Embracing Skepticism To Enhance Financial Modeling Techniques With Geoff Robinson

You rarely see someone in their 40s just starting in the typical investment banking route. Usually you see it maybe out of undergrad, grad school, maybe a couple years of work.

The first weird thing was having to do my regulation and securities exams at the age of 43. But I'd have to say I think being an educator was just a wonderful way to then be a research analyst because it's a very similar skill set. You are you're trying to tell investors something insightful that you've come up with that they hopefully if they thought about it, it's a pointless conversation. So I've got a company new and that's hopefully not priced into the market and that's different and I've got to teach it to them and I've got to make it digestible.

Tell me about the worst financial model you ever saw. Jeff Robinson is The founder of InvestmentAnalyst.com he has a wealth of experience in financial modeling and has taught financial modeling throughout his career. He has also worked as a research analyst and Managing director at UBS for eight. Jeff is a seasoned professional with analytical and insightful financial knowledge that he is eager to share with his audience. @investmentanalyst.com the users can experience the best of edtech, which offers a unique learning platform for investment enthusiasts and professionals.

There's quite a few to pick. The slightly amusing and horrifying story is I'm not saying which bank it was, but looking at a free statement, valuation model, balance sheet, income statement, cash flow and balance sheets. So no matter what happened, even if it was absolutely mashed up, the diagnostic said it was fine and when you went into the detail, there was a $29 billion hole in that balance sheet. So that's pretty bad.

Welcome to Financial Modeler's Cor where we discuss the art and science of financial modeling with your host, Paul Barnhurst. Financial Modeler's Corner is sponsored by Financial Modeling Institute. Welcome to Financial Modeler's Corner. I am your host, Paul Barnhurst. This podcast is where we talk all about the art and science of financial modeling with distinguished financial modelers from around the globe. The Financial Modelers Corner podcast is brought to you by Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling. I am excited to welcome on the show Jeff Robinson. Jeff, welcome to the show.

Hi Paul, nice to meet you. Thanks for having me.

Yeah, really nice to meet you and have you on the show. We're going to start with this is the question we ask every guest because I know anyone who's spent A lot of time modeling has their horror story. Tell me about the worst financial model you ever saw.

Oh, my goodness. There's quite a few to pick from. I think the slightly amusing and horrifying story is, and I'm not saying which bank it was, but looking at a three statement valuation model. Balance sheet, income statement, cash flow, and balance sheets, as, you know, need the balance. They have checks and they have diagnostics. And the diagnostic on this model was it said if total assets equals total liabilities and equity comma, okay, close bracket. So no matter what happens, your balance sheet, even if it was absolutely mashed up, the diagnostic said it was fine. And when you went into the detail, there was a $29 billion hole in that balance sheet. So that's pretty bad.

Yeah, $29 billion. I mean, what's 29 billion among friends?

Oh, between friends, it's, you know, it's just, you know, it's pennies.

Yeah, we used to joke. So I started my career in procurement working for the government. You know, one of the jokes we'd say sometimes just because you spend so much money with the US Government, especially Defense, which is where I was, we would occasionally joke, you know, a billion here, a billion there, pretty soon it adds up to real money, you know. So what was your kind of key takeaway or learning from that experience? From seeing that model?

I would say, I suppose the blunt thing to say is don't trust anything until you understand it. I think the more professional way to describe that, and it's a great term I got from one of the guys I really looked up to at ubs, was to adopt an air of professional skepticism. And so I need to understand what's going on before I'll start to align it. I need to have that trust, that understanding that the robustness is there. And so there are various different checks I will do as soon as I get into these financial models, which we can talk about, and I'm sure people will know what I'm talking about here. But you need to have that kind of confidence before you can even start implying the insights that you're looking for.

Got it. So, okay. And okay is both true and false is not a robust check.

That's not good. It's not a good start, is it? You know, so I mean, you know, it's things like that. But, you know, you see very, you know, I think financial models are real kind of insight and window into the personality of the modeler. You know, you can see how they think, you can see how they over complicate things. You can see if they're, if they're sloppy, if you're picking up hard codes, if you're picking up formulas that have a cell reference plus a cell reference plus a hard coded 20. I think when you start to pick up these little kind of tails and ticks, I think, well, I certainly start to, you know, form a view on the personality of the person that I'm dealing with and the integrity that they have.

I know those are some really good points. It's amazing how much you can learn about somebody by looking at the work they do.

