Unraveling the Mystery: Roderick Mckinley's Guide to Navigating Tokenomics

Tell me about that worst financial model you've ever seen.

The one that comes to mind was absolutely shocking. It was absolutely terrible. It had no structure. What passes as good work can change very dramatically from country to country and sector to sector.

Meet Roderick McKinley. A financial modeler with a background in economics and renewable energy. He transitioned into blockchain and crypto advisory. His approach is to capitalize on the design and structure and financial model. He also emphasizes the potential of new technology like ChatGPT and modeling and shares some deep insights from Treasury. Moreover, he is inclined toward tokenomics and pursues a career in it.

That financial modeling has a lot to do with client and industry expectations. That's something you should be aware of if you do. I ended up getting in my own way, sadly.

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

Hey, Paul, thank you so much for having me on here.

Yeah, we're really excited to have you. I was very excited to bring you on after you and I had an opportunity to chat and learn a little bit more about yourself. So let's get started with the first question we ask everybody. Tell me about that horror story, the worst financial model you've ever seen. I know you have one.

Yeah. The one that comes to mind was this real estate model that was passed to me by professional working for a famous billionaire real estate developer in the US And I was just absolutely shocked at this thing. It was. It had no structure. There was some giant matrix in the middle, hard codes everywhere. And I was just in disbelief because this guy, you know, he was famous and he had all of these guys working for him on things that were projects that were high stakes and high value. But this was absolutely shocking. It was. It was absolutely terrible. You know, it was just that disconnect between the seriousness of the. Of the deal and. And the shoddiness of the work that was, you know, shocking.

Interesting. That's surprising. Yeah, for that big of a deal. So what was maybe the key takeaway or learning experience from Seeing that that.

Financial modeling is often a lot to do has a lot to do with client and industry expectations. What passes as good work or what passes as enough can change very dramatically from country to country and sector to sector. And it's not that, you know, that was a cue for me to feel like I could start being sloppy whenever I was working in real estate, but it was just an incredible example of how standards vary. And that's something you should be aware of if you do sort of like if you're someone who moves around the space a fair bit.

No, it's a really good point. And I think beyond that, it also varies a lot. If you look at FP and A versus more, you know, investment banking versus, you know, different kind of areas within the finance sector, there's definitely different standards. Many are trained to a different level depending on kind of how you got into financial modeling. Plus all that difference sometimes when you just move industries or countries or companies. So why don't you go ahead and tell our audience a little bit about yourself and your background, help them become familiar with you.

Sure. Thank you. Well, I originally started out economics at school to master's level. And I started my professional life motivated by, you know, wanting to work on the climate problem. I started out at Bloomberg covering renewable energy and climate tech. Quite a data centric role. I was working with XL a lot for sure. I then pivoted over into M and A and then a project finance role in renewable energy. And that's when I got really deep into the weeds with serious sort of financial modeling. And project finance models are among the most elaborate and technical that I've seen in the space. So it was, yeah, baptism of fire of sorts. Anyway, eventually I got burnt out. Things were, were not working for me where I was and I quit, started traveling and accidentally ended up working as a freelance financial modeler while traveling around the world, long before COVID made that sort of thing fashionable. And during the pandemic, I, I was lucky enough to get stuck on a tropical island with some creatures, crypto and blockchain entrepreneurs. They saw that I had awesome spreadsheet skills and ability to think my way around a problem logically. And they found that very valuable. And that's how I came into that industry and fell in love with blockchain and crypto, which is where I work today.

Great. And the one question I have to ask, can you really get stuck on a tropical island?

Yeah. I mean, people were choosing, people were choosing to leave. I had the benefit of having become familiar with Thailand after having been in and out over many years and even before it became clear how bad things were going to get, I felt confident saying, no, I'm going to stay here. This is where I want to be and I'm totally cool with that. And it turned out to be one of the best decisions of my life, if not the best one possibly.

You know, that's great. I know you really love what you're doing. You've had some great opportunities. So I'm going to start with a question here. As I was doing a little bit of research before we chatted on your LinkedIn profile, you mentioned you love problem solving, geeky coding and number crunching. I think those are the three things you listed. I'm curious, have you always had a passion for those three things? Has that been since you were a child or how did that kind of come about?

