In Module 2, we'll look at default trends and market indicators. What we're now going to do is step beyond analysis, and then look at data trends over a period of time to look at default rates. Do they peak? Are there clusters of default? Are there periods of stability? We're going to look at this in terms of the entire corporate industries globally, US, by certain industries. We look at some trends, were there periods in the dot-com craze when the default rates increase? What about the financial crisis of 2008, 9 and 10, what about default rates there? Then of course, what about the pandemic and global recession of 2020, what happened to default rates? Now we suspect that the operating environment deteriorates, when the macro-economy deteriorates or any number of operating environment factors, it could contribute to increases in the default rates. We do analysis, but we also want to look back as we did in the first module, we'll look back a little bit more closely to see what's going on in terms of default trends among corporates over the last decade or so. We're also going to look at market indicators. Market indicators is that, you have bond markets and credit markets. You have credit models that are also looking at companies, not just the rating agencies, but other types of markets, and they're forming perceptions. They have a viewpoint of possibility of decline or higher probability false for certain names. I want to get a sense of what they are thinking. That's not a substitute for analysis, but it can complement the analysis and risk management that we actually do. In some ways, a negative impact on the company by the market, the bond market, the loan market, the bank market, the equity market, the stock market, the derivatives market, that negative impact could shape the company as it goes full it. It could have impact to the extent that it might limit the amount of borrowing that the company can do as it goes fall. That negative perception, even though it may not be realistic or fair, it could lead to certain problems in the future. As I said, another reason why I like to look at market indicators is that other analysts, other traders, other investors, and lenders might have a more acute understanding of the risks than I do, or a more conservative interpretation of the risks than I do. If I rate a name, a single A, Fitch rates the name a triple B, and the market rates the name based on probability to fall a triple B minus. I want to get some sense, what are they seeing that I am not seeing while I'm looking at the name at the same time. That's how we use market indicators. Let's start with Lesson 1. This is default trends and credit market. In Lesson 1, we'll look at current issues and topics as it relates to global debt markets, including corporate default. We'll take a peek at what has been going on in 2020 in terms of debt defaults and bankruptcies during the pandemic, and then we'll look at some other periods of stress. We've seen periods of stress in corporate markets, debt markets, company exposures. We've seen that in the financial crisis of the mid 2000s, of course, in the dot-com years of 1999 to about 2002. Then we'll try to see whether defaults do they occur in clusters, is there some form of correlation? See how we could project or predict, at least in markets, if not a name by name, when there could be a rise in defaults. We certainly saw a rise in corporate defaults last year. How can we project a rise in defaults in the market place as we go forward? Then lastly, in lesson 1, we're going to look at credit deterioration versus default. Default is non-payment or non-performance. Credit deterioration is, as we proceed from quarter to quarter, period to period, is there deterioration in the financial condition or an increasing higher default probability? Credit deterioration is, the company is still viable, it is still solvent, but the probability to fall has actually increased. Let's look at some trends. This is going back over about 40 year periods. You can see that whether this is in the US or whether it's global, that we do go through periods of peaks and valleys. A lot of this is scored as I had mentioned. A lot of this is defined by the global economy, the global operating environment during that period. If we go back to the early '90s, you can see that there was global recession during that period. We go through here up to the turn of the century, this is '99, 2000. I had called this the dot-com craze. But this is also where we had the Russian debt crisis, we had foreign currency, Asian crisis during that period as well. As we look at what's happened in the market place, it does appear that defaults tend to appear in clusters. This leads to the concept that we'll talk about little bit more closely in Module 3 is default correlation, the domino effect. Every exposure has a default probability, but what's the probability that they will all default at the same time? That's what we've talked about in terms of default correlation. I can do the analysis to default probability of a five years can be 0.1 percent. Another company does the analysis and default probability is 0.2 percent. But if I look at this in terms of what's the probability that if they're correlated in some way, that they can also happen at the same time? Default clusters and default correlation. Obviously, you see here that's the crisis of the mid 2000s, the global recession, the great recession, the financial crisis. Then if this were to extend out to 2020, we're going to see another cluster of defaults as a result of global recession and the impact of pandemic around the globe. What we also see here, again, this goes back to my peaks and valleys as it takes into consideration a little bit more recent data that include beyond 2011, we can see what helped cause defaults. Some of this is related to bankruptcy. Remember in the previous lesson in Module 1, we talked about how do we define default? If it's certainly in bankruptcy, that would be a definition of default and you can see more specifically what were some of the reasons behind the fall during this period. You can see that in 2009, a number of defaults are basically bankruptcy related, a number of defaults are based on missed principal and payments. Some other defaults are also related to regulatory intervention as well. As we get towards 2018, '19, and 20, we see the recovery period after the financial crisis. Then we get at the beginning of the crisis. This takes to 2020, but it's the beginning of the pandemic crisis of 2020. In some cases, companies can foul themselves, let's keep that in mind. When we talk about bankruptcy, a company itself can file for bankruptcy to ask for reorganization. It's creditors can put it into bankruptcy as a part of their default acceleration after a mispayment. Or in other cases, companies might miss a payment of principal and interest, and that's a default, but it's not yet in bankruptcy. It is definitely a bankruptcy and the banks creditors can accelerate, but it's