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With accounting earnings, we might use a somewhat complex term to explain what is meant. The concept at issue is epistemology. Epistemology is an understanding of the theory of knowledge and how and in what ways and degrees things are known. In some situations you have a very high degree of accuracy attached to a given dollar figure – for example, printed stock prices on the financial page. The degree of reliability of reported stock prices should be understood in relation to the reliability and accuracy of reported accounting earnings. With respect to reported accounting earnings, some companies apply very conservative, withdrawn, and limited accounting principles to recognize revenue in a very limited way and recognize expenses more rapidly. Other firms do precisely the opposite, recognizing revenue in an expansive fashion, while slowly writing off expenses. In the boom years of the 1990s, many firms (technology firms in particular) engaged in the latter practice of reporting current earnings at as high a level as possible through rapid revenue recognition and delayed expense write-offs. Thus, the reported net income figures of firms using greatly differing accounting principles are inherently dissimilar in a qualitative fashion and simply cannot be compared without adjustment.

Accordingly, we must apply a far lower level of reliability to reported accounting earnings (and the comparability of this figure across different firms using different accounting conventions) than we do to reported stock prices. In essence, there is a significant epistemological difference between these two concepts. And again, there are no rules: Two financial statements can have the same dollar per share of earnings and mean something very, very different in one case versus the other. So it’s hard to speak in terms of rules governing financial affairs.

If we had to list ten immutable rules with respect to finance, all ten of them would be, “Be skeptical.” Just as they say about real estate, “Location, location, location,” we should say, “Be skeptical of what you’re told” ten times. Look beneath the simplistic nature of things. There truly are no immutable rules other than obvious mathematical relationships. If you invest a dollar today, and you get two dollars back at the end of ten years, every financial analyst knows that your rate of return is 7.2 percent compounded annually. This financial relationship between inflows and outflows of funds will not change over time. To compute the present value of a financial commitment, once the inflows and outflows of funds on the commitment are known, every financial analyst will get the same answer. This is an unchangeable rule. You put the numbers in the computer and press a button, and everyone will agree as to the outcome. But it’s only those kinds of things that are hard and fast rules; everything else is highly changeable. The really important, significant, and interesting issues relate to the inputs (usually the inflows and outflows of funds). Once we can agree on these inputs, we will all get the same answers. But agreement on these inputs is not easy and depends on the many assumptions that are made in any such determination of project cash inflow and outflows.

Nevertheless, there are a few formulas, theories, and hypotheses that have risen to a high level of ascendancy in terms of understanding financial markets. Again, none of these models is believed in the sense of immutability.

CAPM, as noted, is simply a way of relating risks and returns. CAPM was originally developed as a means of valuing financial assets. However, it has been used less successfully in capital budgeting, which refers to the valuation of real assets.

There is also the Black-Scholes model, which is an options pricing model that has additionally been extended in terms of contingent liabilities and other areas. Black-Scholes has been found to be a very useful model with respect to many valuation problems.

But there have been a number of departures from both of these – the so-called arbitrage pricing theory is one. All of these models are fairly old and well used. There was a time, 20 or 25 years ago, when in the classroom we would use these terms and teach our students these things, and find that in executive development programs in the investment community, they weren’t widely used. Now they are. Beta and other measures of risk are as common in the contemporary investment community as blood pressure, PSA, and other measures of physical health are in the doctor’s office.

In essence, what were once purely academic models have now risen to the status of general acceptance in the professional business and investment communities, a development of the last 20 or 25 years. Before that time, these academic terms and concepts weren’t quite as popular as they now are in the professional world. One of the primary reasons for the adoption of these academic concepts by the professional business community is the trend for students to be trained in the academic world before entering business. The MBA is now virtually a necessary credential for entry at the highest levels into the business community. Executive training programs have become commonplace. And some of the highest-level academics are now going to work in the business world, either as consultants or as employees in some of the biggest Wall Street firms. Many academics have done that – and in so doing have fattened their pocketbooks considerably. But the overall result is a greater closeness over the past quarter century between the academic and professional business worlds.

