The Critical Role of Communication and Standard Definitions in Financial Modeling with John Yeldham
In this episode of Financial Modeler’s Corner, Paul Barnhurst sits down with John Yeldham, a leading expert in financial modeling and founder of Lodeum, a global online financial modeling training platform. John shares his experience leading and training teams of financial modelers across corporate and project finance, and discusses the importance of standardizing language, modular model design, and building trust in complex Excel models.
John Yeldham has 20 years of experience leading and training financial modeling teams in project and corporate finance, supporting deals and valuations for global funds, energy, and infrastructure, as well as smaller businesses. He created and refined modeling methodologies at BDO UK, Forvis Mazars, and for Lodeum, a global financial modeling training platform launching in 2026.Expect to Learn:
Models build trust, not just numbers
Connect finance with operations for better decisions
AI is a helpful junior analyst, but human judgment matters
Scenario analysis and storytelling are essential in models
Here are a few quotes from the episode:
"Standardizing language allows AI and people to understand and use models effectively." – John Yeldham
"A good modeler breaks down a complex model into functional blocks; this is key to building trust and usability." – John Yeldham
John provides actionable strategies for financial modelers to improve clarity, scalability, and trust in their work while leveraging advanced Excel techniques and modular design principles. This episode is a must-listen for anyone looking to elevate their modeling skills and build reusable frameworks for complex financial analyses.
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In Today’s Episode:
[00:00] – Trailer
[02:28] – Worst Model Horror Story
[05:20] – Transition to Own Business & Lodeum
[08:51] – Communication & Language Challenges in Modeling
[16:03] – Standardizing Language & AI Integration
[24:25] – Lambda Functions & Dynamic Arrays in Excel
[30:19] – Online Modeling Training Advantages
[36:43] – Favorite Excel Shortcuts
[38:22] – Most Challenging or Fun Spreadsheet Created
[39:42] – Rapid Fire: Modeling Opinions
[47:16] – Advice for Becoming a Better Modeler
[49:31] – How to Connect & Learn More
Full Show Transcript:
Host: Paul Barnhurst (00:00):
Financial Modellers Corner is the world's Premier modeling podcast. It is brought to you by Financial Modeling Institute, the world's leading financial modeling accreditation organisation. Welcome to another episode of Financial Modellers Corner. I'm your host, Paul Barnhurst. This is a podcast where we talk all about the art and science of financial modeling with distinguished modelers from around the globe. The Financial Modellers Corner podcast is brought to you by the Financial Modeling Institute. FMI offers the most respected accreditations and financial modeling, and that is why I completed the Advanced financial modeler this week. I'm thrilled to welcome our guest on the show, John Yeldham. John, welcome to the show.
Guest: John Yeldham (00:50):
Hello. Good to be here. Yeah,
Host: Paul Barnhurst (00:52):
We're excited to have you. So let me give a little bit about John's background and then we'll get to the questions. So John has 20 years experience in leading and training teams of financial modelers in both project and corporate finance, supporting deals and finance for global funds and developers in energy and infrastructure, as well as working capital valuations and deal models for smaller businesses. John created the BDO UK modeling methodology, renewed the methodology for makers, and recently created the methodology for the Lodeum Modeling training platform, which I know we'll get to talk a little bit about today. He specializes in utilising the latest Excel functionality including dynamic arrays and lambda functions, which are particularly helpful in creating functional modules for large global asset portfolio models. He is a regular contributor to the I-C-A-E-W Excel thought leadership, such as the 20 principles for good spreadsheet practise. I've read that one before. Great one and the Excel spreadsheet Competency framework. He is a founder of Lodeum, a global online financial modeling training platform, which will launch later this year. In fact, when you listen to this, it may have already launched, but it's launching this year. So John, love the background. I have to start every episode. Tell me your horror story, worst modeling experience, worst model you inherited, you built whatever it might be. Tell us that horror story.
Guest: John Yeldham (02:28):
While I have had clients who have hated models, not so much recently, but a long time in the past when I was getting started, actually the horror stories are really where it's most stressful, I think. And I did have one model. The model was complicated. It's gone through these changes and when you have models where they decide to change the periodicity or things don't match, the timelines don't match in various parts of the model, it gets complicated really quickly. And this was a difficult model to work with and they were in the process of an early part of their project and were just running out of money and they needed to get the covenants passed. And it was perhaps a real education to me about how much leeway there is in bank contracts around covenant testing that we could tweak this and that and that and say, well, if you move that from that company and that from that company and move that there and there without doing anything in terms of their operations, we can get that fixed.
(03:26):
And the reason I recognise as a horror story is that I had the FD sat there almost having a nervous breakdown, and at the end we got that number and he gave me a hug. He's the only client who ever hugged me at the end of a modeling session. That is my story. I can't say if I'm just talking about the badness of the model. The worst feedback I ever got was from a model that just didn't look very good. Actually, it was perfectly correct. They said, this is the worst model we've ever seen. It's not doing what we want. I went back, I changed some formatting, I reordered some cells, no changes, and they go, oh, well done. You managed to fix everything so quickly.
