The Critical Role of Communication and Standard Definitions in Financial Modeling with John Yeldham

Speaker A: Finance is a bit of a weird case because who's really the customer there? But for financial modeling, it's quite clear I'm provider, uh, and someone's my customer, uh, if I can't talk their language and I'm just talking in financial modeling gobbledygook, it's getting in way of procurement. So there's these bubbles and in terms of what happens in a financial modeling model that 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.

Speaker B: Financial Modeler's Corner is the world's premier modeling podcast. It is brought to you by Financial Modeling Institute, the world's leading financial modeling accreditation organization.

Speaker C: Welcome to another episode of Financial Modelers Corner. I'm your host, Paul Barnhurst. This is a podcast where we talk all about the art and science of, uh, financial modeling with distinguished modelers from around the globe. The Financial Modelers Corner podcast is brought to you by the Financial Modeling Institute. FMI offers the most respected accreditations in 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 Yeldom. John, welcome to the show.

Speaker A: Hello. Good to be here.

Speaker C: Uh, yeah, 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 uh, financial modelers in both project and corporate finance, supporting gills 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 at UH Forvis Mazars, and recently created the methodology for the Lotum modeling trading platform, which I know we'll get to talk a little bit about today. He specializes in utilizing the latest Excel functionality, including dynamic arrays and lambda functions, which are particularly helpful in creating functional models for large global asset portfolio models. He is a regular contributor to the ICAEW Excel thought leadership, such as the 20 principles for good Spreadsheet Practice. I've read that one before. Great one. And the Excel spreadsheet competency Framework. He is a founder of Loadum, a global online financial modeling training platform which

Speaker B: will launch later this year.

Speaker C: 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.

Speaker A: 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, uh, 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. And they were in the process of an, uh, 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. And the reason I recognize it as a horror story is that, you know, 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 to ever hugged me at the end of a modeling session. So that is my story. I mean, 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. But 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, well done. You've just. You managed to fix everything so quickly.

Speaker C: It's amazing how formatting perception can impact a model, Right? In this case, your model was fine. They just didn't like the way it looked.

Speaker A: Yeah, but they get the impression that it wasn't fine because of the way it looked.

Speaker C: Yes, we draw conclusions. We all have our biases. But on your hub 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. Like, never had a massage in his life. 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, you know, 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 like the joke for my last two weeks. He's like, yes, I'll even give you a hug if you see stay. So. Well, you said he gave you a hug at the end. That's what it reminded me of. So, yeah.

Speaker A: Uh, and what percentage, salary wise was that equivalent to? You know, that's the real question.

Speaker C: Yeah, okay. Like how much of a salary increase do I get with this hug? Um, that was the real question. So I know you've 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.

Speaker A: I like being a modeller. I like doing financial modelling. 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. You know, I could have gone to a different path in my career and ended up being like a computer programmer or working for an investment bank or something like that. But I ended up, for various reasons in practice, I like doing that work. And I was, uh, an expert. I liked the feeling of being an expert. I like helping people. So, uh, like 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 just keep, you use your brain, you keep churning out the hours and it's kind m of tied to hours very often in firms in how you work. 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, um, and I helped businesses, um, 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 on 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 Lodium, um, the training, uh, platform. Put that knowledge into Lodium. 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 of my head into something else. It's there, it's encapsulated and it can deliver to what I can only deliver to 10, 20 people in a room. It can deliver to thousands across the globe and make me money. And so I get equity and I get a profit. I get a pension. That's not a pension. Um, so that's why 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, um, yeah, I just made a decision at one point. It really came about because, um, Ian, my colleague at Lodium, came to me. We actually, um, just played football together, um, previously and he just had this idea for the training platform. So I was really looking for this, um, 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.

Speaker C: Got it. Well, I appreciate you sharing and I, I understand what you mean. The, uh, you know, 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, talk about how a big part of it is, uh, communication and a language problem, often book model, like 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.