A concept not yet developed in this chapter that has considerable current interest both in academic and professional business circles is the efficient markets hypothesis (EMH). EMH is the notion that information drives stock prices, comes to the market randomly, and is instantaneously impounded in these stock prices. A clear implication of EMH is the disutility of attempting to predict stock price movements. Many studies have tested the validity of EMH, and while some exceptions have been found to this concept, most of these exceptions are to be expected on the basis of institutional characteristics of markets and other such factors. In essence, while some inefficiencies have been found in major markets, such as the United States, this condition is somewhat unusual and rarely represents viable investment opportunities for the individual investor. In general, market inefficiencies are far greater in emerging markets than in developed markets. Yet, anyone who has lived through the last ten years understands that inefficiencies can plague developed capital markets, as well.

A word of caution: All models make assumptions. For example, CAPM assumes no transaction costs, no taxes, and, among other things, the stability of beta over time. You have to understand the assumptions behind these models, and all of them abstract and go into somewhat unrealistic assumptions to make reasonable projections of risk-return relationships. It’s not enough just to say, “I know the equation of CAPM.” That’s easy enough. You actually have to understand what’s behind the equations – basically the assumptions on which these equations have been built.

The most important thing about finance, if one had to elevate a single concept to the top of the scale, would be the relationship between risks and returns. All financial models that have come into ascendancy – original academic models that are now used in the professional world, as well – deal in terms of the relationship between risks and returns.

However, when financial analysts speak of risks and returns, they often forget about entry and exit mechanisms. People sometimes buy securities and go into other forms of businesses with respect to physical assets without a real understanding of how they’ll get out or the costs of doing so. High-level professionals certainly consider that. But sometimes in the euphoria of understanding a prospective business deal, or even equities in emerging markets, people forget about exit mechanisms. With respect to financial assets, these are things that can significantly affect risks and returns.

Particularly if we’re speaking of comparing stocks (or even bonds), we usually don’t think in terms of buying and selling costs – economists would call these items transaction costs. In effect, entry and exit mechanisms should be added to the risk- return equation. Many financial markets are very, very thin, especially in so-called emerging markets. And so, particularly with respect to institutional investing in these emerging markets, the very buying and selling action of investors can sometimes drive stock prices through their own activities. So while risks and returns are probably the two most important aspects in contemporary finance, and all models deal with these two elements, many of these models forget to explicitly include entry and exit mechanisms in the equations; it is often assumed that entry and exit costs are negligible.

It is also assumed that transaction costs will remain constant throughout, and that is not the case. Particularly in times of crises, even in mature and well developed large capital markets – our capital markets, for example – these entry and exit costs (usually now we’re speaking exclusively of exit costs) – rise precipitously. There was a time during the market crisis of 1987 when NASDAQ dealer-market-makers were reputed to have backed away from investors wishing to liquidate their positions – they simply wouldn’t answer their phones. Dealer-market-makers were required to deal once they picked up their phones, according to NASDAQ rules, so they just wouldn’t answer their phones.

And so these things are not factored into the models, and that makes it difficult to rely on just a notion of risks and returns. It’s actually far more complex than just saying risks and returns are the sole significant aspects of financial concern because there are many aspects of consequence. For example, past volatility, which we usually take as a measure of risk, and some computation of past returns or even a projection of future returns, are normally considered the elements of primary consequence. However, because markets often do not remain constant over time, the risk and return parameters of the past are frequently not accurate predictors of the future.

Valuation is, of course, the key to financial analysis. One could well have said that valuation is the most important aspect of contemporary finance, and valuation is best approached through an effective understanding of risks and returns. Further, valuation can be comprehended from many perspectives. The most common method now employed is the Capital Asset Pricing Model (or CAPM) and other related models. This approach understands the value of a capital asset as a function of the “riskless” return, the market return on financial assets in general, and the degree of risk inherent in any particular financial commitment in relation to the risk of financial assets in general.

Yet, one of the cardinal aspects about finance is that there are no rules, or practically none. There are some mathematical relationships that will always hold true. If you were to, say, compute rates of return from looking at cash inflows and outflows and you find a 5 percent or 10 percent figure, that’s a reasonably hard and fast mathematical relationship that will almost never change. We say “almost” because even here you can have some gimmickry and tricks and complexities. But other than this, basically there are no rules. And probably the single most important rule in finance is that there are no rules that can be relied upon at all times and under all circumstances.

