The Ultimate Guide to Economic Analysis in Private Equity, Venture Capital, and Startups

Doug is the Founder and CEO at Hypernomics.  It’s a software company that helps fund managers, investors, and investment bankers to conduct economic analysis in the 4th and 5th dimensions. He’s worked with companies like NASA, Virgin Galactic, Lockheed Martin, Northrup Grumman, Raytheon, and more…

What this means is that Doug and his Hypernomics Software are where investors can go, to create and understand multivariate analysis that leads them to create market-beating returns from their analysis in their pursuit of Making Billions.

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[THE GUEST]:  Doug is the Founder and CEO at Hypernomics.  It’s a software company that helps fund managers, investors, and investment bankers to conduct economic analysis in the 4th and 5th dimensions. He’s worked with companies like NASA, Virgin Galactic, Lockheed Martin, Northrup Grumman, Raytheon, and more.

[THE HOST]: Ryan is a Venture Capital & Angel investor in technology and energy. He achieved

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Transcript

Ryan Miller  
My name is Ryan Miller and for the past 15 years have helped hundreds of people to raise millions of dollars for their funds, and for their startups. If you're serious about raising money, launching your business or taking your life to the next level, to the show will give you the answers so that you too can enjoy your pursuit of making billions, millions. Let's get into it. 

Look, if you want to make billions, you're going to need an edge. My next guest is about to give you just that join my guest, Doug Howorth and me as we discuss his hyper genomics tool and how to do an economic analysis in the fourth and fifth dimensions. Plus, you're gonna want to listen all the way to the end, where Doug makes our community, the offer of a lifetime. Let's get into it. Hey, welcome to another episode of making billions. I'm your host, Ryan Miller. And today I have my dear friend Doug Howarth. Doug is the founder and CEO of Hypernomics. It's a software company that helps fund managers, investors and investment bankers to conduct economic analysis in the fourth and fifth dimension. He's worked with companies like NASA, Virgin Galactic, Lockheed Martin, Northrop Grumman, Raytheon, and more. So what this means is that Doug, and his hypernomic software are where investors can go to create an understand multivariate analysis that leads them to create market beating returns. So Doug, welcome to the show, man.

Doug Howarth  
Ryan, thank you so much for having me on. I've seen virtually all your episodes and listened to listen to what's going on. And I really feel honored that you taking me on for an episode. Thank you.

Ryan Miller  
It's certainly my privilege to have someone with your background and your knowledge about economics. And then that's even aside from all the amazing things you've done with these huge companies, and you're building hyper genomics just to help fund managers, investment bankers, entrepreneurs, anybody that's interested in private markets, or economics in general, which is pretty much everybody that listens to the show in over 100 countries in the world. Wow. So that being said, I'm just curious, how did you become an expert in this field,

Doug Howarth  
I went to school for economics, and I got a degree and I never quite believed that. And I never quite believed the Cartesian coordinate system that we were taught when we were in junior high, or in high school, the 2d and 3d coordinate systems, I thought that was greatest Rene Descartes was who invented these things, I thought there was something else out there. So I kind of had this idea percolating in my brain that there was there were other things out there for us to see, decades after I had this view about what happened in with cartesian systems. I was out one day with my wife buying a washing machine of all things. And my wife said, you know, I'd like to buy a washing machines, it's got a larger drum than we have at home. And I thought to myself capacity, she wants more capacity, there's a higher price for capacity. So I said, Well, that's a 2d thing she's going into, she says, also, I want to have more cycles than I have at home, I saw I thought, I need a delicate cycle, she said, so I said, cycles versus price. She's just added another dimension. So she's got cycles, and she's got capacity. And she's got price. She's up to three dimensions there. So I see the next model up the line, I say, Well, what about this one, because I liked it. And she says, It's too expensive. We can't afford it. What I realized was she she was she and everybody else in the store that was buying a washing machine or anything else was part of a demand curve. And we were fixing our place on the demand curve along with everybody else. And so we were part of the quantity and price solution. So I realized that she had quantity capacity and cycles as say, horizontal axes. And she had prices, a vertical axis that adds up to four dimensions, she was solving a four dimensional problem in her head. And so I instantly had this what they call a flash of genius. And I went home. And I started to draw this I got myself engineering plotting system, and I figured out how to plot and four D and then eventually formed a company around this, you know, the rest, as they say, is history. So now we've been at this now 11 years, just about ready to break through, I've got the book, I mean, the software is released, it's gonna go up on the internet, right now it's downloadable, we feel very, very good about where the company is going right now.

