The connection between stories and numbers has been a central theme of this book, but the balance between the two can shift as a company moves through the life cycle from start-up to growth company and into maturity and decline. In this chapter I first introduce the notion of a corporate life cycle with defined stages in corporate evolution and transition points, and then I look at how the connection between narrative and numbers changes as a company ages, with narrative driving numbers early in the life cycle and numbers driving narrative later. In the last part of the chapter, I look at the implications for investors, arguing that the qualities needed for investment success vary across the life cycle and that the valuation and pricing metrics used should be adapted accordingly.
The Aging of a Business
Businesses are born, grow (sometimes), mature, and eventually die, some sooner than others. The corporate life cycle reflects this natural evolution of businesses. In this section I begin by delineating the phases in the corporate life cycle and explain how the demands on storytelling and number crunching change as a company moves through the cycle.
The corporate life cycle begins with a business idea, not always original and sometimes not even practical, but designed to meet a perceived unmet need in the market. Most ideas don’t make it past that stage, but a few go from idea to a product or service, thus taking the first step toward a viable business. That product or service has to make it through the gauntlet of the marketplace, and if it does, it generates revenues, and at successful companies, those revenues translate into growth. Once that transition is made, successful businesses not only are able to scale up, maintaining growth as they get bigger, but are also able to profit from that growth, fending off competition along the way. Once scaled up and profitable, the mature business goes into defensive mode, building barriers to entry (moats) that allows it to maintain profits. Eventually, those barriers get smaller and fade, setting up the pathway to decline for the business. Figure 14.1 captures the stages in the corporate life cycle.
Figure 14.1
The corporate life cycle.
Note that as businesses transition from stage to stage, there will be changes in how success is measured. The focus for both investors and managers will also change with each stage, bringing different challenges and requiring different skills. Early in the life cycle, the transition points test a company’s survival skills, since large proportions of firms fail at these stages: converting ideas into products/services and products/services into sustainable businesses. Later in the life cycle, the transitions tests are reality checks, when companies are confronted with a shift from one stage to another (growth to mature, mature to declining), with some accepting the new realities and adjusting to them and others going into denial and trying to fight the aging process, often at substantial cost to themselves and their investors.
Notwithstanding those differences, there are universal themes that cut across the life cycle, and my analysis is built around three broad ones. The first is that as much as we, as investors and managers, dislike uncertainty, aging is a feature, not a bug, in businesses. The second is the oft-repeated theme of this book, that is, while we tend to think of valuation as spreadsheets, models, and data, it is just as much about storytelling as it is about the numbers. The third is that we often use the words “price” and “value” interchangeably, but they are determined by different processes and estimated using different tools and metrics, as I noted in chapter 8.
Determinants of the Life Cycle
While every company goes through a life cycle, the duration and the shape of the cycle can vary across firms. Put differently, some firms seem to grow up faster than others, making the transition from start-up to successful business in years, rather than decades. By the same token, there are firms that stay as mature firms for long periods, while others seem to fade quickly from the limelight. To understand why there are differences in corporate life cycles across firms, I look at three factors:
1. Market entry: Some businesses have substantial barriers to entry, either because of regulatory/licensing requirements or due to capital investments that need to be made. In other businesses, entry is much easier, often requiring little or no regulatory approval or intensive capital investment.
2. Scaling up: Building on the first point, once you enter a business, the ease with which you can scale up will vary across businesses, some requiring time and substantial capital investments to get bigger, and others not.
3. Consumer inertia/stickiness: In some markets, consumers are much more willing to shift from established products to new ones because they have little attachment (emotional or economic) to the products and/or because there are low costs to switching to a new product.
Other things remaining equal, if market entry is easy, scaling up can be done at low cost, and consumer inertia is low, the growth phase of the life cycle will be much more rapid. That good news, though, has to be offset with the bad news that the same factors will make it difficult to reap the benefits of being a mature business, since new competitors will use the same route to shake it up, setting up the process not only for decline but for rapid decline, as shown in figure 14.2.