If you look for simplistic answers, you frequently get fooled. It is often rather easy to get fooled if you are uninitiated and unsophisticated in financial matters and look to solve this problem through simplistic answers. Accounting earnings are a very good example of this, and people are finally coming to understand the qualitative nature of accounting earnings.

Corporate finance focuses on how to manage a firm’s assets and liabilities so as to create value for investors. We generally believe that firms that are most efficient in creating value will survive, and those that are not will become extinct during takeover or bankruptcy, so it is up to managers to create value for their organizations.

A major field in corporate finance is capital budgeting, which examines how firms should make decisions to invest capital. All investment decisions have a common feature: In exchange for a certain cost, the investment is expected to generate a stream of uncertain earnings. The dominant tool used in capital budgeting decisions is the net present value rule, or the NPV rule.

The concept of present value recognizes that a dollar tomorrow is worth less than a dollar today. There are three reasons for this.

First, there is a time value of money. For example, if today I loaned you $100 for a year, I would effectively give up the use of that $100 for a year. So I would charge you interest to compensate me for deferring my use of that $100 for a year. This is referred to as the time value of money. It is generally accepted that the time value of money is worth about 3 percent.

Second, a dollar tomorrow is worth less than a dollar today because most of the time the overall price level is expected to be higher tomorrow than it is today. So the purchasing power of a dollar tomorrow is less than the purchasing power of a dollar today if the overall price level increases. When inflation is expected, the rate at which future dollars are discounted includes a premium for inflation expectations. In countries with hyperinflation, such as Brazil 20 years ago, interest rates are enormous because of this inflation expectation. In rare cases where the price level is expected to decline – that I, in deflation, the “inflation” premium in the discount rate is actually negative.

Third, a dollar tomorrow is worth less than a dollar today because a dollar tomorrow is uncertain, while a dollar today is certain. If I told you that a firm is expected to generate $100 million in cash flow in five years, but that there is some risk associated with this, you would prefer $100 million today over a promise to receive the firm’s expected cash flows of $100 million in five years. This is just another way of saying, “A bird in the hand is worth more than a bird in the bush.” Consequently, the more uncertain a future dollar is, the higher the rate at which we discount it. This is why the discount rate used to value high risk firms and projects is so high. For example, venture capital firms typically apply discount rates of at least 40 percent to the future cash flows of startup companies, largely to compensate them for the high risk associated with these ventures.

So there are three reasons for discounting expected future dollars: the time value of money, inflation expectations, and risk. A major part of any capital budgeting project is selecting an appropriate discount rate, that is, a rate at which to discount the expected future dollars from a project. Discount rates vary widely across projects and firms. For example, a low risk project in the food industry might have a discount rate of less than 10 percent, while a high-risk project in the biotech industry might have a discount rate of over 20 percent.

To compute the present value of a project, one projects the cash flows that are most likely, and then discounts them by a factor related to the discount rate. Obviously, the quality of this calculation depends on the quality of the assumptions made about expected cash flows and discount rates. Anyone working with the present value model quickly appreciates how sensitive value is to assumptions about future cash flows and discount rates. That’s why we should be very skeptical of any one particular present value calculation. A golden rule in doing this type of analysis is to conduct lots of sensitivity analyses.

After calculating the project’s present value, its net present value is calculated. The net present value is simply the present value minus the cost of the project. Under the NPV rule, if the net present value is positive, the project should be accepted. If the NPV is negative, the project should be rejected. Although there are many technical complications associated with the use of the NPV rule, this in essence is how it works. It remains the dominant capital budgeting rule.

The major limitation of the NPV rule is that it ignores the value of managerial flexibility. In projecting cash flows, the NPV approach does not allow managers to ramp up production if cash flows turn out to be better than expected, or to cut back production if cash flows turn out to be worse than expected. In recent years, financial economists have developed a new tool, referred to as “real options,” which is designed to value the value of these so-called flexibility options. The real options approach attempts to apply variants of option pricing models, such as the Black-Scholes model, to capital budgeting decisions. While it is theoretically appealing, as a practical matter, the real options approach is very difficult to apply in most settings, since some of the key variables in the model are usually unobservable. In my opinion, despite its shortcomings, the NPV model, with sensitivity analysis, is still the most reliable approach to capital budgeting.