Yeah, it's that logic. You know, their personality goes into this model is that it's their DNA. I mean, I, I've had situations where I've walked into a bank and I've looked over somebody's shoulder and I've seen their model. I thought, hang on a second, that's got a bit of me in there. And then I've talked to the, the person and it turns out, you know, I trained them 15 years ago when they were 21 years old at Citigroup. And, you know, they've adopted the, you know, those best practices that we stall at that stage. I know you had Mr. Bennett on the podcast a couple of weeks ago. So, I mean, both him and I just cut from the same hymn sheet really, that you need. Those best practices is in place. So, you know, there are bits of DNA that you see in other people's models where you think actually that's possibly come from me as an influence over the years. Because I think, you know, over the years I've probably trained 20,000 plus analysts. So, you know, you know, good and bad, I think, on my part. But they're all there in the market.

Yeah, you start to see that DNA makes a lot of sense. So why don't we go ahead, go ahead and have you tell us a little bit about yourself, your background, let the audience get to know you a little bit before we carry on.

I would say I'm, I'm a teacher, I'm a financial modeler. I happen to have eight years working as a managing director at UBS and Equity Research, which was kind of an afterthought. But the bulk of my career has been building training businesses and getting to the stage where we were pretty dominant again. You've had Scott Rossen on the, on the, on the podcast. He was a compatriot of mine. Competition. To me, he was us, we were European. And I did that for the best part of 20 years. But we sold the business to Fitch in 2013. And then UBS came along and said, listen Jeff, you know what you're doing at the moment? I was like, hey, nothing really. Just sold my business, I'm kind of hanging out here, I'm a bit bored. And they said, well, why do you not come to be a research analyst? And I hadn't done that before. I trained Morgan Stanley's research business for 15 years. So I had an outside view into it and I pitched up to UBS with no experience and built a research franchise there and had eight wonderful years speaking to investors, financial, building models, trying to get people to build better models, seek better insights and kind of advise the market. So it's been a varied career. It's not a traditional route into investment banking is to kind of have your first day at the age of 43 walking into the business. So it was, I was a late starter, put it that way.

Yeah, definitely. You rarely see someone in their 40s just starting in the invest the typical investment banking route. Usually you see it maybe out of undergrad, grad school, maybe a couple years of work, but generally it's a very early one of those first type things. So talk a little bit about that experience maybe how, you know, having all that prior experience teaching you prepared, prepared you for it or how it was different for you coming in at that stage.

Well, the first weird thing was having to do my regulation and securities exams at the age of 43. I think in the US it's series 63 and series 7 if I remember correctly. I'm doing those exams with a bunch of 21 year olds and you know, I'm the granddad in the corner kind of trying to work out is it T +3 that a Japanese bond settles at. So that was a little bit weird, but I'd have to say I think being an educator was just a wonderful way to then be a research analyst because it's a very similar skill set. You are, you're trying to tell investors something insightful that you've come up with that they hopefully, if they've thought about it, it's a pointless conversation. So I've got to come up with something new and that's hopefully not priced into the market and that's different and I've got to teach it to them and I've got to make it digestible and you've got to get your point across in a way that you are not coming across too complicated. It's simple, it's digestible, it's takeaway and those are Teaching skills. And I think that's one massive thing. And the other massive thing was it's ironic that most equity research analysts haven't ran a business before. And I'd ran a business for 20 years. So probably the first four, five months at UBS I just watched the other teams do what they do and built up a sense of, okay, there's a bunch of good ideas that they do, but here's a whole bunch of areas where they could do it better and they could analyze the data and they could, you know, one of the reasons I would say we were, we were majorly ranked number one in the first attempt that we went at it. And I went at it really hard because I'm that kind of person. I thought I've got to beat the person that's been number one ranked for six or seven years. How do I do that? And I just worked out the rules of these rankings and there are rules behind it. It's a game. And I got the data and I just kind of found out, okay, I'm not going to ring 300 people because I know these 15 people carry more weight in the vote than the other 285. So I just went through a very targeted cross sectional data analysis. And I think we won because we were smarter playing the game as well as having a good product, but as being, you know, known the rules of the game. Been able to play that game hard.

Now you're not competitive when you played car games, board games, sports, any of those type of things, right? Not at all.

I'm competitive. Yeah, yeah, yeah, I am. I'm currently trying to figure out if I can out bench my son and he's 16 year old and built like a brick and I'm miles off him, but I'm hoping by the summer I can get pretty close to him. So I'm into census games where, you know, I try and turn everything into a game.

To be honest, I kind of figured as much as you kind of reversed engineered it and figured out, okay, what's the rules that go into us? How do I make sure I maximize my score here? Yeah, how do I win?

Yeah, yeah, definitely.

