Chapter 20

IN THIS CHAPTER

**Finding ways to improve estimates of cash flows and weighted average cost of capital**

**Looking at estimates from the client’s perspective**

**Identifying which assumptions of financial models are most important**

Investment bankers spend a great deal of time constructing financial models on spreadsheets and manipulating them to arrive at values for companies, divisions, and potential projects. These models are often very complex and involve many assumptions and inputs. This chapter provides some ideas on how investment bankers can improve their analyses and deliver greater value to clients.

In many disciplines mathematical calculations need to be carried out to several significant digits and the results applied to complex processes. For instance, when NASA is launching rockets to “infinity and beyond,” calculations involving satellite orbits and descent angles need to be exact. Minor mistakes can be disastrous and result in aborted missions and losses of millions of dollars. Likewise, when engineers are building bridges and buildings, the calculations need to be quite precise. Given that physics and engineering are two of the more popular backgrounds for investment bankers, it isn’t surprising that many young investment bankers bring that precise quality to the spreadsheets when building their financial models.

Should that sales growth rate be 7.65 percent or 7.6 percent? Should the after-tax cost of debt be 5.35 percent or 5.37 percent? These types of questions on inputs to the financial models are often agonized over, and models are revised with very minor changes. But financial analysis isn’t physics. The goal of any financial model is to provide a very rough *estimate* of the value of a company, division, or potential project — not a precise value.

Several very successful investors, including Warren Buffett, Benjamin Graham, and Seth Klarman, focus on a concept called *margin of safety.* This simple notion is that investors should not purchase a stock because they believe it is worth $95.37 a share and it’s selling in the market for $91.25. Instead, investors should buy a stock with a market price of $91.25 a share that they believe is worth in excess of $140. In other words, many of the assumptions the investor made in computing his value may be overly optimistic, yet there is enough wiggle room that the investment will still make him money even if his optimistic projections don’t pan out.

Manipulating financial models involves making numerous assumptions on many variables. Sales growth rates and gross margin percentages are just two of the multitude of parameters that investment bankers need to forecast when making their cash flow forecasts.

What investment bankers realize, however, is that not all values entered into financial formulas are created equally. Some inputted values have a much greater impact on bottom line estimates than others, and it’s incumbent upon investment bankers to realize which estimates are more critical than others. They’ll then focus their efforts on making sure they spend more time on the more critical inputs than the less critical ones.

The way investment bankers can discover which inputs truly matter is to perform a *sensitivity analysis.* With a sensitivity analysis, the analyst will simply vary one input at a time and see how much the bottom line cash flow forecast is affected — for instance, changing the sales growth rate from 8 percent to 9 percent, or changing the gross margin percentage from 20 percent to 21 percent.

The advent of high-speed computers with large data storage ability has been a boon to investment bankers and their model making. Although any investment banking deal that is made will have only one ultimate outcome attached to it, technology allows investment bankers to develop financial models to provide a more complete analysis of potential outcomes of an investment banking deal and provide clients with a probability assessment that a deal will be profitable or have a return that exceeds a certain dollar amount or percentage return.

The goal of *Monte Carlo analysis* is to simulate the process for a particular investment and run the analysis over and over again to obtain a range of likely outcomes to assess the attractiveness of a given investment. The investment banker and client can then make a more reasoned decision regarding a specific investment opportunity.

With Monte Carlo analysis, the key is, of course, the financial model. The model should provide estimated ranges and probabilities for key variables — such as sales, interest rates, and the like. The model should also have important interrelationships (or correlations) between various inputs embedded in it. For instance, in certain industries when interest rates are high, sales may be low, or when interest rates are high, fuel prices may be high.

In Monte Carlo analysis, the financial model is run literally thousands of times — ten thousand or more “What if?” simulations being generated is typical — and the results are summarized in a *probability distribution* of returns. This probability distribution is likely to look like what we all know as a “bell curve,” often associated with education and grading on the curve. The client and investment banker can make a more informed decision about the likelihood of success of a particular project. More important, perhaps, they can see the likelihood of failure and the potential cost of failure in terms of dollar amounts. They can also assess whether they’re willing to accept a worst-case scenario.

