Episode 13: How Financial Modelers build a goldilocks “Just-right” Financial Model with Brian Egger
Show Notes
Welcome to Financial Modeler's Corner (FMC) where we discuss the art and science of financial modeling with your host Paul Barnhurst. Financial Modeler's Corner is sponsored by Financial Modeling Institute (FMI) the most respected accreditations in Financial Modeling globally.
In this episode, Paul Barnhurst is joined by Brian Egger.
Brian is the Global Head of Financial Modeling, Senior Gaming/Lodging Analyst at Bloomberg Intelligence.
He has been publishing models professionally and writing investment research since the 90s on the sell-side as a brokerage analyst. His industry focus has always been broadly centered around consumer gaming, lodging, and leisure.
Brian has also worked in player-coach roles, whether it's being a research director or team leader or his current role, head of financial modeling, and outside the realm of being a practitioner, he has experience teaching as well.
Listen to this episode as Brian shares:
· His experience with finding the right balance as a Modeler.
· His learning from the worst models he has come across.
· His role as the Global Head of Financial Modeling at Bloomberg.
· Everything about Bloomberg’s Interactive Calculator.
· Trends of the Industry over the years.
· His thoughts on the FMI program and why continuous learning is so important.
· His position on controversial modeling issues, including circular references, dynamic arrays, modeling standards, AI in modeling, and more.
Quotes:
“Models should be dynamic and not static; they should be integrated and not sort of siloed pieces not speaking to each other.”
"There's a lot of merit just to continuously reinvest in both your skill set and your credentials, and that it's really important to never stop doing that. The FMI has created a really intriguing platform as a way to do that.”
“The CFA exam prepares you in many ways to be a capable securities analyst.”
Sign up for the Advanced Financial Modeler Accreditation or FMI Fundamentals Today and receive 15% off by using the special show code ‘Podcast’.
Visit www.fminstitute.com/podcast and use code Podcast to save 15% when you register.
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December 7, 2023 - December 9, 2023.
Sign up for the event today and receive 10% off by using the special code ‘FP&AGUY10’. FMWC – Financial Modeling World Cup | $25,000 Prize Fund (fmworldcup.com)
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Follow Brian Egger:
LinkedIn - https://www.linkedin.com/in/brianegger/
Brian’s article on the balance between complexity and simplicity -https://www.linkedin.com/pulse/what-level-detail-enough-financial-model-brian-egger/?trackingId=08lGH6pSQ865qLFJar2nzQ%3D%3D
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In today’s episode:
(00:22) Intro;
(00:46) Welcoming Brian;
(01:02) The worst financial model Brian has ever seen;
(02:37) Brian’s key learning experience from the worst financial model;
(03:13) Brian’s background;
(05:05) Role as a Global Head of Financial Modeling at Bloomberg;
(06:19) How to find the right balance while Modeling?;
(08:08) Signs that a Model is too detailed;
(10:57) Signs that a Model is too simple to make decisions;
(13:43 - 14:30) Validate your Financial Modeling Skills with FMI’s Accreditation Program (ad);
(14:39) About Bloomberg’s Interactive Calculator;
(15:58) Brian’s key learning from that Project;
(20:25) Guidelines on Balance;
(23:52) Brian’s thoughts on FMI;
(27:44) Trends of the Industry over the years;
(29:58) Rapid Fire;
(34:31) Advice on being a successful Modeler;
(35:23) Connect with Brian;
(35:50) Paul’s Top Picks -Finding the balance between complexity and simplicity
(39:32) Outro.
Show Transcript
Host: Paul Barnhurst
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 Modelers Corner. I am your host Paul Barnhurst. This is a brand new podcast where we will talk all about the art and science of financial modeling with distinguished financial modelers from around the globe. The Financial Modelers Corner Podcast is brought to you by Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling.
I'm super excited today to welcome our guest on the show. Brian Eager, welcome to the show!
Guest: Brian Egger
Nice to be here, Paul! Good to see you.
Host: Paul Barnhurst
Yeah, good to see you as well! Really excited to have you here!
So this is a question we like to start off with, kind of have a little bit of fun, but I think it also makes a real serious point. Tell me about the worst financial model you've had to work with or you've seen in your career.
