Spatial Finance: How Satellite Data Is Enhancing Financial Models, with Jorge Rojas

We all know that governments manipulate, delay, or outright lie about national statistics. One of the most provocative findings in the field is that private satellite companies now produce economic estimates so good that they are reducing the market reactions to official government announcements. Meet Jorge Rojas, an adjunct professor of finance at Tulane University and Pan American University, also a managing partner at Pentandra Capital. He is CFA and AFM certified, a member of the FMI's Global Leadership Council, also holds a PhD in finance. He authored books on corporate finance, capital budgeting, and derivatives. What spatial finance is, Earth observation basically is the convergence of Earth observation, AI, cloud computing with mainstream finance. You cannot cheat a satellite around Earth. You cannot greenwash it. Satellites allow institutions to bypass opaque self-reported government data from foreign markets by directly measuring physical assets. For example, you can check the fullness of crude oil tanks, you can monitor. So imagine having all this data available for your financial model. It will make it supremely more informative and current than we have today. And there are other areas like real estate, for example.

Now, any final advice you'd like to offer people to kind of help them with modeling?

Well, yeah, first of all, and I'm doing that, it's—

Financial Modelers Corner is the world's premier modeling podcast. It is brought to you by Financial Modeling Institute, the world's leading financial modeling accreditation organization. Welcome to another episode of Financial Modelers Corner. I'm your host, Paul Barnhurst, aka the FP&A Guy. This podcast is where we 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 the Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling, and that's why I completed the Advanced financial modeler. This week, I'm thrilled to welcome our guest to the show, Jorge. Jorge Rojas, welcome to the show.

Thank you for having me, Paul.

So let me give a little bit of Jorge's background, and then we'll jump into our questions. So Jorge has a PhD in finance. He's earned his CFA and his AFM. His PhD is from Tulane University, where he currently is in adjunct professor of finance and international programs. In fact, we'll talk about one of the articles he recently published. He also has his CFA and he's a member of the Society in Mexico. He earned his Advanced Financial Modeler, and he's also a member of the Financial Modeling Institute's Global Leadership Council. He's been a lecturer at several different universities all over the world, including the US, Spain, China, Chile, Peru, Mexico, Panama, and Guatemala. He's authored several books in corporate finance, capital budgeting, risk management, and derivatives. He's published them in English, Spanish, and Mandarin. He has been working with Excel and financial models for many years, developing custom models for financial valuation in many industries such as agribusiness, real estate, and land development. Food and energy production, and others. He commonly uses Monte Carlo simulation in most of his models. And his latest article, which we'll talk more about, Earth Observation Data for Finance, is a, a bibliometric review and directions for future research on Earth observation and was recently published in the Journal of Economic Surveys. Love the background, Jorge. Great background.

Thank you.

First question I'd like to ask you. Everybody has that horror story. What's your worst modeling story you ever dealt with?

Well, I think I have— it would be fair to say that I have a recurring financial modeling nightmare where students or customers send me their Excel model. And what I do is I have an analogy of how you sometimes need to untangle that Christmas tree light series that, you know, looks like a big ball of wires and looks like a tumbleweed. That's exactly what I think when I get some of these Excels. Probably one of the worst I have to deal with is this customer sends me an urgent model for a loan application. And it's the perfect example of the anti-modeler. It had all of the worst practices, no inputs, all cells hardcoded values everywhere, no formulas. Each scenario was a different sheet. And it was a mess and no units, no text, and those horrible data islands all around, links to external workbooks. And, you know, as usual, it was urgent. So sure enough, I was better off throwing it away and start from scratch, which I did. And eventually, you know, their loan was approved. But unfortunately, it's very common to me to get models like that.

I hear you. It is not uncommon. It's amazing. How messy it can get when somebody just thinks of Excel as a playground and brings no structure.

Yes, exactly.

We've all seen that. So I'm curious, you've spent a lot of your career teaching about financial modeling. What is it you love about teaching? Why teaching?

I see teaching as helping people cross a bridge, a bridge that takes you from something that you do not know to something that you want to know. And eventually learn. When you help someone transform in a positive way, they're better prepared to achieve their goals, and that gives you satisfaction. And also, what I, at the end of the day, what I want to do is provide them with a friendly and enjoyable journey in crossing that bridge in their learning journey. I like sharing my knowledge. That's what I like doing. And I try to explain in a manner that I wish it had been explained to me when I learned it. Easy, digestible, you know, non-complex way of explaining. Also, teaching a subject forces you to really master that subject. And that's why today I'm also a CFA and an FMI prep provider, which is really satisfying when the topic is really a tool that you will use every day. So overall, it's an experience that I enjoy. As we all have heard, you need to do what, you know, makes you happy, what you enjoy, and what you are good at.