The poet Robert Burns once wrote that “the best laid schemes of mice and men often go awry.” He obviously wasn’t referring to investment bankers’ cash flow forecasts, but he certainly could have been. Investment bankers are often prone to making overly optimistic projections regarding cash flow forecasts and underestimating the risks of things not turning out the way they would like them to. Investment bankers often talk about pro forma financial statements. *Pro forma* simply means “for the sake of form.” But how often do things go as to form?

How does an investment banker take into account the unexpected? After all, if you could anticipate something, you would input that into your model. Investment bankers aren’t required to be omniscient regarding unanticipated circumstances, but recognition that things likely won’t turn out as expected is important. There is no systematic or quantitative methodology to factor in the unexpected — remember, there are elements of both art and science to investment banking. The best remedy for the investment banker is to adopt a conservative bias in estimating cash flows. This may involve slightly tempering assumptions — perhaps something as simple as making a sales forecast a percentage point or two lower.

Many wonderful quotes have been attributed to that great American baseball player and philosopher Yogi Berra, but none more fitting to the investment banker than “It’s tough to make predictions, especially about the future.” While Yogi was more at home behind home plate than in an investment banking war room, investment bankers should consider his sage wisdom.

Many financial spreadsheets manipulated by investment bankers involve estimating and discounting cash flows for 5, 10, and even 15 years into the future. They involve assumptions about future sales growth that can be accurately characterized as nothing better than “wild guesses.” In fact, much of the value of the Internet companies that sold shares to the public in the late 1990s and early 2000s was estimated to be realized from cash flows that were only going to turn positive several years into the future. In the case of these firms, the positive cash flows never materialized.

The takeaway for investment bankers of the difficulty of predicting the future is to shorten the time horizon of many models. Focusing on the near term — the next three years — and simply making a conservative assumption about long-term future growth will serve the analyst better than projecting cash flows over an extended time period. In the case of financial models, simpler is often better.

Investment bankers want to view the world out of the windshield instead of the rearview mirror. Yet, there is so much historical information available and the easiest assumption to make is that the past will continue into the future. It may seem as good a place as any to start in coming up with a sales growth estimate for next year for a firm that realized 18 percent sales growth last year is 18 percent, but such an assumption is fraught with peril.

What simply extrapolating past growth into the future fails to take into account is a simple truism in economics: High returns in a particular industry often attract new competitors, and the influx of new competitors drives down returns in that industry. Quite simply, that’s the basis of the competitive free market economic model.

Now, the influx of new competitors is driven by how easy it is to enter and succeed in a particular market — in effect, how high are the economic barriers of entry in a particular industry. Some industries have lower barriers to entry than others. For instance, if a particular kind or style of food becomes popular, you’ll see many restaurants serving that kind of cuisine springing up and the returns in that industry tend to fall. On the other hand, the aerospace and nuclear energy industries have very high barriers to entry due to the investment in plant and equipment and the long lead time it takes to enter a market and compete.

As shown in Chapter 14, those barriers to entry may also involve brand names, as well as physical investment. Coca-Cola and PepsiCo have built huge economic moats. These economic moats — big advantages the companies have that are difficult to copy, like brands — make it very difficult to compete with those two goliaths in the soft drink industry. Economic moats can be huge barriers to new competition, even though the physical barriers to entry in the soft drink industry are quite modest.

With all the detailed financial models and computing capacity available to investment bankers, how can they make mistakes? How have financial pros been so wrong about valuing the complex real estate securities — mortgage-backed and asset-backed financial instruments — that were central to the financial crisis? How did investment bankers and the ratings agencies (like Moody’s and Standard & Poor’s) miss the boat on the valuation of these securities and not anticipate the housing bubble disaster that ultimately befell the residential real estate market?