Host: Paul Barnhurst (04:04):
It's amazing how formatting perception can impact a model, right? In this case your model is fine. They just didn't like the way it looked,
Guest: John Yeldham (04:14):
But they get the impression that it wasn't fine because of the way it looked.
Host: Paul Barnhurst (04:17):
Yes, we draw conclusions. We all have our biases, but on your hug story, so I think you'll appreciate this, you'll get a laugh out of it. I had a boss I worked for, he hated physical touch for whatever reason, never had a massage. He was alive, never had no chiropractor, hated hugs. And I decided to take a new job and one of the other employees went to him and he was trying to convince me to stay, so they were trying to keep me and employee went to him. I go, I bet I can get Paul to stay if you're willing to give him a hug. And so that was the joke for my last two weeks. He's like, yes, I'll even give you a hug if you stay, but he said he gave you a hug at the end. That's what it reminded me of.
Guest: John Yeldham (04:56):
Yeah. And what percentage salary rise was that equivalent to? That's the real question.
Host: Paul Barnhurst (05:01):
Yeah. Okay. How much of a salary increase do I get with this hug? That was the real question. So I know you spent most of your career working at firms, but in the last year or so you started your own business. How did that come about? Tell us a little bit about the journey and what it's been like so far.
Guest: John Yeldham (05:20):
I like being a modeler. I like doing financial modeling. I like my time at the firms that I've worked for. I was often, because my background is a little bit more mathematical and more technical than perhaps the typical financial modeler, I could have gone to a different path in my career and ended up being a computer programmer or working for an investment bank or something like that. But I ended up for various reasons in practise, I liked doing that work and I was an expert. I liked the feeling of being an expert. I like helping people. So people always said I was patient and this is throughout the career, but what I didn't like was I guess the sausage factory, as some people call it this idea, you use your brain, you keep churning out the hours, and it's kind of tied to hours very often in firms in how you work.
(06:11):
So there's this pressure to work efficiently enough but not too efficiently. So you want to be as good as people need you to be, but no more because it confuses the way that people procure financial modeling work and you'd work through. And I did good modeling and I helped people and I helped businesses grow their technical capabilities through what I did. But I never really got a payoff for that work other than that hour by hour accrual and the salary that came to me and what I really wanted ever since I was young actually is my own business, my own equity and the idea that I could take this knowledge that I built up now in my head, put it into something, and that is lium, the training platform, put that knowledge into lium. And it's not true that you can just sit back when you're running a business, but at least I don't have to do the daily grind to get that information out from my head into something else.
(07:11):
It's there, it's encapsulated and it can deliver to what I can only deliver to 10, 20 people in a room. This can deliver to thousands across the globe and make me money. And so get equity and a profit, I get a pension that's not a pension. So that's why, and there was a certain point at which I just needed to step away from being in employment, otherwise it would never happen. I was getting too old and it was just going to just carry on drifting until I retired. So yeah, I just made a decision at one point. It really came about because Ian, my colleague at Lodeum came to me. We actually just played football together previously and he just had this idea for the training platform. So I was really looking for this, something from my side that gave me equity and used my knowledge and this was a perfect fit, just came together coincidentally, and we set it up and that was that.
Host: Paul Barnhurst (08:11):
Got it. Well, I appreciate you sharing, and I understand what you mean, kind of the sausage grind, whatever you want to call it, the daily versus doing something on your own. I went out on my own four years ago now and for the most part I've loved it. It's been a great journey. Definitely. It's an adjustment at first and it's a learning, but congratulations to you. So I would love to know, when we chatted about modeling, you talked about how a big part of it is communication and a language problem. Often with modeling there's a communication and language problem. I'd love for you to elaborate a little bit on that, why you think that is the case. Maybe give us an example of what you mean there.
Guest: John Yeldham (08:51):
I think I'm always conscious of this. I've always been conscious of maybe my language being slightly different from other people's, and I think in the world of financial modeling and the world of finance as well, but separately and in the world of accounting, there are certain sets of languages that have arisen. There are words that are used and those words, they're not necessarily consistent, they're slang and they really do a few things, some positive and some negative. Sometimes they provide a quick way to describe something so that people inside that bubble can communicate quickly and get things done. I know in financial modeling people say IDC, meaning interest during construction, you have to know it, but it's obviously quicker than saying interest during construction. At the same time, it creates a barrier for people coming into that business. It also creates a barrier for procurement because coming at it from the outside, I'm talking to people now that don't speak my language.
(09:56):
Finance is a bit of a weird case because who's really the customer there? But for finance modeling, it's quite clear I'm the provider and someone's my customer. If I can't talk their language and I'm just talking in financial modeling, Gogo, it's getting in way of procurement. So there's these bubbles and in terms of what happens in a financial modeling model, rather these cause problems because financial modelers don't know quite what finance people want, what words they want to use, and they end up having a mishmash of stuff in their own model, which includes some of the financial modeling slang as well. And you end up with this dissatisfaction with the end product. It's not clear, it doesn't communicate very well. It also, and this is something that I've thought about deeply recently, it inhibits the ability for financial modeling to break out from a set of individual experts.