Love that. I heard a couple of things there, you know, first, obviously enjoy it. And that's one of the most important things. Nobody wants to go through life doing something that they hate. I've seen people like that and no thanks, I'll pass, you know, and then the others you mentioned, you wish you, uh, had a teacher like you, right? Like being able to teach students the way you wish someone had taught you, incredibly valuable to have teachers that want to do that. Cause we've all had the teachers where I used to joke in one of my classes, me and another person would turn to each other and it's like, I feel dumber for having come to class today because it was always so complex and never explained in a way I could understand it. I always walked out going, what's wrong with me?

Yeah, I just, for example, it's a non-monetary, like a convenience yield type of benefit where people, you know, you can look at their faces and know if they're enjoying, if they're learning. And if they come up to you and say, George, I was really inspired today. I'm not really well-versed in finance, but you really made it easy and enjoyable. And I'm really enthusiastic about this topic. So that has a lot of value for me.

I hear you. I love when a student, you know, when I do training, they come to you and they say, oh, this will save me hours, or thank you for teaching me this. You know, those are the moments that make it worth it versus the times when you wonder if anyone's listening. Which we all have.

Yeah, exactly.

I'm curious, you have both your CFA designation— yeah, yeah, you 100% agree, you can tell, no, no question. The worst is when I'm teaching remote, nobody's on camera, you can tell they're paying no attention, you're just like, okay, when is this done?

Exactly, exactly.

Yeah, so I'm curious, you've earned your CFA designation and, and your Advanced Financial Modeler, so you did both. What was your motivation for earning them?

Well, initially my motivation for the CFA program actually was mainly academic. I was then the director of the Master of Finance program, and I wanted to elevate it by making it CFA recognized, as it was called then. But not too long after that, my encouragement was extended well beyond academics, as I saw it its potential in the investments and consulting areas. And I would say that my main incentive for AFM was very specific. Through the years, I had observed mainly through consulting projects that many people were good and they were bragging about how they would project P&Ls and budgets, but very few people could project a balance sheet that actually balanced 5 years into the future. That's not easy, And that's one of the things that caught my attention about AFM. And overall, I also have an engineering background, so I'm very comfortable with numbers, with Excel, CFA, AFM. So I found it to be a perfect complement for me, both in the classroom and as a financial consultant, since I use Excel most in most of my day-to-day activities. So that's my motivation for both of them.

Makes a lot of sense. And I really like what you said on the AFM of the balance sheet balancing. That was the part I worried about the most because most of my models had been P&L driven. And I, uh, when I first finished the test, they didn't balance. Fortunately, I found why before time ran out.

Yeah. And if I may ask, Paul, what's your main motivation for AFM?

You know, so originally when I first decided to take it, I wanted to work for some smaller companies and I knew I'd need to do three-statement modeling because most of my career had been with large companies. So American Express, where you're not building the balance sheet and the, the cash flow, you're, you're modeling the P&L and maybe you're providing a few key items from the balance sheet or other places. And so that was my main motivation. I had originally signed up for it before COVID hit. COVID hit, it got canceled, and then I ended up staying at the job I was at and I delayed it and delayed it. And then I started my own business, so I delayed it some more. I started the podcast and FMI sponsored the podcast. And one of the agreements was, you know, if you're going to run the podcast, you need to have your AFM. So I finally finished it. You know, I had started, I'd signed up for it in 2020, I think. And I finally took it in 2024.

Yeah.

Yeah.

I recall. I like your post about that. It's very satisfying.

Yeah, it is. And then I signed up to do the CFM and I ended up with some health challenges last year. So it got put on hold, but it's still on my, uh, lists to do because I think it's just incredibly valuable to have a really good base for modeling.

Yes, for sure.

So yeah, I love that question. So that was my motivation. I'm curious, any advice you would offer for someone studying for either of these, for CFA or FMI?