Guest: Brian Egger
Yeah, I have to generalize maybe anecdotally looking across models that did not work maybe the way they should have. I would say it's really challenging to make use of a model either where there's no kind of formula connection between the income statement balance sheet and the cash flow statement, in other words, you lose that interconnectedness, or when you kind of begin with your conclusion, so to speak, you begin with net income and you kind of reverse engineer and work your way backwards to what revenue should be. So these things tend to work a lot better, obviously, when the various statements sort of speak to each other, are connected, and when they're kind of bottom-up as opposed to top-down. So those would be observations across models I've seen that really are doing what I think we would hope they would do.
Host: Paul Barnhurst
Yeah, so it sounds like kind of a disconnect in the three statements, for whatever reason, they're not connected properly. Balance sheet doesn't balance, they didn't do a cash flow, whatever it might be. And the second one there, if I heard you right, was really around, they've already had the conclusion in mind, so now let's engineer it to get to that conclusion.
Guest: Brian Egger
Yeah. Ideally, you'd like to arrive at that conclusion through a series of logical inferences, that's the way it should work.
Host: Paul Barnhurst
Totally agree with you, and I've seen that. I've worked on some deals where it's like, oh, wait, just put more revenue in there, just assume this will be three months later because we have to hit this number for it to make sense. And it's like, well, but we're not really hitting that number because we know that won't happen. We've all been there where it's like, well, just plug the number- never ends well.
So what are the key takeaways from seeing those types of models? What's the learning that people should take away from that?
Guest: Brian Egger
I think my overall takeaway, and it's probably applicable to a lot of different aspects of modeling is, models should ideally have an internal logic and consistency to them and that there really should be connections across financial statements, that models should be dynamic and not static, that they should be integrated and not sort of siloed pieces not speaking to each other.
Host: Paul Barnhurst
Makes sense. I get that. Dynamic, integrated, not siloed, those are all good points. Can you just take a minute and tell us a little bit about yourself, your background, how you ended up where you're at today in your career, and what you're doing?
Guest: Brian Egger
Sure! So I studied business as a student. I went to Wharton School undergrad and got my MBA at the University of Chicago Booth School. I've really been publishing models professionally, writing investment research since the 90s on the so-called sell-side as a brokerage analyst. And my industry focus has always been broadly centered around what I called consumer gaming, lodging, leisure.
So I've always had this sort of industry specialization. But I've also worked in player-coach roles, whether it's being a research director or team leader or my current role, head of financial modeling, and outside the realm of actually being a practitioner, I've had some experience teaching.
I taught as an adjunct professor of finance at Columbia Business School. I've done various types of training in research management, so I've had a fairly broad set of experiences. Lucky to have them across my career.
Host: Paul Barnhurst
No, it sounds like a lot of really good experiences there. And I'm curious what kept you in the kind of the gaming hospitality, that area of the industry. What is it you like about that?
Guest: Brian Egger
I think once you develop a specialization, you find that as industries go through these long sort of arcs and changes, there's actually always something new to bring to your analysis. It is kind of a nice thing to be able to balance having that long perspective, but always for the sake of one's own professional stimulation, always encountering something new. For example, sports betting, a relatively new phenomenon, really wasn't pertinent to my job until five years ago.
Host: Paul Barnhurst
Yeah, I think that coincided with, wasn't there a change in a court ruling that allowed the nationwide sports betting and now yeah, I remember that I hear the ads all the time now of the different betting services.
I know you're currently the global head of financial modeling at Bloomberg. So what does that entail? What does that role involve?
Guest: Brian Egger
So I think beyond the sort of operational, administrative aspects of being a manager at a place like Bloomberg, what I've tried to do is provide sort of an analyst or a user's perspective on the structure, the format, the function of this interactive modeling tool that we have. So in that sense, I focus on use cases, standards, best practices. Like any complex project at a large organization, we've got product people, we've got marketing people, we've got data people, analytics people, and I come from the perspective of having been a longtime user and builder of models., and so I come to it as sort of like the first pass of what a user would want from it and also how they would react to it in terms of the conclusions they draw.
Host: Paul Barnhurst
Now, this is the Bloomberg Interactive Calculator, right? Is that what you're referring to here, or is this a different tool?
Guest: Brian Egger
Yeah, Bloomberg Interactive Calculator. Exactly.
Host: Paul Barnhurst
Okay, great. And we'll get into a little more depth on that here in a few minutes. Next question, I just kind of want to ask, you talk about models you've built a lot, you've been involved in the Calculator there, and how do you find the right balance? I think something everybody struggles with is, hey, keep the model simple, but yet everybody keeps asking you to add more and more stuff to it.