I can tell we have a deep analyst that thinks about a lot of things. So I'm curious, you know, what is it that you love so much about kind of modeling valuation and stock picking and that, that kind of whole exercise with being an equity analyst and trying to find those unique angles to bring to your, you know, your customers?

Yeah, I think, is it being an equity analyst? I mean, Obviously, financial modeling is a real core part of what we do. And what I love about it is the only way you make money is by being able to articulate why you think the market is wrong. And the market's not particularly. It's pretty smart. So to be able to identify an opportunity that you think the market isn't grasping correctly is where models come into me. I mean, you know, I think you've got a question later on, it really made me think about the answer. But to me, models are question asking tools. You know, I think one of your questions was, you know, is it a primary decision maker, a financial model? And, and I don't think models are decision makers. I think models are the way you test a hypothesis. You know, you have an idea, and I want to break that idea. I want to cover that idea in different angles and I want to ask what would happen if. And there's a very high likelihood I'm going to be wrong because I'm trying to forecast the future, you know, and I'm not, you know, none of us have a crystal ball. So I am going to be wrong. And I want to know if I'm wrong, how wrong I'm going to be, and at what point does that break my hypothesis. And I find that fascinating. And then to go out with an idea and then speak to usually a bunch of people that are super smart and usually smarter than I am and tell them, I think you've got it wrong. I really thrived on that. I mean, I really loved it. I mean, it was scary the times, but it was, it's a real way to kind of intellectually test the robustness of your thought process. And it was great. It was great.

Yeah. And it sounds like a lot of times you were right, obviously. I think you mentioned being ranked number one. Was it eight, nine years in a row? Is, you know, your team for the institutional research there and emea. So obviously you had to have. A lot of times you were right and doing good research. So I'm curious, you know, how did you go about that process? Like maybe when you started researching a company before you built that model and stock, what were you looking to learn about them to help you in building the model?

That's a really great question. And I think it varies depending on the circumstances that you're in. Sometimes there's just a catalyst that happens in the market. I mean, Trump getting elected in 2017 was really interesting because of the tax reform that he was. And he did an act going from 35% down to 21%. And what I wanted to do there is think about what the legislation was saying and then think about as analysts, as investors, how historically we've thought about that legislation and possibly got it wrong. So it's almost, you get very behavioral or you've got the theory and the legislation and then you've got the behavioral implications and then you're looking for misalignments. And you know, my view was, listen, I think this is a, is a big deal. We're going from 35 to 21%. But in reality, nobody pays 35% tax, nobody actually will pay 21% tax. The goal posts are different to the headline. And you were then trying to link that back into how are people actually reacting to this in the market and is there scope there for possibly being a little bit smarter in the analysis? And you know, for, you know, that legislation took, I think it is until I want to say, April 2018 to be enacted. And we, in our publication, we were, we were kind of like four months ahead of what people were saying, you know, because Trump wanted to go down to 15 at one stage. And we were like, listen, there's no way that's going to happen. You know, 21 to 23 is probably where it settled. And then the market caught up with that. So, you know, and we were talking to some huge investors. I mean, I had a two hour sit down with Carl Icahn one on one talking about US tax reform because he had a trade on the S&P 500. And I was saying something quite different to what his trade was. And again, that was something that I'll take away to my grave, really is a phenomenal experience to take your work to somebody of that level and that kind of standing in the market.

Yeah, I know that sounds like that was a fascinating conversation, kind of. Any takeaways from that two hour conversation that you can share or what was that like? I could imagine that had to be a little nerve wracking, exciting, a little bit of everything.

Nerve wracking, terrifying, amazing. I turned up to his office, which is right next to the apple store on Fifth Avenue, and his assistant said, Carl's going to be 45 minutes late. Go make yourself comfortable in the boardroom. And the boardroom was like entering a museum of just artifacts on the wall of deals that I'd grown up with as a kid, into my teenage years. So Texaco, Marvel, Pan Am, you know, people listen to this, are old enough to remember Pan Am, they know how all that deal is. And So I had 45 minutes of just entertaining myself with this kind of deal history. And he then turned up and sharp as attack. I mean he must have been early 80s at that stage. And for a guy who's not a deep numbers guy like myself, I mean he just got stuff so quickly, got straight to the point and had such experience over the markets. It was just an incredible experience, quite frankly.

Yeah, you know, I can imagine. Like I said, you'll remember that one for the rest of your life for sure.

Yeah.

And so I'm curious, as you mentioned, you know, trying to, you gave the taxes an example. Trying to look at things from not just, hey, here's what's happening, but that behavioral. And trying to find where there's misalignments. How did you think about that? Was it mostly kind of looking at the historic data and saying, okay, everybody's predicting this will happen, but historically it's been a little more like this. Like there's, there's a little bit of room here to take advantage of the opportunity because we think people are going to behave different based on history. Is that a lot of times what you were looking for? How did you think about that?