(11:00):
You and me we're experts at financial modeling and Excel, and obviously we may not want to break it out, democratise the process of financial modeling, but if standards could be introduced for language, it allows it to break out that you can now start carving up the information in understandable nuggets. And you don't need these kind of little units of financial modeling expertise as we are to kind of absorb everything from everywhere. You've got a clear communication channel, it's all about inputs and outputs and interfaces between different groups of people. And the way that works in a model, it's between different parts of the model just to speak the same language. So it's really important and it's a big impediment in the whole process from action modeling. And I'm really coming at it from the point of view of a professional financial modeler who provides financial modeling services, but the same problems are going to occur within businesses as well. Very often the financial modeler is the geek and the attic kind of thing. They have one person they go, oh yeah, he's the guy or the girl that can do that stuff. And they do that stuff, but that weight of that expertise is sat with them because it can't be disseminated very well. The language hasn't been standardised and it just becomes a silo.
Host: Paul Barnhurst (12:26):
Why do you think the language hasn't become standardised? I mean obviously we had a lot of financial modeling training, we spent a lot of time focusing on functions model building in Excel. So why do you think the language is so fractured?
Guest: John Yeldham (12:41):
I think part of it is that in the world of finance, different banks may have different slang they use. And so certain terms are ambiguous and actually part of your badge to know that you can talk to a particular bank is the language they speak. And very often there's an attitude that you don't understand their business if you don't say the same words, even though you've said the same words to someone just like them and they understood that, but it can get in the way that way, and that's coming the world of finance as financial models don't control that world. Within financial modeling, there are lots of terms and lots of standards being written, so they exist, but they talk about things in financial modeling terms, not in terms of the outside world. And what's really missing is that standardisation of the interface between the two.
(13:46):
And they'll take an example of how it's easy to not use the right standard. So this isn't a standard, it should be, and it certainly would be advised by certain methodologies, but when people write or when people do an IRR calculation in the Excel, they often use the XIRR formula and they will write in their spreadsheet XIRR as the row label as the label for that item. But XIRR is just an Excel function. It's not a real thing. And yet you see more than half of all the models out there that have IRR, it's very often labelled XIRR. I mean that's clearly financial modeling slang and maybe it is derived from Microsoft, so it has a reason why where it came from, but it's meaningless in terms of that interface with the finance people. They don't use the word XIRR, you are making a model for them. So that's just a digital example, but that's the sort of thing that I think has risen and because the finance models are not necessarily finance people, some of 'em are, they start from the Excel, they've almost learned finance from the Excel, right? And so they talk in that language and really they need to talk in the finance language.
Host: Paul Barnhurst (15:16):
XIRR is a great example, right? There's no concept. If you Google search XIRR, you're only going to find the Excel function. You're not going to find a concept that of IRR yes has to do with how it's calculated and how it handles the period, whether it's beginning or end or to always try to remember. I don't do a lot of, I rrr in my calculations I was int and a usually you're just building a forecasting model for the p and l, not calculating IRR. So I can never remember was like which one's IRR and which one's XIRR? But that's not the point here. I think the point is it's confusing, right? For the average person, I think that's a great example. So I mean what do you think it's going to take? I mean, how do you think about this as far as standardising language?
(16:03):
Because obviously you have companies that have their own slang they want to use and may not want to give it up. And then you have non-finance people that are seeing things where certain slangs being used. So we've done a pretty good job of putting standards out there for models, whether it's fast or smart, not saying everybody's adopted, it still quite a bit depends on the industry and what you're doing and your level of modeling how much you adopt. But what do you think it will take to do a better job standardising maybe adopting something on the language front?
Guest: John Yeldham (16:37):
I think what it'll take is an investment by someone to make a modeling process that works really efficiently and really fast because the end game from all the standardisation isn't so much the small improvements that just come from tweaking language here and there. It's the big improvements that come from once you've standardised it, you can create libraries, you can create modules, you can create reusable code, you can create reusable code that's well documented and understood and those modules can be understood by an AI and therefore the AI has Lego pieces it can work with well and communicate well with people who want to buy your services. Those people who want to buy your services are speaking a particular language. And I'm actually writing a paper at the moment about the way that AI examines models and what's clear is that AI looks very much from a financial modeling viewpoint.