Ryan Miller  
That's incredible. So your wife, it sounds like she's trying to solve a four dimensional problem in her head, you know, light bulb goes off, and you're like, wait a minute, this is kind of what I've seen before, and how many dimensions we can do. And so you created hyper nomics, don't let me put words in your mouth. So you create hyper nomics. With the design to solve these multivariate problems in different 4D, 5D, I think you've told me offline, like 10, or 16, or something like that, you've you've gone up to some very impressive analyses to really pinpoint where something needs to be or where you want it to be. And, you know, with an investor that whether it's stocks to say there's multiple variables that are going on in the economy, where do we find those given 18 different variables? I'm just making this up. Where do we feel that there's a price-EPS between whatever real estate, there's so many different areas that you can start to zero in and find an unlock value? Rather than, like you said, the Cartesian coordinate system, which is just 2D. So now you with hyper genomics and your wisdom behind planning that out investors, investment bankers, fund managers, and anyone else interested in economics can really start to zero in on market multivariate analyses to help to understand where investments should be. So you mentioned a book, I'm curious, what's what's, what's the name of the book, maybe we could talk about your book and everything else that you're up to today?

Doug Howarth  
Yeah. The title of the book is hypernomics: Using hidden dimensions to solve unseen problems. 

Ryan Miller  
Okay. And he does that on Amazon Wiley. Where

Doug Howarth  
do we find that Amazon ally Lee and also it's already on Barnes and Noble too. So you there's multiple platforms and then fine bookstores come January.

Ryan Miller  
Love it. That's perfect. Yeah. And what else? What are the things you've been up to?

Doug Howarth  
Well, we've we've used the the technology to put up a test fund. And we've been running the test fund with real money, my money, and it's been running for 41 months, and we're doing twice as well as the s&p 500, from which we do all of our stocks. So we feel like we were getting validation from that we were not doing that we're not soliciting offers on the fund, we can't. But what we did do is we've proven that it works in a way in which we think it would work for the for the entire market, we did that doing 1000s of back tests and up markets and down markets. And the data suggested it should go better than the average when it's markets are going up and not hurt as much going down. And basically, that's been true. We've had enough cycles now that we can figure that out. So that's what it's doing. I should probably tell you, too, that the way we came about this is I kept wondering why certain programs would fail. And I realized that there was an analogy that could be made between the stock market or any kind of market and physical obstructions. And so based on that, what I did was I did some research into where people have made some really bad physical mistakes over the years. And so there's an admiral closley Schaible, who in the late 1700s, was on a saline is flotilla of British naval ships from the Mediterranean around Spain and France. And he was headed to the English Channel, and it was a stormy night, and he thought he was at one position. And he was really in another position. So he gave the order to sail forward and he crashed his boat, and several of the other ones that were with him and drowning, died himself and somewhere between 14 118 100 of his people and just charged they went down with him. And we see the same thing of business people run into obstacles that they didn't see they were effectively hidden. And back to the title of the book, they didn't see the problem, the the dimension was hidden, and the problem was unseen. And so what this does is it lets you see how the markets take shape, they actually do have a shape to them. And it lets you see where the limits are and how the market responds to things and figures out what things are underpriced and overpriced and where there are gaps in the market.

