistencies are the result of deliberate intent (where you are trying to justify an unjustifiable value), they are more often the result of sloppiness and too many analysts working on the same valuation, with division of labor run amok.
2. The Dreamstate DCF: It is easy to build amazing companies on spreadsheets, making outlandish assumptions about growth and operating margins over time. With attribution to Elon
   Musk, I could take a small, money losing automobile company, forecast enough revenue
   
   growth to get its revenues to $350 billion in ten years (about $100 billion higher than  Toyota or Volkswagen, the largest automobile companies today), increase operating margins to 10% by the tenth year (giving it the margins of  premium auto makers) and make it a low risk, high growth company at that point (allowing it to trade at 20 times earnings at the end of year 10), all on a spreadsheet. Dreamstate DCFs are usually the result of a combination of hubris and static analysis, where you assume that you act correctly and no one else does.
3. The Dissonant DCF: When assumptions about growth, risk and cash flows are not consistent with each other, with little or no explanation given for the mismatch, you have a DCF valuation
   where the assumptions are at war with each other and your valuation error will reflect the input
   
   dissonance. An analyst who assumes high growth with low risk and low reinvestment will get too high a value, and one who assumes low growth with high risk and high reinvestment will get too low a value.  I attributed dissonant DCFs to the natural tendency of analysts to focus on one variable at a time and tweak it, when in fact changes in one variable (say, growth) affect the other variables in your assessment. In addition, if you have a bias (towards a higher or lower value), you will find a variable to change that will deliver the result you want.
4. The Trojan Horse (or Drag Queen) DCF: It is undeniable that the biggest number in a DCF is the terminal value, and for it to remain a DCF (a measure of intrinsic value), that number has to be estimated in one of two ways. The first is to assume that your cash flows will continue
   
   beyond the terminal year, growing at a constant rate forever (or for a finite period) and the second is to assume liquidation, with the liquidation proceeds representing your terminal value. There are many DCFs, though, where the terminal value is estimated by applying a multiple to the terminal year’s revenues, book value or earnings and that multiple (PE, EV/Sales, EV/EBITDA) comes from how comparable firms are being priced right now. Just as the Greeks used a wooden horse to smuggle soldiers into Troy, analysts are using the Trojan horse of expected cash flows (during the estimation period) to smuggle in a pricing. One reason analysts feel the urge to disguise their pricing as DCF valuations is a reluctance to admit that you are playing the pricing game.
5. The Kabuki of For-show DCF: The last three decades have seen an explosion in valuations for legal and accounting purposes. Since neither the courts nor accounting rule writers have a clear
   
   sense of what they want as output from this process (and it has little to do with fair value), and there are generally no transactions that ride on the numbers (making them "show" valuations), you get checkbox or rule-driven valuation. In its most pristine form, these valuations are works of art, where analyst and rule maker (or court) go through the motions of valuation, with the intent of developing models that are legally or accounting-rule defensible rather than yielding reasonable values. Until we resolve the fundamental contradiction of asking practitioners to price assets, while also asking them to deliver DCF models that back the prices, we will see more and more Kabuki DCFs.
6. The Robo DCF: In a Robo DCF, the analyst build a valuation almost entirely from the most recent financial statements and automated forecasts. In its most extreme form, every input in a
   
   Robo DCF can be traced to an external source, with equity risk premiums from Ibbotson or Duff and Phelps, betas from Bloomberg and cash flows from Factset, coming together in the model to deliver a value. Given that computers are much better followers of rigid and automated rules than human beings can, it is not surprising that many services have created their own versions of Robo DCFs to do intrinsic valuations. In fact, you could probably create an app for a smartphone or tablet that could do valuations for you. (I had originally listed Morningstar as a service that produced Robo DCFs but was alerted to the fact that it has substantial analyst input into its DCF.)
7. The Mutant DCF: In its scariest form, a DCF can be just a collection of numbers where items have familiar names (free cash flow, cost of capital) but the analyst putting it together has
   
   neither a narrative holding the numbers together nor a sense of the basic principles of valuation. In the best case scenario, these valuations never see the light of day, as their creators abandon their misshapen creations, but in many cases, these valuations find their way into acquisition valuations, appraisals and portfolio management.