The simple answer is that the valuation models that both investment bankers and the ratings agencies used to value and rate these securities had a fatal flaw: They failed to take into account the likelihood that one mortgage will default is related to (or *correlated* with) the likelihood that many mortgages will default.

In statistical terms, the likelihood that you’ll default on a mortgage is not independent of the likelihood that your neighbor will default on a mortgage. They’re positively correlated — and highly positively correlated at that. This is due to the fact that, among other factors, the valuations of real estate in a particular area are very much related to each other. When the market in a particular area softens, the valuation of all properties in the region falls even though some properties may decline in value more than others.

When various entities were valuing these securities, they relied on the same principle that insurance companies use to price car insurance. That is, insurance companies know that some policyholders will experience losses, but that the large pool of policyholders allows the company to diversify and predict fairly accurately the level of claims. This is because auto insurance claims typically aren’t highly related to each other. This is not the case with valuing a large pool of mortgages. The probability of default on any one mortgage is related to the probability of default on other mortgages.

The mathematics of valuation seem to make it clear that when interest rates are low — or are in the process of declining — investment banking deals look more attractive. The cash flows are in the numerator of the valuation equation, and the discount rate is in the denominator. When interest rates are low or are falling, the analyst is dividing the cash flow by a lower number and — *voilà!* — the valuation is higher.

But the linkage between interest rates and valuations isn’t as direct as it seems, and lower interest rates aren’t always better for the investor and the investment banker. In fact, investment bankers are often seduced by lower interest rates, and the volume of investment banking deals generally rises with falling interest rates.

There are two types of analysis that many stock analysts employ — *top-down* or *bottom-up* analysis. Oftentimes, the analyst adopts one orientation at the exclusion of the other. In top-down analysis, the analyst starts first with a projection of general economic activity, proceeds with an industry or sector analysis, and, finally, does an analysis of a company. In bottom-up analysis, the analyst begins with the company. Neither type of analysis is right or wrong — they’re simply different ways of getting to the same place. However, the danger of ignoring or mitigating the broad economic effects is that the analyst may ignore why rates are low and fail to adjust cash flow projections accordingly.

Investment bankers often have a vested interest in making a deal happen. After all, they’re paid to generate ideas for deals and to shepherd them through to fruition. If investment bankers can convince a client firm that the best course of action is to acquire another company or to spin off a division of the existing company, this leads to investment banking deals taking place, generates fee income for the investment banker, and results in higher year-end bonuses and larger bank accounts for the investment banker.

However, just like a golfer who is well advised to read the break of a putt from several angles, the investment banker is well advised to look at the attractiveness of a deal from not only the investment banker’s perspective but also the company’s perspective.

After cash flows from a firm or project are determined, they must be discounted back to a present value at the cost of capital. That is the method by which value is estimated and drives the entire valuation process. Chapter 12 describes the concept of weighted average cost of capital (WACC) and how it represented the average cost of a firm’s sources of financing. The concept of WACC is very simple — the average cost of capital is a weighted average of the individual component costs. But things are rarely as simple as they seem. Investment bankers can and do differ upon the weights of each component cost of capital and how those weights are computed.

Most analysts agree that basing the computation of WACC on book values of debt and equity is flawed. A better weighing scheme is based upon market values instead of the historical book values that you’ll find on financial statements. So, if the market value–to–book value ratio is two-to-one for a given firm, that firm actually has twice as much equity as it would seem by simply looking at historical book values listed on the financial statements. Likewise, if interest rates have risen substantially since debt was issued by the firm, the book value of debt could overstate the true amount of debt in the capital structure of the company.

A second point of contention among investment bankers centers around using current weightings in the computation of cash flows versus using *target weightings* — weights the firm is likely to have in the future. If the investment banker believes a firm is changing its capital structure — and investment bankers are often well informed as to this fact because they often work with the firms on altering their capital structures — then using target weights makes the most sense to generate a WACC estimate.

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