Yeah, both the CFA and the AFM designations are outstanding and they do open professional doors that likely are more difficult to open otherwise. I use a very simple analogy. I haven't gone recently, but at Disney, they used to have this FastPass where you could, you know, get faster into places. And in a good sense, this will open doors for you and make processes and overall interactions smoother. If you're in either program, just keep in mind that the rewards are well worth the effort. It's going to be tough. As, uh, especially the CFA, it's more extensive and you need to finish the 3 levels. But if you can get them both, wow. I use the analogy of epoxy glue, you know, epoxy glue, when you mix 2 components and the result is a super strong cement. That's how I see if you can get both designations, the result will be just about, you know, as strong, as powerful, like, and as you can imagine.

Yeah. I love the example and great advice there. I think they're both great programs. I actually started the CFA. I didn't finish it as I decided, you know, I'm really spending my time in corporate finance. And so, I decided to focus elsewhere, but I did Level 1 and I agree with you. You definitely have to put aside a lot of time. There's a lot of study that goes in.

Yes. But the satisfaction is just, you know, and the results and the professional leverage that it gives you, it's well worth the effort.

Yeah, I know, a lot, a lot of great benefits that can come into it. So I want to, I want to switch gears here and dig into, uh, spatial finance and Earth observation. So you recently wrote an article on this topic which you shared with me. I found it really fascinating. Can you start by telling your audience kind of what spatial finance is, Earth observation? Let's start there.

First and foremost, I would like to mention and credit my co-author, Professor Christian Pinto. He's from the University of Talca in Chile, who was actually the lead author of this article. I just wanna, you know, mention that. So satellite data has been around for decades, but mostly for weather forecasting and climate modeling. We all know about that. But in the last few years, Earth observation or spatial finance has been growing significantly and now further driven by AI, making it one of the fastest growing alternative data sources for finance in general and also for financial modeling. What's spatial finance? Spatial finance refers to the incorporation of geospatial data and analysis into financial theory and practice. Basically, it's the convergence of Earth observation, AI, cloud computing with mainstream finance. Uh, its importance in financial modeling, I believe, is that most useful models are or should be forward-looking. Real-time current satellite data is unbiased. You cannot, you know, cheat a satellite around Earth. You cannot greenwash it. You cannot, you know, I don't know if you have heard about the Hawthorne effect that mentions that people react differently, behave differently when they know they're being observed. And additionally, it's— if you have satellite data, it's a much better input to models as compared to historical data that could be biased, manipulated, or with too much of a lag. It could be delayed. I believe in a few years, as analysts have today a Bloomberg terminal, there will be some sort of geospatial data available in different forms and sources. So basically, geospatial data means data that you get from satellites and you can somehow process it to help you in financial decision analysis. That's, you know, in a nutshell what it's about.

You know, it's funny, when you first mentioned it, I'd never thought of it because I'd never thought of having geospatial data for a model. But the more I think about it, I could see how it could be incredibly valuable, right? If you take over a month period, seeing every day how many cars or what the, you know, the traffic footprint is for a mall or a location or so many different things like that. Okay, what's the climate like and how is that going to impact us building something here at a level you're not going to get without that, that data. So really, I was fascinated by the article and just the whole idea when you brought it up.

I think it will change and evolve, you know, exponentially in the next few years. And if you add AI now, it's going to be even stronger. I think it's going to be one of the main alternative data sources for financial analysts in the near future.

Like you shared the example of kind of having a terminal or having access, right? Cloud where you can go out and just search for those type of satellite images. I'm sure the companies will be more than happy to offer subscription service to their images. I'm sure they're out there now and it will just continue to grow.

Yeah. And that's already happening, by the way. Yeah.

Yeah. Well, my background is in FP&A. I am also passionate about financial modeling. Like many financial modelers, I was self-taught. Then I discovered the Financial Modeling Institute, the organization that offers the Advanced Financial Modeler program. I am a proud holder of the AFM. Preparing for the AFM exam made me a better modeler. If you want to improve your modeling skills, I recommend the AFM program. Podcast listeners save 15% on the AFM program. Just use code PODCAST. Totally makes sense. In the article, I know part of what you did is you re— you reviewed a lot of the research that's been done, kind of what's been done so far. And there were 3 dominant kind of streams that you talk about in the article or that you recognize people are talking about. Can you share what those are, what those different kind of 3 key themes were?