Yeah. So yes and no. Not in the sort of like super obvious savant way I was. No. Sort of like Alan Turing or anything. I used to enjoy puzzles. I used to enjoy taking gadgets apart. I used to play, enjoy playing with, you know, my chemistry set and blowing things up. But you know, along the way I ended up getting in my own way. Sadly, you know, as I moved into high school, I became, I felt too socially insecure to embrace my love for mathematics and science. And I also didn't really have much of a growth mindset. I believe that some of this more advanced stuff was beyond me. I remember when I went, I saw my, my peer coding up in VBA for the first time in. My first reaction to that was like, oh my God, that's so hard. I'm never going to be able to do that. But you know, thank goodness, eventually that shifted. The best motivator for me to learn coding was figuring out ways to get out of the office before 3am in this process, you know, spending so much time with a computer, I eventually reconnected with this, with this joy of this puzzle solving. Okay, I really want this. I really want to get out of the office. But you know, how do I do that? And, and it began to offer me its own sort of like pleasures and enjoyment, which brought me back to those sort of childhood experiences of immersion in that kind of problem solving. So yeah, got it.

I can appreciate that. I like that example. And you mentioned the chemistry set and blowing things up. Any big blow ups? Anything kind of fun in there?

Oh, you know, I was, I was cleaned up and hid the broken glasses before my parents came back, so nobody was any the wiser. It Was fine.

Smart, smart decision. So, you know, when you and I chatted and you've mentioned a little bit about it now, you shared how you got into the crypto space. You know, being stuck, as we mentioned earlier, in Thailand as a digital nomad during COVID So could you talk a little bit more about that experience and how you became so interested in, you know, crypto? You mentioned they noticed you were doing financial modeling and maybe just tell a little bit more of that story and that experience.

Yeah. So there was, you know, we were all just there and, and we were all stuck together and it was such an interesting rag bag of people. There was nowhere for us to go. These guys wanted to start a co living space, a crypto themed co living space on the island. So actually the first point of contact was like, we want to raise some equity, we want to pitch to investors, you know, can you help us do the financials for that? And, and when they saw, you know, what I could do with that months later, they said, okay, we've decided we want to launch our own token. Can you do the tokenomics for that? And I'm like, the token what? And like, don't worry, don't worry. And they gave me this model and I tried to reverse engineer it. I then went project finance on the whole thing and tried to take it to the nth level before realizing that, you know, it was impossible to sort of solve some of some of the things. And I eventually worked out what the minimal kind of model necessary to get, you know, what they needed across the line was. And very fortunately for all of us, their, their listing went well. And then basically people kept on knocking at my door asking for more of this work eventually, you know, so this is, this is mainly. This started out as sort of like structuring a token sale, so deciding the terms that are sold to investors, how many tokens there are which get released when. But eventually people started coming to me and asking me for like economic designs for these projects, for these ideas. And I'm like, each time I'm like, I don't. I just started this like a month ago, or you know, three, six months ago. They're like, we don't care, just do your best and all this. It was a very hot market, so, so there was money to be made. And as I went into that problem, I started reconnecting with the studies that I'd done at university. And because, you know, I was a keen student, I was genuinely curious about why is our world the way it is? Why are we, you know, messing up with our resources. Why is there so much inequality? And it's kind of, you know, they call economics the dismal science. And, and you know, some of the stuff they teach you is that, well, the world's this way because it can't be any other way. And, and you pass exams, sort of like explaining why it's impossible to fix things. But suddenly blockchain technology actually gives you a completely different kind of tool chest that, you know, opens that up again. And there are a lot of very deep seated problems that, you know, we can now look at again and say, well, hold on a second, maybe if we can actually build a system of rules here, which nobody can break, no matter who you are, that allows us to do different things to what we were able to do before. Maybe if we have a technology that allows us to monitor things at low cost automatically without loads of humans being involved, we can solve all different kinds of problems that we can solve for and so on. And so my curiosity just drew me into this stuff and I became fascinated with it, irrespective of the excitement. But, you know, it was a wild ride that eventually, you know, I ended up interviewing the ex Prime Minister of the United Kingdom about blockchain technology at a conference in Singapore through one of my clients. I mean, that was, it was such an incredible journey from knowing absolutely nothing of this stuff to being in that position. It was an absolutely wild ride. And the last two years have been very quiet though, after everything blew up. But hopefully sweeter times are around the corner again.

