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Episode 17: Precision in Modeling: Sam Sivarajan’s Wisdom on Balancing Art, Science, and Decision-Making

Paul Barnhurst

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 accreditation in Financial Modeling globally.

In this episode, Paul Barnhurst is joined by Sam Sivarajan, who is a Speaker, Wealth Management Consultant, Behavioral Scientist, Stoicism Padawan, Fintech Advisor, Podcast Host, Bestselling Author and an Adventurer. He has a law degree, an MBA, a PhD in behavioral science, and has also earned a CFP.

 

Sam’s work is focused on how we can make better decisions so that we can get to better results – in investing, in leadership, and in our personal lives.

 

Listen to this episode as Sam shares: 

·      His journey working in Telecom during the tech boom.

·      Advice on balancing the art and science of Modeling.

·      His advice on making smart assumptions.

·      The importance of Decision Making.

·      How to think critically when building a model and making assumptions.

·      Everything about his books ‘Making your money work’, ‘Uphill’ and ‘Am I Ok?’.

·      His position on controversial modeling issues, including circular references, dynamic arrays, modeling standards, AI in modeling, and more.

 

Quotes: 

“Never forget and lose sight of why you're creating the model, and who will use it.......”

“In my view, the model is simply a numerical representation of some prior deep-rooted strategic thinking, and you're bringing that to bear in that model as a representation.”

 

“A model is a simplification of reality. It isn't reality. And to remember that the model is but one tool in making key decisions. It shouldn't be the be all and end all......”
 

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. 

Go to https://earmarkcpe.com, download the app, take the quiz, and you can receive CPE credit. 

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In today’s episode: 

(00:22) Intro;

(00:45) Welcoming Sam;

(01:01) The worst financial model Sam has ever seen;

(02:57) Sam’s background;

(04:22) Sam’s experience at Telecom;

(10:27) How to balance the art of Art and Science of Modeling;

(12:27) Why is the process often more important than the actual Model?;

(17:07) Advice on Assumptions;

(21:51) Sam’s technique to get better at Modeling;

(22:30 - 23:15) Validate your Financial Modeling Skills with FMI’s Accreditation Program (ad);

(23:21) How people think about making decisions;

(24:35) How to make better decisions;

(27:40) Frameworks that help with Decision Making;

(31:57) Sam’s experience while writing his books - Making your Money Work, Uphill and Am I Ok?;

(34:20) Rapid Fire;

(37:38) Connect with Sam;

(38:17) Sam’s advice on being a successful Modeler;

(39:34) Paul’s Top Picks

             - The importance of the Thinking Process

             - Validating Assumptions

(41:00) Outro

Show Transcript

Host: Paul Barnhurst

 

Welcome to Financial Modeler’s Corner, 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.

 

Welcome to Financial Modelers Corner. I am your host, Paul Barnhurst. This is a brand new podcast where we talk all about the art and science of financial modeling with distinguished guests and 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 am excited to welcome on the show Sam Sivarajan.

 

Sam, welcome to Financial Modeler’s Corner!

 

Guest: Sam Sivarajan

 

Pleasure to be here, Paul.

 

Host: Paul Barnhurst

 

Really excited to have you! So, you know, one of the things we like to do before we introduce the guests, we have a fun question we ask every guest that we start our show with. Tell me about the worst financial model you've ever seen.

 

Guest: Sam Sivarajan

 

Well, Paul, I believe that models should have a purpose. If it is a quick and dirty calculation, it's one thing. But if it is meant to drive key decisions, there needs to be some rigor and best practice behind it. I think the worst model I saw was one created by a client, and it basically used historical P&L numbers and grew it over a number of years. And, of course, to make things worse, many of the assumptions were hard-coded in the cells. So basically, you couldn't really use the model for anything useful.

 

Host: Paul Barnhurst

 

Hard-coded is the worst because you try to untangle them. I still remember, and I think you'll appreciate this, I had the sales commission model. I inherited it from somebody else, and it was horrible. And there were hard-coded numbers, and thing was terrible. And the guy who was the director of the organization, he calls me in a panic, going, ”you realize this model will fail audit, like, if our risk audit looks at this from compliance”. Yeah, it's been that way for two years. Where have you been? That's nothing new. I'm trying to fix it, but I can't do it overnight. We had, like, ten different models, they took like, two weeks to run from the prior guy. I had to streamline the whole thing.