Certainly. Yeah, we found 3 main clusters of research development. The first one is information asymmetry and corporate governance. The core idea of this first cluster is that satellite-derived in the, in the, in the articles that we studied, car counts, car counts from retail parking lots can tell investors what companies still don't report at that time. So imagine knowing how Walmart will do before Walmart tells you. So, uh, of course this works for certain retailers on certain types of, uh, locations that have open parking spaces, but still it gives you it levels up the information that, you know, investors can, retail investors can get in real time. So that's one of the areas that we saw that it's been explored, the car counts on parking lots. That's one. Then the second one is, and it makes a lot of sense, alternative to government statistics. We all know that governments manipulate, delay, or outright lie about national statistics. One of the most provocative findings in the field is that private satellite companies now produce economic estimates so good that they are reducing the market reactions to official government announcements. The articles I mentioned— I mentioned the country because it's in the articles. For example, in China, there's a lot of announcements, and these articles use as control variables the cloud cover. So cloud cover was used to test when there is a lot of clouds, when you have clear skies, how the— how it affects the government, uh, preciseness on, on their information. So the articles are very robust statistically, and it makes sense that, you know, that governments usually their information is not quite reliable. Is not quite current or it may be even manipulated. And the third one, which is probably the one that is growing and getting the most attention, it's ESG, environmental risks, sustainable finance. And it's arguably the most policy-relevant cluster since satellite information, it's independent, can verify if companies and governments are, uh, doing what they say they're doing about environmental performance. And that's a game changer for ESG investment and green finance. As I said before, you cannot greenwash a satellite. So satellite information overall is providing very interesting data that it's, you know, it has been, uh, has been used and developed in different articles in these three areas. Uh, and that's basically the, the clusters that we identify.

And those were the three I remember reading about. I thought the example of the cloud cover in China and government is fascinating. I think you make some great points. We've all seen numbers that we are skeptical of coming from a government or doubtful, or we've seen, hey, we're going to revise the way we calculate this when the new person's in office, or, you know, all those type of things. And given they have an incentive for the economy to be doing well. I think we all take some of those numbers with a grain of salt, especially given they're always revised 6 months later. They're almost always revised downward. Why are they always worse after the fact? Yes, I think that's a, you know, fabulous use case is satellite data to help kind of validate, augment, improve what you can get from government resources.

And of course it has errors. It needs to be, you know, worked around and there's a lot of area for improvement. But in general, if you ask me, I will trust more a satellite data unbiased than a report from a government. It's pretty obvious, right?

Yeah, no, I'm with you, right? We, it's not a perfect, there's, there's no perfect system. Just like there's a reason why we're making assumptions in modeling versus providing answers. 'cause we don't know the exact answer. I can give you an estimate and a range, and we'll talk about that with Monte Carlo. But, you know, as I always like to say, if I give a perfect answer, I'd be in Vegas right now getting rich, not here on a podcast.

Yeah, probably me too.

You know, so I think we're on the same space there. What do you see as the biggest, maybe real-world example for the average modeler to use this type of data? Where do you see it kind of most being used by modelers?

There are many areas, real estate, insurance, and I mean, I'm going to give you one very specific example. If you incorporate Earth observation data, your model may shift from relying only again on backward-looking information, actually use real-time objective physical truths, for example. Let's say you're doing a model on commodity trading and supply chain.

Okay.

Traders usually require accurate global supply and demand forecast so you can price future contracts or fund agricultural supply chains. Now, satellites allow institutions to bypass opaque, self-reported government data from foreign markets by directly measuring physical assets.. For example, you can check the fullness of crude oil tanks. You can monitor shipping container congestion at major ports. You may observe crop health in key agricultural belts. For example, AI allows, you know, again, tank measuring, crude oil storage. And in agriculture, for example, you can do macro crop yield forecasting that uses deep learning models to track agricultural indices like chlorophyll absorption. You can tell the health of the agricultural, you know, crops from space, how they are absorbing chlorophyll. So imagine having all this data available for your financial model. It will make it supremely more informative and current than we have today. And there are other areas like real estate, or for example, now there— I don't know how recent they coined this term, but in insurance, you have also the ability, for example, earthquakes, floods, you can very quickly assess the damage that otherwise will take you probably months to assess. So there's different areas where you can get very quick and very current information, like, you know, crops and agriculture, for example.