Yeah, I think I read a lot of kind of Warren Buffett's work, Charlie Munger's work. I'm a great believer of looking at things through multiple lenses and reading broadly, applying different frameworks and just trying to look for an angle that, you know, most people are looking like this because they're under such pressure in their day to day work. You know, to have that bandwidth to look at things at different angles is really difficult. So yeah, history has a rhythm to it. It's very interesting. For instance, looking at the Magnificent Seven and I just have these echoes of okay, I've been through a dot com boom before. I think structurally we're in a different situation. In today's markets. I'm hearing people saying this time it's different. I think it was John Stapleton, sorry Templeton, who's who described those the four most dangerous words in finance. This time it's different. That's what we heard in 1990 2000, 2001. And I think you've really got to think on the, from the, you know, if you try and visualize life as a normal distribution curve, consensus and the herd is all crowded around that kind of central point of, of the meat. I think my job is to force myself to think on the periphery of the tails of where things that could go wrong and, and I don't. When I sit down and build a financial model, I, I don't put a lot of credence on my ability to forecast the future, as I said before. So what I'm usually doing is I'm trying to figure out what's the market pricing in, and then do I believe the company can fundamentally deliver on the expectations that the market has priced into that stock? I mean, Peloton was a phenomenal example of this because when it IPO'd, I couldn't get anywhere near its IPO price. And then, I mean, its IPO's price was 29 a share. Then it hit, what, 154, something like that. And I could never get those numbers to work. It was never worth that amount. So it's just a question of waiting for the fall. And, you know, the fact the stock went down, it's, it's obviously suffered. But, you know, the fact that stock is trading on single digits now is absolutely no surprise to me. If you were able to run these models in a consistent manner where the narrative actually start stood up, you could. You'd never be able to get those numbers to work. So when you do something similar to Nvidia, I think it's a useful exercise to say, okay, I don't know what Nvidia is going to do in the future, but its stock price is way up here. What can I do in my model to try and replicate that answer? And what does that tell me about the business and sustainability of generative AI expansion and GPU development?

Now, I think that's a great point, is. All right, the stock price is at 300 today. I would value that at $50 or whatever the number is, right, you know, 50 billion and it's worth 400 billion. What would it take to get there? And are those realistic assumptions? And, you know. Exactly. I had a guy, a Sam Savarian on the show, and he shares the story where there was an analyst on, you know, kind of Wall street that did some research, said, hey, this stock is going to do this. They had doubled the last two years or something. And he assumed that would happen for like the next eight years. And that's like never happened to a stock but in isolation because he wasn't looking at, you know, broad was going very narrow and just taking the history, he thought that made sense. And he got, you know, roasted. When I went out public, when one of the guys got a story of stock market goes, you realize this has never happened with the stock. What you're saying is going to happen here, like, how did you think that was a good idea? And So I think, you know, putting pressure, testing it and really digging in.

I said you're stress testing, you're trying to break it, you're trying to break a hypothesis. And if you can't break it and you are coming out with a view that's different to what the market's pricing and quite frankly, that's when things get interesting because you're in a position to say to investors, listen, I think I've got something here that the market's not capturing and that gives them opportunity to some alpha there, some gains in excess of the market basics.

Sure. And so what advice would you offer to audiences that are listening who maybe are doing valuation models? You know, we've talked a little bit about behavioral and the hypothesis and things, but what would you say are the kind of key two or three things they should be really thinking about if they're doing valuation models? From your experience in today's business world, financial modeling skills are more important than ever. With Financial Modeling Institute's Advanced Financial Modeler Accreditation Program program, you can become recognized as an expert in the field by validating your financial modeling skills. Join the Financial Modeling Institute's community of top financial modelers, gain access to extensive learning resources and attain the prestigious Advanced Financial Modeler accreditation. Visit www.fminstitute.com podcast and use code podcast to save 15% when you register.

I think there are certain areas not to spend too much time on. So my view, which may be slightly controversial and people may not agree with it, is, that's okay.

We like controversial. Go for it.