Figure 14.2
The corporate life cycle: drivers and determinants.
This perspective on life cycle can be useful in examining differences in life cycles across sectors and businesses. Take, for instance, the technology companies that have increasingly come to dominate the market in the last three decades. Technology businesses tend to have low barriers to entry, allow for easy scaling up, and have consumers who generally are much more willing to experiment with innovation and new products. Not surprisingly, therefore, tech companies have been able to grow faster than non-tech companies, but just as predictably, barring a few exceptions, they have been quicker to age, as shown in Figure 14.3, and many (Yahoo, Blackberry, and Dell, among others) have transitioned from high growth to decline in a few years.
Figure 14.3
A comparison of corporate life cycles in tech and non-tech companies.
In the next section we will build on this foundation of a corporate life cycle to talk about how the emphasis shifts from stories to numbers as companies age, and how it is important to be realistic about where you are in the life cycle when telling your story about a business.
Narrative and Numbers Across the Corporate Life Cycle
While it is true that every valuation is a combination of narrative and numbers, the importance of each component changes as you move through the life cycle. Early in the life cycle, when the company has posted few historical numbers and its business model is still in flux, it is almost entirely narrative that drives the value. As the company’s business model takes form and it starts delivering results, the numbers start to play a bigger role in driving value, though the narrative still has the upper hand. In maturity, the narrative starts to take a secondary role and the numbers come to the fore.
Narrative Drivers Across the Life Cycle
The components of a compelling narrative shift as you move through the life cycle. In the start-up phase, investors are attracted to expansive narratives that can lead to big markets and are often willing to reward companies with high value for big stories. As companies try to convert ideas into products and services, the questions center around plausibility, and it is at this stage that narratives either become narrower, as businesses struggle with resource constraints and market constraints, or break down. Once the product or service is introduced, the narrative focus turns to costs and profitability, which in turn requires grappling with competitors in the product market. If you pass the profitability test, the emphasis in storytelling becomes scalability, that is, the capacity of the company to make itself bigger, testing the limits of productive, managerial, and financial capacities. Assuming that you make it through all these tests to become a profitable, mature company, the attention in the narratives shifts to barriers to entry and competitive advantages that allow the mature company to harvest markets for earnings and cash flows. In decline, the story turns to the endgame, when the company maps out a plan to shrink and perhaps disappear while generating as much as it can for investors on its way out. Figure 14.4 summarizes the narrative drivers across the life cycle.
Figure 14.4
Narrative drivers across the corporate life cycle.
There is nothing more disconcerting in business than watching a narrator (who can be a founder, a top manager, or an equity research analyst) tell a story about a company that does not fit where the company falls in the life cycle: an expansive growth story for a company in decline or a story about sustainability for a young start-up.
The Constraints and Story Types
The best way to describe how your narrative gets more constrained as a company ages is to think like a writer who has been asked to come in and complete a book whose author is deceased. Early in the life cycle, the company is like a book just beginning to be written; the story is unformed and you will be able to create your own characters and mold it to your tastes. Later in the life cycle, think of the company as a book that has mostly been written; you have less freedom to change characters or to introduce new story lines.
It follows then that the type of stories that you will tell will also vary depending on where in the life cycle you are. Early in the life cycle, your stories will be big market and disruption stories of a young start-up entering a business filled with giants and vanquishing them in the market. As the company’s business model gets more established, your stories will tend to get less ambitious, partly because they have to be consistent with the numbers you are delivering. Telling a story of expansive growth and high profit margins will become more and more unsustainable if your revenue growth lags and you are having a difficult time making money. Once a company becomes mature, your story can either become one about preserving the status quo (and the profits that come with it) or about reinvention and exploring the possibilities of rediscovering growth (perhaps through acquisitions or by entering new markets). In decline, the story may be tinged with nostalgia for glory days past, but to be realistic, it should reflect the company’s charged circumstances.
