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So, a credit model is simply a framework that allows you to evaluate the credit risk of an issuer. That primarily means estimating the risk that the issuer might default. There are many ways you can do this. You can evaluate a company’s financial statements, and that means evaluating the company’s capital structure. It could mean estimating future earnings, or debt servicing costs as well. Alternatively, you can take a quantitative approach. Ultimately, the quality of the inputs and the underlying assumptions that the model is grounded in will determine the quality of the model. One very established way of doing this is to model a firm’s balance sheet. And that means assuming that a default will occur when the value of a company’s assets are not sufficient to cover the debt obligation of that issuer. The key is placing a future expected value on those assets, which isn’t straightforward just by looking at a company’s balance sheet, because those don’t reflect market values. So, what a credit model like the one from Moody’s Analytics does is model the probability that the future value of those assets will decline to a point where default occurs. One of the key inputs in determining the market value of assets is a company’s equity price, and from that, you can derive the expected market value of the assets, and then determine the expected probability of default, which is also known as the “expected default frequency.” We think that a quantitative approach is actually a very smart way to approach the investment grade market, first because it’s forward looking by nature. It’s not based on historical data, it’s an estimate of future credit risk. Second, it’s issuer and bond specific. It’s more granular than a credit rating, and it takes the issuer’s capital structure into account, as well as the individual terms of the bond itself. And lastly, because it’s driven by market based data, it provides you with signals with a higher frequency that is needed in order to identify value opportunities in the marketplace as they arrive.


So, I mentioned probability of default, and that’s really the key output, because ultimately the risk of default is what drives credit risk, and that’s what you’re trying to put a value on. Or, in other words, to understand the risk that, you, as a creditor of the firm, may not get paid back. From that expected default frequency, there are other useful metrics that can be used to build a portfolio that’s value and quality oriented with upside potential. So, first and most importantly, if you can accurately evaluate that credit risk of a bond driven by expected default frequency, you can put a price on that risk. In other words, given the embedded risk of the bond, you can quantify how much you need to cover that risk. For corporate bonds, that means how much spread is needed over the risk-free rate. And we refer to that model spread as the “Fair Value Spread.” Ultimately, default risk is pretty low within the investment grade market, and your risk of actually experiencing a default is very low, or it certainly has been historically. But what we’re mostly concerned about is avoiding bonds that have an increasing level of default risk. You want to avoid those deteriorating credits, because as the marketplace begins to recognize that deterioration, you’re going to see wider spread is in a negative price impact. So, it’s about identifying those and making sure you’re getting paid for the risk you’re taking. And related to that is another metric that can be useful for investment-grade investors as well, and that’s downgrade risk. Or, in this case, we’re talking specifically about the risk of a bond being downgraded from investment grade to high yield. Downgrade risk can be impactful for investment-grade investors, and especially for BBB bond investors, because of the distinction that exists between the investment-grade market and the high yield market. And before selling that can occur, as the marketplace begins to anticipate a downgrade in the future, you can actually parse the expected default frequency data into a forward measure of credit risks, and marry that with actual ratings data to determine the probability of a downgrade to high yield in the future. And that can be used as an additional early warning signal within a portfolio bonds to avoid the bonds with high downgrade risk.


So, there are two primary ways that the Moody’s Analytics credit model’s being used by these strategies. First is to select the most attractively valued bonds, and that means comparing the market spread to the fair value spread. If you have a high market spread relative to fair value, that means you’re getting rewarded more for holding that bond, versus what the model says you need. In other words, you’re buying undervalued bonds, and that represents upside potential. Another consequence is that you’re avoiding bonds that are overpriced. So, those would be bonds where the spread you’re getting in the market is not sufficient to compensate you for the risk that you’re actually taking. Now interestingly, we’ve found that there’s a huge dispersion in the investment grade market and the BBB segment as well in terms of pricing. You’ll see bonds that have very different levels of risk and very different levels of pricing, even if they have the same credit rating, the same duration, and the same amount of bonds withstanding. So, the Moody’s Analytics data allows us to select only the bonds that have the most attractive evaluations relative to their expected default frequency. The second screen is that early warning I mentioned related to downgrade risk. So, after selecting the bonds with the most attractive valuations, our strategies also remove bonds that exhibit excessively high level downgrade risk. We want to avoid the potential of holding a bond that gets downgraded to high yield in the future so that we can avoid the negative price impact we often see with those types of bonds. So, even if the absolute level of default risk is low, we want to avoid those bonds that could be downgraded. There’s always a portfolio of attractively valued bonds with controlled risk and upside potential.


So, Moody’s Analytics is the industry leader in credit risk modeling. Their experience goes back several decades, and they’ve drawn on that experience as well as an unrivaled data set of global default and recovery data, and also a team of thirty researchers who are dedicated to credit risk modeling. And that’s why we’re excited to partner with Moody’s Analytics on these two new ETFs, which follow a rules-based investment strategy that’s driven by the same process, the same platform, that over six hundred fifty of the largest institutions globally rely on for their credit risk management decisions.

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