In this episode of Careers in Finance on FinPod, we sit down with Brian Egger, Global Head of Financial Modeling at Bloomberg LP. Brian brings a wealth of experience from his time at Goldman Sachs and Bloomberg, where he has developed and led the Bloomberg Interactive Calculator financial modeling platform.

Brian talks about the complexities of equity research, such as his analysis on Las Vegas airline service capacity in the late 1990s, which challenged conventional thinking and demonstrated the critical interplay between demand and airline service. He also discusses the importance of modern data analytics, highlighting the role of coding and tools like Bloomberg Query Language (BQL) in enhancing financial modeling.

Listeners will learn about the essential skills for equity research, including meticulous attention to detail, the ability to synthesize data into clear viewpoints, and effective communication tailored to different audiences.

Tune in to learn from his expertise and gain insights into equity research and financial modeling!

Transcript

Scott (00:13)
Hi, and welcome to another FinPod. My name’s Scott Powell. I’m CFI’s Chief Content Officer, and I’m also one of CFI’s co-founders. And I’m here together with my CFI colleague, Duncan McKeen who is our Executive Vice President in charge of financial modeling.

Brian (00:23)
Thank you.

Scott (00:31)
And Duncan and I are delighted to be talking with Brian Egger. Brian is the global head of financial modeling and senior gaming and lodging analyst at Bloomberg Intelligence. And in his dual role, he has managed development, training, and marketing for the Bloomberg Interactive Calculator financial modeling platform. While at the same time, he oversees Bloomberg Intelligence’s North America gaming and lodging.

Equity Research franchise. Brian, thank you so much for being here.

Brian (01:05)
Nice to join you, thank you.

Scott (01:08)
Duncan, do you want to take the first question?

Duncan McKeen (01:10)
Yeah, I’d love to. So Brian, we wanted to start off by asking you if you could share with us what initially drew you into a career in equity research.

Brian (01:21)
No, that’s a great question. I think really visually drew me into research was, I would say, my, you know, an analytical nature, a desire to become an industry expert and also the opportunity at the time when I started for a wide range of interaction, interaction, at least in an investment banking environment with sales and trading, with bankers, with traders.

Really, I think those were all the motivating initial reasons.

Scott (01:54)
Awesome. And hey, Brian, we’ve left a lot of our learners at CFI are starting their careers. And so one of the questions we have for you is, what was your first role in equity research and how did you get started in the field?

Brian (02:06)
So, I began as a financial analyst at Goldman Sachs. And I think the way I got introduced to it was I was an undergrad in finance at the Wharton School. And it was probably through a combination of classroom discussion, interaction, and on -campus recruiting that I became aware of equity research. It was probably somewhat lesser known at the time, perhaps, than sales and trading or investment banking.

But there were a lot of things that really appealed to me about it. As I mentioned, a lot of the opportunity for industry expertise and analysis was quite unique to this field within the larger realm of Wall Street investment banking.

Duncan McKeen (02:45)
Now, the other, go ahead, Brian.

Brian (02:46)
So, yeah, I can elaborate further if that’s helpful.

Scott (02:49)
No, I think that’s great.

Brian (02:49)
Yeah, I think, okay, yeah, we live there.

Duncan McKeen (02:53)
And I guess we wanted to take you forward many, many years in time now to ask you about, and I think our learners will be really interested in if you could walk us through what a typical day looks like in your current role at Bloomberg.

Brian (03:09)
So I spent about 50 % of my time as an analyst looking at news, looking at forward thinking themes, thinking about industry analytics, data trends, really trying to assess the landscape for the industry

on both a company-specific and an industry-general level. And then the other 50% of my time is really focused on my head of modeling role. And that involves a wide range of functions, including from a financial modeling perspective, policy implementation, product development, quality control, marketing, really a full range of functions that is involved in developing our modeling platform, which I’m happy to talk more about.