 

Guest: Sam Sivarajan

 

No, look, you're 100% right. I think it's the bane of anyone's existence. I think I would say it's commandment number one, right? Do not hard-code. And you find out, like, you're pulling your hair out, which is one of the reasons I don't have any at this stage, is to go through and try to decipher somebody else's model. And I think it's hard at the best of cases because everybody's got their own style of creating it. But when it's hard-coded or when you can't make out what are assumptions or what are given values, I think it just makes that even that much more complicated in the exercise.

 

Host: Paul Barnhurst

 

100% agree, I mean, I would say 80% of models that use hard-coding are probably the vast majority of those models that people scrap as soon as that person leaves because they try to understand them and it's just not worth it. You finally say, forget it, I'm starting over.

 

Guest: Sam Sivarajan

 

Exactly.

 

Host: Paul Barnhurst

 

Well, why don't you tell us a little bit about yourself and your background, what you're doing today, kind of how you got started?

 

Guest: Sam Sivarajan

 

Yeah. So I started my career, believe it or not, as a corporate lawyer in Toronto. And then I moved to London, England to become an investment banker. And I worked on telecoms and technology deals during the booming 90s and the tech boom and the tech wreck. And I was doing telecoms and technology deals around the world, based out of London and Vienna, Austria. I then moved back to Canada about 20 years ago, where I helped build and lead three wealth management businesses. During this time, I also did a doctorate in behavioral science, because one of the things I'm interested in is really about how we make decisions. And so now I actually write, speak, and consult on helping individuals and companies make better decisions.

 

Host: Paul Barnhurst

 

So let's see, we got the PhD, we got the law degree, MBA, if I remember right. Do you have your CPA in there?

 

Guest: Sam Sivarajan

 

No CPA, no. I did have a CFP at one point, but, yeah!

 

Host: Paul Barnhurst

 

That's quite a bit of schooling, I'm sure! Sounds like you love to learn.

 

Guest: Sam Sivarajan

 

I think I love to learn, I think part of what I like is the blend. I think learning is great, but being able to apply it in practical and useful ways, I think is really where knowledge becomes useful.

 

Host: Paul Barnhurst

 

I would agree with you. I mean, I've seen people that they're always learning but never applying, and there's definitely a balance to it.

 

Guest: Sam Sivarajan

 

Correct.

 

Host: Paul Barnhurst

 

As you mentioned, you spent the early part of your career supporting the telecom industry, I think you mentioned over in the UK, and it sounded like you did a lot of work from the M&A perspective, from what I remember. Can you talk a little bit about that experience, that you said, the booming 90s, that kind of time and what it was like.

 

Guest: Sam Sivarajan

 

Certainly, as you mentioned, at the start of my investment banking career, I spent a lot of time building models. And so, for example, we won the mandate for the first satellite IPO in Europe, in large part due to our clear view on valuation supporting how we would price and market the deal. Look, I mean, on a personal level, this was very exciting time because lots of things were happening and London was a really cool place to be as a young person. As I got more senior, I started leading deals. And so I led the team that advised Hutchison Whampoa on its European 3G mobile license strategy. We built models there to value and support our bids for licenses in the UK, Germany, Sweden, Italy, and Austria. And what was really cool, Paul here, was that we had to model services, including demand and pricing for things that hadn't yet been invented. So services like FaceTime, YouTube, Yelp.

And I think what's really interesting is that keep in mind that this was in 2000, so long before these services were on the scene. So the challenge that we faced in building these models, was in making reasonable assumptions about unknown services and markets. Billion-dollar decisions were based on the output that we were able to produce.

 

Host: Paul Barnhurst

 

No stress at all! It's only a billion dollars!

 

How did you make those assumptions? Walk me through kind of the decision process, I know you talk a lot about that. You've thought a lot about that with your behavioral science, our behavioral finance. So how did you guys think about kind of making those assumptions and feeling comfortable that they were good assumptions, given the huge dollar amount and all the risk in these types of deals for something that really hadn't been invented?

 

Guest: Sam Sivarajan

 

Look, I think this is where, as you said at the outset, there's an art and there's a science to financial modeling. So the science is the technical stuff about how you put models together. And I think it's very important, but the art is really, what are the assumptions? Why did you make them? And more importantly, how do you defend them to those that are asking you about it? So, in this case, there were a few things that we had that were working for us. So mobile penetration, we knew always was trending towards, call it, 95-90% of the population. So we knew what the potential market was. We also knew that roughly you would have a share as a mobile company, you would have a share of the market of 1 over X, where x is the number of licenses in that market. So if there were four licenses, you're likely to get a target share of 25%. If there were six licenses in the market, your target share was 18%, roughly, okay, less 16.7.