DCF Checklist

I see a lot of DCFs in the course of my work, from students, appraisers, analysts, bankers and companies. A surprisingly large number of the DCFs that I see take on one of these twisted forms and many of them have illustrious names attached to them. To help in identifying these twisted DCFs, I have developed a diagnostic sequence that is captured visually in this flowchart:

You are welcome to borrow, modify or adapt this flowchart to make it yours. If you prefer your flowchart in a more conventional question and answer format, you can use this checklist instead. So, take it for a spin on a DCF valuation, preferably someone else's, since it is so much easier to be judgmental about other people's work than yours. The tougher test is when you have to apply it on one of your own discounted cash flow valuations, but remember that the truth shall set you free!

Attachments

1. DCF Checklist (as flow chart)
2. DCF Checklist (as check list)

DCF Myth Posts
Introductory Post: DCF Valuations: Academic Exercise, Sales Pitch or Investor Tool

1. If you have a D(discount rate) and a CF (cash flow), you have a DCF.  
2. A DCF is an exercise in modeling & number crunching. 
3. You cannot do a DCF when there is too much uncertainty.
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.
5. If most of your value in a DCF comes from the terminal value, there is something wrong with your DCF.
6. A DCF requires too many assumptions and can be manipulated to yield any value you want.
7. A DCF cannot value brand name or other intangibles. 
8. A DCF yields a conservative estimate of value. 
9. If your DCF value changes significantly over time, there is either something wrong with your valuation.
10. A DCF is an academic exercise.

How low can you go? Doing the Petrobras Limbo!

A few months ago, I suggested that investors venture where it is darkest, the nether regions of the corporate world where country risk, commodity risk and company risk all collide to create investing quicksand. I still own the two companies that I highlighted in that post, Vale and Lukoil, and have no regrets, even though I have lost money on both. At the time of the post, I was asked why I had not picked Brazil’s other commodity colossus, Petrobras, as my company to value (and invest in) and I dodged the question. The news from the last few days provides a partial answer, but I think that the Petrobras experience, painful though it might have been for some investors, provides an illustration of the costs and benefits of political patronage.

Petrobras: A Short History

Petrobras was founded in 1953 as the Brazilian government oil company, and for the first few decades of its life, it was run as a government-owned company from its headquarters in Rio De Janeiro. Until 1997, it had a legal monopoly on oil production and distribution in Brazil, when the domestic market was opened up to foreign oil producers. Petrobras was listed as a public company in 1997 on the Sao Paulo exchange and as a depository receipt on the New York Stock Exchange soon after. The arc of fortunes for the company can be traced in the changes in its market capitalization over time, reported in US dollars in the figure below:

Market Capitalization & Enterprise Value at end of each year

In the last decade, Petrobras has seen both highs and lows, becoming the fifth largest company in the world, in terms of market capitalization, in 2011 and then seeing a precipitous drop off in market prices in the years since. To understand where Petrobras is now and to make sense of where it is going, you have to look at both its rise in the last decade and its fall in this one.

The rise of Petrobras from minor emerging market oil company to global giant between 2002 and 2010 can be traced to three factors. The first was the discovery of major new reserves in Brazil in the early part of the last decade, which catapulted the company towards the top of the list of companies with proven reserves. The fact that these reserves would be expensive to develop was mitigated by a second development, which was the sustained surge in oil prices to triple digit levels for much of the period, making them viable. The third was an overall reduction in Brazilian country risk from the stratospheric levels of 2001 (when the country default spread for Brazil reached 14.34%, just before the election of Lula Da Silva as President) to 1.43% in 2010, when Brazil looked like it had made the leap to almost-developed market status. In 2010, the company signaled that its arrival in global markets and its ambitions to be even more by raising $72.8 billion from equity markets.

The hubris that led to the public offering may have been the trigger for the subsequent fall of the company, which has been dizzying because of the magnitude of the decline, and its speed. After peaking at a market capitalization close to $244 billion in 2010, the company has managed to lose a little bit more than $200 billion in value since, putting it in rarefied company with other champion value destroyers over time. While a large portion of the blame for the decline in the last few months (especially since September 2014) can be attributed to the drop in oil prices, note that Petrobras has already managed to destroy $160 billion in value prior to that point in time.

