Claude for Finance: Building a Live Merger Model with AI

How good is AI at building investment banking models? 

In this episode, Debs and Graham put Claude for Excel to the test by prompting it to construct a full merger model from scratch, using GameStop's $56 billion bid for eBay as the live case study, but with the focus squarely on the AI workflow rather than the deal itself.

Graham walks through the merger model framework from first principles before opening Claude for Excel and giving it a single instruction: build me a merger model for the proposed acquisition. 

What follows is a live demonstration of what AI can and cannot do in a real M&A modelling workflow.

The verdict is nuanced. Claude sources factual data quickly, structures the model sensibly, makes a credible first pass at sources and uses, and saves the kind of analyst time that used to go into manual press release scrubbing and 10-K data extraction. 

But it also makes errors that anyone trained in proper modelling would catch immediately, hardcoded assumptions buried in cell formulas, fiscal year mismatches between acquirer and target, missing synergy inputs that were publicly disclosed, and modelling practices that would never pass a senior banker's review.

The takeaway: Claude for Excel is a powerful first-pass tool that can compress hours of analyst work into minutes, but it is dangerous in the hands of anyone who cannot audit the output. 

The fundamentals of modelling, accounting and finance still matter - arguably more than ever, because the cost of accepting AI output without scrutiny is now embedded in every workflow.

Key Discussion Points:

Merger model framework: accretion, dilution, sources and uses, pro forma adjustments, LTM calendarisation. 

Prompting strategy: what a minimal prompt produces versus what structured prompting would deliver. 

Where AI saves time: factual data sourcing, model structure, first-pass build. 

Where AI fails: modelling best practices, hardcoded inputs, technical errors, judgement calls. 

Stress-testing in real time: how to use AI to iterate on synergy, consideration mix and financing assumptions. 

AI in finance careers: why the fundamentals matter more than ever in an AI-enabled workflow.

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Transcript

SPEAKER_01 0:00

So today we thought we'd just live see what Claude for Excel does and puts together if we ask it to build a merger model.

SPEAKER_02 0:09

And which deal are we going to be building a model for, Graham?

SPEAKER_01 0:11

The hottest MA, the best MA deal in the market. It's the ingenious GameStop acquisition of eBay.

SPEAKER_03 0:19

GameStop has made a bit of a crazy offer for eBay, a company four times its size.

SPEAKER_01 0:24

In a lot of cases, it's not really clear immediately if something's going to be accretive or dilutive. You actually have to run through the run through the model math to figure it out.

SPEAKER_03 0:33

The merger model allows us to see how the business would look combined, how they would look, and what some of the key metrics that investors look at.

SPEAKER_01 0:41

GameStop cash available, 800 million. I thought they had a ton of cash devs.

SPEAKER_03 0:48

This step as an analyst would take you quite a lot of time to bring all this information together, and it's done it in just a matter of minutes.

SPEAKER_01 0:54

A hundred percent.

SPEAKER_03 0:57

Welcome to all of Alice Ness. Welcome to this week's episode of What's the Big Deal, where we take a look under the hood at major deals in the public and private markets and explore finance industry developments. My name is Debs Taylor, and I'm going to use my experience from my career in investment banking to bring a public markets perspective to our discussions.

SPEAKER_01 1:14

And I'm Graeme Smith. I'll use my background in investment banking and private credit to bring the private market perspective here.

SPEAKER_03 1:22

Excellent. I think we're going to have to draw on quite a lot of our experiences to talk about today's deal. Graham, what is the big deal this week?

SPEAKER_01 1:30

So today the big deal is we uh we've been talking a lot about AI, obviously. Uh we we've done some live modeling together. I mean, not really modeling, we've done some deal analysis together in Excel. So today we thought we'd just live see what Claude for Excel does and puts together if we ask it to build a merger model. So a little bit of live demo with some of the some of the new AI Excel tools.

SPEAKER_02 1:55

And which deal are we going to be building a model for, Graham?

SPEAKER_01 1:58

What else do you think the hottest MA, the best MA deal in the market that the market has ever, ever seen? God, I sound like Donald Trump here. Uh obviously, it's the uh it's the ingenious GameStop acquisition of eBay. Like what else, what else could we do?

