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Express Video Transcript
Analyzing Financial Condition: Basics
Topics
· Top-down analysis of Intel Corporation’s balance sheet:
o Financial leverage
o Working capital
o Current ratio
· Asset risk
· Common-size balance sheets
· Take-aways
Transcript
We began this chapter with a top-down analysis of balance sheets. Remember, we started with the primary elements, analyzing financial leverage. Then we looked down at the major categories and the concept of working capital and current ratio helped us get a better understanding of the timing of the assets’ benefits. And then that wasn’t enough. We went down one level lower to line items and looking at line items allowed us to assess the asset risk — the risk that the benefits associated to the assets would not be realized.
So what’s different? We’re going to do an application, first of all, of Intel’s balance sheet, which will help you better understand how to use these concepts, and the other thing we’re going to do is elaborate on these concepts and the reason we can do that is we’ve been behind the numbers. We now have a better sense of what the asset risks are and how we can tie those to the concept of measurement and the concept of judgment and measurement. So let’s get started.
Financial leverage
Scenic video 1 looks at the financial leverage formula, and we reviewed the formula up front in that scenic route, by looking at how to compute it, and one of the things we did is we got Intel’s 2009 financial leverage ratio. But then we said, if you just look at that ratio in a vacuum, you really can’t draw many inferences. What you need to do is to say, “Well, how has that ratio changed over time?” And that was one type of benchmark data called time series benchmark data. We also said, “Well, it’s important to compare that ratio to other companies.” That’s cross-sectional benchmark data, and in particular, we looked at AMD which is the biggest competitor of Intel, and what we did is got a much deeper analysis of Intel’s ratio by comparing it to benchmark data. To learn more about those comparisons, look at scenic route 1.
Another thing we did in scenic route 1 is that we looked at the consequences of financial leverage and those consequences were flushed out in a very rich example that underscored the importance of two factors. First of all, the magnitude of financial leverage itself, which folks often incorporate into their analysis, and then second, and this is often overlooked, is the risks and rewards associated with assets. So when you’re analyzing the consequences, you must look at both of these factors and that means you need to understand risks in order to assess financial leverage. By going through these examples, we were able to underscore something we have mentioned earlier in the chapter, which is that financial leverage amplifies the owners’ share of the risks and rewards associated with the company’s assets. Very important. And because asset risk differs across industries, we also found that financial leverage ratios tend to vary across industries.
Now, there was one more thing we did in this scenic route that was very important. We highlighted two possible distortions in analyzing financial leverage ratios. One is that some assets and liabilities are not recognized on the balance sheet. In that case, if you calculate your financial leverage based on the numbers on the balance sheet, you’ll be distorting the true analysis, which also should include the numbers off the balance sheet. And the other is, even if they’re on the balance sheet, often some measures are measured very accurately, meaning experts would agree strongly on those measures. And other times the measures are dispersed more widely, in which case you shouldn’t have as much confidence in the numbers. But if you shouldn’t have as much confidence in the numbers, well, then you shouldn’t have as much confidence in the ratios that are based on those numbers. Now, that led to the notion that we had to understand the risks better and we had to understand the measurement dispersion better, which we did later in the scenic routes.
Working capital
The second scenic route focused on working capital, and you might recall that working capital is connected to the major categories — current assets, current liabilities, for example — and that working capital is fine for analyzing one company over time; but not so good for comparing across companies. There we looked at the current ratio. By looking at Intel, we found two things. First of all, it allows you to determine whether the company is meeting its current liabilities. You can think of that as covering the downside. Are they going to get into any trouble because their current ratio is too small? And the other thing it can do though is it can tell you something about the financial conditions of the company to support future growth plans. And what we saw, for example, was Intel was in very good position in this regard.
As we went into our analysis of current ratios for Intel and AMD, there were some really important lessons that came out, and in particular, the importance of understanding what’s going on behind the ratios — being able to connect the ratios back to the business. That’s covered in these examples for Intel and AMD, and in particular for AMD, there’s just some wonderful lessons to be learned there. The key concepts are captured in this quote. When you’re analyzing these ratios, you have to analyze them in the broader context of the current economic environment and the entity’s financial leverage. You need to understand asset risk. We mentioned that earlier. And you need to understand their capacity to raise money from other sources. Those lessons are so clearly underscored with the AMD example in scenic route 2. I strongly recommend you go look at them.
Asset risk:
Uncertainty and risk
Scenic route 3 is by far the longest video, but don’t let that discourage you because the concepts in there are remarkably important. They cut right to the intersection of finance, accounting, and measurement, and a good deal of that video is spent explaining concepts before we applied them to Intel.
One of the things that’s very important is to make the distinction between uncertainty and risk. These are related but they’re not the same. So what is uncertainty to begin with? Well, you kind of have an intuitive idea of uncertainty. If you expect the future benefits for an asset to be $1,500 for sure, well then there’s no uncertainty. But if they can fall along a range between $1,000 and $2,000, well then you’re pretty unsure and that’s a critical aspect of uncertainty: that there’s a range of possible future benefits, and in fact, that’s all you need to have uncertainty. But to measure uncertainty, you also need to know the probability of how those possible outcomes are distributed.