Ryan Miller  
And so this essentially reveals those hidden rocks that Admiral Cloudslee wish he could have seen. And I'm sure 1800 of those people in his charge, wished he had the tools and instruments back then it was nothing compared to what we have today. Just like with naval expeditions, you do that with economics and say, Well, yeah, there might be some sharp rocks on this investment deal that you're going to crash into the you can't see because they're below the surface. You need better tools, better insights and better analysis to understand the path to safety. Would you say that's an accurate metaphor on?

Doug Howarth  
I would say it's exactly accurate. Thank you, Brian. Appreciate that. Yeah,

Ryan Miller  
yeah, I love it. So you've written a book. You've built hyper nomics. And you're you've built a testing fund, and I believe you said you beat the s&p, is that right?

Doug Howarth  
Yeah, we've been up against for 41 and 41 months, and we're doing 2.2 times as well as they are over that period, using only an s&p 500 stocks. So it's a conservative value based fund, no leverage, no shorts, no cryptocurrency, no fiat currency, it's just the stocks, something that Warren Buffett would approve of that kind of is designed to be safe. It's designed to satisfy eventually a fiduciary kind of position. To be clear, we can't do that yet. But eventually, when we can, if we can, well, we'll do that kind of arrangement.

Ryan Miller  
Yeah, Awesome. Happy to support you there. So with that being said, we promised our audience that we're going to give some cheat codes and really open up so I'm wondering if you'd be able to just demonstrate the power of hyper genomics on the show? Sure, really walk us through just maybe two or three things that you find quite interesting that might appeal to emerging fund managers, entrepreneurs, investment bankers, economist, anything, or just private investors in general? What are some of those more interesting findings that you found? 

Doug Howarth  
Well, there's several and let me show you a couple here. We'll start with something we call walk this way. Do you see my screen there? I do. Okay, well, this is a study of 60 properties in the LA area in 2020. Each one of these dots represents a commercial piece of real estate available for sale back at that time. And what we did here is we're plotting household income by zip code against these points. And over here, we're plotting the square footage of these properties against their price. And there's an outfit out there not not associated with hyperdynamic St. But a very clever outfit entitled, though, I think it's called Walk Score, they rank the properties based on their ability people to get out of the front door and walk to any services that they want to have. And the shorter the walk, the higher the Walk Score. So here's the surface for the Walk Score, if the Walk Score is 45, again, with the household income going in this direction, square footage in this direction. So you see the plane is going up with household income, which makes sense is going up with square footage, which also makes sense. And so what we've done here is we've plotted a point where there's 10,000 square feet, and $100,000 household income. And with this as for the if the Walk Score is 45, that property would be worth 4.0 3 million. So now over here on the right, what we've done is we've taken the same parameters here, we've established a household income of $100,000. And we're looking at a 10,000 square foot property. And what we've done here is we've taken the Walkscore up to 90. And what we discovered here is this whole plane you see the plane here is here, and here it rises. And what it means is that the price just slightly more than doubles for this particular condition here where the Walk Score went from 45 to 90. And so what this means is if you can find something that's undervalued in this market, you might be able to get a That's pretty spectacular deal. That's what we're going to try to help people out with.

Ryan Miller  
That's awesome. For those of you who are listening to the shows, what Doug is doing is he's showing the four dimensional actual physical model, it's a four D will say scatterplot, that is able to help you really wrap your head around a lot of the values in real estate that contribute to the price increases. Now, while that's cool on the surface, we can take it a step further and go even deeper, and you can say, and you can also find ones that are undervalued. So this gives you a graphical representation. If you reasonably believe that the data in here can show you where things are accurately priced, overpriced, and more importantly, underpriced. In the words of Warren Buffett, I make money when I buy, not when I sell. So this helps you to make money when you buy. So you're not sending a prayer to heaven that you get the exit price, you need to hopefully return something to your LPs. So if you can get greater insight, 3D, 4D and beyond models that actually graphically help you to understand where pricing is and where it may be in the future. That my friends is the holy grail, in my opinion. Now, with better analysis comes better pricing and better deals. So the stuff that Doug and his company at hyperdynamics have done is absolutely groundbreaking is going to change the game for many emerging fund managers and private investors. But what other tips have you found, or maybe some other industry verticals that you found?