And many of those aspects of the role are quite collaborative. I really want to emphasize that because in the financial modeling role, such as the one I have, it inevitably involves working with a combination of not only other research people, but our data teams, our product teams, our external marketing teams. And so given the kind of the data-intensive nature of modern integrated financial modeling,

it is by necessity really a very collaborative effort.

Scott (04:19)
Got it. And could you, I know some of our listeners and learners will know about your financial modeling platform, but I know some won’t. Could you speak a bit to that platform, the interactive calculator financial modeling platform?

Brian (04:30)
So the board work director of calculator, which we develop jointly, not only within the research team but also with our product and our data teams, is really a consensus-driven bottom-up financial modeling platform. And the basic structure of it is it is intended to create and launch an integrated three-statement financial model. But instead of having my own analytic contributions as assumptions and drivers.

It relies on consensus brokerage analyst estimates to really serve as the drivers of the model. So whether we’re looking at growth rates or specific values or other assumptions, we rely on an average of brokerage analyst contributors. And we’ve had access to this data for years. We have a huge repository of data about analyst estimates, not only for high-level things like revenue or earnings, but also for very deep key performance indicators.

And what we do is really take that data. We use an API to bring in that data to basically populate drivers that make the model work and that in a bottom-up way build a three-statement model. And that’s essentially what our platform does. So, the user does have the ability to override those consensus-based assumptions with their own inputs and assumptions. So it’s kind of consensus-driven with the ability to test sensitivities and scenarios based on.

One’s own assumptions, but it is kind of at the heart of it. It starts with the consensus thinking as a starting point.

Scott (06:05)
Wow, that sounds fantastic. What a resource.

Duncan McKeen (06:07)
That is great. Yeah, and I imagine a lot of the buy side clients would really appreciate the ability to override those inputs and put their own estimates into the models as well.

Brian (06:18)
Yeah, definitely. I mean, I think it’s the idea of it is to really, it is partly maybe a modeling workflow solution on our end, but really it’s meant to enable users, whether it’s our own users at Bloomberg or our clients to test sensitivities of a model to different assumptions. And that’s pretty typically what a model does is you’re testing the sensitivity of outputs to inputs of your choosing. But we sort of pre-populate our inputs with consensus assumptions.

So the model kind of works and gives you sort of a sense of the wisdom of the crowd, if you will, in terms of what consensus thinking is. But again, the ability to test those assumptions and how the model reacts to assumption changes.

Duncan McKeen (07:02)
wonderful way to test sensitivity. We wanted to ask you also, you mentioned like a 50 -50 split between your two roles, so I guess we wanted to ask you about the true equity research role and ask you what are some of the most, what do you think are some of the most challenging aspects of working in equity research and then also how do you manage them?

Brian (07:24)
Sure, so, you know, as a background, I cover, I guess it’s 19 companies and across really several different industries because I cover casinos, hotels, cruise lines, online gaming, which are all really interesting industries. But I think there are three key challenges that come to mind. What is the challenge of prioritization and time management? Because there are a lot of companies and a lot of industries and industry dynamics.

In a lot of data and news flow, one really has to kind of prioritize and decide where the focus should be in looking at companies. I think the second thing is really knowing how to gauge the materiality of whether it’s events or news or research findings, it requires really constant prioritization in that sense in terms of determining what variables are material and which are not.

And then I think, you know, finally, you know, we increasingly work in a very data-intensive world. And so, you know, there’s a paramount need to manage, organize, and analyze the data we have. So we have the benefit of access to large amounts of data. And we’ve had to get more sophisticated about managing that data and adjusting it with our own systems and looking at it. But knowing how to organize it, analyze it, and draw the right

Scott (08:25)
Thank you.

Brian (08:42)
conclusions and inferences from that data is quite challenging, but one of the more interesting parts of the job for sure.

Scott (08:50)
Thanks for that, Brian. And I’m now thinking about our learners and our listeners who are considering a career in equity research. What would be your recommendation or what are the skills that really are essential to succeed in equity research, in your opinion?