SPEAKER_03 2:16

It is so hot. Uh, and I cannot wait for this episode. I think it's gonna be an interesting one. Uh, we will start off with a quick summary of the deal, just in case you haven't been keeping track of what's been going on in the markets. Uh, GameStop has made a bit of a crazy offer for eBay, a company four times its size. And actually, bizarrely, that's that isn't the craziest thing about this deal. Um, the second thing we'll do is, well, we'll have Brahim live on air uh showing us how to build a merger model using AI. And I promise the numbers will surprise you. They might surprise us as well. Who knows? Birdling. Um, we'll have a quick look at uh the latest on the deal where eBay has rejected the offer from GameStop on the grounds that it's neither credible nor attractive, which really is quite brutal as a rejection. Uh, we'll find out why they've said this. So let's kick off with a quick summary of what's been happening, what the deep what the offer on the table is from GameStop. Um, so we've got GameStop offering $125 per share to eBay, and it's a mix of cash and stock, 50% cash, 50% stock, valuing eBay at $56 billion. That's almost four times GameStop's market cap. And that pretty much makes it hostile to take over. So um, it is an interesting deal. Um, the financing for the deal, well, GameStop, they're sitting on about 10 billion of their own cash. And they've also highlighted a commitment letter from a bank for about $20 billion of debt. Uh, but we'll see later on maybe why this is less reassuring than it sounds. There is going to be a lot of equity to finance a deal, um, and that's gonna mean that GameStop has to issue a whole load of new shares as part of this share consideration. So that is gonna create a lot of dilution as a result of the deal if it goes ahead. Um, and the amount of dilution, just to put it in context, well, we're gonna end up with GameStop's shareholders owning roughly a quarter of the combined business and eBay shareholders owning about three-quarters of the combined business. So very dilutive as a deal in terms of the shares. Um, and in terms of strategic rationale, this is a really interesting one. Um, I think the main strategic rationale is to use GameStop's retail network, their physical stores, as a drop-off and fulfillment hub for eBay's transactions. But I know their CEO also talks about expanding their market for collectibles, which both eBay and apparently GameStop also have quite a big presence in. So there is some strategic rationale there, but the big thing really is the synergies. Uh the CEO has talked about potentially huge synergies for the deal, about $2 billion a year, and achieved within 12 months of the deal closing. So some big numbers being bandied around. Uh, we're gonna put those numbers to the test, I think. Um, Graham, do you want to start us off with, you know, building a merger model? And in fact, let's start off with what a merger model actually is.

SPEAKER_01 5:18

Yeah, so merger model, I mean, both of us, I'm sure, have done plenty of these. I mean, I did my fair share in my investment banking days back way, way back in the day now. Basically, what we're trying to do is put two companies together. And in some ways, it's as simple as A plus B equals C. But it's really, it's really more a matter of A plus B plus some adjustments equals C. And when we're talking about the combined entity, we've got a bunch of different metrics that we are that we can evaluate. The thing that we're most commonly looking at is earnings per share for a public company. And in particular, is the deal going to be accretive to earnings per share or dilutive? And all that means is our EPS going up or down. It's kind of as simple as that. So we think about some of the transaction adjustments we make. Say literally we're going to take company A net income, add company B net income, divide by pro forma shares outstanding. We think, all right, in the in the numerator and the income adjustments, we've got adjustments for things like synergies, maybe new interest expense on debt we're taking on, lost interest income on cash we're using. That, that kind of that kind of adjustment. And then the main adjustment we're making to the denominator is how many shares are we going to issue to finance this transaction? Now, obviously, as Deb's mentioned, this proposed, proposed merger that's never going to happen has got a huge component of equity. GameStop is issuing a ton of stocks. So we're going to see some adjustments to both the numerator and the denominator. Uh, because in a lot of cases, it's not it's not really clear immediately if something's gonna be accretive or dilutive. You actually have to run through the run through the model math to figure it out. Um so that's the that's the high level. And we're not even gonna get into any of the kind of more complicated balance sheet adjustments that we talk about when Debs and I teach this in the classroom to to people who are actually say starting to work at investment banks. We're just gonna keep it simple for today.

SPEAKER_03 7:16

Um so Crayon, there's one thing you just said there, pro forma. Let's just uh be clear on that. What do you mean when you say pro forma adjustments?

SPEAKER_01 7:25

Pro forma just means these are these are the adjustments that we are gonna make when we put something together. This is like the the new, it's the new company, it's the new it's the new metric we're looking at. So pro forma, we use this, we use this concept or this term all the time in finance to basically just say, all right, if we're starting, we're starting with something, say standalone or something that's unadjusted, by the time we make our adjustments and we're looking at the new, the new figure we're analyzing, this is the pro forma number.

SPEAKER_03 7:57

Okay. So you're basically saying that the merger model allows us to see how the business would look combined, so GameStop and eBay together, how they would look and what some of the key metrics that investors look at, how they would look on a combined basis, taking into account all the effects of the transaction.

SPEAKER_01 8:15

Exactly.

SPEAKER_03 8:16

Okay, great. Well, that sounds simple. Um, you say we're gonna do this with AI. Tell us a bit about how we're gonna start off. What do we surely we need some really structured prompting? We need lots of inputs to be provided. How are you gonna go about this, Graham?

SPEAKER_01 8:31

I mean, we do. I mean, look, if we were doing this, if we're doing this for real, then we would have a really structured prompt. And like say we're just saying we were doing this in an actual day job in an investment bank, we would put a lot of effort into engineering the prompts here, maybe collecting some data that we wanted, in this case, we're gonna use Claude to use to put the merger model together. As kind of a case study exercise, we just want to see what happens when you tell Claude, literally build me a merger model for the proposed merger of GameStop and eBay or proposed acquisition of eBay by GameStop. I just want to see what happens. Because by the way, I feel like this is something like these tools have come on so much in the last year, two years. Like, can you even imagine being able to give a prompt to GPT or Claude like this, like a year or two years ago? I I could imagine it being semi-helpful, but not to the point where you could just you could just prompt it and say, hey, go off and do it. So let's see what we get.

SPEAKER_03 9:29

Absolutely. And I think this does show the differences in our personalities. When you suggested doing this live, I was like, absolutely no way to go. I mean, I know that we you know we we use it daily, don't we? This stuff. Um, but actually, you don't necessarily know what you're gonna get. So let's go for it and see how it works.