(17:44):
If you ask it to review a financial model, it kind of puts its financial modeler hat on. It doesn't put its finance hat on. So if we standardise the language, we could create these modules and libraries that an AI can understand from a finance perspective, utilise all its finance knowledge, talk to finance people, and it completely changes the way we think about building a financial model because now a financial modeler's job isn't actually the assembly of the financial model because the AI can do that. It's probably to some degree understanding the client needs, but the AI can do quite a lot of that. But what a financial modeler can do is create these modules and designs. So as a modeler, you are creating a reusable pattern that other people can use. So your value moves from being churning from the sausage factory, churning out lots and lots of stuff to creating the best example you can of a particular set of functionality. And the people who can do that can do the best financial modeling in certain modular elements, I think will succeed. And the ones that just turn the handle, they may not be so much of a place for them in the future.
Host: Paul Barnhurst (19:04):
While my background is in fact, I am also passionate about financial modeling. Like many financial modelers, I was self-taught. Then I discovered the Financial Modeling Institute, the organization that offers the Advanced Financial Modeler program. I am a proud holder of the AFM. Preparing for the AFM exam made me a better modeler. If you want to improve your modeling skills, I recommend that AFM program podcast listeners save 15% on the AFM program. Just use Code Podcast. So a lot of this all, but there's a big, and that makes sense. I hadn't thought about the AI side of it, but standardising this makes it much easier to go quicker that speed as you have a common language for AI to work off versus today every model it's trying to, what does this mean?
Guest: John Yeldham (20:06):
And it's about the, I'm going to use the word memo sphere, I'm not sure it's the right word, but the sense of ideas in the globe. One of the things that standard language also does is provide a marketplace for people who want to buy financial modeling service or need them to understand what's going on because you have a front which has a common sense of language. And if they can understand that, they can then compare all the various people who are trying to provide financial modeling services to them. At the moment, if they look at different financial models, they're all saying slightly different things. It doesn't make sense. It's difficult to compare. So it really helps in the procurement aspect as well. I dunno if you've heard of hotels law or it might be hotel is or I think it's hotels law about the proximity of things.
(20:53):
The famous example is on a beach, a long beach, the two ice cream vendors would sit themselves next to each other in the middle of the beach because everyone from one side would go to one and everyone would go from the other side would go to the other. There isn't actually any benefit for them moving apart even though they're competing against each other. And similarly, if the financial modelers can go into a space, a kind of a semantic space and share a common place, then people will find it. And let's face it, AI is going to be finding them. So we can talk specific, sensible, standard language. The AI is going to find that marketplace much more easily than someone who does talks complete different words over here. Well, the AI will just stick around the group of people. You speak the same language and that's where you'll get your procurement from.
Host: Paul Barnhurst (21:42):
And I would imagine the language, and I want to get your thoughts kind of like we have different modeling standards is that language is going to be probably a little different. There's areas be standardised, but project finance versus fp and a versus m and a, I wouldn't imagine across all of you're going to get to the exact same standard, but would love to get your thoughts. You see it as standards across the entire modeling industry or sectors or how do you think about it?
Guest: John Yeldham (22:09):
Yeah, I mean I think I base it on teams actually rather on sectors or departments. The way I think about it is humans obviously evolve their businesses to work in certain teams and they've chosen to work in those groups and those are the same groups by the way that procure for services and will try and develop an outcome. So let's align those bubbles with the teams. So I don't never really understood what's the difference between, I don't know, m and a and other types of deal advisory. It can be very fine details, but if a team calls itself m and a and you know what m and a does as that specific team, then create that bubble around them and actually create the AI that they can talk to directly in a very familiar language and then that plugs into the modules. So these modules aren't mutually exclusive, I guess they overlap, they combine just like human teams do. There's no limit. There's no reason to have to make them mutually exclusive. You just make as many as you need that fit the roles you need to make them
Host: Paul Barnhurst (23:25):
Sure. And you could build your skills and your instructions for your AI so that it understands your standard language and your modules that you built. So you're kind of viewing each team or each group having those standardizations. That helps. That makes a lot of sense. The next question I want to shift a little bit of gears. We'll get into training here in a minute before we get there. When you and I chatted, it was last week, we talked about lambdas and dynamic arrays in models and there's a lot of differing opinions on this. I mean, I love dynamic arrays. I can't say I've got much into lambdas. I've used Let some, I don't model as much any ev do a lot of modeling these days, but would love to get your thought of what's the right approach, how do we balance the benefits of these functions with the increased complexity, right? Because there's a tonne you can do with Lambda. There's a lot you can do with dynamic arrays, but they can also get really complex and long in a hurry.