 

I think the key is not that you can't get a higher share of market, but you would better be able to articulate why you think your service was going to do better than what was there in the market. So this explained, for example, why we were ready to pay about $8 billion for the license in the UK that we won, okay? And that license was one of four in a country of 60 million people. But when we went to Germany, where they were auctioning off six licenses, it was a country of 80 million people. And the bidding war and it was a war, got to over $10 billion, and we pulled out at that point. And we pulled out because, for a market share of 25%, it sort of made sense to pay 8 billion. For a market share of 16-17% we didn't think that it made sense.

 

Other things that we were looking at, it's really an iterative process that we kind of talked it out to say, okay, what kind of services were you going to buy? We did think that the higher bandwidth that 3G allowed for was going to drive data-driven services. That was the whole reason why 3G and why firms were paying 8 billion and $10 billion for licenses.

 

Host: Paul Barnhurst

 

Sure!

 

Guest: Sam Sivarajan

 

But you were trying to model things like video conferencing. So we did model video conferencing and how likely that was going to be used. How likely is it that an employee really wants video conferencing to use it to be able to show their employer that they're actually on the beach somewhere, etc? But so as a joke, we were thinking about those things. We were going back and forth and testing what was reasonable and what was not. Most of those things we got right. Maybe the form was different. I'll give you an example.

 

We thought that video conferencing was going to be a separately charged item, right? That you would pay as part of your mobile subscription for video conferencing. Now, that's not the case in most markets right now. Facetime is free, but you do pay for it in the bandwidth that you're paying your telecoms provider. It's a different pricing mechanism than we had envisioned 20 years ago, 25 years ago. But still, that was the sort of thinking that we were going through, a combination of top-down and bottoms-up type of approach.

 

Host: Paul Barnhurst

 

Got it, thanks! Really interesting to listen to you talk about that and how you thought about it, because I can see what you're saying makes sense, hey, if there's this many licenses, we can get an idea of what our rough market share will be, gives us a starting place all right. We know these types of things will be coming just because of technology and where we're heading, and you could start just piecing something together that at least logically makes sense that you can support.

 

So when you're doing that, any advice to modelers on how to manage, as we said, the art and science, how do you balance the two? Because I've seen sometimes where people get all focused on the art, sometimes you get heavily focused on the science, and I got to follow these 100 pages of rules and everything has to be in line with that for a model.

 

Guest: Sam Sivarajan

 

Yeah, I think that's a great question and I don't know if there's an easy answer. I think to some extent it comes from experience. And of course, experience is hard to speed up. You can't get ten years of experience in a year, right? So I think part of it is keep looking for experience in building different types of models for different uses, but I also think the key is to never forget and lose sight of why you're creating the model, who will use it, what decisions will it support the users to make, how much of what you're actually modeling is knowable and how much is assumptions.

And at the end of the day, I think it's never forget that a model projects future outcomes. So the outcomes, by definition have to be probability-based. There is no certainty that your forecasted outcome is going to occur. So that needs to be supported by scenarios where you're being very clear what your base case is, what your bear case is, what your bull case is. And finally, I think it's important to have a degree of humility in the process to point out the limitations of the model, because a model is a simplification of reality. It isn't reality. And to remember that the model is but one tool in making key decisions. It shouldn't be the be all and end all, and nobody should want that, not the modeler and not the decision-maker.

 

Host: Paul Barnhurst

 

I really like how you said it shouldn't be the end all, be all, because I think there's a lot of truth to that. Sometimes you go, well, whatever the model says, that's what we're going to do. A model is just a representation, it's based on assumptions. There's often qualitative factors and other things that you need to make sure you're thinking about. I think it's a really good point and kind of along those lines, when you and I chatted previously, you mentioned that the process is often more important than the actual model. Can maybe elaborate a little bit on that, how you think about that?

 

Guest: Sam Sivarajan

 

Yeah, I think that building a good model forces the modeler to go back to basics. What are the key drivers of the business, for the costs, for the revenues and so on. And by thinking about these drivers and actually modeling them out for the forecast period, it forces both the modeler and the person who uses the model to think carefully about the business, its operating environment, and to focus on what really moves that business forward. In my view, the model is simply a numerical representation of some prior deep-rooted strategic thinking, and you're bringing that to bear in that model as a representation.

 

Host: Paul Barnhurst

 

I like you said some deep-rooted strategic thinking, because it really is around strategy. The one thing I've seen on more than one deal or the stories is just keep adjusting the model till it makes sense. Right? You're laughing, you've never seen that, have you?