SPEAKER_01 9:46

You don't. And of course, I like I teach with this a lot now. Uh and a lot of a lot of what I do in the classroom is there's a lot of trial and error because uh we're still we're still in the phase where things are improving, right? So you don't you don't necessarily know what you're gonna get out of a lot of these tools. Some do some things better than others. Um there are you know, there are some ways that I think we can kind of rely, rely on these AI tools to do a pretty good job. But in general, what I found is there are some pretty specific applications and there's a decent amount of prompt engineering you've got to give these tools to get something that's like actually quite usable. So let's just see what we get. All right, I'm gonna open up Claude and wait for it to load. My computer sucks, I'm not gonna lie. Um build me a merger model for the proposed acquisition of eBay by GameStop. I'm getting asked my my first question. Now, I'm I'll I'll be honest. I did we did try this out before to see what we got, and by the way, it was different every time. So this is still gonna be a pretty, pretty live reaction. Deal consideration mix. This this is probably this is probably the aspect of this deal that is the biggest meme right now, just because the GameStop CEO had that crazy interview on CNBC where he basically just said 50-50 cash stock for like an hour. Um repeat. Right. Yeah, exactly. And it doesn't seem like Claude has figured this out. So I'm gonna say find this from public sources and oh, pull from my filing.

SPEAKER_03 11:31

I always like to give an answer. That's interesting. You just say find find your own answer.

SPEAKER_01 11:37

For things it it depends, right? For things, for things where obviously that that that component of this deal is very, very well out in the public domain. I'm always also interested to see how good it is at finding that kind of stuff. So here we go, right? SEC filings, assumption sources sheet, standalone financials, sources and uses, combined pro forma accretion, dilution, sensitivity tables. Okay. Something went wrong with your request. Please try again or start a new chat. Okay, there we go. Maybe it's maybe it's the internet here in this hotel room and I don't know.

SPEAKER_04 12:31

We'll see.

SPEAKER_03 12:39

So while it's working, should we start looking at the tabs which have been updated, or is that like kind of jumping the gum?

SPEAKER_01 12:45

Yeah, let's uh let's just go through some of the assumptions because you know you know some of the you know some of the figures here, right? So like let's let's go through and see if it's at least pulled some of that. 50-50 cash stock, okay. It got something, right? Okay. Share price, GameStop $22, diluted share count. I mean, I don't know, I don't know if you've checked if if you have any of these to hand. It just kind of assume that. Well, see, actually, one good thing I've noted about Claude's Excel tool, by the way, is that it actually inserts comments in cells and tells you where it where it got stuff. I mean, for things like a diluted share count, it's just grabbing this stuff from a 10K, 10Q. You know, if you're doing this for real, obviously we would we'd do our own diluted share count calculation. Um for the sake of this, just build me a merger model from scratch with no other kind of guidance. Let's just kind of run with this and and assume this is good enough for the time being. eBay share price. Uh this is share price, not offer price. This is as of May 13th. That's yesterday. Offer price per share 125. Is that right?

SPEAKER_03 13:57

Yep, that's correct.

SPEAKER_01 13:58

Yeah, okay. Equity purchase price 56, 57 billion.

SPEAKER_03 14:02

Just one thing to say is on the premium, it looks quite low there. Normally we see premium of around 20, 30 percent. That's because the share price has risen since the announcement. Uh, I think the premium was closer to around 25, 27% when the deal was actually announced.

SPEAKER_01 14:19

Um, so just to flag that, that normally it's kind of much higher than what's I mean, it's it's by the way, it's typical for the target share price to rise after an acquirer announces that they're going to complete a transaction. I'm actually kind of surprised personally that eBay's share price rise rose hardly at all just on the basis that this offer seems so outlandish.

SPEAKER_03 14:43

Um, because it reflects the probability that the deal will actually go ahead at that price.

SPEAKER_01 14:49

Yeah, yeah, exactly. I know, I know, seriously. Uh okay, but we got the right, we got the right offer price, 50-50 cash stock mix. Uh it's you know kind of run its calculation based off of based off of eBay's market cap. Of course, Scott, we're talking market cap offer value, not enterprise value. So we'll see what it's done in sources and uses with eBay's, uh eBay's net debt. Um we were talking about this before, before we got on the phone. Like, not that from a first, a first pass accretion dilution analysis, we almost really need to have that on our sources and uses on the basis that the impact of that, of that, of that eBay debt is gonna get netted off anyway. Like either we've got to just assume it or we've got to raise new financing to refinance it. In either case, it's not really gonna change. It's not gonna change much about our accretion dilution analysis unless we've got materially different lending costs from one to the other, which here you'd assume is probably the case, to be fair. Okay, financing. GameStop cash available, 800 million. I thought I thought they had a ton of cash debs. I thought they were quoting that like nine billion of cash.

SPEAKER_03 16:14

Yeah.

SPEAKER_01 16:15

Which I I mean, which I just I kind of just almost I'm not gonna say I don't believe it, but it just seems crazy that GameStop has nine billion of cash. But here we go. I'm looking on their on their standalone financials, cash equivalents. This is 11125. I was I'm doing a double take. I'm not sure if this was in European or American date format for a second. Uh $8 billion of cash. What's in our sources and uses? GameStop excess cash, $300 million, new debt issued. So this this doesn't really look right, does it?

SPEAKER_03 17:03

It doesn't, does it? Uh right.