You are also more likely to see big differences in narratives earlier in the life cycle, as observers of a company have more room to create their own pathways for the company. As a company ages, its history begins to restrict the potential narratives that different investors can derive for it. Investors looking at a company like Uber, for instance, will diverge on everything from what business Uber is in to what type of networking effects it will have to how much risk it is exposed to, and that will create a greater spread in the values investors attach to the company. In contrast, investors looking at Coca-Cola or JCPenney are likely to agree on most parts of the story and deviate only on small pieces.
CASE STUDY 14.1: VALUING A YOUNG COMPANY—GoPro IN OCTOBER 2014
On June 26, 2014, GoPro, a company that makes action cameras you can use to record yourself doing a sports activity (running, swimming, hiking), went public, with its stock price jumping 30 percent on its offering date (from $24 to $31.44) and then continuing its rise to $94 on October 7, 2014, before falling back to $70 on October 15, 2014, at the time of this valuation. The stock had accumulated a large number of vocal short-sellers who were convinced that this was a highflier destined for a fall, and many of them were burnt in the price run-up.
At the time of the valuation, the company produced three models of its camera (the Hero, the Hero 3, and the Hero 4), multiple accessories, and two free software products (the GoPro App and GoPro Studio) to convert the recordings into viewable videos. The company’s cameras had found a ready market, with revenues hitting $986 million in 2013 and increasing to $1,033 million in the twelve months ending in June 2014. In spite of large investments in R&D ($108 million in the trailing twelve months), the company still managed to be profitable, with operating income of $70 million in that period. Capitalizing R&D increased their pretax operating margin to 13.43 percent, impressive for a young company. Figure 14.5 looks at the evolution of revenues and units sold over the history of the company through the time of this valuation.
Figure 14.5
The history of GoPro.
In valuing GoPro, I faced all of the typical challenges associated with valuing a company early in its life cycle: determining the business it was in, the market potential, and imminent competition. GoPro was nominally a camera company, but in my narrative, I argued that the action-camera market was a subset of the smartphone market and its customers would be physically active people who also happen to be active on social media (overactive oversharers). I estimated the market for action cameras to be $31 billion in 2013, and applying a 5 percent growth rate on this market yielded a potential market of $51 billion in 2023. Figure 14.6 captures the sequence of assumptions that yielded this number.
Figure 14.6
Estimating the potential market for GoPro cameras.
GoPro was the first mover in the market, and to gauge the expected market share that GoPro could get of this market, it was necessary to take into account that competition was starting to form—upstarts, established camera makers, and some smartphone manufacturers. I did not see any potential networking advantages that GoPro could bring to this process that would allow it, even if successful, to control a dominant share of this market as the market grew. Drawing from the established camera-business market shares, I assigned a market share of 20 percent (resulting in revenues of about $10 billion for GoPro in 2023, i.e., 20 percent of $51 billion), roughly similar to the 20 percent share of the camera market in 2013 held by Nikon, the leading camera maker. On the profit margin, GoPro’s first-mover advantage had given it a head start in this market, allowing it to charge premium prices, and I assumed it would earn a pretax operating margin of 12.5 percent in the future, slightly lower than the margin (13.43 percent) posted by the company in the most recent twelve months but reflecting the trend lines over the life of the company (as seen in table 14.1).