Bad financial models can lead to bad decisions or worse. So how do you minimize the risk of a bad model? You make sure the models you build are great. Financial Modeling Institute developed the Advanced Financial Modeler Accreditation program to help modelers like you. The AFM program offers a step-by-step approach to building world-class financial models. The program ensures that you know the best practices in model design and structure and will help you brush up on your Excel and accounting skills too. Be the one on your team to build great models. If you want to impress your boss and your clients, get AFM accredited. Podcast listeners save 15% on the AFM program. Just use code PODCAST at fm institute.com/podcast. Thank you for sharing that. I hadn't thought of all those, but crops makes a lot of sense. Earthquake, right? An insurance company could go in and with the data, do an assessment of what they think, you know, the cost may be better than they'd be able to otherwise with a bunch of images to start looking at things and getting an idea of what the true damage is. Lots of great examples there. So I want to shift gears a little bit and talk Monte Carlo simulation. I know you're a big fan of it, so I'm curious. Let's start with this. How do you decide when to use Monte Carlo simulations in models you're building? Like, why do you use it? When do you use it? Maybe let's start there.

Yes, for sure. Indeed, I'm a Monte Carlo fan. Uh, let's put it this way, I rather be without my cell phone than without my Monte Carlo plugin. That, that how— that's how extreme I use Monte Carlo. Um, and actually for me it's the other way around. I will use Monte Carlo in most of my financial models and think about which ones I will not use it. It's, it's— I use— I do it the other way around. A good example is, for example, projecting financial statements. It does not make a lot of sense to use Monte Carlo as the projected results are accounting static measures, estimates. So it doesn't make much sense to simulate them, at least for a projection of, you know, the typical financial statements. Now, my simple answer is I use Monte Carlo when I have stochastic variables. Stochastic, by the way, comes from the Greek stochastikos, which means skillful in aiming. Stochos is target, aim, or guess. So that's exactly what Monte Carlo does. So the way I do it is, since most of my models have anywhere 5 to 20 inputs with very random probabilistic distributions—uniform, triangular, normal, log-normal—these variables may be, you know, interest rates, exchange rates, commodity prices, inflation, growth rates. Cost of capital, and, and so many more. So since there is no way you— there's no human being that can forecast perfectly all these variables, and they all play into your model, uh, you— if you rely on Monte Carlo simulation the same way you rely on a pilot training on a flight simulator, or a doctor, a medical doctor training on a dummy before they actually, you know, cut a human being or do surgery. The same way the simulation allows you to test, to stress test, to do scenario sensitivity analysis and see what could go wrong. And these variables will usually produce— if you— the, the key insight of the Monte Carlo simulation is that you need to understand that it relies on the law of the large numbers. And the central limit theorem. If you know that and you can grasp that idea, the output, be it net present value, the intrinsic value of a common share, you will have a very accurate number with probabilistic ranges, and you will understand much better what's going on. You will decide better of what's in front of you. I don't know, by the way, have you used, do you use Monte Carlo simulation often?

I didn't use it in my career. I've seen it a little bit. I learned it in school. If I was to go back now, I'd use it more. I think it is valuable. I've definitely seen people who used it, but it was just something I know, but I saw nobody using in my work. And so it's not something I really use. I did do some statistical modeling, but not Monte Carlo, but I do think it's valuable. That's why I love to hear about it is. You know, if I went back and did— I don't do much modeling nowadays with my business, but if I did more, I would definitely want to do more Monte Carlo.

It's very powerful.

Why do you think it's not used more? Do you think it's people fear it, or, you know, they just kind of go with an estimate and a best and a worst case? Because it feels like to me it's not used near as much as it could be.

Totally agree. I think one of the reasons may be that the Monte Carlo plugins are expensive. They are not there. They are very expensive. They are not cheap. They usually run only on Windows. I think that's by design. For Mac systems, I think it's more limited, but I don't think that will be the main reason. One of the other reasons that I have thought about it is because I've learned from CFA behavioral studies and that area is that people usually are not very good at statistics. People usually don't like statistics. They think it's, uh, like a black box and some mysterious, uh, formulas. Or— but it's actually so powerful that I share your, your question. I don't know why, uh, people use it more. Every time I teach it, every time, always, people are like— their eyes are like this and say Wow, I didn't know this was available. And it's really powerful. For example, one of the typical cases is if I tell you that a project has a positive net present value and I ask you, should we go ahead and do it? You would probably say yes, of course. You know, financial theory tells you to do, go ahead with a positive net present value. But if I tell you The net present value is positive, but the probability that the net present value is zero or negative is 45%. Then you will probably see, even though the theory tells me to go ahead, most investors will say, uh, no, no, I'm going to take a step back because this is— this looks too risky. Or if I tell you the intrinsic value of a common share is $25 versus I tell you it's $25 and there is a 90% probability that it's in the range of $22 to $28. So that's the power of the simulation technique. And I agree that I, I wonder why more people don't use it because it's so powerful. Maybe it's an expensive software, but at company level, it shouldn't be that expensive. I mean, for personal level, maybe yes, but at company level, you should pay, you know, it should be paid, it should pay itself very quickly when you use it to take decisions.