Guest: Brian Egger
Yeah, no, that's a really good point. I mean, I think sometimes you can tell better whether it's too simple or too complex by what it's not doing for you than what it is doing for you. Because I think it's arguably too simple, if it can't provide insight into very basic questions or can't adapt to new information, it's not adapting to your needs as a user. But I think you could say if you add too much onto it, as you mentioned, it may become too complex in the sense that it takes too long or it's too cumbersome to maintain or update. And in that case, the value of a model as a decision-making tool really begins to diminish because I think you have to remind yourself when you're building some model, the temptation is to want to add to it and make it reflect the best of your thinking, but you always have to revert back to, will this assist me in making informed decisions? Whether your role is that of a corporate finance analyst or an investment analyst or financial planning and analysis type of professional, you really have to think about whether or not it can assist you in the decision-making you need to do, or the type of decision making that you're enabling the model to help other people, your clients do.
Host: Paul Barnhurst
I really like how you focused on is it helping the client or you or whoever that may be the user of the model make good decisions. Is it aiding in this decision-making process? Because if it's not, maybe it's too simple, maybe it's too precise in that it's overly complex and they can't figure it out. It's going to be one of the two, typically. Obviously, sometimes bad assumptions play into that as well. But what are some of the signs to you that a model has become too detailed? Because I've definitely seen some where it's just overkill, right? You hit that point where it's like, how do you maintain this model kind of fells under its own weight so to speak.
Guest: Brian Egger
Two things come to mind. One is that it takes a very long time to answer a simple question, or it takes a very long time to simply update. There's always a maintenance element to having any model. You've built it, and now you have to adapt either to new information, company resegmentations or restatements, or you want to test the sensitivity of your model to different scenarios and assumption changes, and you've provided yourself with levers to push, so to speak, to test those sensitivities. But if you provide yourself with too many levers to push, it can become fairly cumbersome, and you can very easily get lost in the tree, so to speak, and lose the sense of what the forest is. So if you make it too detailed, you make it too detailed for you to usefully update and maintain. That could be a challenge.
I think also what we've tried to do here, certainly, is try to make sure you're not devoting too much detail to those aspects of a model for which you're not likely to get that much additional precise conclusive power out of adding that much more detail and save that granularity for something where maybe you can bring some insight into it. Because there may be things that you can model at extreme detail, but not necessarily be bringing any particular unique insight as a modeler.
Host: Paul Barnhurst
Yeah, if you're modeling out every trip for travel and you're talking a large global company, probably not adding much value, even if it's highly accurate.
Guest: Brian Egger
Yeah. Or even there are certain aspects of financial statements for which we've taken maybe more of an abbreviated approach because our analysts, our fundamental industry analysts, they have great insight into industry trends, growth rates, shipping volumes, average selling prices. And we certainly try to create a tool that gives people a lot of flexibility to really flex their intellectual muscle on those fronts in particular.
Host: Paul Barnhurst
Yeah, and when you said too complex, it reminded me of a story. I had someone on other podcasts we do called FP&A Today, and this was a budgeting and forecasting model. And he goes, I still have night sweats about it because he was telling me each model was 150-something worksheets, and he had four of them that he had to update because there were four different legal entities. So he said like when I did a budget or forecast, I had 600 worksheets. I mean, you can't manage that. You know how many errors there probably was in that thing? That's the example to me of the extreme, where it's like, all right, you're trying to do too much in one file. If you got 600 different tabs to work with.
On the other side, you sometimes see people take it very simple and back of the envelope is great as a start, but what are some signs? Or how should I think about if maybe the model is just too simple to make good decisions, it's not going to drive that value?
Guest: Brian Egger
Yeah, well, I'd say aside from the kind of financial statement interconnectedness issues we talked about before, that being lacking would be a major evidence that maybe it's too simple and not really functioning right. Another thing is have you provided the user with enough tools to approach modeling the way they would on their own? Because we're developing a tool very often for a third-party user and I've been a modeler sort of on my own in various ways, sort of self-styled. But as an analyst, I know that if I change for a company that makes widgets of some type, let's say you've got a certain volume shipment number, you got a volume number and you've got a price number average selling price and it's great to have the ability to change those assumptions, but I also know as an analyst that depending on the company's fixed costs and the grave operating leverage, I'm probably going to want to change some cost assumptions or margin assumptions as well. And so a real sign that it may be too simple is that you haven't given the user the tools to change those other variables. In other words, if it's too simple and too unidimensional, you may be denying the user the opportunity to look at all the knock on effects. If I change volume shipments, yes, it obviously has implications for revenue, but depending on the cost structure of the business, it has implications for margins and cost absorption. And if you're not getting below the top line and giving equal devotion of time to creating a modeling structure for cost assumptions, for example, then you're limiting the utility of that model for yourself or another user.