You know, like cost of capital. I think you've got two options in the cost of capital, which both involve you being wrong. So you've got a choice of you can be wrong quickly or you can be wrong slowly. So I prefer to be wrong quickly and then run sensitivity analysis to find out and quantify how wrong I could be. So there are certain areas there. I think you've just got to take a view, then run the sensitivity behind it and then figure out, okay, if I am wrong, this is what the impact is. But I would say the recurring. Let's pick one big recurring theme that I see in valuation, because it's all forecasting, it's forward looking, is making sure that you've got a consistent narrative behind your story. And what I mean by that is there's an awful lot of time spent forecasting out revenue and then costs are usually a derivative of revenue. And then it's almost like the analyst gets a little bit bored with working capital and Then, right, we'll do some capex and we'll run that down to depreciation. What can. And it happens a lot. It happens in my work. I do this myself and I mess it up, is that you have the operations of the business almost getting carried away and inconsistent with the reinvestment requirements needed to support that operations. And you know, there's a million other things that could go wrong. I can feed into this issue. But that lack of a consistent narrative I think is probably the biggest recurring error theme that I see in people's work. Because the ramifications to this are it'll start blowing up things like terminal values, it'll overstate cash flows. I think, personally, a dcf, it's much easier to overstate the valuation than it is to understate it. And a lot of it comes through the lack of, lack of consistency in this because you're storytelling at the end of the day, you know, that's what it is. And if your story is not consistent, it's like having plot holes in a Tarantino film. You know, people will spot it and you'll get called out.

Yeah. But if it's a flick that's just there to have a lot of fun, you know, then the holes are okay. Probably doesn't work though. When it's your money.

Yeah, when it's your money or other people's money and your job.

Yeah, exactly. So that lead me to a question. There is you. You talked about often, you know, kind of getting lazy the further down you get. As you work through everything right. You really focus on revenue. You feel like you got that right. And so I think it leads to two questions. The first is, okay, any advice? As they get further and further in the P and L, is it just spending more time? Is it really? Are there certain areas like the opex and capital, you think more, need more work generally, or what's your thought there?

I think it all starts off with the accounting knowledge because I'm, I'm a chartered accountant in the uk. I was, I was not a very good one. I didn't do it for very long. I qualified and I got out pretty much straight away. But it gave me a super solid grounding in how the numbers work. Now, you know this and I know this accounting is not a fascinating subject. It's not particularly well taught. Most people that I come across get taught with T accounts and debits and credits, which is great if you want to be a 16th century monk looking after a book legend. But if you want to be an analyst. Debits and credits don't really help you because that's preparer's language. And as a consequence, I think people just don't learn the accounting as well as they could do and then they start relying on the numbers and you can be misguided by companies reports incredibly easily. You know, we talk about, you know, I've been writing quite a lot on just having a rant about ebitda. I mean, EBITDA can tie you in knots and you are completely implicitly unaware of it. So I think the core kind of ground zero there is people can always improve the knowledge of the numbers and that can. You know, the best analysts I've come across are the ones that were forensic with those numbers and then could see the commercial implications within the stock price.

Yeah. So they really understand the implications of those numbers, how the accounting work behind it really that language of numbers and were able to translate that into the commercial impact and how that actually impacted the business and what it meant.

And you don't have to be an accountant to do this, you know, you've just got to have taken your accounting training on your grad program seriously and also to keep working on it, you know, and that's essentially, you know, for eight years, nine years at ubs, you know, I was basically talking to people saying, listen, your valuation's wrong because you know, your accounting is letting you down here, here and here.

Interesting.

Yeah, that.

I mean, it doesn't surprise me. I've definitely seen a lot of times, you know, my experience is mostly FP and A, so financial planning analysis, which is forecasting. You're usually just forecasting the P and L for big companies, small companies, you're doing a three statement forecast. And there were definitely times when you'd see lack of understanding of accounting get impacted in those models. And like, oh no, that's capital or no, you can't think of it that way. Or did you remember this? And you know, so I know exactly. Particularly sometimes with revenue. And how are you treating the net discounts and promotions and all kinds of fun little things.

I mean one of the biggest issues is just getting a balance sheet to balance. I mean, I mean, I mean it sounds like it's super easy, but you know, we have a case study that I use in my training business that I've ran past maybe a thousand analysts and it's not an easy one, but it's not rocket science either. I've had two, I've had two people get that right out of a thousand and it's not because necessarily about a lack of accounting knowledge. I think it's more to do with a lack of technique of how to apply these accounting numbers in a financial model. Because we're so groomed to pass exams. I think the way people put numbers together is like, basically, I'll do the income statement, right? I've done the income statement, I'll do the cash flow. Done the cash flow. And it's almost like you've got hundreds of one side of a double entry that you've got to put all those jigsaw pieces into place in the balance sheet perfectly and hope to God that you've done it perfectly. And the thing balances and guess what? It doesn't. If you follow that technique and then you're looking for a needle in a haystack, you're under pressure, your boss wants something, you want to go home, it's 10 o' clock at night. And then I can see why people then think, oh, you know what, I'll just put a cheeky little formula in here that's just going to plug that balance sheet through retained earnings. And I know it's not right, but it looks good, my boss is happy, everybody goes home and hopefully it doesn't blow up in my face.

