Narrative and Numbers: The Value of Stories in Business

CASE STUDY 10.1: ALIBABA VALUATION—THE CHINA SCENARIOS

Lead-in case studies:

Case Study 6.6: Alibaba, the China Story, September 2014

Case Study 7.4: Alibaba, the Global Player

Case Study 8.4: Alibaba—From Story to Numbers

Case Study 9.4: Alibaba—The China Story

In case study 9.4, I valued Alibaba’s equity at the time of its IPO in September 2014 at $161 billion and its value per share at $65.98, under the assumption that its revenues would grow 25 percent a year for the next five years and that it would be able to generate a margin of 40 percent. Those assumptions were built on the expectation that the Chinese online retail market would grow at 25 percent a year and that Alibaba would be able to maintain its market share. Consequently, the value rested on my assumption that the Chinese economy would continue to grow, carrying the online retail business with it.

There is the possibility that I was wrong in that assumption. In particular, growth in China could drop off in the coming years, or it is also possible that I have underestimated China’s growth potential. In table 10.1 I look at three scenarios built around growth in China and value Alibaba under each one.

Table 10.1

Alibaba—Value Under China Growth Scenarios

Scenario	Revenue growth rate	Target operating margin	Cost of capital	Value per share
China growth lower than expected	15.00%	35.00%	9.00%	$40.06
China growth as expected	25.00%	40.00%	8.56%	$65.98
China growth higher than expected	30.00%	50.00%	8.25%	$98.89

The results are not surprising in terms of direction, but the magnitude of the change, a loss of more than a third of value under the China low-growth scenario and an increase of value of almost 50 percent in the high-growth scenario, is an indication of how exposed your narrative is to macroeconomic risk in China.

CASE STUDY 10.2: ALIBABA—A MONTE CARLO SIMULATION

The Alibaba valuation was based on a set of inputs that were estimated with error. While I believe that the expected values for these inputs reflected the company, at least as I saw it in September 2014, it is true that I faced uncertainty in estimating each of the inputs. To capture that uncertainty, I ran a simulation, with probability distributions for the inputs, rather than single expected values. With each input, the expected value from the distributions matched my assumption in my base case, but the probability distribution provides my judgment on the uncertainty that I feel about each input. For example, my estimate for the target operating margin is centered around 40 percent (my base case assumption), but I am assuming that the outcome can lie between 30 and 50 percent, with equal probabilities for each outcome (a uniform distribution). I make similar judgments about revenue growth (with 25 percent as my base case value), the cost of capital (base case value of 8.56 percent), and the sales-to-capital ratio (2.00 in the base case), with different assumptions about the distributions around the base case values for each one. In the simulation I draw from these distributions to estimate Alibaba’s value, which I present as a value distribution in figure 10.1.

Figure 10.1

Alibaba valuation simulations, September 2014. Mean = $66.45; median = $65.15; lowest value = $38.11; highest value = $153.10.

Note that the mean and median across the 100,000 simulations track closely the base case value of $65.98/share, which should not be surprising, since the expected values of the inputs are the same in both analyses. The additional information is in the percentile distribution of values, with the lowest value being $38.11 a share and the highest one being $153.11. Not only do I get a richer information set to base my decisions on, but I am also reminded of how much error there is in my own estimate. That, in turn, will hopefully make me less likely to label those who disagree with me, in either direction, as wrong and more open to suggestions that I can use to improve my narrative and valuation.

CASE STUDY 10.3: AMAZON IN OCTOBER 2014—MARKET BREAKEVEN POINTS

Lead-in case studies:

Case Study 6.5: Amazon, the Field of Dreams Model, October 2014

Case Study 7.3: Amazon—Alternative Narratives, October 2014

Case Study 8.3: Amazon—From Story to Numbers

Case Study 9.3: Amazon—Valuiong the Field of Dreams

In chapter 9 my valuation ($175.25) of Amazon diverged sharply from the market price ($287) at the time. My valuation, though, was driven by my narrative for the company and the revenue growth rate (15 percent for the next five years, leading to revenues of $240 billion in 2024) and operating margin (7.38 percent) that I estimated for the firm. It is clear that investors, or at least those bidding up Amazon’s stock price, were more optimistic than I was. To get a measure of how different the market’s assumptions were from mine, I estimated the value per share as a function of revenue growth and target operating margins (in ten years) in table 10.2.

Table 10.2

Amazon—Value and Price Breakeven Points

The shaded areas represented values that exceeded the price per share ($287) at the time of the analysis. If investors were pricing Amazon on the basis of intrinsic value, they were clearly expecting Amazon to deliver higher revenues than I was estimating, with much heftier profit margins. At the time of the valuation, my judgment was that these numbers were too high for my tastes and that I would stay with my assessment of value.

In fact, the persistence of Amazon’s price climb suggested to me that investors were not valuing Amazon but pricing it and that they were therefore likely to be impervious at least in the near term to fundamentals. That is also why, notwithstanding my assessment that Amazon was overvalued, I did not take the obvious next step and sell short on the stock. Cowardly on my part? Of course, but I think it would have been foolhardy for me to take a position based upon intrinsic value on a pricing stock, if I did not control my time horizon, and in the case of a short sale, I did not.

