Factors Driving the Dispersion of Ideal Measures

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Scenic Video Transcript

Factors Driving the Dispersion of Ideal Measures

Topics

·  What are ideal measures?

·  Why study ideal measures?

·  Factors affecting dispersion of ideal measures

·  Examples:

o  Fair value

o  Value in use

o  Historical value

·  Take-aways

Transcript

Introduction

In this video we’re going to look at factors that affect the dispersion of ideal measures and that’s distinct from factors that affect the dispersion of actual measures, which we’ll look at in the next video of this series. The first thing we’re going to do is to define what we mean by ideal measures - those are the measures that users would like to have if they could have the best numbers possible. They’re not the ones who are going to get it. They’re going to get the actual numbers. Now, we’re interested in the actual numbers but we’re also going to have an interest in the ideal measures because the factors that affect their dispersion are among the factors that affect the dispersion of the actual numbers.

We’re going to look more generally at other motivations of why we look at ideal measures, above and beyond the notion that they share factors. And then we’re going to look at the factors themselves that affect the dispersion and then we’re going to look at some examples for fair value measurement objective, value in use and historical value. And what we’re going to see is that the dispersion of the ideal measures can differ across these measurement objectives for exactly the same asset and then we’ll do some take-aways.

What Are Ideal Measures?

So what’s the definition of an ideal measure? Well, by an ideal measure we mean those that are created by objective experts. So when we’ve been looking at measures throughout the chapter, well we’ve been talking about ideal measures because we’ve been saying, over and over again, imagine you had a thousand objective experts, what would their measures look like? So those are the ideal measures. If preparers were all objective experts and users all share their expertise so they understood exactly what the preparers were doing, there would be no need for any restrictions such as GAAP standards for example. And the reason they wouldn’t need to restrict managers is because everyone’s objective, everyone’s expert, everyone’s honest, their users were honest, so the supply and demand for information would take care of itself without any interference on the part of regulators.

In fact, such perfection is a long way from reality. Some preparers are dishonest or stretch the truth to serve their own interest. We know this, there’s plenty of empirical data to support this - Enron, Parmalat, other scandals around the world during the early 2000s for example. Other managers are honest but they lack the requisite expertise needed about business, accounting, and users decisions to create the ideal measures. Now, you might think we’re being cruel here but imagine how complicated it is to try to apply all the accounting rules. We’ve just began to touch on them, and if you have to do this for hundreds of millions if not billions of transactions every year, with literally thousands of pages of accounting standards, well that’s tough.

Why Study Ideal Measures?

So why do we study ideal measures if we really care about actual measures? Well, understanding the factors that drive the dispersion of ideal measures helps us qualitatively gauge the dispersion and that can help us in return in three ways. First of all, it helps us understand the reasons GAAP restrictions exist in the first place and how they evolve overtime as the factors that affect the dispersions change. Let me be specific here. In this prior video we looked at adverse selection. We said there’s adverse selection when the measurement distributions overlap for, let’s say, a good company’s financial statements and bad company’s financial statements. So that a user can’t tell a good statement from a bad statement, or in our examples, it can’t tell a good car from a bad car because there’s so much room for overlap.

Well, when this happens the standard-setters basically say “Well, look. If we just look at the ideal measures, the ideal measures for good financial statements overlap too much with the ideal measures for bad financial statements. We need to do better.” And the way we can do better is by tightening this variance. And if we can’t do that through economic means, well maybe we can change the measurement methods that are available or the measurement techniques that are available and the measures that we get out, well, they’re not going to be as good as they would have been for the good, honest companies had they used the true underlying distribution. But those measures are still going to be good for those entities because they’ll be able to separate themselves from the other entities.

So we won’t get near as good a measure perhaps but if we don’t get as good a measure at least we’ll have a measure where you can separate those managers that are competent and honest and those managers who either lack this level of competence or in fact they’re totally dishonest.

So that was our big objective and if we understand the factors that are driving these dispersions then we’ll begin to know when the distributions naturally separate for example because there’s more benchmark data available as the economy has evolved, then we get separation. And when we get separation, well we actually see standards putting on less restrictions. We now allow fair values to be measured more than we did before because have better benchmark data available than we did historically. All of this is important. If you want to understand the evolution of accounting rules, it really helps to understand the theory of dispersion.

So that’s the first reason we want to know about the dispersion of ideal measures because every once in a while, the ideal measures are exactly the ones we get to see and then when we don’t, there’s typically a reason like up here, we start getting overlap either because the numbers aren’t comparable across companies or a variety of other measurement problems.

A second reason that we want to study the ideal measures is they’re a great benchmark for comparing reported numbers. So we sort of get a qualitative sense of what is that we’d like to know that is the ideal measures and then what the dispersion is around those ideal measures, then when GAAP forces us to do something that’s not ideal to get the better actual measure, well at least we’ll know for those honest managers what the measures are that we’d like to have.

We’d like to have the ideal measures. We end up with the actual measures. How far apart are these? Well, if we can gauge that qualitatively that means we can say these numbers are pretty relevant. If these numbers are relevant, if these numbers are more reliable in the sense that they measure what they’re supposed to measure, well that’s not a relevant measure anymore because the standard-setters force us away from the ideal measures, well we have to know that.