Guest: John Yeldham (24:25):
It's a question of trust, right? When someone looks at an Excel workbook and a financial model, they want to be able to read it. One of the benefits of Excel is that it's not a black box like computer programmes. If someone has software developed for them, they don't really get to see inside it. So Lambdas only work to the extent that they can be trusted and people can read them. Now if you are making loads of new functions, even if they're little functions, there's a lot of them that's going to be a problem for someone to read. You can kind of expect everyone to learn Microsoft stuff, the functions that they provide to a point I know obviously not everything
Host: Paul Barnhurst (25:12):
The main functions we need for a model. If you're working with finance people, the average person can figure out the 15, 20 functions that cover 90% of what we do. If
Guest: John Yeldham (25:20):
I have a client and I've given them a model, I don't want them to feel that they don't understand that model, so I can't load it with loads of lambdas new functions they don't trust and they have to learn every time they look at the model. Similarly, I can't give them really big lambdas with massive stuff inside because it's really difficult to gain trust in that just because it's so big and complicated. So what we're talking about really is a smaller number, but the way I think about it is it works and the way you get trust is you have a standard, this is coming back to standard language again. You have a standardised set of functions that industry agrees on over and above Microsoft up to now Microsoft dictates what those standard functions are in Excel. If as an industry we can agree on a standard set of functions that we want that make more sense, that do what we need to do in the financial modeling realm, when we stick to those functions, then anyone reading that model just refers to that standard library of functions, those Lambda functions and says, oh yes, well this is the depreciation calculation function.
(26:41):
It's very standard. So for instance, I would be happy to have a depreciation calculation. I wouldn't have a whole fixed assets calculation. I know some people started doing these giant lambdas, but I'd do a depreciation calculation and just make it what we need it to be in a financial model. The financial model has structures over and above what Excel insists on, like broker consistency, all that kind of stuff. So yeah, it only works I think if you have either you have a very small number of Lambdas so that every time a client looks at a model you just have to explain a couple of things to them or you have a standard library. We effectively create a new piece of software as an industry and detach ourselves from Microsoft, the hand that feeds us to some extent and say, look, actually building upon Excel, we can create this new product that we all agree how it works and now you can read it and it makes better models, makes better, more readable models.
Host: Paul Barnhurst (27:46):
Bad financial models can lead to bad decisions or worse. So how do you minimise the risk of a bad model? You make sure the models you build are great Financial Modeling Institute developed the Advanced Financial Modeler accreditation programme to help modelers like you. The AFM programme offers a step-by-step approach to building world-class financial models. The programme ensures that you know the best practises in model design and structure and will help you brush up on your Excel and accounting skills too. Be the one on your team to build great models. If you want to impress your boss and your clients, get a FMI accredited podcast listeners, save 15% on the AFM programme. Just use code podcast at http://www.fminstitute.com/podcast.
(29:14):
It's kind of like the people who have their own code of VVA. So yeah, I see what you're saying and I agree at the end of the day, trust is the key part. If someone has a lot of trust in what you're building, especially if it's a recurring relationship, they're going to trust more and more of the formulas they built. But if you go in especially a first engagement, they're expecting a fairly straightforward model. They can understand it's full of Lambda, you did prep them that you build differently to start with is probably not going to end well.
Guest: John Yeldham (29:44):
It might end well eventually, but it takes a lot of work to make it end. Well, that's the problem. Yeah,
Host: Paul Barnhurst (29:49):
There is a trust you have to gain. I think I like how you said that is in Bennett ahead on the show while back he said we traded trust as models, whether ai it, whether we built it, whether you use lamp, it's doesn't matter. At the end of the day we traded trust and that's what you have to remember. Alright, let's talk training first. Tell me a little bit about your company. Why is the timing as far as now for a modeling training company, the type of training you have, just take a few minutes and tell us a little bit about it.
Guest: John Yeldham (30:19):
So I noticed that normal classroom training and online, which I'll come to consists of a trainer standing in front of a class demonstrating something in Excel and the other people in the class follow on. And because it's a classroom environment, you get to talk to the trainer. That is very valuable but also because a classroom environment, everyone's moving at the same speed and what you are doing in that class, because these training courses are very expensive and you want to cram in as much difficult stuff as possible. In fact, some banks or sorry say elite institutions often use these training courses to try and assess their own staff secretly by how well they get through these training courses. So they move quickly and that can leave people behind. They move quickly through doing the work and it feels like you're learning a lot, but there's a whole host of understanding repetition that's not done and actually you're doing this in two days or four days or whatever it is and then you will forget it down the road because you've only done it once unless you continue to use it regularly.
(31:44):
So you take all of these things together, plus the fact that classroom training is physically difficult because everyone has to go to the same place. It's expensive and difficult to arrange everyone's timetables and I just thought an online training platform works so much better in lots of different ways. Now it doesn't have a trainer you can talk to at front of a class and that is probably the biggest drawback. What it does do is you can train any time you log on, you can do 20 minutes here, 20 minutes there. And actually what the pedagogical pedagogical research shows it's best to do a bit, come back to it later, think about what's happened in the past. It strengthens the learning. So you can space this learning out, you can mix things up, you can get to a much deeper level. You can allow users to go off on their own courses and think, well actually I'm interested about this subject now.