Table 14.1
GoPro—Profit Margins Over Its Lifetime
| |
2011 |
2012 |
2013 |
Trailing 12 months (ending June 2014) |
| Gross margin |
52.35% |
43.75% |
36.70% |
40.13% |
| EBITDA margin |
18.74% |
12.73% |
12.60% |
9.82% |
| Adjusted EBITDA margin |
22.59% |
14.50% |
13.71% |
14.14% |
| R&D adjusted operating margin |
20.24% |
16.70% |
15.98% |
13.43% |
| Operating margin |
16.57% |
10.31% |
10.01% |
6.75% |
| Net margin |
10.50% |
6.21% |
6.15% |
3.27% |
This estimate of the pretax operating margin (12.5 percent) was significantly higher than the 6–7.5 percent margin reported by camera companies and similar to the 10–15 percent margin reported by smartphone companies. I was, in effect, assuming that GoPro would preserve its premium pricing, even in the face of competition. To estimate the reinvestment needs, I made the assumption that the company would have to invest $1 for every $2 in additional revenues generated in years 1–10. This, in turn, would move the return on capital for the company from its current levels to about 16 percent in year 10.
GoPro had a social media focus for its user-generated videos, but in October 2014 the company generated all of its revenues from selling cameras and accessories. GoPro’s focus on creating partnerships with Xbox and Pinterest suggested that it saw the possibility of generating revenues from becoming a media company, with the videos created by its customers as content. In October 2014, though, this was more in the realm of the possible than the plausible or the probable, and I assumed that GoPro’s video generation capability would continue to generate no revenues but would help it sell more cameras. To estimate a cost of capital for GoPro, I considered its prevailing mix of debt and equity (2.2 percent debt, 97.8 percent equity) as my starting point, and estimated a cost of capital of 8.36 percent for the company, declining to 8 percent by year 10.
With this spectrum of choices on the inputs (revenue growth derived from the total market/market share assumptions, operating margin, sales-to-capital ratio, and cost of capital), I assessed the value of GoPro as a distribution rather than as a single estimate of value. Figure 14.7 summarizes my assumptions and results.
Figure 14.7
A simulated GoPro valuation, October 2014.
Reading this distribution, you can see that while the expected value across the simulations was only $32/share, well below the market price of $70, there were outcomes that delivered values higher than the market price. It would have been difficult, but not impossible, to justify buying GoPro on an intrinsic value basis at its $70 price per share. To get to that price, GoPro would have to attract new users (physically active oversharers) into the market and fend off competition with innovative features that create networking benefits. That was a narrow path, and while it was plausible, it did not meet the probability tests that would have convinced me to buy GoPro.
CASE STUDY 14.2: VALUING A DECLINING COMPANY—JCPENNEY IN JANUARY 2016
JCPenney has been a longtime player in the U.S. retail market, tracing its history back to 1902. Started in Wyoming, the company initially grew in the Rocky Mountain states before moving its headquarters to New York in 1914. The company opened its first department store in 1961 and started selling through its catalogs in 1963. When Sears closed it catalog business in 1993, JCPenney became the largest catalog retailer in the country.
In January 2016 the company was facing bleak times, whipsawed by the growth of online retailing in general, and Amazon in particular, and changing consumer tastes. Figure 14.8 summarizes revenues and operating margins for the company by year, from 2000 to 2015.
Figure 14.8
JCPenney revenues and operating margins.
Over this period, the company saw its revenues drop by more than 50 percent, and it reported operating losses from 2012 to 2015.
Given this history and the nature of the competition, my narrative for JCPenney is one of continued decline, in which I see revenues continue to drop 3 percent a year as the company shuts down unprofitable stores. I did have a slightly optimistic ending to the story, in which the company managed to find its place in the retailing business, albeit as a smaller player, and improved operating margins to reach the median for the retail business of 6.25 percent over the next decade. Given the high debt load, there was a significant chance that the company would not make it through the next decade, but if it did, it would be able to survive as a smaller, stable-growth company. I valued JCPenney, using the valuation inputs from my narrative, and Table 14.2 summarizes the numbers.
Table 14.2
JCPenney case study
aDeclining business: Revenues expected to drop by 3% a year for the next 5 years.
bMargins improve gradually to median for U.S. retail sector (6.25%).
cAs stores shut down, cash released from real estate.
dThe cost of capital is at 9%, higher because of high cost of debt.
eHigh debt load and poor earnings put survival at risk. Based on bond rating, 20% chance of failure, and liquidation will bring in 50% of book value.