Speaker A: 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, uh, certain sets of languages that have arisen. There are words that are used and those words, they're not necessarily consistent, they're slangy and they, they really do a few things, some positive and some negative. Sometimes they provide a, a quick way to describe something so the people inside that bubble can Communicate quickly and get things done. Um, 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. Um, at the same time it creates a barrier for people coming into, 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. Uh, finance is a bit of a weird case because who's really the customer there? But for finance remodeling it's quite clear, you know, I'm the um, provider and someone's my customer, uh, if I can't talk their language and I'm just talking in financial modeling gobbledygook, 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, uh, 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, uh, I've thought about deeply recently, it inhibits the ability for financial modeling to break out from a set of individual experts. You and me, we're experts at financial modeling, um, and Excel. And obviously we may not want to break it out, democratize 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. Because you've got a clear communication channel. It's all about inputs and outputs and interfaces between different, 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, financial modeling. And I'm really coming at it from the point of view of a, uh, professional financial modeler who provides financial modelling services. But the same problems are going to occur within businesses as well. Very often the financial modeler is, you know, the geek in 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 you know, that weight of that expertise is sat with them because it can't be disseminated very well. The language hasn't been standardized, um, and it just, it just becomes a silo.

Speaker C: Why do you think the language hasn't become standardized? 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?

Speaker A: I think part of it is that in the world of finance, different banks may have different slang they use. And so certain terms, um, are um, ambiguous. And actually part of your badge to know that you can talk to a particular bank is, you know, 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 that they understood that. But it can, um, get in the way that way. And that's coming in the world of finance. We don't, 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, um, the outside world. And what's really missing is that standardization of the interface between the two. Um, and I'm going to take an example of how it's easy to not use the right standard. So this isn't a standard. It should be, um, and it certainly would be advised by certain methodologies. But when people write or when people do an IRR calculation in 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, uh, it's very often labeled xirr. I mean that's clearly financial modeling slang. Uh, maybe it's derived from Microsoft. So it has a reason where it came from. But it's meaningless in terms of that interface with the finance people. They don't use the word xior. Uh, you're making a model for them. So that's just a little example. But that's the sort of thing that I think has arisen. And because the financial models are not necessarily ex finance people, some of them 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

Speaker C: the finance language, xir is a great example, right? No, 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. It has to do with how it's calculated, how it handles the period, whether it's beginning or end or try to always try to remember because I don't do a lot of IRR in my calculations. Right. I was in stna and you're just building a forecasting model for the P L. Not, not calculating irr so I can never remember. Ah, always like which one's IRR and which one's X irr. 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 standardizing language? 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 slings being used. So you know, we've done a pretty good job of uh, putting standards out there for models. You know, whether it's fast or smart, not saying everybody's adopted. It's 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 uh, do a better job standardizing, maybe adopting something on the language front?

Speaker A: 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 standardization 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 standardized 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 could work with well and communicate well with people who want to buy your services. Those people who want to buy your service are speaking a particular language. And uh, 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. If you ask it to review a financial model, it kind of puts its financial model a hat on it doesn't put its finance hat on. So if we standardize the language, we can create these modules and libraries that an AI can understand from a finance perspective, utilize 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're 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. Um, and the people who can do that can do the best financial modeling in certain modular elements will, will, I think will succeed. And the ones that just turn uh, the handle, they may not be so much of a place for them in the future.

Speaker B: While my background is in FP and A, 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, 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 the AFM program. Podcast listeners save 15% on the AFM program. Just use code podcast.

Speaker C: So a lot of this, uh, not all but there's a big. And that makes sense. I hadn't thought about the AI side of it, but standardizing 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 understand what does this mean?

Speaker A: And um, it's about the. I'm going to use the word memosphere. 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 from natural modelling 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 uh, the moment. If they look at different financial modelers, 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 don't know if you've heard of hoteliers, uh, law or it might be hoteliers. I think it's hotellers law about the proximity of things. The famous example is on a beach, A uh, 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 from 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 semantic space and share a common place, then um, people will find it. And let's face it, AI is going to be finding them. Right? So we can talk specific, sensible standard language. The AI is going to find that marketplace much more easily than someone who does talks completely different words over here. Well, the AI will just stick around the group of people. You speak the same language, right? And that's where you'll get your procurement from.

Speaker C: And I would imagine the language. And I want to get your thoughts. You know, kind of like we have different modeling standards is, you know that language is going to be a little different. There's areas that we standardize, but project finance versus FP and A versus M, M and A. I wouldn't imagine across all of them you're going to get to the exact same standard. But we'd love to get your thoughts. You see it as kind of, you know, standards across the entire modeling industry or kind of sectors or how do you think about it?