Petrobras: Governance Structure
To understand the Petrobras story, you have to start with an assessment how the company is structured. When the government privatized the company, it did so with the objective of raising capital for its treasury but it did not want to release control of the company to the shareholders who bought shares in the company. Using "national interest" as a shield, the government devised a game where it would be able to control the company, while raising billions in capital from investors. The basis for that game, and it is not unique to Petrobras, was to create two classes of shares, one with voting rights (common shares) and one without (called preferred shares, in an Orwellian twist), and offering the latter primarily to investors. The government retains control of more than 50% of the voting shares in the company and another 11% is controlled by entities (like the Brazilian Development Bank, BNDES, and Brazil's sovereign wealth fund) over which the government has effective control. Not quite satisfied with this rigging of the game, the government also retains veto power (a golden share) over major decisions.

Shareholding as of February 2015

Using this control structure, the government has created the ultimate rubber stamp board, whose only role has been to protect the government's interests (or more precisely the politicians who comprise the government at the time) at all costs. Brazilian company law does require that the minority shareholders (anybody but the government) have board representatives, but as this story makes clear, these directors are not just ignored but face retaliation for raising basic questions about governance. To be fair to Ms. Dilma Rousseff, government interference has always been the case in Petrobras, and her predecessors have been just as guilty of treating Petrobras as a piggybank and political patronage machine, as she has. Lula, who stepped down with great fanfare, as president just a few years ago was equally interventionist, but high oil prices provided the buffer that protected him from the fallout.

A Roadmap for Value Destruction
Just as looking at companies that have created significant amounts of value over time is enlightening because of the insights you get into what companies do right, Petrobras should become a case study for the opposite reason. Put in brutally direct terms, if you were given a valuable business and given the perverse objective of destroying it completely and quickly, you should replicate what Petrobras has done in five steps.

Step 1 - Invest first, worry about returns later (perhaps never)
Invest massive amounts of money in new investments, with little heed to returns on these investments, and often with the intent of delivering political payoffs or worse. Between 2009 and 2014, Petrobras stepped up its capital expenditures and exploration costs to more than 35% of revenues, well above the 15-20% invested by other integrated oil companies, while seeing its return on capital drop to 5% (even as oil prices stayed at $100+/barrel for the bulk of the period).

Step 2 - Grow, baby, grow, and profitability be damned
Petrobras has grown its revenues from $17.4 billion in 1997 to $135.8 billion in 2014 and displaced Exxon Mobil as the largest global oil producer in the third quarter of 2014, while letting profit margins drop dramatically. The government contributes to this dysfunctional growth by putting pressure on the company to sell gasoline at subsidized prices to Brazilian car owners.

Step 3 - Pay dividends like a regulated utility (even though you are not)
Petrobras has a history of paying large dividends, partly because it had the cash flows to pay those dividends in the 1990s and partly to supports it voting share structure. The preferred (non-voting) shares that the company has used to raise capital, without giving up control, come with dividend payout requirements that are onerous, if you have growth ambitions.

Step 4 - Borrow money to cover the cash deficit
If you want to eat your cake (by investing large amounts to generate growth) and have it too (while paying large dividends), the only way to make up the deficit is to raise fresh capital. In 2010, Petrobras did raise $79 billion in fresh equity but it has been dependent upon debt as its primarily financing in every other year. As a consequence, Petrobras had total debt outstanding of $135 billion at the end of 2014, more than any other oil company in the world.

Step 5 - Destroy value (Mission accomplished)
If you over invest and grow without heeding profitability, while paying dividends you cannot afford to pay and borrowing much more than you should be, you have created the perfect storm for value destruction. In fact, the way Petrobras has been run so defies common sense and first principles in corporate finance, that if I were a conspiracy theorist, I would be almost ready to buy into the notion that this is part of a diabolical plan to destroy the company hatched by evil geniuses somewhere. I have learned through hard experience, though, that you should not attribute to malevolence what can be explained by greed, self-dealing and bad incentive systems.

It is worth noting that none of the numbers in the last section can be attributed to the drop in oil prices. In the most recent twelve month data that you see in these graphs represent the year ending September 30, 2014, and the average oil price during that year exceeded $100/barrel.The government of Brazil, working through the management that they installed at Petrobras, have pulled off the amazing feat of destroying more than $200 billion in value with no help from outside.