Doug Howarth  
I was just going to try finish up with a real estate example of if I can give some specifics on that if you'd like. Oh, yeah, absolutely. So I was talking about it for those who are listening about a database of 60, Los Angeles county based properties here. So I've made that a list of these 60 properties have included about 12 or 15 columns relating to their their characteristics, what this does for you is it gives you a scatterplot of standard of the of the error terms that shows you the properties that are undervalued here. And so once you get this data, you can discover which properties are if you're looking at real estate, or which stocks if you're buying stocks are undervalued, you can do that inside of just minutes. That's That's how quickly that somebody can do that. And then you can see how the other market behaves in terms of its return relative to the variables that are going on. So that's pretty, pretty handy stuff for if you're trying to invest, that's again, that's what we use in stock market, we use the same thing

Ryan Miller  
perfect. So we've definitely proven that to a real estate investor, for example, there's a lot of value that they can start to see just like those sharp rocks under their feet. And they can say, Wow, I now that I consider more variables in my analysis, I can see that this once believed good deal or bad deal is not quite what I thought it was. And now I'm going to take a new action. And so the better the data, the better the activity, and hopefully the better the returns. So with that, what other verticals have you found to be quite interesting in doing 3D 4D 5D models,

Doug Howarth  
we are invested in the stock market. So let me show you how that works in the stock market. For those of you that are watching a YouTube video, if you're if you're listening on the podcast, you might want to flip over to the YouTube channel because there's some pretty spectacular stuff Ryan has for lots of other people, not just myself, I'm just one of the several really brilliant people he's had on here. So. So here's a database. Again, for people, if you want to try to visualize this back at home, I made a simple database of the s&p 500 on a given day, The Given Day back in February of this year. And so I'm looking at the last price and various variables such as book value per share, and P E ratios are Oh A is ROIs. And what I've done that is I've I've created a model, that's as they say that in the industry is statistically significant that describes the supported price as a function of the book value per share the number of shares the return on assets and the return on equity. As soon as you create this model that what this lets you do then is it lets you find the stocks that are undervalued. For example, here's lumen technologies, we I actually own some of that, and it's still undervalued, this is showing that it's almost four standard deviations too low. And for those of you know anything about that, that means the chance that it's priced correctly, according to this equation is less than one, well, well, less than one and 100, probably closer to 1000. So what this is showing us is that on the left hand value side, if you can imagine, for one axis, call it you know, left to right, call it east to west, there's book value per share. And then on another axis, there might be return on assets. And what this is showing you as the book value per share goes up, not surprisingly, the stock price goes up. And as the return on assets goes up, the price goes up. And what this is showing you that is that there's actually a flocking behavior in what we call this value space, which is to say that everybody shares information. The reason I was able to make this discovery in the first place was that I realized that my wife's behavior was exactly like everybody else's behavior in the store, which is to say she was making decisions and she was weighing them out. Now she didn't come to the exact same decisions everybody else. But collectively, people decide on what things are worth with a with a distribution around. But basically, when you go into a room and say the room is all populated by men, you have an expectation that nobody is going to be less than four foot six, and nobody's gonna be taller than seven foot six. If you go into a room and it's populated by women, you expect that the high distribution would be a little bit lower. If you went into a room that was populated by NBA centers, you'd expect the the height to be higher still. So what you're finding out is this is you know something about, say a human a that it's a man and be that as an NBA senator, you've got this cluster of data that you can try to analyze and that's what this is doing for us. And so what this is showing us on one side is that there's a way to figure out the value of the stock and then on what we call the demand plane over on the the right hand side And when we look at it from the front, there is a upper limit to the market. And we call those the demand frontier. And then there's also an inner bound to the market, which is the minimum demand. So if you're going if you want to flip over and go to the YouTube channel, there's a very nicely tightly drawn upward demand curve. And there's a lower demand boundary that forms here. And what this tells us, this also helps inform people about what they should do for IPOs. Because based on the shape of these curves, it works out that if you price things this curve is, as they would say, in economics, it's elastic, we'd like to just call it flat, because we don't like to use more syllables. And we need, there's a flat curve, that means there's more money out here than there is up here. And so what you want to do, if you were trying to do an IPO, you probably want to price it less highly. And then you could sell all the all the stocks that you have in your portfolio on the first day, that's kind of what you would want to do.