Yeah, that sounds like a pretty wild ride. And I'm curious, so how did you end up getting to interview the ex Prime Minister of the uk? How did that kind of come about?

My client had a successful launch. They, you know, they, they put together a conference to discuss ideas around this technology and, you know, leverage that as a marketing opportunity for, for their project. All of this was set up while the, while everything was still flying high. And then, you know, some of the huge implosions happened, but Boris Johnson was already booked. So it was a very interesting experience and I was lucky. You know, they, my client thought I had good communication skills and they wanted me to interview him. And it was a funny experience because I go and do my research, draw up all these questions, then his team come back and basically ax everything that I prepared and give me a set of questions that I meant to ask him. And then I asked him the questions his team had prepared and he didn't have answers to some of them. But he's A charming guy. He's controversial. I'm not going to play down the fact that there are a lot of problems. There have been a lot of issues with his conduct, but he's very personable and he struck me as very intelligent, and I was certainly very happy for the opportunity.

That's fine. Yeah. No, I. I think we all know anyone who's researched, from Boris Johnson has this controversy, as does a lot of politicians. It will leave it at that. So can you give our guests, you know, our audience is listening, just kind of an overview on token. Maybe a definition of token economics. Help us make sure everybody kind of understands that.

Yeah, nobody knows.

That's why I'm asking you.

I'm only half joking. So, you know, there are no authorities on this. I can't sort of like pick up a hefty book by Oxford University Press and point you to an official definition. I think they're kind of two camps. Right. For some people, there's. There's a sort of narrow definition that leans more into the word token in token Tokenomics. Right. And this is a view of that. What matters is how much of the token there is, how quickly it inflates, who has it and all of this sort of stuff. And these are relevant details, but they reflect very heavily on the earliest cryptocurrency designs, which were very much monetary instruments. Right. So these sort of factors are the most important ones. However, as blockchain, as technology has progressed, it basically has become more programmable. And as it becomes more programmable, there are more things you can build with it and, and more kinds of rules and systems that you can put together. And so tokenomics becomes a more expansive term that has more to do with economics, which is, you know, one way to describe economics is the study of human behavior in systems, how they react to different rules, what other people do, how much resources they have. And so you use that sort of broader economic lens to figure out, to ask a lot more varied questions about the system you may be designing or analyzing as a potential investment. So those are the two kind of definitions, the narrow token centric one and the broader sort of economic definition for tokenomics.

Thank you. Appreciate you taking a minute and doing that. So now we're going to jump into some of our more what I'll call nerdy hardcore modeling questions. So we're going to get started with the first one here. Talk to us a little bit about maybe the similarities and some of those differences between traditional models in other industries and building one for tokens. Just kind of talk A little bit about that.

Yeah. So my answer there is to kind of turn the question on its head somewhat. If I am building a model for a tokenized system in a spreadsheet, I've chosen to do that because it is similar enough to the business or process that we would be handling with the spreadsheet model in the first place. One part of my journey in this effort to sort of like understand what it is I'm doing, what my skills are, is to figure out what the limits of a spreadsheet model are. And you know, we're dab hand with this tool. We know that a lot of people who crap on it don't really understand just how much you can do with it when you're properly trained and skilled. In those contexts where I think a spreadsheet is a good tool to model what you want to model, I would say that they share basic similarities and concerns of tracking flows of resources and understanding the relationship between those flows and what drives them. In this sense, a financial model is part of a family of a more general class of models that engineers will call stock and flow models. Right. So your balance sheet is a collection of stock items and your P and L and your cash flows or flow items. Way of thinking about it, when I'm building one of these models in Excel, that's very much my concern. I want to figure out what's moving these tokens from A to B and how am I tracking where they're landing in different locations. In terms of the difficulty, like the token sale models are incredibly simple. But what matters? There is really a lot more understanding about the context of what's going on in the deal and how different parties see the trade offs between different choices you're making. The model itself is kind of very, very simple to anyone. I'm sure he's listening. You know, I was on a podcast yesterday with a CFO audience and I got a great question. It's like in most of the models I see tokens aren't included in the OPEX line. What is your view on that? And I had to pause and think about this because it was sort of like a tricky question to answer. I understand why it's excluded, but it is a cost. But we don't really have. There are no sort of established or settled ways for deciding how a token is. A token that you spend from your own treasury is a cost because you minted it at no cost out of thin air. And then it's not always, it's not always clear that you should be Looking at the current market price as, as the cost of spend, you might want to be looking at more downward effects of what happens by, to the token price by you adding that token into circulating supply. So you do some dilution analysis. So you know, all of this sort of stuff can, you can think your way around in the spreadsheet. You need to think quite actively and carefully when you decide how you interpret those token flows and token balances. And that's where I would say is the key difference. Thinking about context a lot more really.