 

Guest: Sam Sivarajan

 

I've seen it a lot, and I think I can give you an example. I think that Michael Mabusen, if you know the author and analyst, he talks about the difference, about using an inside view and an outside view, and your listeners might find this useful if they don't know him. He gives the example of an analyst who was forecasting a stock growing at 25% a year for the next ten years. Now, based on this analyst's models, an inside view, this looked like a reasonable assumption. You would go and buy the stock based on this analysis. But Mabusan asked an analyst a very simple question. He asked him how many companies historically had achieved these types of growth rates for this period of time. And this was an outside view, and you can imagine what the answer was. It was completely different, and the answer is none.

 

I think that to your point about keep working the model until you get the right answer, that's the bottoms-up analysis. Sometimes you have to complement it. Usually, you have to complement it with a top-down, out-of-the-box kind of looking in to kind of say, does this make sense? We're so in the model, if you're looking at it from an outside perspective, why would we expect the assumptions and drivers and values that we put in? Why do we expect that to hold water?

 

Host: Paul Barnhurst

 

I like how you said that, to hold water. I mean, I can remember being in a meeting one time where they kept pushing me to cut cost or add more revenue and kept getting there, and remember being in a meeting and we finally came to the conclusion and everybody was like, “why are we considering doing this deal?” The company was shrinking, and they're supposed to be a hyper-growth company. Like they're slowing, not shrinking, but they're slowing. We're not seeing the sales. We keep assuming we can do more with it when they're struggling to do it, and we can do it on low cost. And it was like, that's kind of the eye opener to the CEO. He finally said, just tell them we're out we’re, not going to go forward. I'm like, thank you, that's a smart decision. Why do we continue to waste time and try to make this work when we know it's not going to? We're sprinkling assumptions that just aren't going to happen all over the place.

 

Guest: Sam Sivarajan

 

Look, I couldn't agree with you more, Paul. I think that assumptions look smart on paper, but they're completely impractical in real life to sit there and say that you're going to cut this department or whatever. Yeah, well, it looks easy to remove a line or a row or a cell on paper, but that department or line, I've had CEOs tell me this. These aren't just lines on a spreadsheet. I think those people are the ones that are going to keep this company running, whatever that cost is. That's the cost. I worked at one place where we had an IT team, and they were expensive. You know why they were expensive? It was proprietary code. You can't possibly replace them with somebody else. There's a difference between what it says on the spreadsheet and what it actually tells you when you look at the facts behind that, the numbers on the spreadsheet.

 

Host: Paul Barnhurst

 

Well, an example, we were trying to wind down a traveler's check product, you can imagine how old those are. The system we were using, they were wanting to revamp it to something new because it was so expensive. And I'm like, how old that is, how old the code is, we ended up not going down that route because it's just like you're just opening up a huge can of worms. Sure, you can make an assumption we're going to save this amount, but you may end up killing everything in the process. And kind of speaking of that, on drivers and assumptions, because they're so critical to a model. Any advice you'd offer to modelers to walk through that process, how should they think about that as they're making those assumptions to ensure that they're not making bad assumptions or creating undue risk beyond the risk that's going to be there no matter what with every project

 

Guest: Sam Sivarajan

At the risk of sounding like a cliché, I think part of it is to look at it holistically, and maybe I can illustrate with an example. In my early days, when we were building DCF models for early-stage businesses, we would look to see what percentage of the value was in the forecast period and what was in the terminal value. I've seen several models where the terminal value was over 100% of the total value, meaning that in your forecast period, you don't see a way that this business is making money. How much confidence should anybody have in that model, or the drivers or the business, when you, in your own forecasts, don't have confidence that this is going to make money, but in a terminal value which is infinite, you think that's where we're going to make the money. And again, the assumption is that somebody is crazy enough to invest in this business thinking that I'm not going to make money for ten years, but if I hold it for 100, I'm going to make money. I would say a couple of key questions that the modeler should ask themselves as they're working with the drivers and assumptions.

 

So one, do these drivers make sense? So for a mobile company or a SaaS business, for example, a key revenue driver is the number of subscribers. For a manufacturing company, it's going to be different. The key is to think about each business and their operating model and think about that situation separately from everything else.

 

I think, two, do the values make sense, like whatever the revenue growth numbers or the end valuation, we talked about the Mabusan example. This is a very, I think, cautionary tale of looking at something holistically. The numbers on itself could look like, oh, yeah, that makes sense. 10% year of growth, or 25% of growth for a high growth stock, that seems reasonable. Because it's grown over the last two years, 25% is reasonable. Where it becomes unreasonable is when you think it's going to do it ten years in a row, because that becomes like a unicorn and there are not many unicorns around.

