Seeing the Dark Side of Valuation
When confronted with estimation challenges, analysts have one of two choices. The healthy response is to confront the challenge and adapt existing models to reflect the differences in the company being valued. The more common response is to bend the rules of valuation and use shortcuts to justify whatever price they are predisposed to pay for the company. The latter approach is "the dark side of valuation." This section looks at its many manifestations.
In the input phase, we look for the standard starting points for valuing individual companies—earnings and operating details from the most recent financial statements; forecasts for the future, provided by analysts and management; and data for macroeconomic inputs such as risk-free rates, risk premiums, and exchange rates. We see some standard patterns in valuations:
Base year fixation: Analysts often treat the current year as the base year in valuation and build these numbers in making forecasts. While this is understandable, it can also lead to serious errors in valuation when either of the following occurs:
- Current numbers do not reflect the firm's long-term earnings capability. As we noted earlier, this is especially true of commodity and cyclical companies, but it is also the case for young and start-up companies.
- Inconsistencies in the accounting treatment of operating and capital expenditures are skewing current values for earnings and book value. With technology and human capital companies, this will be an issue.
- Outsourcing key inputs: When it comes to macroeconomic inputs, analysts usually go to outside sources. This is especially true with equity risk premiums and betas, where services offer estimates of the numbers, backed up by volumes of data. While this may give analysts someone else to blame if things go wrong, it also means that little independent thought goes into whether the numbers being used actually make sense.
- Trusting management forecasts: The most difficult task in valuing a company is forecasting future revenues, earnings, and reinvestment. This is especially true with younger companies that have significant growth prospects. When managers offer to provide forecasts of these numbers, analysts, not surprisingly, jump at the opportunity and rationalize their use of these forecasts by arguing that managers know more about the company than they do. What they fail to consider is that these forecasts are likely to be biased.
The inputs feed into valuation models and metrics to provide the final judgments on value. At this stage in the process, it is natural for analysts to feel uncertain about the reliability of these numbers—more so for some companies than others. In the process of dealing with this uncertainty, some common errors show up in valuations:
- Ignoring the scaling effect: As firms get larger, it becomes more and more difficult to maintain high growth rates. In making forecasts, analysts often fail to consider this reality and continue to use growth rates derived from history long into their forecast periods.
- Inconsistencies in valuation: Good valuations should be internally consistent, but it is easy for inconsistencies to enter valuations. As you will see in the coming chapters, assumptions about growth, reinvestment, and risk not only have to make sense individually but also have to tie together. Estimating high growth rates with little or no reinvestment into the business to generate this growth may be possible, but it is unlikely. The assumptions that we make about inflation in our cash flow estimates have to be consistent with the assumptions (often implicit) about expected inflation in interest rates and exchange rates.
- Valuing for the exception: Analysts often draw on anecdotal evidence to justify their assumptions. The fact that Wal-Mart was able to continue growing, even as it became larger, is used to justify maintaining high revenue growth rates for firms for long periods. Analysts point to companies like Coca-Cola and Microsoft to justify assumptions about maintaining high margins and returns on investment for small-growth companies. It is worth nothing that Wal-Mart, Coca-Cola, and Microsoft are the exceptions, rather than the rule.
- Paradigm shifts: When analysts abandon age-old principles of economics and valuation, talking about how the rules have changed, it is time to be skeptical. It is true that economies and markets change, and we have to change with them. But we cannot repeal the laws of demand and supply or the notion that businesses eventually have to make money to be valuable.
- Black-box models: As data becomes more easily accessible and building bigger models becomes more feasible, one response to uncertainty is to build bigger and more complex models. Two problems come out of more detailed models. One is the fact that they require far more inputs to arrive at a number. Uncertainty often multiplies as we add more detail, and it is "garbage in, garbage out." The other problem is that the model becomes a black box, with analysts having little sense of what happens inside the box.
- Rules of thumb: If one response to complexity is to build bigger and better models, the other response is to look for a simple solution. In many valuations, this takes the form of using a rule of thumb to arrive at the value of an asset. An analyst faced with a particularly troublesome set of inputs may decide to value a company at three times revenues because that is what investors have traditionally paid for companies in this sector. While using these shortcuts may provide the illusion of precision, it is far better to confront uncertainty than to ignore it.
In many cases, the real damage to valuation principles occurs after the valuation has been done—at least in terms of mechanics. At least two common practices wreak havoc on valuations:
- Valuation garnishing: This is the all-too-common practice of adding premiums and discounts to estimated value to reflect what the analyst believes are missed components. It is not uncommon in acquisition valuations, for instance, to add a 20% premium for control, just as it is standard practice in private company valuation to reduce value by 20 to 25% to reflect illiquidity. Similar premiums/discounts are added/subtracted to reflect the effects of brand names and other intangibles and emerging-market risk. The net result of these adjustments is that the value reflects whatever preconceptions the analyst might have had about the company.
- Market feedback: With publicly traded companies, the first number that we look at after we have done a valuation is the market price. When analysts are uncertain about the numbers that go into their valuations, big differences between the value and the market price lead to their revisiting the valuation. As inputs change, the value drifts inexorably toward the market price, rendering the entire process pointless. If we believe that markets are right, why bother doing valuation in the first place?
In summary, the dark side of valuation can take many different forms, but the end result is always the same. The valuations we arrive at for individual businesses reflect the errors and biases we have built into the process. All too often, we find what we want to find rather than the truth.