Yeah, no, we're. We've all been tempted. I've been there when I was taking the Advanced Financial Modeler from fmi. Yeah, I'd done very little. This was just a couple months ago. I'd done very little three statement modeling. So the hardest part was kind of the balance sheet, just building out all those pieces because I was a P L guy for my career and so I'd done it a few times and got in there taking that test. I've been super tired from trying to study and run a business during the week and it didn't balance and I was about ready to say, just screw it. I fell, you know, spent about an hour and it turned out it was something as simple as I had my parenthesis. It's always easy, isn't it? It's always something, yeah. My working capital. I just put the parentheses in the wrong place. It was summing it wrong. And as soon as I fixed that formula, voila.

You know, it's interesting you say that, Paul, because, you know, I, I reckon it wouldn't surprise me that half the balance sheets you see around the planet probably don't balance and there's little cheeky formulas in there. But if you gave me a balance sheet that wasn't working the first place, I would go to is the cash flow statement. And in the cash flow statement I've got a working capital because it's like there is a human mental block on what signs you put for an increase in receivables. Is it a negative or is it a positive? And depending how tired you are, you can convince yourself of the incorrect answer very, very easily. And we've all been there, you know, I've done it myself, you know, so you know, you do see patterns in human behavior and where those errors go wrong. So you know, there's like a heat seeking radar for me when I look at people's models and thinking okay, that's not working right. Win capital cash flow statement. If not, let's go check balance sheet. Let's check capex. If not let's go check adding back DNA in the cash flow statement. And you know, do you find where the, the issues are?

Yeah, no. I saw in Schnorr recently did a 10, 10 part video series of okay, so your balance sheet doesn't balance right. And just walk through where you should be looking. And I remember one time I had it not balancing and I went through his list and he'd only done seven or eight of them at the time and it was none of those. It was just something a little stupid where I forgot to copy a formula over and I didn't highlight it when I was working. And I'm just like.

And try and find that when you're tired, I mean, I mean you've got to be holding on to a cross hopefully to be able to find.

Otherwise I got lazy and just not document, you know, not made sure it was clear that I had done something there that I wasn't supposed to as I was trying to figure something out so I'd know to come back and fix it later and I forgot.

Yeah, and you don't want people interrupting you halfway through a modeling exercise. You know, I used to wear headphones on the desk at UBS and it kind of became an unwritten rule. Like if I've got my headphones on, stay the hell away, you know, because I'm in the middle of something. Because if you're in the middle of modeling something and somebody comes across for a chat and you lose your flow, trying to pick it up again is really, really difficult.

No, I agree with you. It was one of those where I had left it and come back. Right. I had to go do something and came back to it. So you'd forgot what you'd done and you didn't document it, we've all been there.

Yeah.

I mean, it should happen, but it's when we get, when we get lazy is when it happens. So, you know, I'm curious. You talk a lot about behavioral side of modeling, right? We'll talk a little bit about that. So how big of a role do you think human bias plays in the modeling process? And then any thoughts on how we kind of compensate for that?

Yeah, I think it's huge. I spend more of my time these days talking about the behavioral aspects. I mean, today's bit of a sad day. I don't know if you saw Daniel Kahneman pass.

I did see that.

Thinking 90, I mean, yeah, thinking fast. I mean, that's had a huge impact on how I go about my, my modeling and forecasting. The idea of having an inside view, which is our expert view on life, and then just having this more data driven kind of context, agnostic, outside view is one of the ways that you can deal with bias. Because we're all biased. We're all biased by recency, by confirmation, by affirmation. It is in our work all the time. That's why as analysts, we're just wrong a lot, you know, because of those biases. So I think one way to try and manage this is, you know, look, you said earlier in the podcast is, you know, history's got a rhythm and irrelevance and, you know, if you're looking at something that has not happened before ever, it's not to say it's wrong, but what's your evidence to back that up that comes outside? There's no point you saying, I think this is going to happen. It's all right. I don't really care what you think. Where's your evidence to support that? And so it's using that outside view. So, I mean, Daniel Kahneman's work has been huge in that respect. The other guy, and he was deeply influential at UBS, kind of 2015-20, was a chap called Phil Tetlock, who wrote a book called Superforecasting, which talks about how there are certain skills that we can work at that make us better forecasters. You know, some of it's fairly simple stuff of, you know, if you make a mistake, analyze why you made that mistake and do a postmortem, but also do a pre mortem and try and figure out, okay, if I was correct. I mean, how often do we do this? We get something right. We're normally just like, yes, got it right, brilliant. You don't tend to go through and figure out, right, how did I get it right? Is that repeatable? Was I lucky? And just trying to work on that information. And I think you can work at managing those biases by looking at some of those techniques. So those are two books. Absolutely. I recommend that people pick up and have a look at. Conmar's is very well known. Phil Tetlock's Super Forecasting is pretty well known, but it's a. It's an excellent read. And, and I think that's what it is. It's read a lot. You know, I read more than ever now. It's hard to do it when you've got a kind of, you know, Wall street job and you're trying to kind of get the job done. But, you know, I think Charlie Munger said he never met a smart person that didn't read all the time.