 

So I think it really is a question of, number one, tailoring it to each particular model, and there isn't a holistic answer, but there's a framework or process that modelers can use. And it's really to kind of challenge, from an outside perspective, each driver or assumption that you're using. And can it hold water when you're having to defend it to someone that isn't involved in building the business? It could be your boss, it could be the decision maker, it could be an analyst from the street.

 

Host: Paul Barnhurst

 

Kind of a funny story. When you said, have to support it or defend it, I still remember I had a boss and he had been in investment banking for years, had been in the UK probably in the 90s, early 2000s, doing a lot of financial modeling, and he looked at one of the models I'd built and he goes, “this looks like government work”, is the way he put it. Then he proceeded to shoot holes and all kinds of things in the model, I had to go back and rethink the whole thing because he's like, “yeah, this assumption doesn't make sense”. And he was right and he was nice about it, but it was a really good learning lesson to really try to think. It's something I hadn't done before. It was a new way, and it took us quite a bit of back and forth to get to where, okay, we're comfortable with this. Yes, this could make sense.

 

Guest: Sam Sivarajan

 

And I think you make a great point, Paul. I think building a model is and should be an iterative process. You're testing assumptions. It's like writing anything. I think the assumption that you can write a first draft and that it's going to be perfect, etc is the same kind of mistake to say that a model that you create is going to be your final version. I think you should be testing the assumptions and drivers. You do look at the output, you do look at the scenarios, and you're going to want to try different things. And working with other people, showing it to other people, and verbally explaining your model, and defending your assumptions is a very good test internally with low stakes to see whether that model is really fit for purpose or whether you're going to have to go back and revisit some of the key drivers and assumptions, so that's a great point.

 

Host: Paul Barnhurst

 

Yeah, I appreciate that. One other thing I wanted to ask you, kind of shifting gears a little bit, we've talked a lot about the assumptions, the drivers, the behavior, all those things, but there's the technical side of the model, which 90% of the time is building in Excel. So anything you learned that kind of helped you, maybe a formula, a technique, something that helped you get better at building those models. I mean, obviously shortcut and practice, but maybe is there any kind of technical nugget, something you would advise people to focus on on that side, to be better at modeling?

 

Guest: Sam Sivarajan

 

Learn your macros, learn your VBA. I can say that as having done it in the past and having promptly forgotten it, if you'd asked me how to build a macro or a VBA kind of scenario, I couldn't do it today. But I do remember spending a lot of time learning about how to do those, and I think that was critical to kind of move things forward.

 

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Host: Paul Barnhurst

 

So, moving on, I want to talk a little bit about something I saw on your website. So, I know you do your own business today, and one of the things you had right on the front of your website, it said the average person makes 35,000 decisions daily. Can you maybe talk about that, where that number came from, and how people think about making decisions?

 

Guest: Sam Sivarajan

 

Sure. So there's research that shows it. Of course, nobody is counting it, but again, I would say to keep in mind that most of those 35,000 decisions are automatic. So things like getting out of bed in the morning or brushing your teeth or putting on your shoes, you're not consciously thinking each step through. That is your brain being efficient. And that's good, because otherwise we would be completely overwhelmed. But this is also dangerous. And it's dangerous because if we let our brains go on autopilot, even when making critical decisions, like where to invest, shutting down a business line, or launching into a new market, so we have things like biases, emotions, our own previous experiences that can all play a role, an outsized role, in making key decisions without us even being aware of it.

 

Host: Paul Barnhurst

 

That's a great point. So, I know one of the things you do is help people make better decisions. How do we make better decisions? How do we help eliminate those biases and those automatic reactions that may not be what's best, that may lead us down that wrong road, how do we get better at doing that?

 

Guest: Sam Sivarajan

 

I would say a couple of things. I don't know that you're ever going to eliminate biases, it's just what is hardwired into us. And remember, as I say, these biases come from the fact that our brains have learned to be efficient and to try to automate things. So we don't want to get rid of that, because otherwise, we're going to be spending an hour trying to figure out how to put our shoes on, right? But on the other hand, I think being aware of it and letting your rational brain work through the decisions, the critical decisions that you want to go through, I think, can lead to better outcomes.

 

So, in my writing and speaking work that I do, I share research and evidence, as well as real-life cases of how these factors impact decision-making and how to get better outcomes by following a more rigorous process. So I talk about doing a pre mortem, for example, on key decisions, like walking through a very rigorous pre mortem process to kind of think what could go wrong and how to prepare for that.