A Contrarian Bet?
When a company falls as fast and as far as Petrobras has, it attracts the interests of contrarian investors and the company looks attractive on the surface, at least using some conventional multiples.

Petrobras looks very cheap, at least using equity multiples (PE and Price/Book) but the results are mixed with enterprise value multiples.

All of these multiples are affected by the fact that oil prices have dropped dramatically since the most recent financial statements and that the earnings numbers, in particular, will dive in the coming quarters. Given that Petrobras was already reporting sagging profits, before the oil price drop, I am almost afraid to think of what the numbers will look like at today's oil prices (which are closer to $50)., but I will try anyway. Looking at the annual revenues over time at the company and relating them to the average oil prices each year, here is what I find:

Revenues at Petrobras = -4,619 million + 1276 (Average Oil price during year)     R squared = 92%

Thus, if you assume that the current oil price of $51.69 is close to the average for this year, the normalized revenues for Petrobras will be $61.3 billion, a drop off of about 55% from the $135.8 billion revenues in the 12 months ending September 30, 2014.

Revenues at Petrobras = -4,619 million + 1276 (51.69) = $61,337 million or $61.3 billion

If you apply the operating margin of 10.82% that Petrobras reported in the trailing 12 months to these revenues, you arrive at an operating income of $6,638 million, prior to taxes. At that level of earnings, the value that I get for the company is $62.4 billion, well below the $135.1 billion owed by the company, making its equity worth nothing. In the matrix below, I look at the value per share under different combinations of base year income (ranging from $6,638 million at the low to $28.7 billion at the high) and return on invested capital on new investments (again ranging from a low of 2.67%, with income normalized for low oil prices, to 13.36% as the high):

Assuming no high growth period, stable growth rate of 2% and cost of capital of  11.17%. Adding a high growth period reduces value in all the return on capital scenarios, except one (average over last 10 years)

The red numbers represent the dead zone, where the value of the business is less than the debt outstanding and they dominate the table.  In spite of the reckless abandon shown by its management, there remain some bright spots, if you are an optimist. The first is that the company is one of the largest oil producers in the world and if oil prices rebound, they will see a jump in revenues. The second is that the exploration and investments over the last decade have given the company the fifth largest proven oil reserves in the world, though the proportion of these reserves that will be viable at today's oil prices is open to question. The third is that if the Brazilian government stops pulling the strings and management stops its self destructive behavior, profit margins and returns will improve. In the most optimistic spin, you can assume that Petrobras will be able to keep its trailing 12-month intact at $135.8 billion, improve its operating margin to the 21.1% that it earned in 2010 and its return on capital to 13.36% (10-year average), while reducing its debt ratio to 43.5% (average over last 5 years). With those assumptions, which border on fantasy, Petrobras would be worth $8.11/share (R$ 22.55/share) well above the current stock price of $3.28/share (R$ 9.12/share).  You are welcome to try out different combinations of your assumptions in this spreadsheet and see what you get.

Unsolicited (and perhaps unwelcome) advice for a new CEO

A couple of weeks ago, Ms. Maria das Gracas Foster, Petrobras CEO since February 2012, stepped down, and the Brazilian government announced that it has chosen Mr. Aldemir Bendine, former head of Banco do Brazil, as the next CEO. The market response was almost universally negative, partly because Mr. Bendine does not have any experience in the oil business and partly because there is no trust left in the Brazilian government. I do not know Mr. Bendine and it would be unfair of me to tar him as a government stooge, just because he was appointed by the government. In fact, I am willing to not only cut him some slack but to also provide advice on what he should do in the coming weeks. Here are my suggestions:

1. Hire a chief operating officer who knows the oil business and turn over operating responsibilities to him.
2. Fire anyone in the top management who has any political connections. That may leave lots of empty offices in Petrobras headquarters, but less damage will be done by no one being in those offices than the current occupants.
3. Side with directors for the minority stockholders and push for a more independent, accountable board.
4. Refuse to go along with the cap on gasoline prices for Brazilian consumers, a subsidy that has already cost the company $20-$25 billion between 2011 and 2013. With oil prices low, the consumer backlash will be bearable.
5. Push openly for a move to one class of shares with equal voting rights. Accompany this action by cutting dividends to zero.
6. Clean up the investment process with less auto-pilot exploration, production that is in line with oil prices and less focus on growth, for the sake of growth.
7. Start paying down your debt.