SPEAKER_01 17:07

I thought Yeah, I thought GameStop, I mean, especially, I mean it got there, you know, let's assume, let's assume as of November 1st this was the right cash number, 7.8 billion. Um oh and sorry, I've been I'm not even looking at the line below, cash and and marketable securities. You got $8.8 billion of cash. So there's there's our nine billion dollars of cash, right? So why why in our sources and uses?

SPEAKER_03 17:34

So do you know what the best thing I think is you can actually ask Claude. Have you thought about just set saying?

SPEAKER_01 17:41

Yeah. GameStop has nine billion of cash per your last uh financial statement input. Why is this not used on the sources and uses fixing? I mean, by the way, so here as we're as we're kind of going through this and and talking about it, and you know, now now this is looking this is looking a bit more a bit more reasonable in terms of sources and uses, right? Because we know, you know, if you've if you've read the news or followed this deal, you know there's a there's basically a letter from a bank saying we'll we'll fund about $20 billion of of new debt if this deal happens. So there's our you know, there's our 19 billion of new debt in our in our sources and uses. You know, here here this model is assuming there's $2 billion of eBay excess cash that we can use. Uh like what's what's actually in eBay's eBay's got what, $3.5 billion of cash. So maybe, maybe that's a reasonable assumption. You know, question mark, not not too crazy. Um like but just as a as a quick kind of break here, like one thing one thing about these tools right now is they're incredibly powerful. I mean, like talking about this before, like being able to just do this based off of a quick prompt. If we'd actually taken the time to properly, properly prompt and get something really specific, I think we'd probably get a much, a much we definitely would get a much better result. We're having to iterate a little bit more right now. But I think a lot of people are very, are very trusting of a lot of these tools quite quickly. I've I've noticed that in the classroom recently a lot. Uh and I basically had to say, did did did Claude or GPT do this? And they're like, yeah, yeah. I'm like, you know, like it's great, great for a lot of stuff, but still requires some manual intervention, manual intervention to make sure, make sure you know what you're actually getting. Um there's some pretty fundamental, yeah, it hasn't hasn't taken much to get us in a better place, but pretty fundamental errors here that if you didn't know what you're looking for, you might just you might just gloss over.

SPEAKER_03 20:04

So I think this is actually, with a bit of correction and guidance from us, done a pretty good job. And this is absolutely fundamental to our model, isn't it? The source and user funds is a key starting point. You know, it basically says how much cash is going to be coming in to finance the deal, how's that cash being deployed? We can now see the numbers. The numbers look pretty sensible to me. And it kind of sets you up for then doing the next step of the model, which, you know, this step as an analyst would take you quite a lot of time to bring all this information together. And it's done it in just a matter of minutes. So that's a pretty good step.

SPEAKER_01 20:37

100%. 100%. Yeah. I mean, you know, the I don't know about you. I found when I was an analyst, the thing that took the most time was not necessarily pulling in stuff like consensus forecasts, because like there was always cap IQ and fact set and that kind of stuff to automate that pretty easily. But any kind of scrubbing of press releases, trying to pull, pull external sources and say, okay, what did they say about the the funding sources, like the debt they were going to bring with them? Like all of that stuff just took a long time because you had to pull it manually and copy paste, triple check that it was right, all that, all that kind of stuff. Whereas this has saved quite a bit of time just right off the bat. So we're saying, all right, we've got we've got 50, 57, 57 billion of uh of cash we got to come up with. We're we're funding this through, you know, about $20 billion of debt. We've got $8.3, you know, $8.3 ish billion dollars or saying of excess cash used. I guess it's just applied some kind of some kind of minimum cash. It's assumed that we're gonna use $2 billion of eBay's cash to fund the deal, and which in essence is basically uh just a dividend getting paid out to to eBay shareholders.

SPEAKER_05 21:58

Uh-huh.

SPEAKER_01 21:58

Uh and then the rest is getting. Is getting funded through new stock issued by by GameStop. So GameStop rather. So, you know, not horrible as a as a first pass. All right. Okay. Let's uh should we take a look at the what is done on the Proforma tab here?

SPEAKER_03 22:18

Yeah. So you mentioned the ProFormer is kind of showing the numbers kind of squished together as if it's already a combined business. So let's just why don't you just tell us what we're looking at here?

SPEAKER_01 22:29

Yeah. Actually, you know, the way just like looking at this kind of like really quickly in terms of the whole A plus B plus some stuff equals C, right? You got A plus B plus the stuff, the adjustments in this adjustment column is your pro forma. So let's see. It's actually kind of a kind of a helpful, kind of a helpful layout. And sometimes in terms of some of our teaching materials, it doesn't look that that dissimilar in terms of, you know, you've got an adjustment column, and what are we going to put in there? So revenue, we're basically just adding the two together, cost of sales, adding the two together, gross profit, we're saying that's the same. It's making some adjustments here for operating expenses. So here's here's one area where you compare to what I know has been put forward in the in the public announcements, and this just seems massively, massively understated based on uh based on what they've what they've said. Oh, as soon as you realize 75% of run rate in nine months.

SPEAKER_03 23:28

Yeah. So it's done everything on a nine-month basis. So I wonder if we need to go back to Claude and tell it to run it on a last 12-month basis to make the numbers a bit more realistic because we want annualized numbers, don't we?