Yeah. Yeah. I was talking to the guy who created one of the main ones, AtRisk, which you may have used, you know, the Monte Carlo simulation. They, uh, he was the founder of the company originally. I was chatting with him the other day and they're coming out with some new Excel, uh, sensitivity type stuff. And I agree with you. I think, you know, statistics, I saw that not even Monte Carlo, but just I'd use some statistics regression and stuff and it was woefully underused. Underutilized. And I think you're right, a lot of it is people don't understand it or they just don't want to spend time on it because it can— it takes some time to understand it and to be used to it. And it's different than just doing, you know, the basic model or whatever. But when done right, it can be incredibly valuable. Yes, for sure. Yeah, so I'm with you. All right, so I want to ask a question. When you and I chatted, you talked about some of the challenges you face modeling agriculture projects particularly in Guatemala, you know, and the risks to crop growth. It's something I haven't thought about because I've never modeled anything like that. So it was fascinating hearing you talk about it. So I'd love to hear you to share a little bit of what are some of the challenges, the risks you face when you're modeling agriculture.

I have developed several agribusiness financial projections, sugarcane, palm oil, coffee, and, uh, they're very, very, uh, you know, particular. Businesses and projects. First of all, the families and companies that usually have this have more than just agricultural businesses. They have, for example, hydroelectric power generation. So sometimes they mix, they want to know what's the free cash flow of their combined businesses. So it makes it even more challenging. But going back to specifically agricultural projects, they have several characteristics. One is that their price Usually it's set in international commodity markets. Palm oil, you use the Rotterdam CIF price, coffee, sugar, all that is in international prices. So price is a big metric that you cannot just say, "Well, I'm going to assume it's going to be that." No, that's not good enough. Then agricultural projects are exposed to significant risks such as tropical storms. We have one coming our way right now. Seismic events, earthquakes. Guatemala is split in half by continental tectonic plates. So, it's— we are very seismic. We're— Guatemala is about, give or take, about the size of Louisiana or Tennessee. And we have 37 volcanoes, out of which 3 or 4 are active all the time. Insect plagues. The poor plants take a lot of beating around here. So, What I did with this project is, well, there is no way I can predict all of this. So what we did is we went back with the owners, the experts, and say, okay, how often do you have one of these exogenous events? Okay, one significant event every 4 to 5 years. So in this case, for example, I model a Bernoulli variable on Monte Carlo. Which may take a 0, 1 value, but you can assign a probability. So you, every year you have a Bernoulli exogenous event variable that may be 0 or 1 with a 20% probability, 1 every 5 years. So the owner knows and tells me if this happens, the agricultural result is that the crop is 20, 25% less efficient or whatever he tells me, he or she tells me what's the effect on the plants or how much more he has to spend on whatever needs to be fixed. So in this case, I use commodity prices model as typically a log-normal or exponential distribution. The good thing is that the Monte Carlo software can give you, if you give it the data, it can suggest distribution probability. So you can use that as an input for the commodity item. Then you have the external events, and then you have your usual cash flow, sales, costs, expenses, taxes, and all of the free cash flow to the firm. But basically Monte Carlo, and for example, in the hydroelectrical business, you have data of the quantity of rain, so that affects the level of the dam. And at the same time, you can model it together with the agricultural business and have a joint free cash flow. And you model that in Monte Carlo. And, and, and of course, the model needs to be, you know, garbage in, garbage out. You have to have a great model before you apply simulation. But that's basically like, uh, like the typical process I use with this, uh, kind of, of of projects here.