Interesting when you mentioned the OPEX part. Never even thought about that. But yeah, right. The only cost was the computer that created it or whatever. Yeah, you could argue maybe there's a little bit of energy that was used, was probably on a server that it's going to be used anyway. So it's hard to argue there's any real cost to creating it. And that value, you know, especially with volatility can be so different. How do you capture that? How do you think about it? How do you estimate it? I would imagine especially with the volatility we've seen over the years with tokens, you know, some of the implosion and the ramp ups, it would be really hard to around assumptions. You probably have a wide range sometimes for sensitivity. Is that a fair guess?

Yeah, I mean when I, when I work on a project, I always try and design in, you know, a process that generates or collects revenue in what we would call stablecoin, you know, which is just a representation of the US dollar in a blockchain token.

Sure.

And sometimes I get pushback from this because I'm like, oh no, you're not really making use of the native token. I'm like, dude, I've seen projects blow up because they capitalize. They end up collecting revenues and something that's here today, gone tomorrow. And, and if you're serious about this project, it's going to take you years to build out. So that's the way how I try to build in some control of the system. To your point about volatility, I'm actually doing a research exercise right now. I've been teaching myself SQL and learning how a lot of these sort of markets function on the blockchain in terms of their programming code. And then I go to the blockchain and get a readout of everything that the, the, the pro, all the things that the program did and decode that into an understanding of how many buyers, sellers there are, what the variation in prices are and what I'm working to eventually Is, is coming up with a representative sort of history of, you know, volatility and that I could then plug into an Excel model. You know, once I have those sort of high level. Pretty much the same way as you would do in an FPA model. Right. You've got some historics, you, you, you forecast out some sort of predict from the historical data. But this, this historical step now is, takes place with these amazing on chain platforms. It's absolutely amazing. I've only worked on one huge large FPNA project but you know, there was so much difficulty in like getting, integrating the point of sale data and figuring just getting the stuff to go in. With blockchain, everything happens through data. There is no difference between the action and the data are the same thing. So all activity is self document and that potentially gives you the opportunity to set up real time accounting or monitoring of what's going on. And that's, that's an incredibly low cost. It's just for me it's mind blowing. So that's what I'm working on at the moment in order to sort of like create some empirically informed balance for ranges and volatilities of key drivers in my modeling.

Yeah, well, you'll have to let us know kind of what you find there. I'd love to learn a little bit about that. It's fascinating that you're kind of doing all that work because. Yeah, very true. Right. It's all digital, it's all recorded, the data is there. It's accessing it and analyzing it that sometimes is, proves a little challenging. But it's all there.

That's right.

That's pretty amazing. So you know, one other kind of question I have here is you mentioned a little bit about treasury, you know, dealing with the balance sheets. Talk about how companies, these crypto companies manage their treasury. Do they hold so much in US dollar? How do they think about those reserves and just kind of Treasury Departments? Because I'm sure with the volatility and other things it's going to be managed a little different than your traditional treasury department. And I can tell that's true by your laughing.