CASE STUDY 10.4: THE PRICING FEEDBACK—UBER, FERRARI, AMAZON, AND ALIBABA

In chapter 9 I valued Uber, Ferrari, Amazon, and Alibaba and I would be lying if I said that the current pricing of these companies did not influence my valuations.

• With Uber, a nontraded entity, the feedback from the market came in the form of the implied valuations in venture capital investments. My interest in Uber was triggered by a news story that it had been priced at $17 billion in its most recent venture capital round. That news colored my perspective on Uber, and while my valuation on Uber was only $6 billion, I was inclined to give Uber the benefit of the doubt on almost every aspect of my valuation.

• With Ferrari, my valuation was ahead of its IPO, and the IPO delivered a value of about €9 billion, much higher than my estimated value of €6.3 billion (under my exclusive club narrative). I did go back and review my valuation (and story) after the offering, checking to see whether there were parts of the narrative where I could look for higher value, but found no reason to change it.

• With Amazon, the disconnect between my estimated value ($175.25) and the actual price at the time of the analysis ($287.06) was stark and led to some soul-searching for what I might be missing. One reason that I did compute the breakeven points in the last case study was to get a measure of what the market was assuming in its pricing of the stock.

• With Alibaba, my estimated value per share was approximately $66, ahead of its offering. Shortly after I did my value estimation, the bankers set an offering price of $68 for the company, uncomfortably close to my estimated value. Why uncomfortably? Given that bankers price IPOs (rather than value them) to increase the odds that the shares will be bid up on the offering date, I did not view the closeness of my value and offering prices as anything other than pure coincidence. On the offering date, the stock opened at about $95 a share, indicating that investors were much more optimistic than I was about the future of the company.

In each case, the market price did affect my valuation, at least implicitly, and that is almost always going to be the case. When you first start valuing publicly traded companies, the market price will often end up driving your narrative, because you feel safest (even if it is only a perception) when your value is close to the price. As you become more comfortable with both your narratives and your valuation skills, you will become more willing to attach values to companies that are very different from their prices and perhaps even to act on them.

CASE STUDY 10.5: UBER—THE GURLEY COUNTERNARRATIVE

Lead-in case studies

Case Study 6.2: The Ride-Sharing Landscape, June 2014

Case Study 6.3: The Uber Narrative, June 2014

Case Study 8.1: Uber—From Story to Numbers

Case Study 9.1: Uber—Valuing the Urban Car Service Company

After my valuation of Uber in June 2014, I received a gracious email from Bill Gurley, an early investor in Uber, telling me that he was planning to post a counter to my Uber valuation and that it would not pull punches. A little while later, I started getting messages from those who had read the post, with some seeking my response and some seeming to view this as the first volley in some valuation battle.1 The post did provide a very interesting and provocative counternarrative to my urban car service one, and it was interesting to me for several reasons.

1. Like everyone else, I like being right, but I was far more interested in understanding Uber’s valuation, and the post provided the vantage point of someone who not only was invested in the company but knew far more about it than I did. Rather than berating me for not getting the new economy or abusing DCF valuation as a tool from the Middle Ages, the post focused on specifics about Uber and the basis for its high value.

2. If it is true that valuation is the bridge between numbers and narrative and that neither the numbers nor the narrative people have an automatic right to the high ground, Bill Gurley’s post brought home that message by laying out a detailed and well thought through narrative.

Gurley’s narrative lent itself well to a more grounded discussion of Uber as a company and I am grateful to him for providing it. As a teacher, I am constantly on the lookout for “teachable moments,” even if they come at my expense.

In my Uber narrative, I viewed Uber as a car service company that would disrupt the existing taxi market (which I estimated to be $100 billion), expanding its growth (by attracting new users) and gaining a significant market share (10 percent). The Gurley Uber narrative was a more expansive one, where he saw Uber’s potential market as much larger (drawing in new users) and its networking effects as much stronger, leading to a higher market share. In many ways, this is exactly the discussion I was hoping to have when I first posted on Uber, since it allows me to see how these narratives play out in the numbers. In table 10.3 I contrast the narratives and the resulting values.

Table 10.3

Uber Narratives—Gurley versus Damodaran

	Gurley	Damodaran
Narrative	Uber is a logistics company (moving, delivery, car service), and it will use its networking advantage to gain a dominant market share, while cutting its slice of revenues (to 10%).	Uber will expand the car service market moderately, primarily in urban environments, and use its competitive advantages to get a significant but not dominant market share and maintain its revenue slice at 20%.
Total market	$300 billion, growing at 3% a year	$100 billion, growing at 6% a year
Market share	40.00%	10.00%
Uber’s revenue slice	10.00%	20.00%
Value for Uber	$28.7 billion + option value of entering car ownership market ($6 billion+)	$5.9 billion + option value of entering car ownership market ($2–3 billion)

The valuation that I produced for Uber with the Gurley narrative was $28.7 billion, much higher than my estimate of $5.9 billion.