SPEAKER_01 23:40

You did the pro forma on nine months, year to date, do everything on LTM. And also, we were talking about this just kind of in the prep, just the calendarization aspect here. We'll see if we'll see if it'll do this well. Basically, when we're looking at accretion dilution, we're looking at accretion dilution for GameStop shareholders. So we're gonna take earnings per share for GameStop as of their fiscal year. GameStop and eBay don't report in the same fiscal year. So we've got to, we've got to calendarize. We get on the same, we need to get on the same basis. LTLC, do everything on LTM basis and calendarize eBay to GameStop.

SPEAKER_03 24:22

So and that calendarization, actually, it's not a complicated calculation, is it? Because you're just basically saying, let's take, you know, some X percent of eBay's numbers from one year, remaining percentage from another year, just so that it's as if they had an end of January year end. The calculations aren't complicated, but it's just another thing that you have to build the calculation. Well, you used to have to build the calculations for. Whereas now, as we've just seen, you've just done that by prompting Plaude, can you adjust eBay's numbers to GameStops year-end? Done. Yeah, it'll do all the heavy lifting for you.

SPEAKER_01 24:58

So while it's doing this, the the the synergy number they talked about is nuts, right? Was it $2 billion or something like that?

SPEAKER_03 25:05

Within 12 months. Don't forget that fact, which is pretty incredible. Normally, when we're looking at deals, we're thinking three years for most synergies, sometimes out to five years. And in fact, we used to always assume no synergies in the first 12 months, just costs for that that initial kind of integration process.

SPEAKER_01 25:24

So exactly.

SPEAKER_03 25:25

I was a bit flabbergasted, I think, is the word to see that uh suggestion that they're gonna get all of those synergies within 12 months.

SPEAKER_01 25:34

I mean, it's it's completely it's completely outlandish. And what let's see, this is all still on nine months do we know, do you know off the top of your head what the let's say like operating profit and like eBit DA for GameStop were like last year?

SPEAKER_03 25:50

So their eBit DA was 148 uh million, and I've got their revenues of three point six billion on an uh annual basis.

SPEAKER_01 26:03

So 150 million, right? So synergy, synergy target on a company that size.

SPEAKER_02 26:08

This revenue is running about three point six trillion.

SPEAKER_01 26:11

I know.

SPEAKER_02 26:13

Crazy. It's a Welcome to the crazy world of the deal.

SPEAKER_01 26:18

I know. And and by the way, I mean, one thing, one thing I talk about a lot in the classroom when I'm kind of teeing up a discussion around synergies is you know, ask students to kind of think about merger announcements or press announcements that that they've read and say, like, can you ever can you ever tell me a time where you've seen a public MA announcement where the acquire has said in their press release that this is going to be dilutive to our earnings per share? Right. Not no, right? And the the reason, one of one of the main reasons is because the synergy number is basically up to up to that acquire company, just to almost make up two billion dollars of synergies on a company that does 3.6 billion of revenue and under 50, 150 million of EBITDA sounds sounds pretty made up to me. Having said that, I actually never's gonna be wildly accrued.

SPEAKER_03 27:10

I know a synergy consultant. Uh I can't believe that's a job. Uh but they basically advise companies going through MA on the sorts of synergies they can expect. And they're usually pretty careful about saying, well, whatever you think you can achieve, take a good haircut and include that reduced number in the press release because you do not want to disappoint on those synergies. So usually the number that you see in the public is actually lower than they think they can achieve. So is that really the case for this deal? Who knows?

SPEAKER_01 27:39

I mean, real questions on are there like did they even go through that kind of process for a deal like this where I don't think anyone ever expected this to be a real, a real transaction. Like I I don't know. I do do you do you have to? Like, I don't I don't think so. I don't know, actually. If any if anyone knows the answer to that question, let us know. But my guess my guess knowing uh what the the kind of high-level terms of this deal are is that GameStop did not go through that whole, that whole process or found someone that was very flexible, for lack of a better word. Uh okay, so now we've got we've got LTM. So we've got GameStop revenue, okay, 3.8 billion. I think you quoted 3.6. So we're not, we're not like a million, a million miles apart.

SPEAKER_00 28:30

Yeah.

SPEAKER_01 28:31

Right. So Proforma, Proforma revenue 14, 14.5 billion dollars. All right, so we've now got a $300 million synergy target. So okay, this says run rate synergies full year. So obviously this is not right per the announcement. Now let's see where it grabbed this from. One thing, like I said, one thing I do illustrative management has not disclosed synergy estimate. Well, they they have.

SPEAKER_03 28:59

Except you, I think it was in an interview, so I wonder if that doesn't count. It's not necessarily a reliable source, but who knows?

SPEAKER_01 29:06

Well, yeah. Okay. Well, let's just we'll continue ticking through this adjustments and we'll get onto the onto the actual you know, kind of accretion dilution. Um okay, so intangible amortization. Let's, ooh, has this has this done kind of purchase price allocation and like an asset and asset value step up?

SPEAKER_03 29:30

So this is like a really super keen analyst, isn't it? It's kind of gonna be above and beyond. Because normally we wouldn't bother including that in our EPS. In fact, we used to refer to it as cash EPS because it would be EPS excluding adjustments, accounting adjustments that arise as a result of MA. So I would almost be tempted to zero this out, but hey, that's just me.

SPEAKER_01 29:54

Done. All right, we'll be good. Oh look, it looks like a better deal now. Yeah, right. It's great. We'll make it make it blue so we can we can go back and change it later. Okay-ish modeling modeling practice. Uh okay. Okay, okay. So that's an interesting thing. That's a big adjustment. This is gonna be a big one, right? So this this should be, in theory, the the new interest expense on the additional or incremental debt rates, which we know is gonna be about twenty billion dollars, right? So let's just audit and see how this is being calculated. So we've got new debt issued, 18, 18.6 billion, 7% interest rate on new debt.