The revenues I projected for JCPenney ten years in the future were about 15 percent lower than prevailing revenues, and the value that I obtained for the operating assets of the company was $4.36 billion. That value was well below the outstanding debt (inclusive of leases), reflecting the tenuous state of the company and the very real possibility that this story could have a bad ending.
CASE STUDY 14.3: NARRATIVE DIFFERENCES—UBER IN DECEMBER 2014
In chapter 9 I valued Uber, based on my narrative for the company in June 2014, as an urban car service company with local networking benefits, and I arrived at an estimate value of $6 billion for its equity. In chapter 10 I valued Bill Gurley’s counternarrative for the firm, as a logistics company with global networking benefits, and arrived at a value of $29 billion for equity. These are only two of many stories that you can tell about Uber as a company. To understand how narratives drive values for young companies, and how narrative differences can result in divergent values, I broke down the narrative process for Uber into steps and looked at the choices investors looking at the company could make at each step:
1. Business and potential market: The business that you see Uber in sets the limits of growth for the company, and the more broadly defined the market, the larger the potential growth (and the higher the value). Your choices are listed in table 14.3, with the consequences that I see for Uber’s total market next to each one.
Table 14.3
Uber’s Business and Potential Market
| Uber’s business is |
Market size (in millions) |
Description |
| A1. Urban car service |
$100,000 |
Taxi cabs, limos, and car services (urban) |
| A2. All car service |
$150,000 |
+ Rental cars + non-urban car service |
| A3. Logistics |
$205,000 |
+ Moving + local delivery |
| A4. Mobility services |
$285,000 |
+ Mass transit + car sharing |
2. Effect on total market: In my valuations of Uber in chapters 9 and 10, I pointed to the possibility that it could attract new users to the car service market, thus increasing the size of the market over time. In table 14.4 I outline four possibilities for this growth effect.
Table 14.4
Uber’s Effect on Total Market
| Uber’s effect on total market |
Annual growth rate |
Overall effect over next decade |
| B1. None |
3.00% |
No change in market size |
| B2. Increase market by 25% |
5.32% |
Increase market size by 25% over 10 years |
| B3. Increase market by 50% |
7.26% |
Increase market size by 50% over 10 years |
| B4. Double market size |
10.39% |
Double market size over 10 years |
3. Networking benefits: In my valuation of Uber, I assumed that it would have local networking benefits, allowing it to capture a 10 percent market share of the total market, but in an alternative narrative in chapter 10, I pointed to the possibility of global networking benefits, giving it a much larger market share. Table 14.5 lists the possible choices.
Table 14.5
Uber’s Networking Benefits
| In its business, Uber will have |
Market share |
Description of networking effect |
| C1. No network effects |
5% |
Open competition in every market |
| C2. Weak local network effects |
10% |
Dominance in a few local markets |
| C3. Strong local network effects |
15% |
Dominance in multiple local markets |
| C4. Weak global network effects |
25% |
Weak spillover benefits in new markets |
| C5. Strong global network effects |
40% |
Strong spillover benefits in new markets |
4. Competitive advantages: The competitive advantages that Uber creates as it builds up its business will affect whether it can keep its slice of driver revenues at 20 percent and preserve strong operating margins or see slimmer profits even if it is able to generate large revenues. Table 14.6 lists the choices on Uber’s competitive advantages.
Table 14.6
Uber’s Competitive Advantages
| Uber’s competitive advantages |
Slice of ride receipts |
Description of competitive effect |
| D1. None |
5% |
Unrestricted entry + no pricing power |
| D2. Weak |
10% |
Unrestricted entry + some pricing power |
| D3. Semistrong |
15% |
Unrestricted entry + pricing power |
| D4. Strong and sustainable |
20% |
Restricted entry + pricing power |
5. Capital intensity: While my initial valuation of Uber assumed that it would be able to continue to grow with its prevailing business model (of not owning the cars or hiring the drivers), there is the possibility that as the company grows, it will have to adopt a model that requires more investments (in cars, technology, or infrastructure). Table 14.7 outlines some of the possibilities.