Ryan Miller  
Yeah, that's perfect for any economist or investment banker who's trying to accurately price an IPO or anything like that. So you can use these numbers. But also, my there was a former life of mine, where I was a CFO, we do what the industry nicknames window dressing. And so a lot of that is just get your financials in place. And so if you understand in conjunction with your investment banker to say we're gonna go public in three years, and when we do it, we got to go at this price, then it's my job as a CFO to say, all right, how do we adjust our operations so that this price is accurate and justified, so no funny business, we just need to if that's the price we need to do, then obviously, we know how to change your business. So there's so many angles, not just with investment bankers and investors. But this also helps people to do big businesses run IPOs there's a whole bunch of different things that we can do. And that's just in equities.

Doug Howarth  
Yeah, and one more thought to hammer home with respect to the equities. This is a 4d model of the s&p 500 from June 20 2019. So this, this red side is the what we call the demand plane. And so these red points are Boeing back then Boeing, Microsoft, Apple and Pfizer, the points that are the highest up here at the formula demand curve. And then over here, on the other side is the value proposition for the stock. So we're looking here at adjusted earnings per share and return on assets, you see that that there's a plane that goes up and describes them and so points above this plane may be overpriced. So here was Boeing back then. And of course, we know what happened to Boeing, it was overpriced. And here's some points that are below the curve, they may be underpriced. The advantage to having hyper genomics is that it actually lets learners that are not oral. So some people are gonna pick this up on the on the in the car, me, I'm a visual learner, if I if I heard this on the on the radio, I'd say wow, I'm gonna go off to the YouTube channel and check it out. So some people are visual learners, but some people are tactile learners. So they could actually you can actually touch this model and literally get a feel for how the market is working. And that that turns out to be pretty important for lots of people. Actually, it ends up making it broadens the ability for somebody trying to convey what's going on to their get the whole audience instead of just part of the audience. So we're pretty excited about that, too.

Ryan Miller  
Love it. So what other industry verticals? Have you seen? Maybe one more?

Doug Howarth  
Well, we've, we've done we've done some analysis of crypto versus fiat currency, and we share what we've done there. Interestingly, what we did back in 2019 so this is this is about four years old now that Bitcoin was not quite at its peak. And then of course, the fiat currencies have existed forever so to describe what you're seeing, you wouldn't see it ladies and gentlemen, they're listening, if you were to go off to the YouTube channel is that we've got the cryptocurrencies plotted, we've got the quantity of units issued on the horizontal axis, and we've got the dollar for currency on the vertical axis to the US dollar courses plotted at one we also have the fiat currency. So the cryptocurrencies are these red circles with white dots except the ones that are on what we call the frontier and we're comparing that to the fiat currency. So there turns out there's an upper for demand frontier for cryptocurrencies, which includes the some of the Gulf states, the Bahraini dinar, the Kuwaiti dinar Omani in the UK pound and the Swiss Franc and the euro and the US dollar, they all form this upper demand frontier. But then at the same time, fiat currencies have an outer demand frontier, which is includes again, the euro and the USD but also includes the Chinese one, the Indian rupee, the Japanese yen, Korean one Indonesian rupee and the Iranian rial. And very interestingly, and very importantly, the slopes for the outer frontier here for the fiat currency and that of the cryptocurrencies is virtually identical, in which when I was knocking this around with Ryan, we kind of agreed that what happens here is the the market is treating cryptocurrency, like fiat currency, not so much as an asset but as a as a holder of value. The other thing that comes from this too, is that when you draw this line for the frontier, there's there's points that you know, from if you know how to do their little math, they call regression, there's going to be points that are what we call above the line or beyond the line, and there's gonna be points below the line. So the points that are above the line, the market tends to favor and the two points that are above the line for the cryptocurrency or Bitcoin, which is slightly behind the curve, but the one from several years ago that was way beyond the curve was ripple. And so we think that that means that the market is favoring these things again, four years ago, but favoring that these these devices more than other exchange mediums that are out there. So maybe you what you want to do, what you want to do is figure out what made ripple and Bitcoin more favored by the market. And that would be something you could use in future analyses to try to say, well, that if we can only figure that out, we'll be in good shape.