When you were mentioning, you know, kind of the Bonoli variable, I could see you were going to go with 20% once every 5 years, and that may lead to 50% less crop or 30% more expense plus less crop. And right, because it's like, you know, people say, well, I can't model because I never know when a black swan event's going to happen. But you can at least say, how would I behave if there was some kind of catatrava? Event? What's the probability that you could still do some work? You can't predict it, but you can at least try to plan for it. And that sounds like what you're really trying to do with these probabilities is plan for these events that we know are going to happen at some point. We just don't know when or exactly how bad they'll be.

It's you, you have to work around expected values. I always start any course saying, listen, the goal here is to be as little wrong as possible. You will never know if you're 'Net present value is correct.' How? There's no way you can know it. So we need to be rigorous so we can be the least possible, uh, you know, we have the least possible error. Other than that, there's no way you can, you know, be sure that your number is perfect. There's no way.

I appreciate all that, and that gets back to like what we talk about— Monte Carlo, you know, spatial finance. All these things are tools, ways to improve your estimate and to better understand the range of possibilities. Yes, totally agree. Yeah. What I want to do now is move into some standard questions we ask every guest. So first, before we get to rapid fire, there's a couple I like to ask. Do you have a favorite Excel shortcut? Yes, I have built-in shortcuts.

I use a lot of Ctrl+Right, Ctrl+D to copy themselves. That's— I use it all the time. I would say it's my favorite built-in, uh, shortcut. And then I have personalized, uh, shortcuts, uh, that I use to, to format my local currency, quetzales. I don't want to go 3 layers with my mouse, so I develop a macro shortcut for my currency and also for center across selection, which is a very common, uh, function that I use. But I, I have develop my own personalized shortcut for that.

Yeah, currency totally makes sense if you're dealing with a lot of currencies, because yeah, you have to drill down. I'm not surprised to hear you say center across selection since they bury it in Excel. Maybe one day they'll put it on the front, front page. Sure. Yeah. So I, that's, that's a common one. All right. What's the most unique or kind of fun thing you've done with a spreadsheet? It could be for personal life or at work, but what would you say is the most unique thing you've created with the spreadsheet?

One very unique and fun was we recently went with my family to a— we took a road trip actually from Albuquerque all the way to Denver. And my kids designed an Excel with AI. So it suggested the national parks where we should go, the cost, the budget, and everything was fed into an Excel. So I told them, you plan this trip, go ahead, we will just get into the car and you will drive. And that was a very fun and useful way that we use an Excel. And it was just a couple of months ago.

That's awesome. I love the Excel and I love that, you know, AI, it's amazing the different ways we can use it that I don't think anyone realized 3, 4 years ago when, you know, 2022 when it first came out of all these use cases. Yes. All right. So this next section is called rapid fire. I do this with every guest. So I'm going to lay out the ground rules of how this works and then we'll go through it. So the idea is you have to pick a side. I realize there's nuance in all these questions. So you could give the answer, it depends, but that doesn't make for fun listening. So you have to choose in most situations, yes or no. So if I was to ask circular references, you would say which one? Then when we get through all the questions, you can elaborate on a couple of them because I recognize there's nuance to all of them. So ready? Yes. Circular references in models, yes or no? No. VBA, yes or no? Yes. Lambdas in financial models, yes or no? Yes. External workbook links, yes or no? No. Yeah, I get a lot of that of no, please don't, don't do it. Should modelers primarily use keyboard shortcuts, yes or no? Yes. Okay. Should models always be print ready? Yes or no? Yes. Are merge cells, is there ever a situation where you should use merge cells? Yes or no?

No, there should be jail time for using merge cells.

I had one guest that his saying was, use merge cells, you go to hell. So kind of a long jail time. So yeah, that's, that's one of the strongest ones yet. Do you think financial modelers should learn Python in Excel? Yes. What about Power Query? Yes. Power BI? Yes. Do you believe every financial modeler should be able to build a fully integrated three-statement model?

That's a painful no, but I will take no.

I actually say no, but I can understand the pain. So we're out. I imagine we'll talk more about that one in a minute. Will Excel ever die? Yes or no? No. Have you used AI to help you build a model in Excel?

No, not so far. No. Okay. Not to build it.

No. What if you had to pick just one? What financial statement is most important for modelers? Is it the P&L, the balance sheet, or the cash flow statement?

In my case, the cash flow statement. Do you have a favorite LLM?

Do you like Cloud, Copilot, ChatGPT. Do you have one you turn to the most?