What is the worst that can happen to you? If the government is set on a path of self-destruction, you will be fired. If that happens, wear it as a badge of honor, since your reputation will be enhanced and you will emerge looking like a hero.  If you go along with the status quo, you will preside over the final destruction of what was Brazil’s crown jewel and face the same fate as your predecessor.  Unless the new CEO can come up with a way to remake the company,  my guess is that, at least for the next few months, here is the song that will be playing out in the market:

Final Thoughts
There are always lessons to be learned from every calamity and Petrobras qualifies as a calamity. The first is to recognize that there every reason to be skeptical when politicians claim "national interest" and meddle incessantly in public corporations. In most cases, what you have are political interests which may or may not coincide with national interests, where elected politicians and government officials use stockholder money to advance their standing. The second is that those who have labeled "value maximization" as the "dumbest idea" and pushed for stakeholder wealth maximization, a meaningless and misguided objective that only strategists and Davos organizers find attractive, as an alternative, should take a close look at Petrobras as a case study of stakeholder wealth maximization gone amok. In the last five years, Petrobras has enriched countless politicians and politically connected businesses, subsidized Brazilian car owners and provided jobs to tens of thousands of oil workers, leaving stockholders on the outside looking in. Anyone who argues that this is a net good for Brazil has clearly not grasped the damage that has been done to the country in the global market place by this fiasco.

Corruption update: I have been asked by many of you as to why have sidestepped the corruption stories that have been swirling around the company. I did so, not because I want to avoid controversy (which I don't mind at all) but because I thought that at least in this case, being subtle delivers the message about political game playing better than brute force. At Petrobras, I treat corruption as a really bad investment with horrible returns to stockholders, but I believe that with its management structure, the company was destined for trouble, and that the corruption just greased the skids.

Attachments

1. Petrobras valuation spreadsheet

Blood in the Shark Tank: Pre-money, Post-money and Play-money Valuations

My kids are inclined to binge TV-watching, especially in the winter, and this Christmas break, when they were all home, they were at it again. Having gone through all the Walking Dead episodes during the summer and  Criminal Minds multiple times, they chose Shark Tank as the show to watch in marathon format. For those of you who have never watched an episode, it involves entrepreneurs (current or wannabe) pitching business ideas to five 'sharks', who then compete (if interested) in offering capital (cash) for a share of the business.  Like some large families, we make even TV watching a competitive sport, especially when there are multiple shark offers on the table, with family members ranking the offers from best to worst. In one episode, a contestant was faced with two offers: the first shark offered $25,000 for 20% of the business and the second one jumped in with $100,000 for 50% of the business. While one family member suggested that the second offer was obviously better and everyone else in my family concurred, I was tempted to argue that it was not that obvious, but wisely chose to say nothing. A late night family gathering is almost never a good teaching moment, especially when your own children are in the audience. 

Pre-money & Post-money: The VC playbook

In public company valuation, the contrast between pre-money and post-money valuations almost never is an issue, but in venture capital valuation, it is front and center. Given the central role it plays in venture capital investing, and the consequential effects it has both on capital providers and capital seekers, I assumed that the venture capital playbook would have detailed instructions on the contrast between pre-money and post-money valuation, but I was wrong. In fact, here is what I learned from the playbook. If you pay $X for y% of a business, the post-money value is the resulting scaled-up value and netting out the cash influx yields the pre-money value:

• Post-money value = $X/y%
• Pre-money value = $X/y% - $X

Using the Shark Tank episode in the last paragraph, you can compare the two offers now in post-money and pre-money terms:

Thus, the two offers effectively attach the same value to the business and at least on this dimension, the entrepreneur should find them equivalent. While the VC definition is technically right, it is sterile, because if you have a pre-money value for a business, you can always extract the post-money value, or vice versa, but both estimates are only as good as your initial value estimate. It is also opaque,  because the process by which value is estimated is often unspecified and and made more so when the simple exchange of capital for a share of ownership is complicated by add ons, with options to acquire more of the business, first claims on cash flows and voting rights thrown into the mix.