(32:54):
And they're not tied into that linear progress that's in the classroom. So it just works so much better. And yes, there are online courses I think probably we're conscious of that and that's perfectly good. You've got stuff like course and so on. They will still generally be of the format of someone delivering a video of 10 minutes or 20 minutes long saying do this, do this, do this, do this, do this. Now you've done that, answer a few questions and it's just not that engaging and it isn't the best learning you can have now accept all of these have value, but we think there is a niche for better learning experience in financial modeling that opens up to the globe as well because there's plenty of countries around the world where people really struggle to get classes. It's fine. In Britain, there's loads of financial modeling courses in London. If you go to Nigeria or South Africa, there is a few but not so many. And then further afield, people still need to build infrastructure, still need to run companies in any country in the world and yet there's no centres of excellence for financial modeling in some of those countries. So it just allows it to spread everywhere and everyone can learn at their own pace.
Host: Paul Barnhurst (34:23):
A hundred percent agree. It's amazing how online training that ability has democratised learning from availability and from a cost standpoint, right? Yeah, there's expensive online training and there should be, don't get me wrong, but it allows it to be delivered at a lower price point everywhere from lots of free stuff out there to expensive but still less expensive than if you have to fly to person halfway across the world and fill 'em in a road for a week. Not just from a cost standpoint but from a time and a commitment and a resource standpoint.
Guest: John Yeldham (35:05):
It's interesting how we'll see because launching in a few months, but obviously there's a premium attached to these expensive courses which are you will do three days in a row and your boss will fly you out to do this course. There is a certain cachet attached to attending those courses and having someone pay for you to do it, you feel like you're an important person, you're a valuable employee and all that kind of stuff. And I get all that, but the market for that is obviously smaller just because like you say, when you open the door it's a bit cheaper and it's cheaper for time, it's a bit cheaper and it's cheaper for time, organisation, money, then it feels like there are other people and I think not everyone is really interested in that prestige element anyway. A lot of people just want to learn and do the stuff. And so that sort of thing suits a much better than a kind of prestige course where they say, ah, hi, I've attended this so I've got taught by this expert. So there should be a market for it. There should be people there.
Host: Paul Barnhurst (36:15):
Yeah, I mean I wish you the best. There definitely is room out there for digital learning. Lots of people have built great platforms, so I hope that all goes well for you, but congratulations on that. Enjoy that journey. I'm going to kind of shift gears here to a couple standard questions we ask and then we're going to move into rapid fire and I'll lay out how that works before we get there. But first I got to ask, I ask every guest this favourite Excel shortcut, what is it?
Guest: John Yeldham (36:43):
Control C?
Host: Paul Barnhurst (36:46):
Ah, the good old simple one.
Guest: John Yeldham (36:48):
Most important. I mean that is the most important court shortcut. People don't copy their stuff enough. Leave it at that.
Host: Paul Barnhurst (36:58):
I would say it's probably the most used shortcut as well. It has to be close maybe F two or F four, but it's definitely up there if it's not number one in the good old days, wasn't it control S before auto save?
Guest: John Yeldham (37:13):
Oh yeah. Right. Yes. We see I've never really been a keyboard type. Well that was a question later, isn't it? And so I'm used to this habitual reach up to the top back in the day when the save used to be at the top of the screen rather than where it is now. I used to reach up and press the save icon and do that every half an hour. Something that'll go in your brain. I remember that time I lost five hours work.
Host: Paul Barnhurst (37:38):
No, I can appreciate that. For a good part of my career, I wasn't much of a mouse, I was much more keyboard. I mean much more mouse than keyboard. I've mouse switched most things to shortcut, but I still mix it up. I'm not one of those. Yeah, the people, they're keyboard warriors. If they use your mouse, you can't be a modeler. Come on. You can could have slow you down. Sure. But it depends on what type of modeling isn't just about speed. That's kind of what I like to think about. If it's just about speed that I agree with you, but it's about so much more. Alright,
Guest: John Yeldham (38:10):
Speed and speed. Yeah.
Host: Paul Barnhurst (38:12):
What's the most unique or fun thing you've created with a spreadsheet? It doesn't have to be in your work life. It could be personal, it could be whatever.
Guest: John Yeldham (38:22):
I wish I could say something was fun. I never really viewed spreadsheets as fun. So I'm going to say the most fun was the most technically accomplished spreadsheet I made, which was one reasonably recently I did one for a large carpark company and it needed to effectively work out the priority of car parking spaces for bookings in advance. And the reason I was so proud of it was that I used dynamic arrays to do the sorting with a bit of PBA, but I realised there's a lot you can actually do with dynamic arrays and then it's just an F nine. There's no buttons to pressed to run a macro or anything like that to process what was a moderately complicated algorithm. So yeah, I think that one, I never described an Excel model as fun. I'm afraid.
Host: Paul Barnhurst (39:20):
That's okay, I get it. Everybody has a different way of looking at it and that one sounds like an interesting challenge for sure to think through that and use dynamic race. We're going to move into rapid fire now, so I'm going to lay out how this works before we get started. The idea is you have to pick a side, a yes or a no, you can't say it depends. So no being a consultant,
Guest: John Yeldham (39:42):
Fine.