Host: Paul Barnhurst
I really like something you said there and the way I thought of it is when you mentioned revenue, right, if you don't have the flow-throughs kind of those key drivers and how they interact, one, you're going to limit the ability to make changes to it and the accuracy of it for changes you make. Because the reality is whether you touch revenue, you touch cost of goods sold, you touch certain opex, there's a number of other things that should adjust in a model.
Guest: Brian Egger
I think so! And I think a model is meant to replicate, can describe it in different ways, but it's what's the company's value creation process that you're analyzing. And a company creates value not only by making products and selling them at an optimal price, but also by managing their costs and gaining efficiencies and managing their capital deployment. If you don't get some insight into that corporate value creation process, then the model is simply going to be less useful.
Host: Paul Barnhurst
Yes, I think that's a really good point and I really like how you talked about the value creation, that, what it comes back to as we're trying to understand what's the value that's going to be created from whatever we're modeling. In the future, from this project, from the company, whatever it might be, we want to understand value.
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Host: Paul Barnhurst
Shifting back, something you mentioned earlier, you talked about you're the head of modeling there, I know you've been involved in the development of the Bloomberg Interactive Calculator.
So first, can you tell our audience kind of what that is and what it's used for?
Guest: Brian Egger
Sure. So the Bloomberg Interactive Calculator is kind of a template, a modeling product we've created. It's available right now for about 1600 companies. It aligns very much with Bloomberg intelligence's coverage of companies. But really what it is meant to do is, it creates kind of a modeling platform, a default modeling platform based on consensus assumptions. In other words, based on broker estimates, not only for high-level things like revenue, but on a very granular level, for deep estimates, for things like all the things we talked about, product shipments and selling price and costs and margins. And because we have data that we've had available that kind of tracks consensus, there's a Bloomberg consensus on a very deep granular level, we found a way to basically use those consensus assumptions based on broker estimates to populate a bottom-up model that takes those deep estimates and kind of feeds up to basically create a full integrated model, which you then can compare to a variety of benchmarks, including the kind of high-level consensus. So it's a consensus populated modeling tool based in Excel. That's essentially what it is, and it's available on the Bloomberg terminal to our clients.
Host: Paul Barnhurst
Cool, thank you. I appreciate that explanation.
What were maybe some of the key learnings you had from being on that project? Because I imagine that took a while to figure out how to automate and how to get the consensus together and agree on what that would look like for all these companies where you get it in a standardized process.
Guest: Brian Egger
So, you know, it's what I described as the potential to be kind of a complex project. Having the resources we have at Bloomberg is great, but it only means something if you have a collaborative approach. Really one thing I've learned is that the effectiveness in building a model in more of like an enterprise or corporate environment, which I do, really hinges on the extent of collaboration and the work relationship among people like myself, who have kind of a traditional financial modeling approach and perspective, but also people that are really skilled and capable in data and analytics, capable in product management, and we're essentially a financial technology firm and so we have some really great product people and also how to kind of market and position it. So you really do need a wide range of capabilities. Much wider than I could have or any analyst could have on their own. Developing this type of thing requires a lot of collaboration.
Host: Paul Barnhurst
I could imagine a lot of cross collaboration, a lot of people involved. Is there maybe one key takeaway from all that, if you would say, if you kind of had to sum it up, is a collaboration on any big project or what's kind of the one kind of key takeaway from that one?
Guest: Brian Egger
I think it's that and it's also that you come to realize that no one professional has the skill set singularly that you need to get the whole thing done. And so we've got people with kind of programming and more quantitative language capabilities. We've got people who really know kind of the ecosystem of how our firm does things and then we have people that have kind of been out there on the outside, as a user. I think really, the key thing I take away is that you have to bring your skills to bear, whether you're working on a model collaborative with colleagues or developing a product or simply using a model on your own, having some way to not only share your insights but share your skill sets is really critical.
Host: Paul Barnhurst
Yeah, I really like how you said that, of sharing skill sets because you think of M&A transactions, and any big model you're doing in just the workplace, a budget you're building, you need a bunch of people. It's not just the person who may have the knowledge to build the model. There's others involved in the project very much like what you were doing there, and to be successful, you need to learn how to tap into those and make sure you're getting the best out of everybody.