While some of the opacity that accompanies pre-money and post-money valuations is related to the fact that you are dealing with young, start-ups, often without operating histories or clear business models, I believe that some of it is by design. By leaving the discussion of value vague and/or making the exchange of capital for proportion of the business complicated, venture capitalists can create enough noise around the process to confuse entrepreneurs about the values of their businesses. By the same token, the sloppiness that accompanies much of the discussion of pre-money and post-money valuations in venture capital can also lead to excesses during periods of exuberance, where the fact that too much is being paid for a share of a business is obscured by the confusion in the process.

Pre-money and Post-money in an Intrinsic Value World

I know that intrinsic valuations (and DCF valuations, a subset) are considered to be unworkable by many in the venture capital community, with the argument given that the young, start-ups that VCs have to value do not lend themselves easily to forecasting cash flows and/or adjusting for risk. I disagree but I think that even if you are of that point of view, the path to understanding pre-money and post-money values is through the intrinsic valuation of a very simple business.

The Franchise Stage

Let's assume that you are politically connected and that the government has given you a license to build a toll road. The cost of building the road is $100 million and to keep things really simple, let's assume that the government has agreed to pay you $10 million a year in perpetuity, that you live in a tax-free environment and that the long-term government bond rate is 5%. To get a measure of the value of the license, all you have to do is take the present value of the expected cash flows, net of the cost of building the road:

• NPV of road = -100 + 10/.05 = $100

While a conventional accounting balance sheet would show no assets and no value for the business (since the road has not been built), an intrinsic value balance sheet will show this value:

Note that the $100 million value attributed to you (as the equity investor) in the intrinsic value balance sheet is based on a notional toll road, not one in existence. 

The Capital Seeking Stage

Now, let's assume that you don't have the capital on hand to build the road and approach me (a venture capitalist) for $100 million in capital that you plan to use to build the road. Assuming you convince me of the viability of the business and that I invest $100 million with you, here is what the balance sheet will look like the instant after I invest.

Note that the business value has doubled to $200 million, with half of the value coming from the cash infusion. That cash is transitory and will be used by you to invest in the toll road, and the minute that investment is made, the balance sheet will reflect it.

While the value of the business has not changed from the post-cash number, the nature of its assets has, with a physical toll road now setting value, rather than a license and cash. Thus, the value of the business after the cash infusion is $200 million and this is the post-money valuation of the company. 

The Negotiation Stage

The question at this point is what proportion of your business I should get as the venture capitalist. At first sight, the answer may seem obvious. The value of the business, after the capital infusion (and investment) is $200 million, and the capital I am providing is $100 million, entitling me to 50%, right? Not so fast! The actual answer will depend upon your bargaining power (as the entrepreneur) and mine (as the venture capitalist), and the easiest way to see this is in the limiting cases:

• Case 1 - Only entrepreneur in market, Lots of capital providers: Assume that you are the only entrepreneur with a valuable franchise in the economy and there is a large supply of capital (from banks, venture capitalists, private equity investors). You (as the entrepreneur) have all the power in this negotiation and I will end up with a 50% share of the post-money valuation ($200 million).
• Case 2 - Lots of entrepreneurs with valuable franchises, a monopolist capital provider: At the other extreme, if I (the VC) am the only game in town for capital, I will argue that without me your franchise is worth nothing, and that I should end up with all of the value (thus giving me close to 100% of the business). 

The reality will fall somewhere in the middle. In general, the value that you will use to compute your percentage ownership will be neither the pre-money, nor the post-money value. It will be the value of the business, with the next best capital provider providing the $100 million in capital. In the toll road example, assume that you can borrow $100 million from a bank at 7.5%, a rate that is much too high, given the risk of the investment (zero). The value of your equity in this toll road will now have to reflect the interest payments on this debt.

Cash flows after debt payments = $10 million - .075 (100) = $2.5 million

Value of equity = $2.5 million/.05 = $50 million

The new balance sheet of the business will reflect this expensive debt:

Note that the bank has effectively claimed $50 million of the value of the business by charging you too high a rate and netting out the bank's surplus yields a value of $150 million for the toll road, the "ownership value", since the ownership stake will be based on it. As the venture capitalist, I recognize that this is your next best option and demand two-thirds of your business for my $100 million. In summary, then the ownership percentage of your business that I will get in return for my capital provision can range from 50% to close to 100%, depending on the relative  supply of entrepreneurs and venture capital in a market.