Yeah, there, there's real value in always being, reading and learning and gaining wisdom from others, whether it's listening to books or whatever. But I'm a huge believer you need to be looking to learn all the time because just part of having that growth mindset. So I appreciate that. We'll throw both those books in the show notes. So this next question is kind of a fun one. We like to ask people, what's your favorite Excel shortcut and why?

I think copy Paste special links.

Why link? Why special links?

I don't know if people use that quite so. I think one of the fairly common errors when people try and link data between spreadsheet tabs is that they just pick up the wrong cell. And I can't explain why this is the case, but I think if you go to the cell you want to pick up and you copy it, and then you go to your destination cell and say, I'm going to do paste special. And if you do pay special, that menu comes up. Bottom left hand corner is a little button that says Paste Links. You hit links and it creates the connection for you. I think you've got a much lower chance of screwing up the data connection. And that is one I use an awful lot. And I think, you know, I'm a huge fan of shortcuts because it makes you quicker, as we all know, and you're probably saving what, four or five times the speed compared to somebody on the mouse. And I just want to make less mistakes because ultimately I want to go home is in the back of my mind. I want to see my family and I want to see my son. And if I'm just in the office because I've Just been a bit rubbish. That is not a good use of time.

Spoken like someone who started banking at 43 instead of the 20 year old that's like, I don't care if I'm a little late in the office, I.

Want to see my family now. I want to go home. Yeah.

So, next question. What is the one lesson you have learned over your career that's helped you the most when building a model plan? Love that. Why?

And I say I see it when I'm dealing with the younger kind of cohort within the banks, is that modeling is quite fun and people want to get on and do it. And it's almost like, can you build this? Yes, I can bang straight into the keyboard. Whack, whack, whack, whack. And if you don't plan, I think you over complicate the modeling process. I think, I think my models are disappointingly straightforward and simple and they do clever things. So being able to build something clever simply I think is the real skill modelers should be aspiring to. But that will only happen is if you have a little think about what you're doing. And I have a pen and pencil and I draw stuff and I think about the layout and I think, you know, is it best doing this vertically or horizontally? And I just take a little bit of time over it. Now as life and time goes on, it's almost like you have this kind of encyclopedia of templates in your head. I know how to build a lost memorandum. I don't need to plan it, but at some stage in the past I planned it. And you know, that's code which could get incredibly complicated. In fact, actually I think in my FMI AFM exam I had to build a loss relief memorandum where the losses only had a five year life, which gets hugely complicated. And if you didn't plan it, oh my goodness, the code would be horrific. But if you do plan it, the code's actually not that bad. And that's when I think you have a situation where your code becomes simple because your planning was good.

Yeah, I absolutely couldn't agree more with what you're saying there. I love when you said the models are embarrassingly simple and easy. One of the sayings I use all the time in my training, especially with Excel, complex is easy. Simple is hard because it takes time to design and really think through it. And often making it really simple is more work than just throwing in that nasty if statement that we've all used before or whatever it might be.

Yeah, Honestly, Paul, you're Making me want to grow a beard now.

Go for it.

You know, we're on the same sheet in that respect. I mean, it's. Yeah, I had a quote and it was my quote. I wrote a book at UBS called Build Better Models, and in massive font on the second page was something along the lines of good models do clever, great models do clever simply. And it's the same thing.

I like that. I'll remember that one for sure. So we're going to move into our rapid fire fire section. Here's how this works. There's about 10, I think we got about 10, 11 questions here. You get no more than 10 seconds to answer. You can't tell me it depends because I know it could be the answer on all of them. You got to take one side or another. And then after we get through the whole list, we'll run through them, then you can expand on one or two answers to kind of give a little more nuance because I recognize there's nuance to all of these. So, circular or no circular references?

No circular.