Speaker A: 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 evolved their businesses to work in certain teams and they've chosen to work in those groups. So let's. And um, those are the same groups, by the way, that procure for services and we'll try and develop an outcome. So let's align those bubbles with the team. So I don't really, I never really understood what's the difference between, I don't know, M and A and other types of deal advisory. You know, it can be very fine details, but if a team calls itself, uh, M, M and A, and you know what M and A does as that specific team, then create that bubble around them, 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 the mutually exclusive. You just make as many as you need that fit the roles you need to make them.

Speaker C: Sure. And you could build your skills and your instructions for your AI. Right. So that it understands your standard language and your modules that you built. So you're kind of viewing each, each team, each group having those standardizations. Okay, that. That helps. That makes a lot of sense. The next question I want to. 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 models. And you know, 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 NV do a lot of modeling these days, but would love to get your thought of what's the right approach. You know, how do we balance the benefits of these functions with the increased complexity. Right. Because there's a ton you can do with lambda. Uh, there's a lot you can do with dynamic arrays, but they can also get really complex and long in a hurry.

Speaker A: 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 programs. 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, if 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.

Speaker C: 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.

Speaker A: If 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 of massive stuff inside because it's really difficult to gain trust in that, um, 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 has come back to standard language again. You have a standardized set of functions that industry agrees on over and above Microsoft at, uh, the moment. 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 and we stick to those functions, then anyone reading that model just refers to that standard library of functions, those lambda functions, and says, ah yes, well this is the um, depreciation calculation function. 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 do a depreciation calculation and just make it what we need it to be in a financial model. Because the financial model has structures over and above what Excel insists on, like bro, 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, you know, 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.

Speaker B: Bad financial models can lead to bad decisions or worse. So how do you minimize the risk of a bad model? You make sure the models you build are great. Financial Modeling Institute developed the Advanced Financial Modeler Accreditation Program to help modelers like you. The AFM program offers a step by step approach to building world class financial models. The program ensures that you know the best practices 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 AFM accredited podcast listeners. Save 15 on the AFM program. Just use code podcast@fminstitute.com podcast it's a

Speaker C: little bit of what Craig Hatmaker with uh, 5G Beyond Excel Lambdas. He's been Creating is a little bit like that, a little bit like all these add ins we see being added. Excel or they're trying to. So I get it, the industry could take it by itself and you know, via put it out on GitHub or whatever and everybody downloads a standard library that's used in modeling. It's kind of, you know, like the people who have their own code of vba. So yeah, uh, 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, you know, fairly straightforward model they can understand. It's full of lambdas. You didn't prep them that you build differently to start with. It's probably not going to end well.

Speaker A: Well, it might end well eventually, but it'll take a lot, it takes a lot of work to make it end well. That's the problem.

Speaker C: Yeah, there is a trust you have to gain. I think I like how you said that is in Bennett I had on the show a while back, he said we traded trust as well modelers. Whether AI built it, whether we built it, whether you use lamp, it's like, doesn't matter. At the end of the day we trade in trust and that's what you have to remember. All right, let's talk training. First, tell me a little bit about your company. Like, you know, why is the timing as far as now for a, uh, modeling training company, the type of training you have, take a few minutes and tell us a little bit about it.

Speaker A: 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 it's a classroom environment, everyone's moving at the same speed. And what you're 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 so we 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. 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 the front of a class. And that is probably the biggest drawback. What it does do is you can, you, you can train any time. You know, you log on, you can do 20 minutes here, 20 minutes there. And actually what the, um, you know, 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, uh, courses and think, well, actually I'm interested about this subject now. And they're not tied into that linear progress that's in the classroom. So it just works so much better. Um, and yes, there are online courses. I think probably we're conscious of that. And that's perfectly good. You've got stuff like Coursera 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 hit answer a few questions and it's just not that engaging and it isn't the best learning you can have. Now, I accept all of these have value, but we think there is a niche for better, uh, 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, you know, Nigeria or South Africa, there is a few, but not so many. And then further afield, you know, people still need to build infrastructure, still need to run companies in any country in the world, and yet there's no centers 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.

Speaker C: 100% agree it's amazing how online training, that ability has democratized learning from availability, uh, and from a cost standpoint. Right? Yeah, there's. There's expensive online training and there should be, don't get me wrong, you know, but it allows it to be delivered at a lower price point regardless. You know, everywhere from lots of free stuff out there, too expensive, but still less expensive than if you have to fly the person halfway across the world and fill them in a robe for a week. Not just from a cost standpoint, but from a time and a commitment and a resource standpoint.