You know, the question should be how do projects not manage their Treasuries? You know, let's be charitable here. We have democratized the ability to issue securities or issue financial instruments and create really complex sort of Treasuries without a commensurate sort of level of education or capacity on, on, on the hands of, on the project side. And that's, that's why I have work to interesting work to do because I want to help, hopefully help build that understanding and best practice over what to do. This group that I was in touch with, that I spoke for yesterday, Web3 CFO Club, they've had some talks on where they talk about, you know, trying to keep a certain amount of their reserves and stablecoins. So, you know, first, first and foremost is the fact that a very large portion of your assets are at risk or will be by default denominated in this extremely vol an asset that's not only extremely volatile in terms of price, but extremely illiquid. There are exchanges that you can sell into, but they are configured in such a manner to be automatic so that if you sell into the market, you cut a huge price, a huge percentage of the token that you're selling. So, so, you know, you look at your balance sheet and you think you're, you're as rich as Jeff Bezos and as soon as you try and spend it, it kind of disappears. It's this sort of like this ironic thing, right? So really understanding that relationship between. And furthermore, actually, this isn't just an asset that's of relevance to the Treasury. It's playing a role in an ecosystem and it's affecting either your investors or it's affecting your users. So what you do with your treasury becomes, you have to take more things into account. And then finally, you're not just having to manage the liquidity needs of, of your own enterprise. You probably have to look out, manage some of the liquidity on the decentralized exchanges, these automatic exchanges that I was just talking about. Because if liquidity gets too thin out there, then as I was selling saying, anybody who, who tries to sell and transact in that decentralized exchange pool will see, really, you'll see very volatile price movements. Because of that, there's a real need to sort of keep an eye on that external liquidity, create incentives, continue making that attractive to people, helping that stay healthy. So all for all these reasons, it's a very different beast and a very interesting challenge.

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This is really, potentially really hard. It's, you know, ideally, not ideally, but in some systems, you know, you really get into, you get into some really interesting sort of design opportunities where you have two people who have nothing to do with each other, and you figure out a way to get, get them to collaborate. So again, the decentralized exchanges is a really great example because you have two groups of people. You have the people who want to exchange one token for another. But the only way that can be automated is if there's a pool, a reserve that's always available for people to make those exchanges, and that pool only gets filled up by a different group of people who are the liquidity providers. We can think of them as being like savers at a bank. And so the problem, a core part of the problem that the decentralized exchange designs had to solve was how do you make it worthwhile for those unrelated liquidity providers who aren't interested in trading to provide that? And it all gets funded by fee that's paid by the people who trade. Very simple, but a helpful, clear example of how you, you can use this technology to create arrangements and interactions that allow two parties to come together to create a service without a middleman, you know, and then the token is also very frequently used, almost all the time used as a fundraising instrument. So you bring the investors on board. But there is, in this case, we have a situation where there are conflicts of interest. Right. And it's very hard to align incentives because what investors want to do is cash out. And them doing so messes the token price up for everybody else and maybe messes it up for the project as well, who may be counting on that to, you know, make some kind of contribution to their ongoing funding. So in certain situations, the, the incentives are way out of line and a lot of work needs to be done on creating new contractual arrangements with investors to not give them a free ride, in my opinion. And we're getting to that. We're starting to see for fundraising, they're starting to mix equity and tokens. So you get an equity offer, a sale of future equity, plus a token warrant. So you have some optionality in the award of tokens that brings on. So we're seeing this space evolve to accommodate some of these challenges. But yeah, it's a Very interesting feature of the space.

Fascinating. And I could tell by the way you mentioned conflicts to describe that I'm like, we're talking to an economist. You tell you have that econ background and, you know, kind of the incentives and behavior of people trying to figure that all out. So I appreciate that. Next question I want to ask is, you've created an online course for tokenomics, modeling and fundraising. Talk a little bit about that. Tell us about that.