SPEAKER_03 30:38

Okay.

SPEAKER_01 30:39

What is this excess cash? Oh, and then okay, what it's doing is this is a net interest income calculation. So what we're saying is you're both increasing your interest expense from the new debt you're raising, and you're also losing out on cash interest income on the cash you're using. All right. So some some okay, some okay things and also some pretty horrific, uh, pretty horrific modeling practices here in which you know you would never have this times 0.04 in the cell here, just saying that's my that's my cash interest income assumption.

SPEAKER_05 31:11

Okay.

SPEAKER_01 31:12

Hopefully it put it put the 4% in the in the comment there. So thanks, Claude, but also don't do that again. Uh what is this? We're just kind of subtotal here. Income tax. This should be, we expect this to be at the acquire tax rate. I don't know if we even have a distinction here.

SPEAKER_03 31:34

So that I think they've just assumed the US tax rate uh as the marginal tax rate for everything.

SPEAKER_01 31:40

Aaron Powell You know, for the sake of this analysis, fine.

SPEAKER_03 31:44

Yeah. Fine. For the sake of a deal, it's not gonna happen.

unknown 31:48

Right.

SPEAKER_01 31:48

Exactly. So our main, okay, so now our main, our main thing, obviously, our main this is our numerator adjustment, right? We're taking the two, you know, essence, the two net incomes and and adding the two together with the adjustments. The main adjustments really here being the synergies and the interest expense on the on the new debt. Now let's get to the denominator. All right, so we've got our diluted shares for GameStop, our diluted share count for eBay. And then we've got the shares issued as part of this equity, the equity issuance that GameStop is making. So let's see how this is being how this is being calculated here. We've got stock consideration divided by, I'm assuming, GameStop's current share price. Not bad as a as a first pass. Let's just check the models actually adding up the right stuff.

SPEAKER_03 32:39

Yeah. Why is it not adding eBay's numbers? I want to know that.

unknown 32:43

Of course.

SPEAKER_01 32:43

Right? When you when you're, I mean, when you're we're acquiring, we're now acquiring all eBay shares in exchange for GameStop share. Well, either GameStop shares or cash. But in essence, now eBay is no longer a thing. The combined entity is now just GameStop or GameStop eBay or whatever it's gonna be called. So all the shareholders are holding GameStop, GameStop shares. So eBay shares just simply go away.

SPEAKER_03 33:12

Okay.

SPEAKER_01 33:13

So diluted earnings per share, pro forma net income, combine that income with adjustments divided by our new diluted share count. We're gonna compare it to GameStop's standalone EPS. Now, I feel like I remember having a discussion with you, Debs, that this number doesn't look right. I feel like GameStop's EPS was what, 90, 90 something cents?

SPEAKER_03 33:35

I think it was more like I've got $1.18 actually in my own.

SPEAKER_01 33:38

$118, okay.

SPEAKER_03 33:39

But it was so there are two different measures for EPS. There are reported numbers. That's what the companies report in their actual financial results. And then there's adjusted ones, which are kind of scrubbed or cleaned for things that maybe distort the number that's being reported. So my number, $1.18, is higher, probably because it's the scrubbed number rather than the reported number.

SPEAKER_01 34:04

I mean, this is simply this isn't even taking any kind of reported EPS uh I was gonna say estimate, any kind of reported EPS figure. This is just simply dividing net income by the diluted share count from like the 10K. So very much, very much unadjusted from that perspective. We're saying, okay, standalone EPS 80 uh 80 cents or so, pro forma EPS 80 cents. So we're basically basically break-even on on this basis, right? 70, 79 to 80. Now, of course, if we go through and we tinker with some of these assumptions, right? Let's go back to our where's our synergy, our synergy target. Did Claude put that up in our assumption page here? We zero that out, we're now at 16% dilution. Um and again, I mean, I don't even know if we're working off definitely the right EPS figure here. If if people are really, if the street is really working off the you know, 90 something, 118, whatever it is, then this is gonna look pretty, pretty dilutive without a material synergy number. Um this is why, you know, this is why that that synergy number is really the uh the focus of a lot of a lot of analysts, I feel like, when when these kind of deals get get announced, just because there's a lot of a lot of scope for that can for that to be managed. I'll subject to your your synergy expert comment from earlier.

SPEAKER_03 35:37

So Graeme, I I think it's kind of intuitive that if synergies go up or down, the consolidated net income goes up or down, and therefore the EPS goes up or down. So I guess we could say that's a key lever in our model. Are there any other numbers we can change that would affect the outcome of that EPS accretion or dilution calculation?

SPEAKER_01 36:02

In terms of checking, in terms of checking these numbers here, um I mean let's see, these these diluted share counts, like would we would we go through and do our own diluted share count, especially for eBay, on the basis that eBay is getting acquired above its current market price and figure out, say, what what options, what options are going to vest that might not be in this diluted share account and come up with a better view, um maybe to really get a view of what what we're having to uh of what we're having to buy here. Yeah, that that could change, that could change a little bit.