Speaker A: It's interesting how we'll see because we're launching in a few months. But, um, obviously there's a premium attached to these expensive courses, which are, ah, you know, you will do three days in a row and your boss will fly you out to do this course. There is a certain cache 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 for. It's cheaper for, um, time, organization, 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, right? And so that sort of thing suits them much better than a kind of prestige course where they say, ah, hi, I've attended this, I've got taught by this expert, right? So there should be a market for it. There should be people there.

Speaker C: Uh, yeah, I mean, I wish, uh, you the best. There definitely is room out there for, uh, 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 is favorite Excel shortcut. What is it?

Speaker A: Control C. Ah, uh, the good old simple one. Most important, uh, I mean that, that is the most important shortcut. People don't copy their stuff enough. Leave it at that.

Speaker C: I would say it's probably the most used shortcut as well. It has to be close, maybe F2 or F4, but it's definitely up there, if it's not number one. In the good old days, wasn't it Control S before autosave?

Speaker A: Oh, yeah, right. Yes, we see. I've never really been a keyboard type. Well, that's a question later, isn't it? And, um, so I'm used to this habitual reach up to the top. You know, 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, you know, and do that every half an hour. Something will go in your brain.

Speaker C: Yep.

Speaker A: I remember that time I lost five hours work.

Speaker C: No, I can appreciate that. I, uh. 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 now switched most things to shortcut, but I still mix it up. I'm not one of those. Like, you have the people that are keyboard warriors. Like, they use your mouse. You can't be a modeler.

Speaker A: Come on.

Speaker C: You can. Can it slow you down? Sure. But it depends on what type of modeling isn't just about speed. That's kind of what I, you know, I like to think about it. It was just about speed then. I agree with you. But it's about so much more speed.

Speaker A: And speed. Yeah.

Speaker C: What's the most unique or, uh, fun thing you've created with a spreadsheet? Doesn't have to be in your work life. Could be personal, could be whatever.

Speaker A: I wish I could say something was fun. I never really view spreadsheets as fun, so I'm going to say the most fun was the most, maybe the most technically accomplished spreadsheet I made, which was one reasonably recently. I did one for a large car park 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, um, with a bit of vba. But I realized there's a lot you can actually do with dynamic arrays. Um, and then it's just an F9. There's no buttons to press to run a macro or anything like that to process what was a moderately complicated algorithm. So, yeah, I think that one. Yeah. I never described an Excel model as Farnum Freud.

Speaker C: 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 arrays. 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, uh, yes or a no. You can't say. It depends. So no being a consultant.

Speaker A: Fine.

Speaker C: And then what happens at the end? And some of these aren't? Yes and no. There's, there's some where you get some options. There's about 15 of uh, them. We're going to go through them in relatively quick order. Then at the end we'll pick a few, maybe two or three that you want to elaborate on. Because I realize 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?

Speaker A: Ready.

Speaker C: All right. Circular references in your models, yes or no?

Speaker A: Yes.

Speaker C: Vba yes. Lambdas in financial models?

Speaker A: Yes.

Speaker C: External workbook links?

Speaker A: No.

Speaker C: I figured as much. Should models always be print ready?

Speaker A: No.

Speaker C: All right. Is there ever a situation where merged cells are acceptable?

Speaker A: No.

Speaker C: Should financial modelers learn Python in Excel?

Speaker A: No.

Speaker C: Should financial modelers learn Power Query in Excel?

Speaker A: Yes.

Speaker C: How about Power Bi?

Speaker A: No.

Speaker C: Should every financial modeler be able to build a fully integrated three statement model?

Speaker A: Yes.

Speaker C: Okay. Will Excel ever die?

Speaker A: No.

Speaker C: Have you used AI to help you build a model in Excel? Know?

Speaker A: Yes.

Speaker C: Okay. What financial statement is most important for modelers? Income statement, balance sheet or cash flow statement?

Speaker A: Cash flow, of course.

Speaker C: Of course. I like it. Favorite? LLM Like Claude, Co pilot, chatgpt or something else?

Speaker A: Claude.

Speaker C: Claude. If you could only pick one for all your models going forward, would you pick me able to do a sensitivity analysis or scenario analysis?

Speaker A: Scenario. I might need the question clarified, but scenario.

Speaker C: 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?

Speaker A: No. Okay, what is back of a fag packet?