Table 14.7
Uber’s Capital Investment Model
| Uber’s capital model |
Sales-to-capital ratio |
Description of model |
| E1. Unchanged |
5.00 |
No investment in cars or infrastructure |
| E2. Moderate |
3.50 |
Some investments in cars or infrastructure |
| E3. High |
1.50 |
Large investments in driverless cars and or technology |
Depending on the choices—total market, growth in that market, Uber’s market share, and revenue slice—the valuation results vary. While the number of combinations of assumptions is prohibitively high to show value estimates under each one, I summarized the value estimates in Table 14.8 for at least a subset of plausible choices.
Table 14.8
Uber—Narrative and Valuations, December 2014
| Total market |
Growth effect |
Network effect |
Competitive advantages |
Value of Uber in $ millions |
| A4. Mobility services |
B4. Double market size |
C5. Strong global network effects |
D4. Strong and sustainable |
$90,457 |
| A3. Logistics |
B4. Double market size |
C5. Strong global network effects |
D4. Strong and sustainable |
$65, 158 |
| A4. Mobility services |
B3. Increase market by 50% |
C3. Strong local network effects |
D3. Semistrong |
$52, 346 |
| A2. All car service |
B4. Double market size |
C5. Strong global network effects |
D4. Strong and sustainable |
$47,764 |
| A1. Urban car service |
B4. Double market size |
C5. Strong global network effects |
D4. Strong and sustainable |
$31,952 |
| A3. Logistics |
B3. Increase market by 50% |
C3. Strong local network effects |
D3. Semistrong |
$14,321 |
| A1. Urban car service |
B3. Increase market by 50% |
C3. Strong local network effects |
D3. Semistrong |
$7,127 |
| A2. All car service |
B3. Increase market by 50% |
C3. Strong local network effects |
D3. Semistrong |
$4, 764 |
| A4. Mobility services |
B1. None |
C1. No network effects |
D1. None |
$1,888 |
| A3. Logistics |
B1. None |
C1. No network effects |
D1. None |
$1,417 |
| A2. All car service |
B1. None |
C1. No network effects |
D1. None |
$1,094 |
| A1. Urban car service |
B1. None |
C1. No network effects |
D1. None |
$799 |
Looking at the range of values ($799 million to $90.5 billion) that I obtain for Uber in case study 14.3, you may find your worst fears about valuation models vindicated, that is, that they can be used to deliver whatever number you want, but that is not the way I see it. Instead, here are four lessons that I draw from this table:
1. Soaring narratives, soaring values: I know that some people view DCF models as inherently conservative and thus unsuited to valuing young companies with lots of potential. As you can see in table 14.8, if you have a soaring narrative about a huge market, a dominant market share, and hefty profit margins, the model will deliver a value to match. It also stands to reason that when you have big differences in value estimates, it is almost always because you have different narratives for a company, not because you have a disagreement on an input number.
2. Not all narratives are created equal: While I have listed multiple narratives, some of which deliver huge values and some of which do not, not all are equal. Looking to the future as investors, some narratives are more plausible than others and thus have better odds of succeeding.
3. Narratives need reshaping: The narrative for Uber that you develop is based on what you know today. As events unfold, it is critical that you check your narrative against the facts and tweak, change, or even replace the narrative if the facts require those adjustments, which was the point that I made in chapter 11.
4. Narratives matter: Success when investing in young companies comes from getting the narrative right, not the numbers. That may explain why some successful venture capitalists can get away being surprisingly sloppy with their numbers. After all, if your skill set includes finding start-ups with strong narratives and picking founders/entrepreneurs who can deliver on those narratives, the fact that you cannot tell the difference between EBITDA and free cash flow or compute the cost of capital will be of little consequence.