Host: Paul Barnhurst (39:42):
And then what happens at the end? And some of these aren't yes and no. There's somewhere you get some options. There's about 15 of 'em. We're going to go through 'em in relatively quick order. Then at the end we'll pick a few, maybe two or three that you want to elaborate on. I realised there's nuance to every one of these questions or almost every one. I'm going to guess there's a few you'll have very strong opinions on. There's others you'll be like, but what about this? But what about that? So you ready? Ready? Alright. Circular references in your models. Yes or no?
Guest: John Yeldham (40:14):
Yes.
Host: Paul Barnhurst (40:16):
VBA?
Guest: John Yeldham (40:18):
Yes.
Host: Paul Barnhurst (40:19):
Lambdas in financial models?
Guest: John Yeldham (40:22):
Yes.
Host: Paul Barnhurst (40:23):
External workbook links?
Guest: John Yeldham (40:26):
No,
Host: Paul Barnhurst (40:28):
I figured as much. Should models always be print ready?
Guest: John Yeldham (40:31):
No.
Host: Paul Barnhurst (40:32):
All right. Is there ever a situation where merged cells are acceptable?
Guest: John Yeldham (40:39):
No.
Host: Paul Barnhurst (40:41):
Should financial modelers learn Python in Excel?
Guest: John Yeldham (40:45):
No.
Host: Paul Barnhurst (40:47):
Should financial modelers learn power query in Excel?
Guest: John Yeldham (40:51):
Yes.
Host: Paul Barnhurst (40:52):
How about power bi?
Guest: John Yeldham (40:54):
No.
Host: Paul Barnhurst (40:55):
Should every financial modeler be able to build a fully integrated three statement model?
Guest: John Yeldham (41:01):
Yes.
Host: Paul Barnhurst (41:02):
Okay. Will excel ever die?
Guest: John Yeldham (41:06):
No.
Host: Paul Barnhurst (41:08):
Have you used AI to help you build a model in Excel?
Guest: John Yeldham (41:13):
Yes.
Host: Paul Barnhurst (41:14):
Okay. What financial statement is most important for modelers? Income statement, balance sheet or cashflow statement?
Guest: John Yeldham (41:22):
Cashflow.
Host: Paul Barnhurst (41:24):
Of
Guest: John Yeldham (41:24):
Course,
Host: Paul Barnhurst (41:25):
Of course. I like it. Favourite LLN. Do you like Claude copilot chat, GPT or something else? Claude. Claude. If you could only pick one for all your models going forward, would you pick being able to do a sensitivity analysis or a scenario analysis
Guest: John Yeldham (41:44):
Scenario? I might need the question clarified, but scenario.
Host: Paul Barnhurst (41:48):
Yeah. And we could clarify at the end if you want because I know that could go in a lot of places on that one. Do you believe financial models are the number one corporate decision making tool?
Guest: John Yeldham (41:58):
No.
Host: Paul Barnhurst (41:59):
Okay. What is
Guest: John Yeldham (42:01):
Back of a fag packet?
Host: Paul Barnhurst (42:02):
Hard to argue with that. Okay. What is your lookup function of choice?
Guest: John Yeldham (42:07):
Some
Host: Paul Barnhurst (42:10):
Ah, old sum. Yeah, you could definitely use that. I can't say I use that one a lot for lookup. I think you're the first one is given that answer. I think I've had some product once, but love it. Alright. I'm sure there are a couple you wanted to elaborate on there.
Guest: John Yeldham (42:24):
Yeah. Okay. Circular references. I said yes because yes, in the future and my light's gone. It's good enough though the lighting isn't it? Right? So circular references I said yes because they're going to make a comeback. Dynamic arrays changes the way that circularity are calculated in Excel, and I think that will change the possibilities of making models that work quickly with circular references. There's a challenge to making them auditable, but I think they are here to stay. They will work better in future.
Host: Paul Barnhurst (43:05):
Okay. Was there another one you wanted to touch on?
Guest: John Yeldham (43:09):
Yes. Will Excel. You said something about will excel ever go away? Will excel
Host: Paul Barnhurst (43:16):
Die?
Guest: John Yeldham (43:16):
Yeah. Will excel ever
Host: Paul Barnhurst (43:17):
Die?
Guest: John Yeldham (43:18):
Right. Excel into three things. There's the Excel you use to make a financial model. There's the Excel you use to use the financial model and there's the Excel format that governs what a spreadsheet is. That is interchangeable. Exchangeable. Will the Excel as a creative tool die? Yeah, quite possibly. You can build Excel files without Excel. Will the Excel output die to a degree and maybe completely because people will make output things that take an Excel file and explain them better. Eventually. Will the Excel file format die? No. So the middle thing will endure and create a kernel that will persist forevermore. Well, until we become robots or whatever it is we become,
Host: Paul Barnhurst (44:11):
There's one person who said yes until we have a g, I then alves are off. And I was like, okay, fair enough. I could live with that one. Are there any others you wanted to elaborate on? I think you wanted to ask for clarification on sensitivity and scenario. You kind
Guest: John Yeldham (44:28):
Pause
Host: Paul Barnhurst (44:29):
There. If
Guest: John Yeldham (44:29):
What you mean is sensitivity that allows people to just kind of randomly go, well, what happens if this goes up by a percent or this goes down by 10%. Whereas scenarios are, this is a case, a thing that could happen. Sensitivities have almost zero value because no one can quantify the variation that they expect. Whereas scenarios are real cases that correspond to something that we know could happen in real life. So they actually give some value back. I just don't think sensitivity is very useful at all.