Speaker C: Hard to argue with that. Okay, what is your lookup function of choice?

Speaker A: Sum.

Speaker C: Ah. Ah, the 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 who's given that answer. I think I've had some product once, but love it. All right. I'm sure there are a couple you wanted to elaborate on there.

Speaker A: Yeah. Okay. Circular references. I said yes because. Yes. In the future. My light's gone. It's good enough though. The lighting isn't It. Right. So circular references. I said yes, because they are going to be. They're going to make a comeback. Um, dynamic arrays changes the way that circularities 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 make making them auditable, but I think they are here to stay. They will work better in future.

Speaker C: Okay. Was there another one you wanted to touch on?

Speaker A: Yes. Um, will Excel. You said something about will Excel ever go away? Will Excel.

Speaker C: Will Excel ever die?

Speaker A: Right. Excel into three things. There's the Excel you use to make a financial model. There's the Excel you use to use a financial model, and there's the Excel format that governs what a spreadsheet is, that is interchangeable, exchangeable. Um, will the Excel as a creative tool die? Yeah, quite possibly. You can build Excel files without Excel. Will the Excel output die? Uh, to a degree. Uh, and maybe completely, because people make output things that take an Excel file and display them better eventually. Will the Excel file format die? No. So the middle thing will endure is created a kernel that will persist forevermore. Well, until we become robots, whatever it is we've become.

Speaker C: There's one person said, yes, until we have AGI, then alt reds are off. And I was like, okay, I.

Speaker B: Fair enough.

Speaker C: I could live at that one. Is there any others you wanted to elaborate on? I think you wanted to ask a, uh, clarification on sensitivity and scenario. You kind of.

Speaker A: Yeah, so if. If what you mean is sensitivity. So they allow 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, uh. This is a thing that. This is a. 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, uh, correspond to something that we know could happen in real life. So they actually give some value back. I just don't think sensitivities are very useful at all.

Speaker C: I think they're a little helpful when the relationship may not be what you expect to figure out how sensitive something is to realize. Oh, that has a much bigger impact than I expect if we move at one basis point to 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 underutilized and often sensitivity is confused for scenario by people. And it's like, no scenarios are specific situations that are plausible that you're figuring out how does that change things and how should I behave based on this situation and how likely it is. So I, I would choose scenario as well for what it's worth.

Speaker A: And unless it's a very numerical thing like um, you know, so I've done um, uh, district heating, which has plants and so on. And when the temperature changes in a city with district heating, it's a non linear 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 it's 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.

Speaker C: Very good example. I appreciate that. I like the way you kind of leave 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 scenario personally. Now no one's ever going to say sensitivity going forward after you listen to this episode. Not I'm sure I think I've only got a scenario so far, but I may take that. I've only asked it maybe five, 10 times. So if I don't get any disagreement then it comes off the board of rapid fire questions because it's like all right, nobody ever disagrees. Last question here before we just kind of wrap up and I let people know where they can connect with you. If you can offer any final advice to somebody listening to be a better model or something they should be doing, what would it be?

Speaker A: I think other than obviously signing up for Loadium when it launches. I think they could do a lot by taking a step back. Zoom out. I think a lot of modellers focus in on the individual calculations step by step and actually return to this idea of modularization. They should 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 picture the entire model in your head in 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?

Speaker C: Yeah, it could feel impossible. If you look at the task in totality versus breaking apart, it can feel overwhelming. Totally agreed. So if our audience wants to get in touch with you, learn more about you, what's the best way for them to do that?

Speaker A: Get in contact with me on LinkedIn. It's John Yeldom. John with an H and Yeldom with an H as well. I'm not quite sure how to say my own name really, so I say it's Yeldom, like Beckham. Um, Y E L D H A M. Anyway, you can find me on LinkedIn. I think I'm the. There may be one or two Johnnyldams in the entire world, but only one of them has a mustache, so I can be found. And then to take a look at, we have a page for lodium. It's L - E-U-M.com where you can find out about what, what will be happening in the future. I mean, 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.

Speaker C: Good luck with that. I hope you get lots of signups. Again, congratulations on the, uh, business. Thanks for joining me today. I've enjoyed chatting with you, John.

Speaker A: Yeah, it's been great. Thank you very much. Thanks for having me.

Speaker B: 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 M at www.fminstitute.com podcast and use code podcast to save 15% when you enroll in one of the accreditations today.