And for example here, we see that anything close to $1,500 has a much higher probability than anything close to $2,000, and that’s what we mean by measuring uncertainty. So to measure uncertainty you need two things. You need to have the range of possible values and you need to have the pattern of probabilities above them.
Now, how does uncertainty differ from risk? Well, if you look at the pictures here, it tells the whole story because the second picture down here deals with risk and the first one up here with uncertainty. And you could see that everything that’s going on in the top picture is down below, that is, every element up here is down here. So, that means that you need uncertainty to have risk, but there’s things down here that aren’t up here, and that means there’s more to risk than just uncertainty.
Now, let’s make this intuitive for you. To have risk, you must have something at risk or you must be anticipating putting something at risk. What does that mean? Well, if you go out and buy an asset, the moment you buy the asset, you put yourself at risk because you could lose money, and that’s the key concept. So if you pay $1,400, for example, to buy something today, an asset, and one of the possible outcomes is the value could drop to $1,000. Well, then you could lose $400. As a matter of fact, you’ll lose money on that asset anytime the realized benefits turn out to be worth less than $1,400 to you. So that defines your possible loss realizations.
And notice there’s no concept of loss up here with uncertainty. To have loss, you must be at risk and you’re at risk by laying down some money on the table and buying the asset or anticipating doing that and therefore anticipating the risk. Once you do that, of course you can have a large range down here of losses, but that doesn’t mean there’s a big risk. You need something else. You need the probabilities above them. The more probability there is above the loss range, well then, the more you’re at risk. So again, this is really important. Understand the distinction between uncertainty and risk. To get to risk, you must be at risk. And of course, when we’re analyzing the company’s balance sheets, we’re looking at where they’re at risk. They bought those assets. They control those assets.
Risk and measurement
Another thing that’s looked at in scenic route 3 is the relationship between risk and measurement, and what we discovered is risk is necessary for measurement to be challenging, that is difficult, but not sufficient. What do we mean by that? Well, first of all, why’s this important? Well, it’s important because accountants focus on measurement, and when you’re looking at a balance sheet, you’re seeing measures. So if you want to assess those measures, they depend on risk, which depends on uncertainty. So we need to understand risk to understand measurement problems, but having risk alone is not going to necessarily give you a challenging measurement problem.
Let’s look at some pictures that will help you out a good deal. Here we see measurement issues. Now, remember how we do this. We envision a bunch of objective experts, maybe a thousand of them, trying to measure, say, in this case a fair value, and if all the experts’ measures turn out to be closely dispersed, that is, they’re closely together, closely connected, well, then that’s not a measurement challenge. We can put a lot of confidence in that number if we see it on the balance sheet. Now, contrast that to over here on this extreme, trying to measure the fair value, but in this case, we see the measures of experts are widely dispersed. So that’s a much more challenging measurement problem and we’re not going to want to put as much confidence on those numbers.
Well, how do we go from over here to over here, and what does risk have to do with all that? Well, let’s suppose we have a really risky marketable security like a stock. Let’s suppose that one company owns stock of another company and that’s a very, very risky security. Well, is it tough to measure, easy to measure?
And the answer is, it depends. It can be very risky and still be easy to measure. What’s the key? Well, the key is to look at the market in which that stock is traded. If it’s actively traded so there’s lots and lots of trades; then in making the measurement, the experts can look at the data for several trades at a specific date, in fact, sometimes for thousands of trades near the close of that date. In that case, they’re going to all agree, or come close to agreement, on the measure. So you can actually have a very risky asset if it’s an actively traded asset, and that is there’s really good benchmark data available and get a very tight distribution of the experts’ measures and have great confidence in that measure.
Now by contrast, if you have a very risky asset, but in fact, it’s not actively traded, then you’re over here in this picture.
Benchmark data is typically market data, but we’ve seen that it can be other types of data too, and there are other ways to calculate fair value. So you look at a balance sheet and you see some numbers that are investment securities, for example, but the video demonstrates it can be receivables, it can be anything, and you say, “How do I make these assessments? How do I determine how much confidence I should put in a number? How do I decide, regardless of how it’s measured, how risky that asset is?” Well, footnotes help you. And in a later chapter, we’ll be looking at footnotes that are going to help you assess the risks and help you assess the reliability of the fair value estimates. How do they do that? Well, they give you data on the types of investment securities that are being held, and then, if you use your knowledge of that type of investment and the kind of market it’s traded in, and how risky it is, you get a much better analysis of the balance sheet.
Risk and expected return
Now, having seen how risk is tied to measurement, we also want to see how it’s tied to returns and the scenic route video does that. And it brings in one of the most important concepts in finance and that is the idea that if you increase the risk, which is going on over here, relative to over here, then you’re going to increase the expected return and that’s a really important concept. The expected return is the return right in the middle here, and so it goes from 7% here to 15% here.
This example is explained in great detail in scenic route 3. It’s a fundamentally important concept because risk and return, again, is not only important to finance, it’s very important to measurement because there’s an interaction, right, because we saw risk can affect measurement. And moreover, when we look at the return, which is an economic concept, that ties to the idea of income, which we’ll be looking at in the subsequent chapter. So really important concepts. If you’re not familiar with these concepts, look at scenic route 3. It makes them very clear. It breaks down this notion of risk and return into its simplest elements.