SPEAKER_03 36:35

I was thinking more about the lever, the levers in your model. So when I think about any model, we're trying to say, you know, the the one of the key outputs here is ETS accretion and dilution. So we can see that synergies are a key lever because if they go up or down, the output goes up or down. What other things do we play around with? Let's, you know, we are in kind of a crazy world where we, you know, if Ryan Cohen can say, you know, we're gonna do a zero on these crazy terms, let's see how crazy we can get to increase that accretion number. Um so what are the other levers?

SPEAKER_01 37:09

Yeah, I mean, the other is really consideration mix here, right? So if we I mean, generally, generally speaking, the well, not generally speaking, I mean the the kind of the old investment banking one-on-one interview question is if you're looking at an all-stock deal, if a company with uh with a high PE buys a company with a lower PE, that's gonna be accretive to earnings per share. So generally you can get a you can get a view on accretion dilution just kind of looking at relative P multiples if the consideration mix is gonna be all stock. Here we've got a few more things going on because in the cash portion of this deal, most of the cash is funded with debt. So we have the interest expense that we need to account for in our accretion dilution math. But let's okay, but we're trying to be we're trying to be crazy now, right? So we're gonna do that. Let's try to be Ryan Cohen.

SPEAKER_03 38:02

Let's go for it. Let's put the cash consideration up to like 90%.

SPEAKER_01 38:07

90%. Oh, so we think we're gonna be able to raise we're gonna be able to raise that much debt, I guess. Let's do it. Why not?

SPEAKER_03 38:14

Let's just see what happens.

SPEAKER_01 38:16

Let's see, cash consideration, 90. I mean, also set we wouldn't be paying 7% on that debt, right? I think like three and a half feels more realistic for that kind of figure. Hey.

SPEAKER_03 38:35

This is like an amazing deal. Okay, so that's quite an interesting thing. I think it's quite a subtle point, isn't it? That if you m change your financing mix, because you have debt as a cheaper source of financing than equity, that it actually makes the numbers it factors them, doesn't it? That we end up with a higher accretion figure. But is that well, yeah.

SPEAKER_01 38:55

Well, so just think, yeah, just I mean, like we'll we'll flip the consideration mix back to back to 50-50 in a second. Here I'm gonna control Z and get back to 7% interest. Right. We're still we're still at this 258 share count adjustment. We've got we've gone all the way down to 146% accretive. So obviously that that interest expense is still very material, especially when in this case we're at a 90-10 cash stock consideration mix. But now if I go back and flip to 50-50, this 258 million shares issued is gonna go up quite a bit. Right, which is going to increase increase our denominator by quite a bit. Right? We're still we're still now at 94% accretion. Of course, we still got this crazy, this crazy synergy target running through. So again, let's go back, let's just make this I don't know, remotely, remotely realistic. Let's okay, let's say there's some there's some synergies, but they don't get to two billion. They get to, I don't know, 300 million, something, something like that. We're back at back at break-even.

SPEAKER_03 40:08

Yeah.

SPEAKER_01 40:09

Um So th I think those are kind of the the high-level levers you think about, you think about pulling, at least in an early stage model like this. What do we think about this result based on the prompting we gave?

SPEAKER_03 40:24

Yeah, I mean, I think it's a if if you had to start off with a blank Excel file like we did, it's done the job much more quickly than we could have done it. Is it more accurate than we would have done it? Probably not. You need somebody who's at the helm who knows a little bit what they're doing. You know, I I personally take the view that AI is very dangerous in the wrong hands because it can give false confidence. So it isn't a substitute for knowing the fundamentals. Um it has found information quite quickly. You just need to be prepared to sense check and validate those outputs quite rigorously to make sure that you understand, you know, if you were going to put that, you know, god forbid, in front of a client, then it would be very embarrassing because the numbers, you know, there's still quite a lot that needs to be improved. You know, there are some, you know, some technicalities which aren't quite there. But ultimately, anyone can type in those words that you did, build me a merger model, and you're away. And then you're just kind of, you know, you're just basically improving what's been given to you. And and that's the reality, even at your desk, you were never really going to start with a blank Excel file. We always used to start with, you know, pre-existing older templates, you know, just update them for a new deal. Um, but what they have done here, what Lord has done as SATA a lot of time is sourcing a lot of that information. As you said, analysts spend a huge amount of time trying to source, particularly the factual information. You know, as you say, the estimates are usually quite easy to find, you know, because you can automatically suck those out of you know other data providers. But the the factual data, the stuff that comes from the press release, that comes from actual reports, financial reports, that's the stuff that used to take a lot of time trying to find the correct figure. And now it's very automated. Claude has done all of that work. We just need to be happy with the outputs, I guess.

SPEAKER_01 42:13

Yeah, I think it's super. I mean, the the fact that we we gave it the most simple prompt we could think of, and then we made a few quick adjustments, what, two or three, you know, really, really not that many, and got to this point is pretty, is pretty phenomenal. I mean, there's some pretty horrible modeling best practices in here, right? It's not all awful. You kind of look at and you're like, all right, we've got the the you know, green for links to different workbooks, blue for hard-coded inputs, and black for formulas general, general methodology in here. But then when you start to dig in a little bit and you see things like that 4% cash interest income rate baked right into the cell formula, you would never do that in in the real world. I mean, I do, I do also feel like like one reason, one reason whenever I'm in the classroom, I get people to do a lot of stuff, either step by step or if it's something kind of simple, maybe like this from scratch, is doing something from scratch teaches you how things are wired up and how things are supposed to work. I think you would be at this point right now, you would be pretty in a pretty bad spot if you took this model and used it as your blueprint for this is how you build a merger model or this is best practice modeling in Excel. So I don't think we're I think we're to the point where we can use it for that level of fidelity yet. We'll probably get there at some point. Right? We probably will, but that's still a little ways off.