So that course collects all of the experiences I had. It was a wild time, but I did manage to help projects gain access to US$150 million in new capital. I saw a bunch of go right and go wrong and sat through loads of phone calls, sat through lots of revisions. And that course is basically distillation of everything and an opportunity for me to sort of like really refine my thinking and, you know, as, as maybe coming through in the things that I'm sharing. I'm not, you know, someone who's just evangelizing the thing without any caveats. I'm. I think I'm pretty realistic. You know, I have a whole section in the chapter in the course where I'm like, these are the differences between tokens and equity. And you should look at this first. You should look at this before you think whether this is the right fundraising instrument for you or not. And there is no free lunch, you're getting trade offs. And some of these trade offs are pretty difficult to assess. So it's a course where I go into all of that background, context and nuance, but then also just show you how to create a very simple model by financial modeling standards, but one that essentially allows you to manage the track the terms of all the offers you're making to various parties and understand the key metrics that help you keep that offer balanced. And I also teach you sort of like approaches to working through the variables, working through the problem in a systematic manner. Because if you just see the thing and they say, here you go, you may not know the most efficient or the best route to take from A to B. You're just flipping switches all the way around. So all that's in the course.

Sounds like a great course. We'll make sure to put that in the show notes, make sure to get that link from you. So anyone who's interested in it can check that out. So I know you're a big believer that a modeler should have more than just Excel in their toolkit. We've talked a little bit about that. So maybe talk about some of the other tools you use and how do you make a decision of when, hey, Excel is the right tool, or I should be using some Python or, or you know, another tool here, some kind of other modeling tool. Maybe talk a little bit of how you think about that.

Yeah, so I don't, I don't think everybody has to be multi tool. I don't know, I guess the more I went into sort of like learning communities around Excel, I did see that people were starting to pick up more than one tool. I think if you, if you become a freelancer, you help yourself by becoming more flexible. And actually, let me just say that there has never been ever, ever, ever, ever. I'm gonna say it one more time. Ever be a better time? I've lost count. I, I can't, I'm so bowled over by how amazing ChatGPT is. I can't contain myself. I've been taking on a really substantial undertaking in SQL and I don't know how much time this thing has saved me because for the first time we can, we can basically detach the core foundations of our value add as analysts, which is understanding business processes and critical thinking. And I can just, if I'm like, oh, damn, I don't know how to loop through an array in SQL, I go, hey, ChatGPT, I want to multiply two arrays together in tree. No SQL. This flavor SQL. How do I do it? It spits out the answer. And I put the answer in. As long as I understand enough about coding a program to evaluate what comes in. I'm doing my quality controls, my checks, my sanity checks as I go through, but it is unreal. It is absolutely unreal. And you, for the first time you. I can, you know, I remember tying my head myself in knots, trying to get my head around DAX and M and Power Query. God, this is just going to be so easy now. I mean, comparatively, it is, it is unreal. It is truly unreal. So there's never been a better time anyway to think about. But you know, if you've got a really deep well of work, if you're a project finance specialist, you may, you may only need to do that. But it's never been a better time to diversify in terms of the different tools that are out there for what I do. There's some really cool stuff. There's a tool called Machinations which was developed for game designers. And imagine a financial model where you would actually build it with little picture Lego blocks. And you have the revenue process there, cost process there, and you wire them together and you hit go and you see how the money is sort of like moving around in your, in your little model and it's, it's very engaging and fun to work with. But you know, coming from the experience I had in M and A in finance, like it doesn't, I wouldn't trust that tool to build anything too complicated because documenting that and quality controlling that is, is really difficult. There are some Python libraries as well. I haven't started working with those just yet. The example I give to people in terms of deciding what's, what's the right sort of tool. If you think about a financial model, our exploration of the system, the explorations we have to make are in some ways really simple and forgiving. If we just consider where trying to figure out the breakeven for profits, all we have to do is look at where the points where costs and revenues are the same. Now there are no surprises that show up as you lower the revenues or as the costs come up. All we need to look for is that break point. However, in some of these systems that I was describing. So again, the decentralized exchange, really good one. The interactions are so complicated that as you move through the values of a particular parameter, you may hit a break in a really strange, unusual place that just happens to be the sum result of all the factors that are leading the system to work in a particular way. That setting Excel is just not granular enough to reveal those kind of strange and unexpected breaks to you. But again, if I'm not really in the business of fine tuning a really tricky detailed design issue like that and just want to know how many buyers and sellers are there on average, I can use my historical data to do that. Then I can go back to a spreadsheet again. I mean, to be honest, this is something I'm working out. Everybody comes to this work with their favorite tools. The engineers love their matlab and their Python. The data analysts love, you know, their, their SQL and their dashboards. I'm sort of like trying to navigate this a little bit myself and be cross disciplinary and figure out what, what fits. For me the analytics stuff is clearly going to be key. We'll see how far I go down the Python rabbit hole. But analytics and Python for now and.