Given that the values delivered by the narratives were so different, the question, if you were an investor, boiled down to which one had a higher probability of being closer to reality and Gurley’s had the advantage over mine for at least two reasons. The first is that as a board member and insider, he knew far more about Uber’s workings than I did. Not only were his starting numbers (on revenues, operating income, and other details) far more precise than mine, but he had access to how Uber was performing in its test markets (with the new users that he lists). The second is that as an investor in Uber, he had skin in the game and more at stake than I did and should therefore be given more credence. The third is that he not only had experience investing in young companies but had been right on many of his investments.

Does that mean that I was abandoning my narrative and the valuation that goes with it? No, or at least not right then, and there were three reasons why. First, it is difficult, if not impossible, for someone on the inside not to believe the best about the company that he or she invests in, the managers he or she listens to, and the products that it offers. Second, an investor in a company, especially one without an easy exit route, is more attached to his or her narrative than someone who has little to lose (other than pride) from abandoning or altering narratives. Third, as Kahneman notes in his book on investor psychology, experience is not a very good teacher in investing and markets.2 As human beings, we often extract the wrong lessons from past successes, don’t learn enough from our failures, and sometimes delude ourselves into remembering things that never happened. I am not suggesting that Bill Gurley was guilty of any of these sins, but I am, by nature, a cautious convert, and I waited to buy into his narrative, compelling though it may be.

The Gurley narrative for Uber made a good case that the convenience and economics of Uber will expand the car service market initially to include light users and nonusers (suburban users, rental car users, aged parents, and young children), but it did highlighted three requirements for Uber’s success:

1. Reason to switch: Uber has to provide users with good reasons to switch from their existing services to Uber. For taxi services, the benefits from using Uber are documented well in the Gurley narrative. Uber is more convenient (an app click away), more dependable, often safer (because of the payment system), and sometimes cheaper than taxi service. However, the trade-off gets murkier as you look past taxi services. Since mass transit will continue to be cheaper than Uber, it is comfort and convenience that will be the reasons for switching. With car rentals, Uber may be cheaper and more convenient in some senses (you don’t have to worry about picking up a rental car, parking it, or having it break down) and less convenient in others (especially if you have multiple short trips to make). With suburban car service, the problem that Uber may face is that a car is usually more than just a transportation device. Any parent who has driven his or her kids to school will attest that in addition to being a driver, he or she has to play the roles of personal assistant, private investigator, therapist, and mind reader.

2. Overcome inertia: Even when a new way of doing things offers significant benefits, it is difficult to overcome the unwillingness of human beings to change the way they act, with that inertia increasing with how set they are in their ways. It should come as little surprise that Uber was initially most successful with young people, not yet set in their ways, and that it was slower to make inroads with older users. That inertia will be an even stronger force to overcome as you move beyond the car service market. The articles that point to young people owning fewer cars are indicative of larger changes in society, but I am not sure they can be taken as an indication of a sea change in car ownership behavior. After all, there has been almost as much written on how many young people are moving back in with their parents, and both phenomena may be the results of a more difficult economic environment for young people, who come out of college with massive student loans and few job prospects.

3. Fight off the status quo: The taxi cab empire, hobbled and inefficient though it may be, will fight back, since there are significant economic interests at stake. As both Uber and Lyft have discovered, taxi service providers can use regulations and other restrictions to impede the new entrants into their businesses. Those fights will get more intense as car rental and car ownership businesses get targeted.

One way to contrast my narrative with Bill Gurley’s is to think in terms of the possible/plausible/probable distinction that I laid out in chapter 7. In figure 10.2 I display how the two narratives vary on this classification.

Figure 10.2

Probable, plausible, and possible—Damodaran narrative versus Gurley narrative.

The second part of the Gurley Uber narrative rests on the company having network benefits that allow it to capture a dominant market share. As Bill Gurley noted, a networking effect shows up any time you, as a user of a product or service, benefit from other people using the same product and service. If the networking effect is strong enough, it can lead to a dominant market share for the company that creates it and potentially to a “winner-take-all” scenario. The arguments presented in his post for the networking effects—pick-up times, coverage density, and utilization—all seem to me to point more to a local networking effect rather than a global networking one. In other words, I could see why the largest car service provider in New York may be able to leverage these advantages to get a dominant market share in New York, but these advantages will not be of much use to it in Miami, if it is not the dominant player there. There are global networking advantages, such as stored data that can be accessed by users in a new city and partnerships with credit card, airline, and car companies, but they are weaker. In fact, if the local networking advantages dominate, this market could very quickly devolve into a city-by-city trench warfare among the different players, with different winners in different markets. Thus, it is possible that Uber will become the dominant car service company in San Francisco, Lyft in Chicago, and a yet-to-be-created company in London. For the Gurley Uber narrative to hold, the global networking advantages had to move front and center.