SPEAKER_03 43:40

Yeah. I guess so the final step really though, as I said, is to make sure you kind of sense check and val validate, but then identify the key takeaways. We kind of gave the spoiler right at the start of the episode that this is a bit of a crazy deal. Um so I think we should kind of follow this up with what. Why is this such a bad deal that the bid was rejected within two weeks by eBay? I mean, that is a pretty swift rejection. As we said, it was a pretty stinging rejection as well. It's not credible or attractive. I can't get that phrase out of my head. And the rejection listed not just one or two concerns, but I think there are about six concerns to do with the deal.

SPEAKER_00 44:26

Yeah.

SPEAKER_03 44:27

Graham, from your perspective, what is it that makes this what would your key concerns be about this transaction? Is it the numbers that we're looking at in the model or is it something more qualitative?

SPEAKER_01 44:40

I think in some ways, I mean, if you if you believe I mean, okay, let's assume this is right for a second and that we're we're we're believing $300 million of synergies and not $2 billion. I mean, by the way, I still think $300 million is probably a pretty big number in the context that we're that's basically we don't have it on this page. It's basically GameStop's eBita. Just that's still that's still even at that level seems uh seems a bit punchy. I just don't think anything here is particularly credible, right? Because we're saying 50-50 cash stock mix. Can GameStop issue issue a bunch of equity to to fund this transaction? Uh you know, in theory, in theory they could. You know, it means eBay shareholders would have to be willing to accept 50-50 cash stock mix and hold cash in a combined GameStop eBay eBay conglomerate. Um the you know, the cash, the cash portion of this offer, you know, GameStop has $9 billion of cash in their bank account, which is, you know, which is real. Um, but most of the financing here is from a bank letter that's not committed. Like it's not nothing's really nothing's really real yet. Uh and then you got you got their CEO going on CNBC, just kind of looking like an idiot, not even not even really knowing what he's talking about or how to back himself up. And he's saying, yeah, eBay is gonna be much better if I'm running it. And I think you just got to take a step back and say, like, what are we what are we doing here? Um so I think very much in agreement with eBay's board from my perspective anyway.

SPEAKER_03 46:19

Yeah, it's interesting because uh some people think that Cohen is a visionary. He has massive support in the retail investor community, but it's quite telling that one of their biggest shareholders sold, uh X tier, that's Michael Burry, uh X tier after the deal was announced. So clearly he's not a fan. Um but yeah, I mean there's I think sort of the financing side, as you said, is pretty scary. I mean, they've they've got this letter that has been touted as a letter of commitment, but it seems a lot less tight than that, and it's been made publicly available, this letter. Um, and one of the conditions is that as part of the financing, they'd have to maintain an investment grade credit rating. But the amount of debt that would have to be taken on to do the deal, Moody's have already come forward and said that it would be a negative credit credit event if this deal went ahead. So it seems like a catch 22. There's no way they would be able to maintain that rating. Um, and that's what's needed to do the deal. So that seems bizarre. And I think another thing that I read, which is really quite astounding, is to do with the incentive structure from for Ryan Cohen, is that he is basically his compensation has been renegotiated at the start of his year. So he gets no salary, no cash bonus, all stock options based on hurdles. The hurdles are that if he has if if GameStop has comb uh has EBITDA of 10 billion and a market cap of 100 billion, then the stock options would be worth an astounding 34 billion dollars. I mean, that is just a crazy number. It'd be a third of the market cap of the business.

SPEAKER_01 48:02

So I mean, all he's all he's gotta do is increase EBITDA by what 20 times? Like that that's easy.

SPEAKER_03 48:08

Well, it's just basically, but he's a visionary who's clearly on the hunt for a deal because the only way you're gonna meet this criteria is by buying a much bigger company, which is what he's trying to do. So it does seem um, yeah, normally stock options are used to align management with the interests of shareholders. And it feels like that is not what is happening here. And that was flagged in the letter rejection. You know, the governance and the incentive structure was flagged, and that's quite a targeted or pointed attack on the incentive uh structure for the CEO. So I think lots to be concerned about. I think the big question is what's next? What do you think? Another offer?

SPEAKER_01 48:52

He's gonna I mean he's probably he's gonna do something, right? I mean, I I can't I can't think this has any legs whatsoever, really. I feel like this is just gonna, this is just gonna die a pretty quick death. Um, but knowing knowing that about his comp structure, will he try to find something else? I'd be shocked if he didn't.

SPEAKER_03 49:09

Yeah. Yeah. Hey, that's just gonna give us so much to talk about there, isn't it?

SPEAKER_01 49:14

We can only congratulate pod merger modeling, crazy, crazy assumptions and all that.

SPEAKER_03 49:20

I know. Fantastic. Okay, well, definitely we'll watch this space. Um, and I hope that everyone that's been listening has learned a little bit about how you use AMI, how you analyze mergers. Um, so I think it's been uh an education for us all, shall we say? Um so thanks very much for tuning in for this week's episodes. Um, tune in again next week for a new deal, some fresh insights um for myself and Graham. Thanks for listening.