Excel for now, trying to be multilingual there. And I love the example, the Python, the Excel. I really like what you said about ChatGPT is I like to say the tool is incredibly valuable and the more you know about a subject to be able to kind of validate and test it, you don't have to have to have super deep knowledge, but it's. It's deadly in what I call the hands of the competent. Right. If you have somebody that doesn't know anything that is just using Chat GPT, it's where there's going to be a lot of mistakes made. But if you know enough to kind of validate things, it can save you incredible time. I'll use it to help write formulas sometimes. Like, all right, this is a really messy indirect. I had to write the other day and it was, you know, a bunch of different levels to it, and I hate that formula because you have quotes everywhere, single and double and ampersands. And I just threw it in and said, here's what I'm trying to do. Here's the cell. Write me the formula. And then I did the next step. After about 20 minutes, I had my six levels that I needed and I threw it in. I moved forward. Would have taken me an hour probably to get the syntax right because I just don't do it enough. So I'm totally with you. It's. It is really neat to watch. So, you know, going back to Excel, this is one question we'd like to ask all our guests. What's your favorite, or could call it most valuable? Excel shortcut.

It's one that I made for myself. It launches a little VBA task. I love working with multiple windows, but I get super annoyed when it resets the formatting every single time I do that. You know, in the financial, I have all the frames locked, all the cells, so I just set up a VBA script to open new windows and keep all the formatting that I have. I reach for that a lot.

So you got your own custom shortcut you love there. I like it.

Yeah.

So this next question is one kind of around modeling. Maybe what's a technical nugget or something you've learned within your modeling, Whether it be something around depreciation or using a certain type of formula with the revolver or whatever it might be that you can give our audience that you wish more people would incorporate when building models.

That's really hard. Technical nugget. I don't know. I just love to see basic structure. Clear basic structure. It is just. It is the be all and end all. Because if you have like clear and basic structure, you can work the rest out. I just think that's so foundational. That's. That's the beginning of everything else. That's what I'll go with.

I love that answer. I would 100% agree with you. As I've heard in Schnorr say, I'm one of my podcasts. You know, the most important principle is design. It almost always comes back to design the issues in a model. He's like, it's about flow, data flow and process flow and design. And I just think that's so true. Almost always. The problem can be solved with thinking more about how you design it. All right, we're going to move into our rapid fire section before we wrap up here. So I'm going to give you the ground rules for this one. You get no more than 10 seconds to answer. You can't tell me. It depends because you could probably do that for all of these. So you have to pick a side and then at the end you can pick one or two to elaborate a little bit on. So you ready?

Ready. Let's do it.

All right, here we go. Circular or no circular references?

Not on sheet.

VBA or no?

Vba. Vba.

Horizontal or vertical models?

Horizontal. Man.

Excel Dynamic arrays. Yes or no?

No.

Interesting. External workbook links. Yes or no?

Go to hell.

Thank you. All right. Named ranges versus no named ranges.

Yes, but sparingly.

Okay. Do you follow a formal standards board? One of the formal standards out there when you're modeling? Yes or no?

I get inspired by them and then work consistently to what I choose to do.

Makes sense. Will Excel ever die?

It hasn't yet. I don't see it.

Alrighty. Will AI build the models for us in the future?

I'm skeptical. There's so much context and nuance. I'm skeptical.

Use of sheet cell protection in your models, yes or no?

Yes. When the employees may tamper with their pay grade and performance.

Makes sense. Do you believe financial models are the number one corporate decision making tool? Yes or no?

Not anymore. What is analytics? Analytics and all this kind of other kind of technology is incredibly important in upgrading the whole finops operations for companies. And a lot of that happens without ever touching Excel.

Definitely. We're seeing more and more of that. And then what is your lookup function of choice? Choose vlookup Index match or xlookup Index match. All righty. That's a pretty common one. All right, so which answer did you want to elaborate on? There?