Cloud, by far. Yeah.

That seems to be coming a favorite for a lot of people. All right. If you could pick only one for all your models, would you choose sensitivity analysis or scenario analysis?

Sensitivity analysis with Monte Carlo.

I figured that's where you're going to go. See, you're the first.

Everybody else has picked scenario.

So I love that. I thought you might go there and say something about Monte Carlo, so I love the different answer. Do you believe financial models are the number one corporate decision-making tool? Yes. That's always a tough one. People think about that one for a minute. And then what's your favorite lookup function? XLOOKUP. Alrighty, I like it. XLOOKUP. So it's funny, you know you're interviewing a modeling nerd when I interviewed the Financial Modeling World Cup champion. And I gave the op— I gave him 4 options. He goes, you do realize there's more than 4 lookups. And I'm like, yes, you know, like immediately wanted to go, well, you could do this one and that one and see how many he could list. It was kind of funny. All right. I think you wanted to elaborate. It sounded like on the 3-statement model, you said you gave a painful no. So what was your, your thinking there?

The reason is that since I did AFM and I'm not an accountant, but doing the projection of the 3-Statement Model, it's so powerful, so productive that I have used that knowledge in other applications that are none related to financial statement projections. So I would say no, because it would be a little unrealistic to expect anyone to project 3 statements. It's very useful and very powerful. I was, you know, between a yes and a no, but I would say probably would be unfair that anyone needs to do that because it's, you can do a lot of models without doing that project, you know, statement project. So that's one of the ones that I would say. I think it's incredibly valuable.

I think it would be good for anyone to be able to do it, but I spent my whole career building P&Ls. I didn't do my first 3-statement model until I started my own business and I worked with a small company. And so I recognize that there's people that can build great models that just don't do it. I'm with you, but it's incredibly valuable. Any others you want to elaborate on?

Yeah, the other one is that it's up my alley is I usually, when I tell people, you know, you have the, this what-if analysis you need to do, they're not mutually exclusive. You need to do scenario, sensitivity, and a Monte Carlo simulation if you happen to have the plugin. For me, the three are critical and important, and I actually do the three. Of course, this exercise is different, but for in the real-world applications, I do the three of them. The three of them are so powerful that you get a lot of information from doing the tree. But other than that, it's—

I'm clear on that. Yeah, it makes a lot of sense. And I realize this is all, you know, just kind of situational. It's not reality. So as we wrap up here, first, thank you so much for joining me. I loved hearing, you know, about spatial finance. Like I said, I hadn't even thought about satellite data and how it would impact finance, so it was great to hear what you shared. And I'd like to give you an opportunity before you wrap up, any final advice you'd like to offer people to kind of help them with modeling?

Well, yeah, first of all, and I'm doing that, it's incorporate AI in your learning. I mean, everyone is, you know, learning AI now and how to incorporate it in your model. Second, you learn by doing and practicing and mainly listening to guys like you, value content from everywhere, many different expert modelers. I see Excel as a blue ocean. You need to go out and shop around knowledge to become an efficient modeler. There's always something you can learn from you know, people that share their knowledge on Excel and build your model as automated as possible. Probably will take you more time upfront, but at the end, I promise you, it will save you time down the road. I have what I call the Friday 5 PM syndrome. It's when you do a model, your boss will call you a Friday 5 PM and tell you, I need this in an hour. So you better build your model so you can respond, and whatever needs to be done very quickly in less than a couple of hours. If your model is built automated and well thought, you will have a model that will, you know, last for a while. So that's what I would like to share.

Great advice. And I can appreciate the 5 o'clock Friday syndrome. We've all been there. So thank you for that. If our audience wants to learn more about you or maybe get in touch with you, what's the best way for them to do that?

LinkedIn, I think, is the way to go.

Yeah. All right. Well, we'll make sure to put your profile in the show notes so everybody can find that on there. And thank you so much for joining me. I really enjoyed the conversation. Appreciate you taking an hour and spending it with me and my audience. So thank you so much, Jorge.

No, thank you. It was an honor and thank you for having me.

Thanks. Financial Modelers Corner was brought to you by the Financial Modeling Institute. This year I completed the Advanced Financial Modeler certification and it made me a better financial modeler. What are you waiting for? Visit FMI at www.fminstitute.com/podcast and use code podcast to save 15% when you enroll in one of the accreditations today.