Damodaran on Valuation

Security Analysis for Investment and Corporate Finance

Aswath Damodaran

• Verlag: Wiley
• Jahr: 2011
• ISBN:
  9781118161081
• Auflage: 2
• Thema: Business
• Thema: Valuation

"Aswath Damodaran is simply the best valuation teacher around. If you are interested in the theory or practice of valuation, you should have Damodaran on Valuation on your bookshelf. You can bet that I do."
-- Michael J. Mauboussin, Chief Investment Strategist, Legg Mason Capital Management and author of More Than You Know: Finding Financial Wisdom in Unconventional Places

In order to be a successful CEO, corporate strategist, or analyst, understanding the valuation process is a necessity. The second edition of Damodaran on Valuation stands out as the most reliable book for answering many of today?s critical valuation questions. Completely revised and updated, this edition is the ideal book on valuation for CEOs and corporate strategists. You'll gain an understanding of the vitality of today?s valuation models and develop the acumen needed for the most complex and subtle valuation scenarios you will face.

CHAPTER 1

Introduction to Valuation

Knowing what an asset is worth and what determines that value is a prerequisite for intelligent decision making—in choosing investments for a portfolio, in deciding on the appropriate price to pay or receive in a takeover, and in making investment, financing, and dividend choices when running a business. The premise of this book is that we can make reasonable estimates of value for most assets, and that the same fundamental principles determine the values of all types of assets, real as well as financial. Some assets are easier to value than others, the details of valuation vary from asset to asset, and the uncertainty associated with value estimates is different for different assets, but the core principles remain the same. This chapter lays out some general insights about the valuation process and outlines the role that valuation plays in portfolio management, in acquisition analysis, and in corporate finance. It also examines the three basic approaches that can be used to value an asset.

A PHILOSOPHICAL BASIS FOR VALUATION

A postulate of sound investing is that an investor does not pay more for an asset than it is worth. This statement may seem logical and obvious, but it is forgotten and rediscovered at some time in every generation and in every market. There are those who are disingenuous enough to argue that value is in the eyes of the beholder, and that any price can be justified if there are other investors willing to pay that price. That is patently absurd. Perceptions may be all that matter when the asset is a painting or a sculpture, but we do not and should not buy most assets for aesthetic or emotional reasons; we buy financial assets for the cash flows we expect to receive from them. Consequently, perceptions of value have to be backed up by reality, which implies that the price we pay for any asset should reflect the cash flows it is expected to generate. The models of valuation described in this book attempt to relate value to the level of, uncertainty about, and expected growth in these cash flows.

There are many aspects of valuation where we can agree to disagree, including estimates of true value and how long it will take for prices to adjust to that true value. But there is one point on which there can be no disagreement. Asset prices cannot be justified by merely using the argument that there will be other investors around who will pay a higher price in the future. That is the equivalent of playing a very expensive game of musical chairs, where every investor has to answer the question “Where will I be when the music stops?” before playing. The problem with investing with the expectation that when the time comes there will be a bigger fool around to whom to sell an asset is that you might end up being the biggest fool of all.

INSIDE THE VALUATION PROCESS

There are two extreme views of the valuation process. At one end are those who believe that valuation, done right, is a hard science, where there is little room for analyst views or human error. At the other are those who feel that valuation is more of an art, where savvy analysts can manipulate the numbers to generate whatever result they want. The truth does lies somewhere in the middle, and we use this section to consider three components of the valuation process that do not get the attention they deserve—the bias that analysts bring to the process, the uncertainty that they have to grapple with, and the complexity that modern technology and easy access to information have introduced into valuation.

Value First, Valuation to Follow: Bias in Valuation

We almost never start valuing a company with a blank slate. All too often, our views on a company are formed before we start inputting the numbers into the models that we use, and, not surprisingly, our conclusions tend to reflect our biases. We begin by considering the sources of bias in valuation and then move on to evaluate how bias manifests itself in most valuations. We close with a discussion of how best to minimize or at least deal with bias in valuations.

Sources of Bias

The bias in valuation starts with the companies we choose to value. These choices are almost never random, and how we make them can start laying the foundation for bias. It may be that we have read something in the press (good or bad) about the company or heard from an expert that it was undervalued or overvalued. Thus, we already begin with a perception about the company that we are about to value. We add to the bias when we collect the information we need to value the firm. The annual report and other financial statements include not only the accounting numbers but also management discussions of performance, often putting the best possible spin on the numbers. With many larger companies, it is easy to access what other analysts following the stock think about these companies. Zacks, IBES, and First Call, to name three services among many, provide summaries of how many analysts are bullish or bearish about the stock, and we can often access their complete valuations. Finally, we have the market’s own estimate of the value of the company—the market price—adding to the mix. Valuations that stray too far from this number make analysts uncomfortable, since they may reflect large valuation errors (rather than market mistakes).

In many valuations, there are institutional factors that add to this already substantial bias. For instance, equity research analysts are more likely to issue buy rather than sell recommendations; that is, they are more likely to find firms to be undervalued than overvalued.1 This can be traced partly to the difficulties analysts face in obtaining access to and collecting information on firms on which they have issued sell recommendations, and partly to pressure that they face from portfolio managers, some of whom might have large positions in the stock, and from their own firm’s investment banking arms, which have other profitable relationships with the firms in question.