Host: Paul Barnhurst (44:58):
I think they're a little helpful when the relationship may not be what you expect to figure out how sensitive something is to realise, oh, that has a much bigger impact than I expect. If we move at one basis point it goes three. So I think the relationship on sensitivity could be helpful in certain situations. But as a general rule, I agree with you. I think scenarios are underutilised and often sensitivity is confused for scenarios by people. And it's like no scenarios are specific situations that are plausible that you're figuring out how that changes things and how should I behave based on this situation and how likely it's so I would choose scenarios as well for what it's worth.
Guest: John Yeldham (45:38):
And unless it's a very numerical thing. So I've done district heating, which has plants and so on, and when the temperature changes in a city with district heating, it's a nonlinear relationship of how this plant turns on, this plant turns off and so on. That's a very specific sensitivity. And their sensitivities are useful because actually you're taking a number, you're extracting a number. It's quite formulaic in the end. It's complicated but formulaic. But if you give someone a graph who's making a decision and you say, well, this is the graph with the sensitivities, look at all those points. You can see it's a thousand points in a line or you give them four things and say these are the four possible things we think might happen. There's really no value to a human because they find it really difficult to make a decision based on a curve, whereas they can understand what those four scenarios mean.
Host: Paul Barnhurst (46:29):
Very good example. I appreciate that. I like the way you kind of laid that out, but you also make a good point when you're dealing with some non-linear stuff, activity can become more valuable. But yes, as far as decision-making goes, I think you have to pick scenarios personally now, no one's ever going to say sensitivity going forward after we listen to this episode, not pussy. I think I've only got scenarios so far. I've only asked it maybe five, 10 times. So if I don't get any disagreement that comes off the board of rapid fire questions like, all right, nobody ever disagrees. Last question here, before we just wrap up and let people know where they can connect with you. If you could offer any final advice to somebody listening to be a better model or something they should be doing, what would it be?
Guest: John Yeldham (47:16):
I think other than obviously signing up for Logum when it launches, I think they could do a lot by taking a step back, zoom out. I think a lot of modelers focus on the individual calculations and actually return to this idea of modularization. They should just take a step back. What does a senior debt module do? What are the inputs and outputs of it? Just take some time to think about it in those terms because I think people aren't abstracting it in that way enough. And when you do that, you turn a business into a series of units and each unit is a little bit more manageable. Otherwise, the only way to really be a good modeler is to have a perfect stream of thought. And you start from the beginning of the model, you've basically pictured the entire model in your head of one go. And that's actually really, really difficult. So decompose, turn into blocks, understand those blocks. And I'm not talking about the little blocks like you get in fast methodology for instance, which is one calculation I'm talking about the whole of senior debt, for instance, as a block. Think about it in those terms and that will give you a grounding for everything you do going forward and make the work manageable in chunks instead of what looks like an impossibility. And let's face it, a financial model as a whole is an impossibility, right? Yeah.
Host: Paul Barnhurst (48:39):
It can feel impossible if you look at the task and totality versus breaking apart, it can feel overwhelming. Totally agreed. So for audience wants to get in touch with you, learn more about you, what's the best way for them to do that?
Guest: John Yeldham (48:52):
Get in contact with me on LinkedIn. It's John Yel, it's John with an H and Yeldham with an H as well. I'm not quite sure how to say my own name really. So I say it's Yda like Beckham, Y-E-L-D-H-A-M. Anyway, you can find me on LinkedIn. I think I'm, there may be one or two Johnny Elms in the entire world, but only one of them has a moustache so I can be found. And then to take a look at, we have a page for Logum, it's L-O-D-E-U m.com where you can find out about what will be happening in the future in a few months, hopefully I'll have another announcement and we'll have the full site up and running. And then please take a bigger look and hopefully some of you might sign up.
Host: Paul Barnhurst (49:31):
Good luck with that. I hope you get lots of signups. Again, congratulations on the business. Thanks for joining me today. I've enjoyed chatting with you, John.
Guest: John Yeldham (49:39):
Yeah, it's been great. Thank you very much. Thanks for having me.
Host: Paul Barnhurst (49:41):
Financial Modeler's Corner was brought to you by the Financial Modeling Institute. This year, I completed the Advanced Financial Modeler certification, and it made me a better financial modeler. What are you waiting for? Visit FMI at https://fminstitute.com/podcast/ and use Code Podcast to save 15% when you enroll in one of the accreditations today.