KBRA Analytics’ Post

Transcript

Hi, my name is Sean McGovern, Associate Director at KBR, a Financial Intelligence. We're here at KBR's Bank Symposium in Washington, DC and we just came off the stage, just did a big presentation up there and we wanted to share that with a much broader audience or at least a part of it. And so here we are. We're going to demonstrate K5 platform live. And to do that, we're going to introduce a little bit of a case study. As everyone knows, the Federal Reserve is now reducing interest rates. And one of the knock on effects of that is the transformation of the M1 and M2 expansion and that's the money. Reply So from 1 monetary aggregate, M2 monetary aggregate in banking terms, you can think of that sort of M1 partially composed of demand deposits and M2 partially composed of time deposits. And that's kind of what we're really going to be focusing in on today because going back to 2022 and 2023, the money supply shrunk. There was a contraction of these monetary aggregates and that had the knock on effect for banks of shrinking the pool of deposit growth that they could pull from. And so not only were banks. Raising the rates that they were paying on their deposits because just interest rates were going up, but they also had to entice depositors into putting a smaller pool of assets at. The same number of banks. So this is what we call a funding cost overhang because a lot of banks locked in time deposits for very long maturities and some of those are still expanding out more than a year. In fact, in the third quarter of 2020, four, 14.4% of all time deposits at U.S. banks had maturity dates greater than one year. And you can see in this table, we have here selected U.S. banks with above average concentration of time deposits maturing in more than one year and the deposit composition of a couple of those banks. Now where do these graphics come from? How do you get them? How do you get data like this visualized with a massive data set like PHYS? Well, it's actually pretty simple. It only takes a couple of minutes and I'm going to show you how right now here in our little demo. So this is our data wizard. This is built directly into Excel and its data can be exported directly to Excel. We're going to start building our peer group with the company Screener. You can do that manually by just typing in a bank's name, or you can take some of these qualitative identifiers like regulatory type. That's we're going to start today with commercial banks and savings banks. That gives us a really wide universe of banks. Now we need to boil that down to banks that just filed for the third quarter, whether that was a call report, Fry 9C. We have a bunch of different regulatory reporting frameworks we include. So we'll do total deposits for the third quarter of 2024 and we'll say between 10 and $200 billion and that will bring our universal banks down to just 123. But let's keep on bringing that down. Let's talk about time deposits and we'll do deposit composition. We'll say we want. Thanks. With retail time deposits that are equivalent to 15% of total deposits or more. So now we have 37 banks. That's a pretty good sized peer group. That's something we can really work with. So let's go to the field selection. We have our categories here, which are just the regulatory reports from where we pull our data. The field section will be the actual data points that we want. So we want jumbo time deposits, retail time deposits and the cost of time deposits and we want that for a quarterly frequency. And we'll just go back to the past three quarters. We could go back much further, but. For the sake of our example, let's keep it short. That will kick on right through the selected fields, and we have 12 of them. Let's preview this report. So there are banks, there's all the data we wanted on them, and just with the click of a button, send it right over to Excel. Now that it's in Excel, we can kind of see how is it displayed. Well, let's throw KBR matrix formula and that takes the banks ID could be KC or RSD, either 1 is fine, connects it to a Nemonic, which is just an identifier for a certain kind of data and it corresponds to a point in time whether it's for this is going to be the third quarter of 2024. So now what if we want to keep on getting more granular, let's say we're going to take 10 banks here. We want to look at them more closely. We move over to our commercial bank time deposit custom template. You can see we have cost of time deposits in here. We have the retail time deposits as well as the jumbo time deposits and the time deposits with the remaining maturity of one year or less. So this is everything. We can compare banks one against another. There's ten banks in here already, but we're just going to paste in our ten banks we just copied and click the refresh button. And right there we have our ten banks in here. All of their data now populates the entirety of the template. Now what if we want to just get like A1 pager here, something we can look at just altogether and really compare these banks across multiple metrics? Well, we have a table built right into the template which pulls in those ten banks automatically. And all of this data on them such as the K5 score, total deposits, time deposits to total deposits, and the increase in time deposits and the cost of those time deposits year over year, which is what? Really focused on so there's also an average for these ten banks that you can compare all of them just in A1 pager. This is super easy to look at, super easy to understand, and this can be expanded to as many banks as you want. We use 10 for this example, but it can be many more. So we move on to our next template, which is going to be the comparison of deposit composition. There's four banks in here now you can see there deposit composition. We're going to replace one of those banks. We will refresh the page to get the name in and you'll see. That it is Synchrony bank right there and Scroll down just a little bit and we will see. We also have in short versus uninsured deposits. So now that we have these four banks, let's just look at one. Let's go to the most granular level we can get and get a breakdown of one banks deposits. This is our deposit composition and uninsured deposits analysis report K5 score up in the top right corner there discover banks and currently we're going to replace that with Synchrony and you can see updates automatically. You don't have to press the refresh button. This one and that compares all of the deposit composition to peer average. This brings in liabilities and funding as well as the year over year deposit change and analysis. So we just went from a universe of thousands of banks down to 123, down to 37, down to 10, down to four and then ultimately down to 1. And we did that in less than 10 minutes. And all along that spectrum K5 has tools to help you analyze these kinds of peer groups, these kinds of banks, break them down even further.