The reward and punishment structure associated with finding companies to be undervalued and overvalued is also a contributor to bias. Analysts whose compensation is dependent upon whether they find firms to be under- or overvalued will be biased in their conclusions. This should explain why acquisition valuations are so often biased upward. The analysis of the deal, which is usually done by the acquiring firm’s investment banker, who also happens to be responsible for carrying the deal to its successful conclusion, can come to one of two conclusions. One is to find that the deal is seriously overpriced and recommend rejection, in which case the analyst receives the eternal gratitude of the stockholders of the acquiring firm but little else. The other is to find that the deal makes sense (no matter what the price is) and to reap the ample financial windfall from getting the deal done.

Manifestations of Bias

There are three ways in which our views on a company (and the biases we have) can manifest themselves in value. The first is in the inputs that we use in the valuation. When we value companies, we constantly come to forks in the road where we have to make assumptions to move on. These assumptions can be optimistic or pessimistic. For a company with high operating margins now, we can assume either that competition will drive the margins down to industry averages very quickly (pessimistic) or that the company will be able to maintain its margins for an extended period (optimistic). The path we choose will reflect our prior biases. It should come as no surprise then that the end value that we arrive at is reflective of the optimistic or pessimistic choices we made along the way.

The second is in what we will call postvaluation tinkering, where analysts revisit assumptions after a valuation in an attempt to get a value closer to what they had expected to obtain starting off. Thus, an analyst who values a company at $15 per share, when the market price is $25, may revise his growth rates upward and his risk downward to come up with a higher value, if he believed that the company was undervalued to begin with.

The third is to leave the value as is but attribute the difference between the value we estimate and the value we think is the right one to a qualitative factor such as synergy or strategic considerations. This is a common device in acquisition valuation where analysts are often called upon to justify the unjustifiable. In fact, the use of premiums and discounts, where we augment or reduce estimated value, provides a window on the bias in the process. The use of premiums—control and synergy are good examples—is commonplace in acquisition valuations, where the bias is toward pushing value upward (to justify high acquisition prices). The use of discounts—illiquidity and minority discounts, for instance—are more typical in private company valuations for tax and divorce court, where the objective is often to report as low a value as possible for a company.

What to Do about Bias

Bias cannot be regulated or legislated out of existence. Analysts are human and bring their biases to the table. However, there are several ways in which we can mitigate the effects of bias on valuation:

1. Reduce institutional pressures. As we noted earlier, a significant portion of bias can be attributed to institutional factors. Equity research analysts in the 1990s, for instance, in addition to dealing with all of the standard sources of bias had to grapple with the demand from their employers that they bring in investment banking business. Institutions that want honest sell-side equity research should protect their equity research analysts who issue sell recommendations on companies, not only from irate companies but also from their own salespeople and portfolio managers.

2. Delink valuations from reward/punishment. Any valuation process where the reward or punishment is conditional on the outcome of the valuation will result in biased valuations. In other words, if we want acquisition valuations to be unbiased, we have to separate the deal analysis from the deal making.

3. No precommitments. Decision makers should avoid taking strong public positions on the value of a firm before the valuation is complete. An acquiring firm that comes up with a price prior to the valuation of a target firm has put analysts in an untenable position in which they are called upon to justify this price. In far too many cases, the decision on whether a firm is undervalued or overvalued precedes the actual valuation, leading to seriously biased analyses.

4. Self-awareness. The best antidote to bias is awareness. An analyst who is aware of the biases he or she brings to the valuation process can either actively try to confront these biases when making input choices or open the process up to more objective points of view about a company’s future.

5. Honest reporting. In Bayesian statistics, analysts are required to reveal their priors (biases) before they present their results from an analysis. Thus, an environmentalist will have to reveal that he or she strongly believes that there is a hole in the ozone layer before presenting empirical evidence to that effect. The person reviewing the study can then factor that bias in while looking at the conclusions. Valuations would be much more useful if analysts revealed their biases up front.

While we cannot eliminate bias in valuations, we can try to minimize its impact by designing valuation processes that are more protected from overt outside influences and by reporting our biases with our estimated values.

It Is Only an Estimate: Imprecision and Uncertainty in Valuation

Starting early in life, we are taught that if we do things right, we will get the right answers. In other words, the precision of the answer is used as a measure of the quality of the process that yielded the answer. While this may be appropriate in mathematics or physics, it is a poor measure of quality in valuation. Barring a very small subset of assets, there will always be uncertainty associated with valuations, and even the best valuations come with a substantial margin for error. In this section, we examine the sources of uncertainty and the consequences for valuation.

Sources of Uncertainty

Uncertainty is part and parcel of the valuation process, both at the point in time when we value a business and in how that value evolves over time as we obtain new information that impacts the valuation. That information can be specific to the firm being valued, can be more generally about the sector in which the firm operates, or can even be general market information (about interest rates and the economy).

When valuing an asset at any point in time, we make forecasts for the future. Since none of us possess crystal balls, we have to make our best estimates given the information that we have at the time of the valuation. Our estimates of value can be wrong for a number of reasons, and we can categorize these reasons into three groups.

1. Estimation uncertainty. Even if our information sources are impeccable, we have to convert raw information into inputs and use these inputs in models. Any mistakes or misassessments that we make at either stage of this process will cause estimation error.

2. Firm-specific uncertainty. The path that we envision for a firm can prove to be hopelessly wrong. The firm may do much better or much worse than we expected, and the resulting earnings and cash flows will be very different from our estimates.

3. Macroeconomic uncertainty. Even if a firm evolves exactly the way we ex...