 

For example, in investing in market downturns, people panic and sell their investments indiscriminately, even though every one of those investors will tell you that when they made the investment, when the markets are good and everything else, they will tell you that they, of course, know that the markets will correct and it will rebound, and they're okay to ride that journey without a problem until the markets correct. And so we know those things happen. So how can we help protect people against themselves? And that's a lot of the work that I do in terms of educating them. But the education itself isn't going to help because this isn't things that they're unaware of. It's being in the moment. What steps, what tools, what frameworks can you provide that is going to help them manage it? And in my consulting work, I apply this firsthand, so I actually do it with the clients to kind of sit there as they're evaluating.

 

I had a client that I was helping who wanted to enter into a new market, and I looked at different options with them, and I kind of helped them walk through in a very rigorous way. What are the pros and the cons? What are the alternatives? What are some of the downstream potential consequences of what's going to happen? What is the impact on their current business? All of those things, I think, need to be looked at systematically and being aware that, as I say, behavioral biases can play a factor that limited perspective, either from a stakeholder perspective or from a time horizon perspective that you're not looking long enough. I think those are all kind of factors that I try to help individuals and companies take into account when they're making decisions.

 

Host: Paul Barnhurst

 

You mentioned one thing people can do is a pre mortem, right, for certain situations. You also mentioned frameworks. Are there any other favorite frameworks you have that you often train clients on that help with decision-making?

 

Guest: Sam Sivarajan

 

So in team situations, for example, there's Edward Debono's six thinking hats framework. And the six thinking hats is, if you've ever known, like the devil's advocate or the red team kind of approach, asking different members of the team to kind of take on a persona, to challenge an initiative with that persona's perspective. So the six thinking hats has different, there's a logic hat, there's an emotional hat, there's a culture hat, and so what you're asking is the team to kind of take turns wearing those hats and look at the problem or the decision from that perspective. If you were someone that only cared about logic, what would you think about this decision? Where does it make sense? Where does it not make sense?

 

If you're thinking about it from a culture perspective, what does this do to the culture of our organization? Why is this a good decision or a bad decision? And I think that kind of framework, and you can use that as an individual as well. All you're trying to do is to force yourself to look at the decision from multiple perspectives and not to be blinded, not wear blinders, to sit there and think, okay, to come back to financial modeling, I've got a financial model, and this makes financial sense. And it may make financial sense, but it may not make strategic sense, it may not make operational sense, it may not make cultural sense, it may not make technological sense, right? But I think if you're not looking at things from multiple perspectives, there's a danger of having groupthink and making decisions on that basis.

 

Host: Paul Barnhurst

 

As you said all that, one decision I thought of when you mentioned kind of the emotional and the logical, there's always people's debates, hey, should I invest the money or should I pay off my house early? And if you look at it strictly from financial, logical, if the interest rates are high enough and your loan is low enough, you should invest. From just a logical run the numbers might be low risk, but there's the, hey, what's the benefit of paying off early? What's the peace of mind?

 

Guest: Sam Sivarajan

 

Correct.

 

Host: Paul Barnhurst

 

There are so many other things that are other perspectives leading to why rational people can come to a completely different conclusion that makes sense for them with the same basic facts.

 

Guest: Sam Sivarajan

 

No, look, you're 100% right. I mean, in the example that you give, that choice only is a real choice if you were going to invest in something that was a good investment and you stayed invested, okay? If you don't and you are playing the markets, you're timing the markets, you're getting in or out, I mean, that's a theoretical benefit that you're not realizing. If you're going to take that money and spend it because you can't control yourself, it makes it a really bad decision. So one of the reasons to pay down your debt might be that, okay, you would rather not have that temptation that you're going to try to time the market or try to buy something that you don't really need, knowing that you pay down your debt, that that gives you a lot more flexibility.

 

So I think you're 100% right. I think that the facts may be the same, but I think each person's preferences and motivations and circumstances, I think, dictate potentially different responses.

 

Host: Paul Barnhurst

 

And I'm sure it's one of the reasons we have behavioral finance as a study, is because economists want to come at it from often just that logical point of view. Well, why aren't people being rational? Because there's a lot more to it than just the rational facts.

 

Guest: Sam Sivarajan

 

Well, and the funny thing is, of course, the same economists that talk about all of that, when you ask them to apply that economic thinking to their own decisions, they're very often guilty about not behaving rationally as well.