Guest: Brian Egger
Yeah, and I think also when you're a securities analyst that's industry or sector specialized like I've been, a lot of your knowledge and skill and expertise is knowing a lot about that industry. You're a fundamental analyst. And so, yes, you're hopefully good at financial analysis and modeling and understand accounting, but you're using that with the purpose of applying that to understanding how companies in a particular industry allocate capital and make decisions and create value. And so that requires just a lot of industry knowledge, and you only get that by following companies in an industry over many quarters, over many seasons and years. That's a time-consuming learning process and it's entirely parallel to, but different from the process of actual model building per se.
Host: Paul Barnhurst
Sure, yeah. Learning a business deeply, understanding it, understanding the trend is different from building the model, obviously. Does it help you build the model? Is it important? As I always say, one of the best things you can do, especially in corporate finance where you're working for one company all the time is learn the business, learn the company. If you're covering one industry, I would imagine learning that industry has helped you as much, if not more, than knowing how to model.
Guest: Brian Egger
Yeah. And you get the pattern recognition as a modeler from not only having seen this model before, but I've seen this business industry, I've seen it evolve. I know what sort of seen this play before. That kind of mindset applies both to, I've seen this model before, I've captured this modeling problem before, but also as a fundamental analyst of industries and companies, I've seen these types of issues taken on by management before.
Host: Paul Barnhurst
Yeah. I'm sure over the years you've seen some repeats too, like, oh, here we go again, probably with some companies and things.
Guest: Brian Egger
Yeah, and I think a sense of what can and can't work is also you can do a model with a lot of assumptions, but if you're an analyst, do what I do, you also have the sense of whether or not what you've put together in a spreadsheet, whether or not that's something that the company can execute on, that requires a bit of a more qualitative sense of how businesses work and how talented management teams are.
Host: Paul Barnhurst
Makes sense. Thank you for sharing that. You recently did a webinar with the Financial Modeling Institute, and you talked about four guidelines to help balance what we talked about earlier, right? Complexity versus simplicity. So could you talk a little bit about those guidelines with us?
Guest: Brian Egger
Sure. Well, and this will probably echo a few things I said earlier in this discussion, but I think it goes back to the question of how much detail. I like to always start to, at least when I create a model, replicate the amount of detail that the company itself provides in its own disclosure. And if you can go deeper, which maybe you can, you want to use certain metrics that the company does disclose. It can be a really creative process. But your starting point, you at least have to be able to replicate the level of the company's own disclosure, because if it can't, then it can't really respond to new information. The way we approach modeling, because it has some consensus basis to it, is we have the ability to create a lot of these sort of fundamental drivers based on these consensus inputs. The way to preserve the type of linkages among financial statements I talked about earlier, you really have to have a very basic sense of the formulas. Like, you can have all the drivers and consensus-based inputs you have, but if a company is increasing its capital spending, or you assume it is, there should be a knock-on effect in terms of what's the impact on property plan equipment. And so the only way you get that internal connectedness is if you have the right types of formulaic connections between parts of the financial statement. So you really have to have kind of a combination of what I would call drivers and basic formulas, formulaic connections.
I think also, as you develop a modeling I've built on most of my models as a modeling practitioner, it's useful to be able to benchmark yourself against what is that forecast that you've come up with, compare to. And we may rely a lot on consensus, kind of brokerage community estimates, but there always are ways to rethink, what are you using either to compare your forecast against or to populate your assumptions. If I decide, rather than using my own assumption about revenue growth or prices for a particular commodity, instead of using my own assumption, maybe I want to inform that assumption with an industry forecast. So there's always a way to rethink whose perspective you put in the driver's seat of the model, so to speak. Do you want it to be your individual assumptions or do you want them to reflect some type of third-party review or some type of composite or consensus? And so it's always useful to challenge yourself by reframing who the modeler's insight is coming from. That could be either you working alone and simply testing different scenarios and assumptions, how about trying this, trying different sensitivities or trying the buller bear case or maybe trying to inform your model with some insights from some other sources that might not be your own?
Host: Paul Barnhurst
Thank you, I appreciate that. And I really like how you said, especially as an analyst, right, the baseline you got to start with is you need to be able to at least model what they have in their financial statements. And that makes sense, right? If I'm doing corporate finance, I need to be able to at least model the basis of the company at the level that they need me to plan. And so there's a baseline you start from, and then it's layering on from there as appropriate.
What do you layer on that's going to help you make better decisions, help better inform your end user? I like the way you kind of shared that there. And so I know you did that for Financial Modeling Institute. And so I just want to ask you, obviously you're involved with Financial Modeling Institute. I know, you know the executive director there, kind of what are your thoughts on what they're doing there, having that formal modeling accreditation to help the industry validate their modeling skills?