The third reason to understand ideal measures is their dispersion contributes to the dispersion of the actual measures. And we have to understand that dispersion to assess the confidence we should put in numbers. So these are three reasons we’re going to look at ideal measures. Let’s get doing it.

Factors Affecting Dispersion

What are the factors that affect the dispersion of ideal measures? First of all, there’s the measurement objective. We’re soon going to go over 2 or 3 examples to demonstrate this. If we look at, say fair values, value in use and adjusted historical cost. Well, those different measurement objectives are going to lead to different measurement dispersions. So if you’re looking at a set of financial statements and you don’t know how it was measured, well you’re not going to be able to gauge the dispersion, that’s a really important lesson. And if you do know what the measurement objective is, then we’re going to show you some questions you might want to ask yourself as you’re thinking about the dispersion and trying to qualitatively get your hands around it.

Measurement scope also matters and that has to do with the number of items that we measure as one unit of measure, we won’t be talking about that here, but it can have a big impact on the dispersion of the ideal measures. And then measurement method matters that is, even if you fix the objective but you go about measure in different ways, you’re going to end up with more dispersion. Uncertainty about the measurement inputs - this is critical. So what we studied earlier is we said, “Look. Risk, which deals with uncertainly, risk is a big contributor of the dispersion of the ideal measures.” We said that risk is a necessary condition, but it wasn’t sufficient because if there was good benchmark data available then you could have all the risk you wanted. Remember the investment securities?

But if you have really good benchmark data available, then you could narrow that dispersion and that means as benchmark data has become more available as we’ve gone to more markets around the world, more trades with global markets and we had better data because of technology and computers, well then we’d narrow the dispersion and as we’ve narrowed the dispersion, then the ideal measures have become more separated and more reliable. And that means fewer GAAP restrictions. We now allow measurements that we didn’t allow before for example and we can do that with great confidence.

So benchmark data is important. Remember there are three kinds: market prices, comparable company data, comparable historical data looking over time. And what you’re going to learn is that you’re going to understand so much about the accounting standards by understanding the availability or the lack of availability of benchmark data and how that forces restrictions on how numbers are measured.

EXAMPLES

Fair Value

Let’s look an example of the fair value measurement objective. And we’re going to look at more specifically a ten-year old building’s fair value. And what we’re going to ask our self is, what drives the dispersion of this estimation process? Now, to do that you first have to say, well, suppose you invited an expert to measure the fair value of a ten-year old building, what would they have to do?

The answer to that question when we looked at what’s behind the numbers - fair values, but let’s remind ourselves right here. So to estimate a company’s ten-year old building’s fair value, the experts would have to do the following: First they’d have to assess the building’s current condition. What kind of shape is it in, which depends on the past usage, how can we use that building? What the maintenance program is and what the quality of the construction is? So all the experts would have to come in and look at this and come to a judgment. What shape is the building in? But once we know what shape the building is in, then we have to figure out how they’re going to go about measuring it. Well, they’re going to estimate the average recent sales price for comparable buildings in similar locations or an alternative; then they’re going to use a different method. All of these factors are going to affect the dispersion of the estimates. Let’s see why.

So the dispersion of the estimates depends on, the extent to which the experts’ assessments of the building’s condition, or the commercial viability of the location differ and thus to which their assessments are what constitutes a comparable building are different. If they can’t agree on what a comparable building is, they’re going to compare that building to a different set of benchmark data, that’s critical. So that can cause a good deal of variation in their estimates simply because they disagree on the condition of the building. And again, think about the large commercial building and how you would come to very different assessments of the condition of that building and what the commercial viability of that building is in terms of the neighborhood it’s in.

The next thing is the dispersion of the recent sales prices of comparable buildings. It could be that comparable buildings’ recent sales prices are all over the place. There’s plenty of them. But for whatever reason they’re jumping all over in the marketplace. And if that’s true, that uncertainty itself in the benchmark data, well that’s going to drive uncertainty when we begin to sample from that benchmark data and that’s the next issue. How big a sample do they take? If you only sampled two buildings, well those two and these two are going to give you a different average than these two for example. But if you sample all of the population then you’re going to get a real good estimate of the average.

So right away we see that the size of the sample, the dispersion of the underlying thing you’re sampling from is going to lead to a different dispersion and that’s if everyone agrees on how they should go about doing this. That is, look at comparable buildings and take the average sales price for the comparable buildings. But what if they don’t even agree on that and they come up with alternative methods for coming to some assessment of this building’s value? Well, if they do that adds more even more dispersion. So what we see is the method can matter, the sample size can matter, the objective can matter, all of these things are fundamentally going to affect the dispersion.

Now, here are a couple of examples to help you refine this. Suppose you were looking at the estimates of a ten-year old building’s fair value. But now let’s give this fact pattern over here where we’ll help tightly distribute a distribution. And that means you can have a lot of confidence in that number if you are a user of the financial statements. Let’s suppose experts generally agree on the comparable building, no problem there. There goes some dispersion. There’s a large number of tightly clustered recent sales prices of comparable buildings, so we’d look out there, there’s thousands of buildings, they look comparable and all the prices are really tightly together and then experts average a large number of these recent sales prices when estimating the fair value.