Let's see. I think the dynamic arrays are an interesting one to talk about. So first of all, they seem like more of a convenience than a necessity. I do use them. They're like. It's like cool. But there are certain things that I find really annoying. Like, you know, I mentioned that formatting and presentations a large part of what I do, they don't track formatting. Then when I have this entire sort of skill set around integrating things with charts, I find that the integration doesn't go all the way into all these classic features. And then because I'm building, I'm often been building sort of spreadsheets in educational contexts and stuff. Then you know, there are compatibility issues or you know, if I want to build and build a Google Sheets copy, I don't fully understand or trust how that migrates over into Google Sheets environment. So all these little frictions, I'll yeah. Mean that I, that I haven't really been working with them so much even though, you know, I was excited to see them. But that's. Yeah, I don't, I don't use them for all those reasons.

Yeah, and we're definitely seeing, you know, some people that definitely have the friction. I tend use them here and there, but I don't use them to build like a whole model. I don't think they're quite there yet for that. But I definitely use them in a lot of areas. I love them in some of my analysis and different things. But I can agree with you that they're definitely some drawbacks still. It'll be interesting to watch and see how that develops over time.

The other thing I'll mention is about. Sorry, do we still have time for another elaboration?

Yeah, go ahead. You can do one more.

Yeah, the circular references thing. So I work in project finance and there are these. Or I used to. And they're these circular calculations that you just can't get away from. They are a feature, not a bug. You know, one of the things I eventually came to learn and understand was this method that Ed Bomer Bodmer popularized where you use VBA to handle the circularity and then suddenly instead of having these clunky machines which would copy and paste values until you would converge, you would be able to get a model that was sort of lightning fast and really reactive. And like literally this thing would have saved hours of my life in my last job, but it required a very different skillset to execute. So there's a really difficult trade off with the maintainability of the model. The likelihood of finding someone skilled enough to keep that model going if you leave. It's very complicated. But yeah, it's an interesting point. You know, again, going into this issue of how broadly skilled should we be as, as modelers and analysts. But yeah, that's a really interesting question there.

Yeah, no, and Interesting you mentioned Edward Boeinger because I've learned a fair amount about him from Heidi Kinniford.

She's working with quite a bit and.

Mentioned him and she was going to introduce me to him. I'm hoping to have him on the show at some point in the future. So we were just talking about him earlier this week, so. Funny you mentioned him. Yeah, she, she's a big fan of his, so I'm looking forward to learning a little bit more about his approach there. So, yeah, it's, it's a fascinating subject and there are times when you have to, to do it, you know, heavy debt models, you got enough interest in there, you can't just avoid circular references and have an accurate number. It's a challenge that everybody fights with. I think we'd all like to say no in the sense that it's kind of a pain and it creates risk. But there are times you have to take that risk one way or another. Even if you do vba, however you solve it, there's still going to be that calculation. All right, we're coming up into our concluding section here, so just have two questions left. The first is if you could offer our audience one piece of advice that would help them become a better financial modeler. What is that one piece of advice you're going to offer?

Try and look for the shortest and most efficient way to express your calculations. Try and maximize the speed of your model.

That's a really good one. Yeah, I like that. I've learned that lesson the hard way. You put in some offsets and indirects and all of a sudden you're like, I can go to lunch and hopefully it will run when I come back. So optimization is a. That's a great one. The last question here, if our audience wants to learn more about you or get in touch with you, what would be the best way for them to do that?

I'm active on LinkedIn. I've got my tokenomics channel token design and if I get round to it, I'll be releasing some old, some project finance stuff that I, that I was working on before COVID and very gradually sort of dusting off. So hopefully that'll be out in a few months. It's my, my other YouTube channel. There is just project finance modeling on YouTube. So, yeah, any of those ways LinkedIn is, is where I'm most active and on the, on the crypto channel as well. So. Thank you.

Thank you for joining us, Roderick. I really enjoyed the chat. I know I learned a lot and I'm sure our audience will as well. So thanks for carving out some time for us.

Thank you so much for having me. Pleasure.

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