Guest: Brian Egger
Yeah, look, I think it's a great way to invest in yourself and get that kind of accreditation. I think aside from the fact that they built a really interesting organization, I think more generally this goes back to the bigger picture of the importance of just continuously reinvesting in yourself and they provide a great way to do it. But I think other people will take approaches of being very focused in their academic studies and trying to gain specialization that way. Some may pursue the Chartered Financial Analyst exam or there may be supplemental courses you can take or, you know, on-the-job types of experiences you can have. And I think that there's a lot of merit just to continuously reinvesting in both your skill set and your credentials, and that it's really important to never stop doing that. The FMI has created a really intriguing platform as a way to do that. I'm a big advocate, having also taught at the graduate business school level of the challenges of being on both the teaching and student side of credential, granting, and gaining. I think it's great to both accumulate skills and credentials because it just makes you stronger and it also constantly gets you to challenge your own thinking. And there's kind of a nice, even if you haven't done some teaching at the university level, kind of a nice symbiosis between being a practitioner, taking a step back and trying to teach it. And as you begin to teach things, or you're exposed to teaching, you frame it and structure it in a different way.
Host: Paul Barnhurst
I would agree. I think it's great to reinvest in your own skill set, and I like how you said you learn to frame things differently when you have to teach it. Because I do a lot of corporate training now and started doing more of that in my business, and there's definitely times when you're like, all right, I didn't train or teach that very well. How do I rethink that? I know the concept, but how do I make sure others understand it?
Guest: Brian Egger
Yeah, and I think even separate from the value of any credential that we've talked about, there's just a value in achieving, actually going through the process and actually achieving a goal. I think this is so professional, it's always impressive when someone can particularly likely taking time away from their day job, so to speak, have the ability and the potential and the devotion to really invest in something that is many cases going to be time-consuming, but really kind of helps crystallize and further your skill set.
Host: Paul Barnhurst
Yeah, I agree with you. I think investing in yourself is always a payoff, and that can be done many different. I was originally scheduled to take the FMI a few years ago, then COVID hit, then I started my own business and some other things, so now I'm hoping to finish it by the end of the year, is my goal right now.
Guest: Brian Egger
Yeah, it's a great set of accomplishments to do that, and they've created a very interesting platform as a way to do that.
Host: Paul Barnhurst
Yeah, they have, and I've done some of their fundamentals and I spent a little time preparing for it, and I think it's a great program they have, and I think there's a lot of great ones out there, like the CFA. It was exciting to see the changes they made where they've added some modeling and Python and some practical to the theoretical side of that.
Guest: Brian Egger
And yeah, you know, we've seen, even the world of training accreditation, Financial Modeling Institute is an example of ways to really hone in on a sort of skill set that's part of a larger constellation of skills. So the CFA exam prepares you in many ways to be a capable securities analyst. Modeling is one important skill there, but then there are people that decide to pursue accreditation because they want expertise as a market technician or have some other alternative asset management. As you get deeper into a practice or a field of study, you realize there are areas of specialization that you can never really get enough of to further your skill level.
Host: Paul Barnhurst
Agreed. There's definitely, there's more out there to learn than we'll ever learn, that is for sure
Now, next question here. I just want to ask you a little bit, kind of going back to the gaming lodging industry. Obviously you've covered that for many years now, a couple of decades at Bloomberg, and over your career, just talk a little bit about what you've seen in the industry, maybe some changes, any insight or trends that you could share.
Guest: Brian Egger
I think the broadest trend, because it's applicable to, I've covered hotels and casinos and cruise lines, so the broadest trend is one of consolidation. If you follow an industry long enough, particularly from an earlier stage in its lifecycle, to a later stage, you eventually see the type of curve of growth and development and eventually consolidation. And so we saw that over time, just because I cover fewer companies now than I used to, because many of them have merged or combined, that it's a phenomenon that has been true across hotels, casinos, it certainly happened to some degree in the online betting industry. It seems to be a pervasive trend that businesses grow, they pursue returns, and over time, the business in some way consolidates and harvests cash and rationalizes in some way. And I think that sort of tends to be the broad pattern of an industry's economic life, if you track it from earlier stages to the more mature stages.