VBA or no vba?

No vba.

Horizontal or vertical model?

Vertical.

Excel Dynamic arrays, yes or no?

Yes.

External workbook links? Yes or no? No named ranges or no named ranges?

Named.

Do you follow a formal standards board when you're modeling?

Yeah, it's in my DNA.

Okay. Will Excel ever die?

It should.

Okay. I'll be curious to learn more on that one.

Yeah.

Will AI build the models for us in the future? Probably be interesting to watch. So should we use sheet cell protection in our models?

Yes.

Okay. Do you believe financial models are the number one corporate decision making tools?

No, they don't make decisions. Humans make decisions.

What is your lookup function of choice? Choose VLOOKUP index match. XLOOKUP or something else. I didn't ask.

XLOOKUP. XLOOKUP's a gift. I don't know why it took 30 years.

Yeah, I love XLOOKUP. It's so much easier to train XLOOKUP if somebody doesn't know Excel.

Yeah, makes sense.

Big fan there. All right, so which the one or two you'd like to elaborate a little bit?

Well, let's talk about will Excel die?

You know, because you said it should. Why should it die? Let's start there because that means we.

Haven'T evolved, you know? You know, this last 24 months in Excel, the evolution has been more than the last 20 years. Huge amount pressure. AI chat, GPT, Python coming in. If you think. What? In 1969 we sent people to the moon with Less computer power than what's in your iPhone.

Say what? Less than what's in my watch.

Yeah, exactly. So what I would hope is, I'm not saying Excel's becoming extinct or obsolete is I would hope it evolves to such an extent that it is almost a completely different beast. In 20 years, if we are still doing something that is just sort of like a user experience evolution of Excel, I think that'd be really surprising to me because look back at history, everything dies.

Yeah, no, that's what I always say. It's a question of when, not if. Just based on history. That's what I say now. You know, some, some people have answered, they'll say yes, but I hope not in my lifetime. I don't want to learn something all new. So any other one you want to elaborate on?

Okay, give me a real rerun of that. Should just run through those questions again.

So, circular. No circular.

Vba, no vba, no circular. So we'll do this quite quickly. There are two occasions I will use circular reference reluctantly, but the dangerous circular reference is as soon as you switch iterations on, you've given the model over to the computer. So I think Ian Chenille's view is it's okay if you understand it. Even if you understand that you switch iterations on, you know, we can all find new ways of screwing things up in a new way. And you, you've got problems at that stage. The VBA one is, in my world is there's not enough people that understand vba. So if I build something in vba, I'm creating problems for people who are using them.

That's usually the biggest, I think issue with VBA is downstream internally.

And it was just my team, we would build things in vba, Python R. But it was for our benefit. It didn't go anywhere else. And we used the data analysis off the back of that in order to.

Figure out that makes a lot of sense. So we're heading into our last questions here, just to wrap up. So the first one, if you could give our audience one piece of advice that they should follow to be a better modeler. What's your one piece of advice?

I think it goes back to what we said before is plan.

That's what I figured.

You know, just take, take a breath, take a knee, get a pencil out and just have a think. Even if you don't write anything down, just having a pencil in your hand is just going to halt you for a second. And you can save yourself an awful lot of time just taking A breath.

Great. Love that. So, last question before we let you go. If somebody wants to get in touch with you or learn more about you, what's the best way for them to do that?

First, if I move out the way, there's that thing there. If you can see it behind you.

You see it now. Investmentanalyst.com I love it. Great marketing there.

You know, so we, we. You know, I just. I'm not trying to build empires here. I'm just, you know, very experienced analyst. I've taught for a long time and I really love the teaching. And I'm not really teaching people Excel. I'm teaching people how to value companies. So, you know, I spend a lot of time with hedge funds, asset managers, bankers, private equity. So that's where I come from. Excel's always been financial. Modeling's always been a tool that I'm using to get to the point of being able to portray an insight.

I love how you said that because that's really how it should be treated. I had a interview who once said, remember, your product is not the spreadsheet. It's the insights and the decision making that you help drive because of the spreadsheet.

Yeah.

And it's basically what you said there. And I think if people remember that, you think about it differently.

Yeah, absolutely. Absolutely. Touche.

Well, thank you so much for joining us. I really enjoyed chatting with you today, Jeff, and look forward to sharing this episode with the audience.

Yeah, absolute pleasure. It's lovely to speak to you, Paul, always and every time. So thanks for having me on your podcast.

Financial Modeler's Corner was brought to you by Financial modeling institute. Visit fmi@www.fminstitute.com podcast and use code podcast to save 15% when you enroll in one of their accreditations today.