not yet in bankruptcy as well. That goes back to our definition of what actually constitutes a default. That's what I'm emphasizing in this course, is that when we have that declaration of default, it does give the financial institution, the lender or the investor, the right to take action. Then that action could lead to a renegotiation, a settlement, or an exchange of debt, or actual bankruptcy and liquidation. Let's look at these default trends in terms of non-investment grade versus investment grade. Let's keep in mind, investment grade as defined by the rating agencies, would be BBB- and higher. So that's the convention, BBB- and higher from Fitch, S&P, and Moody's. You can see that default rates are expected to be lower for investment grade in part because these are stronger firms, stronger obligations, more stable operations and earnings and cash flows, and also higher ratings do tend to have lower default frequencies, and then of course, lower default probabilities. We would expect for all the reasons that we come up with the analysis of a non-investment grade name, what we call the high-yield names, we would expect there to be higher defaults and higher defaults. Default probability, we're going to look at default frequency. That's the takeaway from module 1, and this looks at it a little bit more closely. The other chart show default trends, but now I'm going to start looking at this in terms of industries and ratings. You can see that as we go through economic cycles, as we go from period to period, that investment grade names are subject to default, but they're not going to be as high as non-investment grade. Intuitively, we know this, in practice, it actually shows that. From year to year, our chart show that default trends fluctuate, but then they're also very much high to whether they're non-investment grade or investment grade names. This is presented in very simplistic fashion. In practice, there could be some names that are borderline or have split ratings. What I mean by that, a name could be a BBB- from S&P, but it could be a BB+ from Moody's. In one respect, it's an investment grade name, in the other respect it's a non-investment grade. There's some polishing of how we present this particular material, in part because rating agencies don't necessarily reflect the same rating. Now, we can look at this over a period of time. This was looking at it from 2001, 2006, 2009. Now let's go from 2011-2015. Again, as we had said that global default rates appear to be very much tied to global economies and you have operating environment, but they're very much tied, of course, as we would expect and the data shows this, tied to whether it's investment grade or non-investment grade. Investment grade names continue to perform as expected by the time we get to 2015, and they deserve the ratings that they earn. Note that in certain years, the investment grade default rate was actually zero percent, and certain years it could be as high as 0.03 percent as you see here, or as high as nine percent as we saw during the Great Recession in 2008 and '09. Overall default statistics may also reflect declining performance of one or two significant large bars. What I'm saying here is we're looking at data, we're looking at charts, and we're summarizing and aggregating the data, we're trying to draw some conclusions. But we have to keep in mind that to a certain extent, some of the numbers can be out of whack, some of the presentation of the data because in a certain year, you had a very large default or very large declaration of bankruptcy in terms of the total aggregated amounts. Let's just keep that in mind as we look at data from year to year. A lot of these numbers are corporate-related. If we included financial institutions, they would also include Lehman Brothers of 2008. Which actually happened to be a single a rated name when it filed for bankruptcy in September of 2008, which turned out to be one of the largest bankruptcies in history. In terms of that default, that could distort some of the statistics as it relates to single eight names defaulting in that particular year. But we can still look at the data and trends. You can see here, now I'm going from 2014 up to 2019. I'm looking at S&P data, and I'm noticing again when I expect to see investment-grade names tend to perform well, as we would expect, and non-investment grade there's some fluctuation in terms of default rates. Let's also keep in mind, investment-grade names doesn't mean that they are susceptible, they are not susceptible to risk. It doesn't mean that they're not necessarily performing up to par, it's just that there's still ongoing institutions that can meet principal and interest payments. There could be deterioration and investment-grade name can go from triple B to double B or single B and become non-investment grade, but it hasn't defaulted yet. That goes back to when we come back and revisit the concept of credit deterioration versus credit default. This is a little bit more specific. I'm looking at some of the non-investment great names, and now I'm focusing on the non-investment grade names, but I'm focusing on the rating per se. I'm looking at trends. I would expect, as you see here, the default rates for the triple Cs to be much higher than the single Bs and then a little bit higher than the double Bs. I would expect, this is where I'm going to have my default rates. Again, this shows the prominence and uselessness of coming up and deriving a rating. We derive the rating and guess what happened? What we'd expected to happen, did happen. That the large number of defaults, the real cluster of defaults really occur with the triple Cs to double Cs, to single C names. As I had said, this is the type of data that we can use this very carefully. We're going to use this very carefully. You see from year to year there are fluctuations in default frequencies. There is fluctuation. Then I could take an average, I can take a weighted average, and then use that default frequency to determine what should be the default probability of a double B or a single B. What should be the default probability? There's a lot more art and science beyond just taking the data and then using that as a default probability. As I said, let's keep in mind that sometimes some of the trends could be distorted because there can be cases where a single, as you saw in the financial decision where the Lehman Brothers, can file for bankruptcy or a double B name can file for bankruptcy as you see here. What I also want to remind ourselves is to say that, what we don't do is rate a name as part of the analysis. In the analysis, as we're looking at earnings and cash flow and balance sheets, working capital, and capital structure and debt levels, we don't come up with a probability to fall and then map that into our rating. This is our reminder once more, we do the analysis and come up with the rating and then map that into a probability of default. At least that's the conventional way of how we approach this.