 

Host: Paul Barnhurst

 

Yeah, they do the same. Well, as I always like to joke, when it comes to economists, ask five economists, get six opinions.

 

Guest: Sam Sivarajan

 

Exactly.

 

Host: Paul Barnhurst

 

So take it for what it's worth.

 

Next thing I want to talk a little bit about is you've written three books, so let me see if I have them right here. I believe you've written one called “Am I Okay?” “Making Your Money Work” and “Uphill”. Can you talk a little bit about your experience writing these books and why you wrote them, what you hope people gain from them?

 

Guest: Sam Sivarajan

 

Look, each book was a different experience. “Making Your Money Work” was my very first book, and it was really tied to helping people make better decisions with their money. And it's concepts, fundamental concepts like risk, return, compound interest, etc.

 

“Uphill” was my second book, and it was inspired by the rapidly changing world during the pandemic, and it was focused on taking the lessons that I learned in behavioral science and that I was applying to investing, but really broadening it to say, how can we use those tools to better understand a changing world and to make better decisions at work or in our personal lives?

 

And “Am I Okay?” was my third book, and it was designed to be a short and practical primer for advisors and investors on the questions that they should consider in making key investment decisions.

 

So each of the books started with a fundamental idea and message that I wanted to share, and to be honest, writing was the fun part and the part that I found relatively easy. Editing, now that took much longer and was a lot less fun, I can assure you. I went through multiple edits and drafts over many, many months. And then I decided to self-publish the books, which was a whole other learning process on its own.

 

Host: Paul Barnhurst

 

I bet. And I laughed when you said the editing because I never pictured myself as a writer, I've never considered myself a good writer, and I do a lot of it now for my business. And yes, you might crank out a first draft of something in minimal time, but then you're wordsmithing and changing and where do I add this picture? And, hey, am I saying the right thing here? And constantly editing, at some point you just have to say, okay, it's good enough, I can't keep going.

 

Guest: Sam Sivarajan

 

I swear, every time I look at it, I must have read each of my books at least 100 times and I'm sick of reading it, to be honest, even though I think there's good stuff in there. But every time I read it, I sit there and think, well, why did I, there are still things that I would say, okay, should I put it this way or should I put it a different way? And ironically, I'm sure that I've changed it three times already in the previous edit. So that's the art of writing, is there is no one way of writing it. You can do it three different ways and they're all right, it's just personal taste, right?

 

Host: Paul Barnhurst

 

Totally agree. So we're going to move on to what we call Rapid Fire. This is a section we ask everybody. You get maximum 10 to 15 seconds to answer. If there's something you want to elaborate on at the end, you can, if you don't, that's fine as well. But I have about ten questions I'm going to run through here, and these are all common modeling questions. If you have any clarifications you want, just let me know and I can add more details, but the first one is circular or no circular references in models?

 

Guest: Sam Sivarajan

 

No circular references.

 

Host: Paul Barnhurst

 

VBA or no VBA?

 

Guest: Sam Sivarajan

 

VBA.

 

Host: Paul Barnhurst

 

Horizontal or vertical model?

 

Guest: Sam Sivarajan

 

Horizontal.

 

Host: Paul Barnhurst

 

Excel dynamic arrays, yes or no?

 

Guest: Sam Sivarajan

 

Yes.

 

Host: Paul Barnhurst

 

External workbook links, yes or no?

 

Guest: Sam Sivarajan

 

No.

 

Host: Paul Barnhurst

 

Named Ranges versus no named ranges.

 

Guest: Sam Sivarajan

 

Named Ranges.

 

Host: Paul Barnhurst

 

All right. When you modeled, did you follow a formal standard for your modeling,  one of the formal standards out there?

 

Guest: Sam Sivarajan

 

I was so early on, so the answer is no, but I would say yes. But it's no because it was so early back on when I was modeling that we had our own standards internally, but there wasn't anything formal.

 

Host: Paul Barnhurst

 

Got it. That works. Will Excel ever die?

 

Guest: Sam Sivarajan

 

Not in my lifetime.

 

Host: Paul Barnhurst

 

Fair enough. Will AI build the models for us in the future?

 

Guest: Sam Sivarajan

 

So I'm going to say no, but I'm going to say to expand on it, I would say partly, but the logic still needs to be provided the drivers and assumptions, because I don't believe that AI is ever going to be capable of making those assumptions in a meaningful way.

 

Host: Paul Barnhurst

 

That's what most people are saying is yes, the technical AI will figure out, but you still need someone for the interpretation.