Ryan Miller  
And you know, that's interesting. Thank you for that. So showing that side by side and those clusterings as fiat currencies around the world operate as as well as those global crypto currencies, and there's plenty in there. But the interesting thing that I just want to outline whether you're on YouTube or the other podcast channels that we communicate on is that when you have a visual example of the data, right, you take it out of a table, and you make it visual. Instantly, Doug and his team are able to say, Well, it's interesting what I see how those are starting to cluster together. And crypto, much like these currencies, clustered together. Interesting. Further analysis allows you to create better conclusions, but also new theories, and dare I say, even new thesis for investors. And so now we can say with data, we say, well, it appears that the behavior of the data, they're actually pretty similar. And the thing is, is the debate of is crypto an asset. Is it a currency? Well, if you follow this logic, then the data clusters together much like a currency now, we're not saying it's a currency, or we're not saying it's an asset, but what we are saying is there's similar behaviors in the market. So this can help you for those of my many friends on this crypto space. This can help you whether it is crypto, whether it's real estate, or any other asset class or any market phenomenon. The point is, is that when you start to visualize your data, new findings come and you can add more dimensions to your analysis to find those. So before we wrap things up, Doug, I'm just wondering, is there anything else you'd like are fans around the world to know anything at all?

Doug Howarth  
Yeah, some of you might be interested in my book. And I wanted to tell you that the book is right now available for preorder at Wiley. If you are going to the podcast, you can see it directly there and that it's also avail available on Amazon, and Barnes and Noble. And then also give me my contact data you people, anybody that wants to can try to reach me on LinkedIn or on my personal website in the company website there. So anybody that wants to contact me can use the contact information here. My email for those of you listening as D Howarth, it's h o w ar th@hyperdynamics.com. If you go to LinkedIn, it's just type in Doug Howard's, you'll get me first, I have a personal website, Doug Howard's dot com, and the company website is hypernomics.com. So the first 10 people to contact me will get a free one hour consult if you'd like

Ryan Miller  
No, thank you that is generous, right? making billions of exclusive so one hour console with a brilliant economist that understands how to get your data and everything in plays help you to analyze in multiple dimensions on your investment thing. Whatever that is, whether it's real estate, hedge funds and everything in between. So just understand the value of the data, understand how to analyze it. Utilize good tools with better information to help avoid those rocks that you crash into. You do these things and YouTube will be well on your way in your pursuit of making billions.

Wow, what a show. I hope you enjoyed this episode as much as I did. Now if you haven't done so already, be sure to leave a comment and review on new ideas and guests you want me to bring on for future episodes. Plus, why don't you head over to YouTube and see extra takes while you get to know our guests even better. And make sure to come back for our next episode where we dive even deeper into the people the process and the perspectives of both investors and founders. Until then, my friends stay hungry. Focus on your goals and keep grinding towards your dream of making billions