Brian (09:07)
Sure. So I’ll start out with, you know, certainly a foundation and accounting and financial statement analysis because accounting is the language of business. And I think even with other really well-developed skills, it’s difficult to develop and advance as an analyst without that basic backdrop. But that being said, I think probably the next most important quality, in my opinion, is really attention to detail, kind of meticulousness and attention to detail because…

whatever else you’re looking at or whatever skills you bring to bear, if you’re not really deep in the weeds and looking at the numbers and the meaning behind those numbers, then the risk is you make some type of error that really dilutes the effectiveness of all the other work you’re bringing to bear. So certainly attention to detail meticulousness, I would say is really important. I would say almost kind of like…

not only an inquisitiveness, but sort of a perseverance type of inquisitiveness where you’re constantly looking to get deeper, trying to look at things holistically, ask yourself, what about this consideration? So I think really being an inquisitive thinker, sort of like a relentlessly inquisitive thinker is really important. I’d say thirdly, the ability to know how to synthesize conclusions often from a lot of data and really,

I’ll boil that down to a clear viewpoint, which is not a difficult thing because you have to be deep in the weeds and in command of the numbers and then be able to take a step back and draw something conclusive from that data that makes your audience understand it and kind of related to that maybe is a fourth point is the communication skills themselves because you can do great analytical work, but.

you need to have really well-developed written and oral communication skills. And not only generically good skills, but the ability to customize those skills and the delivery, but to suit your audience. So the way as an analyst, I might talk to a trader in very, very short form would be quite different than talking to a portfolio manager. And then maybe with even greater detail talking to another analyst that is also equally.

Duncan McKeen (11:08)
Yeah.

Brian (11:17)
Involved in the details and the, the number of aspects of what I’m working on.

Duncan McKeen (11:23)
Hmm. That’s, doesn’t that.

Brian (11:24)
So I would say those are kind of like the key qualities, yeah.

Duncan McKeen (11:27)
And those are definitely a lot of skills that you need to bring to the table to be successful in the career. And I guess we wanted to ask you also if you could sort of bring all those skills together and maybe share with us an example of particularly impactful project or analysis that you’ve worked on. Maybe drawing on a lot of those skills that you highlighted.

Brian (11:50)
I’m sure when I think about this question, I’m trying to I think it perhaps may be the most. High impact call as an analyst I ever had and this goes back quite some time ago. It was actually the late 1990s and there was a lot of concern around the preparing bearish sentiment for investors in the casino industry that all of these new casinos were being built as they were back in the late 90s. All these new resorts.

And the concern was that airlines weren’t introducing air service quickly enough to provide an adequate amount of transportation capacity to bring people to Vegas to fill those rooms. It was really just a question of a concern of the adequacy of Las Vegas airline service and seat capacity. And, you know, we kind of took a different view of it. And we really came to the conclusion that as long as the demand is there, the air capacity will take care of itself.

Which is a bit of a simplification, but what that involved was taking a deep dive into not only Las Vegas visitation data, but airline data and looking historically at. Understanding the cause-effect relationship between airline service and Las Vegas visits and what we kind of found was there rather than. Needing the planes in place to bring the people to Vegas if the demand was there for people being in Vegas, the planes would come and so.

Yeah, I think the reason that was important as kind of a career-defining call was number one, you know, a challenge conventional thinking, which is not always easy to do because some is conventional thinking is actually quite right. Secondly, we were able to back it up with data and data is everything because you could have an interesting thesis, but you have to be able to support that thesis with data and good analytics that

kind of analyze that data in a way that’s rigorous and convincing. And thirdly, I also work with other colleagues. I was a hotel and casino analyst. I was not an airline analyst, but I had a great colleague who was a very good airline analyst. And so not only did I rely on him for the data itself, but for the interpretation and understanding of the context of the data to make sure that I was.

Using it correctly and drawing the right inferences. And so, you know, that being able to kind of span the full vertical of that industry, which has suppliers like airlines and operators like hotels really was helpful. And so. It kind of brought together a lot of aspects of what I think are important qualities and equity research, which is, you know, kind of that. Constantly the challenging of conventional thinking, the ability to harness.