Host: Paul Barnhurst
100% agree with you! I'm following an industry now that has gone through an explosion in one of the software tools industries, and you're seeing a ton of tools just popping up left and right, and I've been saying for a while now, it's just a matter of time till we start seeing some consolidation, because you can only have so many in an industry because just the economies of scale, everybody wanting an above average return, which everything tends toward an average return over time. And so you try different things, and one of them is consolidation and mergers and acquisitions and that whole game. So that doesn't surprise me. It makes a lot of sense. I think you see that in a lot of industries, as you mentioned. If you watch it over time, there's usually a similar playbook across industries.
Guest: Brian Egger
Yeah, industry kind of starts off a bit more fragmented, and it rationalizes and consolidates and economic forces underlying it tend to result in very often bigger players getting even bigger and other types of corporate participants making different types of capital allocation decisions.
Host: Paul Barnhurst
Next we have the section this is one of my favorite sections. We call it the Rapid-Fire Questions. So I have a host of questions going to ask you here, I think I have seven in total. You get 10 seconds to answer. You can't say “It depends”. So you got to give kind of a yes or no, one side or the other. And then at the end you can pick whichever one you want to elaborate about.
I've had a few people that they're like, can I say “it depends”? And I'm like, no, you got to pick one, and then you could explain at the end. So we'll get started here, and these are all kind of related to modeling.
So circular or no circular references in a model?
Guest: Brian Egger
No circle references.
Host: Paul Barnhurst
All right. Do you prefer a horizontal layout, so basically many sheets, or more of a vertical layout where you kind of put it all on one sheet or on a couple of sheets?
Guest: Brian Egger
Yeah, I'm going to say horizontal with probably a preference to elaborate in the after-session, but I start up by saying horizontal.
Host: Paul Barnhurst
All right, and we'll give you this opportunity to elaborate on that one. Named ranges versus no-named ranges in models?
Guest: Brian Egger
Named ranges, absolutely. Particularly if it helps.
Host: Paul Barnhurst
Do you follow a formal standards board for your modeling, like Fast or Smart or some of the others out there? Yes or no?
Guest: Brian Egger
No, although I would qualify solely by saying we've tried to create some of our own standards and practices, but to your specific question, no.
Host: Paul Barnhurst
Okay, yeah, and that makes sense, I'm sure you have some own standards that you use at the company.
Next question is, will Excel ever die? Yes or no?
Guest: Brian Egger
I would say no for the simple reason that I haven't come across anything yet to replace it.
Host: Paul Barnhurst
Yep. I think you're not alone in that most people tend toward the no. We've got a few yeses, but… Will AI ever build the models for us?
Guest: Brian Egger
Yes, again, with an Asterisk, it will never replace the modeler’s insight, but it will definitely become a tool.
Host: Paul Barnhurst
I would agree with that, that's kind of my thinking as well. And then the last one, and you get four choices here, as some people like to point out, there's more than four. So if you want to choose a different one, I'll let you. But what is your lookup function of choice? Do you like Choose, VLOOKUP, Index match, or XLOOKUP?
Guest: Brian Egger
I would say index match, but that's partly because that's what I've had experience with.
Host: Paul Barnhurst
Makes sense. So which one did you want to elaborate on there?
Guest: Brian Egger
So I think in terms of the horizontal versus vertical model, my answer is probably a little bit of a hybrid. I like the idea of having and we've graphed towards having different tabs, at the very least a tab that breaks out kind of the key performance indicators and kind of the segment mechanics on one tab and the more traditional financial statements on another tab. And then maybe there's another tab for things like valuation or we also have a tab for kind of like a cover page. So just organizationally and based on how we use these pages, I like the horizontal approach.
However, having said that, within that, when we have like a financials tab, we have collapsed that into an income statement, a balance sheet, and a cash flow statement which is sort of vertically stacking them, the conventional financial statements. But what goes into the modeling process does go beyond those three statements or financials may have two statements, right? So we have kind of dedicated tabs horizontally for things like segment KPI analysis or other types, or summary type of views.
Host: Paul Barnhurst
Makes sense, and in my career, I've mostly been horizontal. And part of that I've worked big global companies corporate FP&A, where when I'm working at American Express, you're typically not doing a three statement. You're doing the P&L, you might be doing some capex stuff, there might be some metrics.
So that's been most of my career. I didn't do an integrated three-statement for work till a year and a half ago. So very different. And so horizontal was very common there because you often had 50 cost centers that you were forecasting and so you'd put one of them on each sheet because it was too painful to kind of have that vertically. But then once I started to do stuff that was three statements, I'm like that's kind of nice. I can see where laying out all three statements on one sheet makes sense. So I've kind of come to where I could see both and often it's a little bit of a hybrid. So it's interesting to see.