Implications

1. A DCF valuation, done right, always yields a pre-money value for a business.

2. The value of a business, after a capital infusion, will have to incorporate the cash that comes into the business, pushing up the post-money value.

3. The "ownership value on which the ownership proportion is negotiated will move towards the post-money value, when there is an active and competitive (venture) capital market, and towards the pre-money value, when there is not one.

The Pricing World: Pre-money or Post-money?

As I noted at the start of the last section, most venture capitalists swear off DCF for many reasons, some justified and some not. Instead, they price businesses using a combination of a forecasted metric and a multiple of that metric (given what others are paying for similar businesses right now). Thus, if you were valuing a start-up money-losing technology firm with no revenues today, you would forecast out revenues three years (or five) from now and apply a multiple to those revenues (based on what the market is paying for public companies in this space) in the third year to get an exit value, which you will then proceed to discount back at a "target" rate of return to get a value today:

Pricing: Pre or post-money?

When you price companies, the question of whether the value you arrive at today is a pre-money or post-money valuation becomes murkier. The forecasted revenues that you forecast in year 3 is not (and often are) only based on the assumption that there is a capital infusion in the firm today but that there may be more capital infusions in the future, in which case it is a post-post-post money valuation and adding cash to this value will be double counting. (As an analogy, consider the toll road example that I used in the intrinsic value section. The earnings on the toll road are expected to be $10 million a year and the toll road should trade at about twenty times earnings, given its fundamentals. Using the VC approach, the value that I would get is $200 million, which is the post-money valuation). 

A pre-money pricing?

Can you modify the VC approach to deliver a pre-money pricing? Yes, and here is what you would have to do. You would have to forecast two measures of future earnings, one with the capital infusion and one without. In the extreme scenario where the start-up will cease to exist without the capital and there are no other capital providers, the expected earnings in year 3 will be zero, yielding a pre-money valuation of zero for the company. Consequently, you will demand all or almost all of the company in return for your investment.

Implications

1. Pricing is opaque: While pricing is market-based, quick and convenient, the cost of pricing an asset rather than valuing it is that the process glosses over details and makes it difficult to figure out what exactly you are getting for your investment today and what you have already incorporated in that number. 
2. The Target rate is Swiss Army knife of VC valuation; In the VC approach, the target rate (though called a discount rate) is like a Swiss Army knife, serving multiple purposes. First, it is a reflection of the expected return you should make, given the risk in the investment, i.e., the conventional risk-adjusted rate.  Second, it incorporates the survival risk in the company, i.e., the reality that many of the companies  that VCs invest in don't make it and that you have to lower the value of start-ups to reflect this risk. Third, it includes a component to cover the future capital needs of the business, with a higher discount rate being used for companies that will need more rounds of capital. Finally, it is a negotiating tool, with VCs pushing up the target rate, if they feel that they have a strong bargaining position. While it is impressive that so much can be piled into one number, it does make it difficult to figure whether you have counted all of these variables correctly and not double counted or miscounted it. It also implies that the actual returns generated by VCs will bear little resemblance to the target returns; the table below summarizes venture capital returns across VC funds over the last year, three years, five years and ten years and compares them to returns on growth equity mutual funds and the S&P 500.

   

   Through Sept 30, 2014; Source: National Venture Capital Association (NCVA)

3. Winners and Losers: It is not clear who wins and loses in the pricing game, when sloppiness rules. In periods where entrepreneurial investments are plentiful and venture capital funding is scarce, it probably leads to venture capitalists claiming too large a stake in the businesses that they invest in, given the capital invested. During periods when entrepreneurial investments are scare and venture capitalists are plentiful, my guess it that it leads venture capitalists to overpay for businesses.

A Plea for Transparency

I am not making an argument that venture capitalists and other early stage investors shift to intrinsic valuation. While I believe that they under use and often misunderstand intrinsic valuation, I think that the attachment to pricing is too deep for them to shift. I do believe though that everyone (founders, entrepreneurs, venture capitalists) would be better served if there was more transparency in the process and we were more explicit about the basis for assessing ownership rights (and proportions). Perhaps, I will start by making myself unpopular in my household and bringing up the discussion of pre and post money valuations during Shark Tank!

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).