And correctly use data and the ability to work collaboratively, whether it’s with other research partners or data partners so that you’re not working in too much of a vacuum and you’re not relying strictly on your own skill set.

Scott (14:43)
Thanks for that, Brian. The next question we have is looking, instead of not looking back over your career, but looking forward, how do you see the field of equity research evolving over the next few years?

Brian (14:56)
Yeah, that’s a great question. I mean, because it’s I’ve seen it evolve and it will continue to evolve in interesting ways. I mean, one is that I think it’s in some ways has become a much shorter form medium, meaning that we also have at Bloomberg our own long form research. And I think it builds a lot of authority and command of an audience to be able to write longer, more in-depth reports. But we also absolutely find it necessary to deliver structure and

construct and deliver data in a shorter form format because it’s suitable to an audience that is inevitably time -constrained and inundated with different types of media and sources of insight. And so, the ability to have short-form ways of delivering research is really important. I think it is evolving more in the direction I think of becoming more data-intensive. I talk about data in both in terms of

Structured well, well-developed industry data as well as unstructured data alternative data and such. And we increasingly work in an environment where we have to be able to respond to and have some command of the implications of those data sets, whether they’re structured or unstructured or traditional industry data or alternative data. And with that, really

leads me to is kind of like a final observation, which is that things like coding, in some way, are becoming important. And I would have never said this even a few years ago. I’m not personally a Python expert, but I’ve had enough experience working with, you know, graphic user interfaces and ways to harness external data and really, you know, we have our own.

Capability within Bloomberg. We have a language called BQL Bloomberg query language. There’s things like Power Query. You know, there are there are query languages and ways to interact with APIs and sources of data so that you can ingest taking that data, take the data you need, organize it the way you need to organize it. And whether it’s a direct experience with coding or at least a facility with working in an environment where you know.

Coding takes place and you have, you know, kind of a series of tools in your arsenal that are ways to use interfaces that can interact with that data. That’s really important because it’s far too much data and often far too unstructured to deal with any kind of manual data entry approach, which is, you know, and having been announced a long time that that was very familiar to me. I think we have to become.

More sophisticated in the types of data analytics and resources out there, and if not having a command of coding or something like Python, at least knowing how to work with tools that have. Those types of analytics and capabilities as a backbone.

Scott (17:52)
Thank you so much, Brian. I think we have time just for one last question and I’ll pass that to Duncan.

Duncan McKeen (17:58)
Sure. Thanks very much, Scott. I guess we wanted to take you back to a backward-looking stance now, just to ask you about what you feel has been the most rewarding part of your career in equity research so far.

Brian (18:14)
Yeah, I think what’s been great about my career is I mentioned earlier in this discussion, the fact that I have a player-coach role and what’s been really rewarding about that is there’s a certain multi-dimensional aspect to both being an analyst and retaining that hands-on experience with doing analysis and research. And at the same time, having been involved in different ways with the more forward-thinking, forward-looking kind of higher level part of

Where is research headed? And so I’ve done that in different ways. While I’ve been an analyst, I’ve also at times been a director of research or associate director of research. I’ve had roles that have heavily involved mentoring or junior analysts, and that’s been tremendously rewarding. And now my current role as head of financial modeling, we’re constantly thinking about ways to advance the state of the art, if you will, in terms of how modeling works and takes place.

And I probably would say, even though I’ve been doing this a long time in that role in particular, I’ve learned a lot about the direction of research and where Excel and all its capabilities is headed, where data analytics is headed, where various types of data analysis are headed. So it’s been really rewarding for me

to always have a hand in the process of producing research, but also have the opportunity to participate in kind of the direction of research.

Duncan McKeen (19:42)
Thanks very much for that, Brian.

Scott (19:44)
Yeah, and Brian, generally, thank you so much. On behalf of Duncan, myself, our listeners, and learners at CFI, thank you so much for these insights, not only into equity research but also into what you’re doing with Bloomberg Intelligence. We really appreciate your time.

Brian (20:00)
Thank you, it’s been my pleasure.