Guest: Brian Egger
Since I’m a securities analyst, not an FPA kind of professional, we're external users of financial statements, right? So we use companies' disclosures and filings as our starting point. So I think it would be a different approach if we were working internally with financial planning analysis. But because we're external users, we naturally start with the prepared financial statements and then we infuse that with as much of our thinking as we can.
Host: Paul Barnhurst
Totally makes sense. I get that. So it's a little bit different depending on what you're doing.
So as we wrap up here, I just have two questions left for you. So the first is, if you could give our audience one piece of advice that you've learned over your career, that would help them be a more successful modeler, what would that advice be?
Guest: Brian Egger
Probably just adhere to a couple of basic tenets or priorities. One is, never lose your attention to detail because as you tend to get more senior, you tend to be a higher-level decision maker, try never to lose that sense of what's in the weed, so to speak. And the second thing would be never lose your sense of intellectual curiosity because it's that constantly asking why, I think, that makes you a better thinker and ultimately a better modeler.
Host: Paul Barnhurst
I'll say those two just to summarize, the intellectual curiosity, always be willing to ask why. And the second is be willing to understand the details and get in there, there'll be times when you need it. Got it.
So the last question, if our audience wants to learn more about you or get in touch with you, what is the best way for them to do that?
Guest: Brian Egger
Probably the best way is LinkedIn.
Host: Paul Barnhurst
Okay. And we'll put that in the show notes for people and just want to close by saying thank you for joining me. I enjoyed chatting with you today and been a real pleasure. So thank you for being on the show, Brian.
Guest: Brian Egger
Thanks, Paul. Appreciate it.
Host: Paul Barnhurst
Thanks for joining me for that episode with Brian. There are a couple things I'd like to highlight as I've had a chance to re-listen to the episode and think about it. The first is how we talked about finding the right balance in your model. So the key takeaway for me was, does it aid in the decision-making process? If the answer is no, then you can probably leave it out of your model or make a very general assumption. So I think the first thing to always ask yourself when you're trying to find that right balance between complexity and simplicity is to always focus on does it aid in the decision-making process.
To elaborate a little further, he emphasized four pointshous in finding that balance between complexity and simplicity. He wrote an article about it on LinkedIn that we've included in the show notes.
So the first one was models should be detailed enough to replicate company disclosures. Obviously, this is going to apply when you're modeling public companies, there are other cases where you may not have that, but at a minimum, if you are modeling a public company, you should have that level of detail.
Next, I really love this, effective models rely on both drivers and formulas. There's places where you may have a driver, a key assumption. There's other places where things may adjust based on formulas and you need to find that right balance and rely on both of them.
The next one he listed is effective modeling requires a balance between granularity and simplicity. The example he gives is you may go really detailed on revenue and certain expenses, but you'll go a lot less detailed on the model for the immaterial nonoperating lines, which you have limited insight on, to begin with. So we talked about how that might be an area where you might want to keep it really simple, but you may want to go really granular on revenue.
And then the fourth area he mentions is data and analytical tools enable users to reframe who is the modeler. And he gives the example of using industry data using crowdsourced inputs. So you don't need to make all the assumptions yourself. You have the opportunity to look beyond consensus and incorporate industry data in your forecasts or other people's opinions. So I really like that and I think it's so important as modelers that we find that right balance. I've always been guilty on erring on the side of too complex. And the older I get, the more I do this, the more I'm trying to find ways to keep it simple, to really step back and think, how do I make this really simple? So my view is to err on the side of simplicity if you have to choose, but always keep in mind, is it aiding the decision-making process?
And then the last thing I want to mention from the episode is just the advice he gave to modelers. He gave two pieces of advice, and I think they're really solid. Never lose attention to detail. Always check and recheck your work. Just don't get sloppy as you get older, don't get lazy. Make sure you take care of the details. And the next, which was my favorite of the two, is never lose your sense of intellectual curiosity. Always be learning, always be growing. Always be challenging yourself.
So that's kind of my summary on a few of my favorite parts from that episode. Let me know in the comments or reach out to me and email me what your favorites were. We'd love to hear from you!
And if you enjoyed this episode, I'm going to ask that you please share the podcast with your friends, subscribe on your platform of choice, and leave a review and a rating. If you could give us a rating, that'd be great. It helps us produce this content. It's a lot of work to do that, so please take that time.
Thanks again for joining us for this episode!
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