 

So what is your favorite lookup function of choice? Do you like using Vlookup, Index match, Xlookup, Choose? Or do you have something else you like?

 

Guest: Sam Sivarajan

 

I use Vlookup, and largely because that's what I grew up using.

 

Host: Paul Barnhurst

 

Understand that one, that's usually how it works.

 

So when it comes to models, are you a fan of sheet and cell protection? Should you have them in your models or not?

 

Guest: Sam Sivarajan

 

Yes.

 

Host: Paul Barnhurst

 

And the last one here is, do you believe financial models are the number one corporate decision-making tool? If no, what do you think is.

 

Guest: Sam Sivarajan

 

So this is heresy, but I'm going to say no on this podcast. And I say no because I believe in a toolbox. Every situation needs its own tool, and models are an important tool, but not the only one, and not necessarily the first one, depending on the decision to be made. So I think it depends on the decision. I think there are ones, for example, I gave you the market situation that we had in mobile markets. Yes, of course we did the financial model, but for us, knowing that we were going to get 25%, our target was getting 25% of the market, that was a more important kind of tool, that kind of rule of thumb and heuristic that we were working towards in that case, than what the financial model was. The financial model helped underlie and support that thinking, but by itself, it wouldn't have been the decision-making tool.

 

Host: Paul Barnhurst

 

You passed, It wasn't total hearsay, I can understand your logic. We won't crucify you or anything. No, I appreciate that, I can totally understand what you're saying there.

 

So, as we wrap up, the first thing I would like to ask is, is there anything you'd like to share with our audience about your services, your business, ways to contact you, anything you'd like to add just kind of about what you're doing and your business today?

 

Guest: Sam Sivarajan

 

Yeah. So, look, I speak, I write, and I consult on decision-making. I have a newsletter, I do my own podcast that's advisor-driven. So people can find me either on LinkedIn or get in touch with me through my website, which is www.samsivarajan.com, and there they can find the links to the books that you mentioned as well.

 

Host: Paul Barnhurst

 

All right, thanks, appreciate that.

 

So, last question we want to ask you here, if you could give advice to our audience about how to become a better financial modeler, if you can give them one piece of advice, what would be that kind of parting advice for them?

 

Guest: Sam Sivarajan

 

I think the key thing I would say is remember who that model is for and what questions it is going to answer. Check your assumptions and the drivers, keep it simple. I think the key thing I would say is the elegance of any model is in the thinking behind it, not the bells and whistles that you put on the page.

 

Host: Paul Barnhurst

 

Thank you. I like that and I think that's great. Keep it simple, focus on those assumptions, and really validating them, and don't add a bunch of bells and whistles.

 

So on that note, we'll go ahead and close, and I just want to thank you so much for joining us. Sam, I really enjoyed chatting with you and learning more about your background, and I'm confident our audience will enjoy this conversation as well. So thank you again for joining me.

 

Guest: Sam Sivarajan

 

Paul, thank you for having me. I enjoyed our conversation.

 

Host: Paul Barnhurst

 

What a fabulous episode that was with Sam. It was a real treat to interview him, what a unique background. A law degree, an MBA, a PhD in behavioral science. He's an author, he's earned a CFP, he worked in investment banking. He just had a wealth of knowledge, and it was a real joy to learn from him.

 

And what I really learned is so much about the thinking process. Again, it validated how important it is to think critically, to understand the art, to be able to validate your assumptions, in particular, how he talked about being able to take an outsider's view. And he shared two examples that I really like there. He talked about, one, would you invest in a model where our project, where the assumption that over 100% of the value is going to be in the terminal value? It's like, I wouldn't. It's like, why would that make sense? And the second was the guy who predicted a stock would have 25% growth for the next like 10-12 years, and he was asked by somebody how many stocks had done that in the stock market, and the answer was zero.

 

In isolation, that may have seemed realistic. Okay, they've done it the last three years or two years, so they can continue to do that. But as you started to stretch that time, it no longer became realistic. And being able to think about that critically and realize that and understand that, oh, okay, for a year or a short period, this can make sense, but not over the long run. So I really appreciated his view, and I loved how he shared about Telecom and talked about, hey, in this case, $8 billion made sense in this market, but $10 billion over here no longer made sense, and how he thought about that. And how he thought about modeling things that had never happened. So what a great episode. I hope you learned as much as I did, because I know I learned a lot from talking to Sam, and I cannot wait for future guests and to continue to learn with and from them.

 

So thanks for joining me for this episode. We'll look forward to seeing you during the next episode. Thanks!

 

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