Implications for Investors
The interplay between corporate life cycles, narratives, and numbers provides a template for understanding differences between investment philosophies and what it may take to succeed with each one.
Investor Skill Sets
As I argued in the last section, venture capitalists, who focus on companies early in the life cycle, will succeed or fail based more on their skills in assessing stories (that founders tell them about companies) than on their number crunching. In contrast, old-time value investors whose attention is on mature companies will be able to make money with mostly number crunching driving their choices, even if their narrative skills are weak or narrow (focused on assessing moats and competitive advantages).
If you are an investor trying to determine where the payoff from investing will be greatest for you, in addition to gauging your storytelling and number-crunching capabilities, I would suggest that you look at how well you deal with uncertainty and being wrong. If you are easily thrown off your game or rendered off balance by results that are different from your expectations, you should steer away from young businesses, in which narratives (and values) are more likely to change over time. In contrast, if it is big shifts in value that attract you to investments, you will not find many opportunities among mature companies.
Investor Tool Kits
As your investment focus shifts from young businesses to mature ones, the tools you use to examine investments also need to change. If you make investments based on value, that is, you buy only if price is less than your estimated value, the valuation models you use should draw on the same fundamentals but the way you build them will change over the life cycle. For young companies, your valuation models will have to start from the total market and work down, just as my Uber valuation did, and they also will have to be much more flexible to allow you to bring narratives into value. For more mature companies, you can build models that build upon the historical data for the company, and as long as the base narrative does not shift dramatically, you can get a reasonable estimate of value. Since this is effectively what many large spreadsheet financial models do, it should come as no surprise that while they deliver reasonable estimates of value for stable firms, they fall flat with younger or transitioning businesses.
If your investments are not based on value but are based on pricing judgments, you will have to make relative judgments, that is, ask whether the company is cheap or expensive relative to other priced companies in the business. That almost always will require you to choose a pricing multiple, where you scale the price paid to a common variable. Early in a company’s life cycle, when there is little tangible data on operations, that variable may be something that you believe will eventually lead to revenues and profits, say number of users, downloads, or subscribers, but as the company moves through the cycle, you will scale value to operating metrics, starting with revenues (for growth companies that are still building up profitability), moving on to earnings (for mature companies), and ending with book value (as a proxy for liquidation value for declining companies). Figure 14.9 captures these shifts in investor skill sets and tool kits over the corporate life cycle, illustrating why there is often a divide between growth investors and value investors and between venture capitalists and public market investors.
Figure 14.9
Investor Challenges across Life Cycle
Using its own metrics and tools, each group will find the other’s investment judgments to be almost incomprehensible. Just as old-time value investors ask, “Who would buy a stock that trades at a thousand times earnings?” about investors who buy growth stocks, growth investors just as frequently wonder why anyone would buy a company whose revenues are expected to decline.
Conclusion
I started this chapter with a description of the corporate life cycle, in which companies move from the start-up phase to maturity and eventual decline, and I used it to examine how the balance between narrative and numbers changes over the life cycle. Early on, not only is it your story that drives your valuation of a business, but you are also likely to see wide variations across investors in story lines and valuations. As a company ages, the numbers start to play a greater role in determining value, and it is possible that you can attach a value for a company based purely on its numbers, perhaps by extrapolating historical data.
If you accept this argument, it follows that your investment philosophy and focus should match your skills and mental makeup. If you enjoy telling corporate stories, are good at connecting these stories to value, and are comfortable with being terribly wrong, you will be drawn to investing in young companies, as a venture capitalist or as an investor in young, growth companies in the public marketplace. If your preference is to work with numbers and you like rigid rules on investing, you will be more comfortable working with mature companies. To each, his (or her) own!
