Episode 63:Understanding Predictive Analytics with Alexander Harmsen

More on YouTube? Check the video version on Youtube

Who is the Guest?

Alexander is an experienced tech entrepreneur, CEO, board member, and advisor, having founded multiple successful companies and organizations. His work has seen him produce multiple AI-driven products, scale sales globally, hire world-class executives, manage hundreds of employees, and raise over $25M in venture capital. He is focused on hard problems that have a meaningful impact on the world, knowing that very few others are in a unique position to take on such challenges.

Visit Him at: 

Website: https://www.globalpredictions.com/

Twitter:https://twitter.com/worldpred

 Facebook: https://www.facebook.com/worldpred/ 

Linkedin: https://www.linkedin.com/company/globalpredictions

 Instagram: https://www.instagram.com/globalpredictions/

They have a free individual portfolio management tool there for self-directed investors!

Start taking action right NOW!

• Goal-setting the right way! 
• Hesitant to make the first step toward real estate investing? Axel learned the hard way- but you DON’T have to start that way. Feel free to talk to him :)

Connect with us through social! We’d love to build a community of like-minded people like YOU!

Transcript

Axel Meierhoefer: 0:02

Hey guys, when you listen to the news, and you're asking yourself, How can you actually make really good decisions when it comes to your investing? Wouldn't you want to know a little bit on? How is the future going to look like? What are the things that are most likely going to happen? And how can you take advantage of those developments when you're investing? Well, today we bring on a guest, Alexander Hanson, who has developed a system called Global predictions that actually uses a lot of data to make as good of an estimate of how certain things will develop in the future to help you as a self directed investor, somebody who wants to use their money on their own to decide what to do with it, on making better decisions. So pay close attention, because there's a lot of really good nuggets in this particular session. Hello, and welcome to another episode of the The IDEAL Investor Show today we want to talk about predictions for the world and for the globe, and all that kind of stuff. And we have a special guest for that with us in the show that Alexander Harmsen. And he is going to be our expert today, Alexander. Welcome to The IDEAL Investor Show.

Alexander Harmsen: 1:26

Great to be here. Axel, thanks for the invitation.

Axel Meierhoefer: 1:29

So I said and before we started recording, it's a pretty ambitious name for your organization. How did you get involved in working on things that have to do with money and where money goes and stuff like that?

Alexander Harmsen: 1:42

Yeah, intentionally ambitious, know what we've built a global predictions as you're basically a digital twin of the global economy. I've always been fascinated in modeling and simulation. And you're even when I was young, you have competed in national science fairs with large scale models of how ecosystems work. How are we not all different populations, I've gone on to form multiple other companies very focused on hybrid AI, in many different domains, autonomous vehicles, I've been involved in a company modeling the human body. And I've always been fascinated with the idea of simulating and modeling very complex systems, especially when other people say it's impossible or it's too complex, or, you know, there's so much to it, I completely understand and admit that it's a very ambitious project. But there are tremendous amounts of data streams out there. In general, we've made tremendous progress and economic forecasts and predictions modeling, over the last 60 years of humankind, there are tons of hedge funds out there that have made tremendous progress in doing this kind of, you know, systematic macro modeling. And, you know, I thought there's a significant amount of inefficiency that happens in the world because of, you know, risk and our lack of understanding of macroeconomic systems. You know, even the Fed uses relatively simplistic models and how they, you know, set monetary policy. And so, you know, I probably, I don't know, a couple of years ago and 2020 2019, I spoke with hundreds of people from hedge funds to central banks, to individual investors, insurance companies, oil and gas giants to try to understand your what is the current state of the art in forecasting, economic modeling? How do people make decisions about what's happening in the economy? And, you know, unfortunately, many people threw their hands up and just said, you know, it's too complex, you'll never get here, except for a few hedge funds. You know, there were hedge funds like Bridgewater, that basically I spoke with people at these kinds of systematic macro hedge funds. And they said, you know, this is all we do, I literally, for decades, we have built these kinds of models. And so mainly think that the real problem is and that it's impossible to build these kinds of models, but that they're locked away in these, you know, private hedge funds. And, you know, there's a real opportunity to democratize access to these kinds of models. And so, you know, what we've built over the last couple of years with, you know, a team of ex Bridgewater folks is, you know, the same kind of infrastructure. We have build sort of ground up models, we're using a mix of AI standard models that are used within the finance industry, we're using some of the same kinds of dynamic factor models that the Fed uses. And then combining all of that into a single approach for you. We basically have hundreds of 1000s of different forecast models, underlying drivers, you're all sitting on top of this knowledge graph. And so basically, within our system, you can connect your model Millions of different factors within the global economy. And, you know, by no means is it predicting everything perfectly. That's not what we're shooting for. But it is definitely extremely helpful for basic portfolio management.

Axel Meierhoefer: 5:13

I have tons and tons of questions about that. So the first thing, obviously, since you trust that follow my ftrs you Ray Dalio just not too long ago wrote a good book that I really liked after reading in the New World Order. So did you put the historic data in and then see if his predictions were anywhere close to what he was saying at the end there?

Alexander Harmsen: 5:36

Yeah. Fantastic book. I read it, too. There's something about, you know, these long term historical shifts that happen. And you know, I think the one of the best things about Ray Dalio is that he thinks about the economy, like a machine, you know, set of pipes and tubes and you know, mechanical winches. And you know, as long as you understand the inputs, and you have enough data, and you make basic assumptions, you can model all of this with certain degree of accuracy. I mean, he's talking about sort of empires changing, right, over a certain period of time, 150 year cycles, but he's written previous books that are much more similar, like, you know, he talks a lot about the debt cycle, or the business cycle. And so these kinds of things like are also modeled into what we built a global predictions, where, you know, we like to think that, imagine a charts where you said, I've had like a certain amount of accuracy from the models, you know, we like to think that the very near term future is very difficult to predict. And so you get to, you know, sort of zero to two months out, is extremely chaotic, and very much driven by the news cycle, it's driven by sentiment, it's driven by individual actors within the economy, within markets. And then, you know, after a year, you sort of get this cone that starts diverging, as well of, you know, millions of different probabilities, and small little things end up adding up and stacking up over time. And so our sweet spot is two to 12 month predictions.

Axel Meierhoefer: 7:12

You know, we're talking today and on a day where they were just the latest CPI data coming out. And if I take my admittedly not expert level economics, understanding, I would say, okay, so all the numbers that came out, were some two pretty significantly higher than any of the quote unquote experts had predicted, which you would think would make the market actually shut out even further than it had in the last couple of weeks. But the exact opposite, right, like where you say, Okay, how crazy is this? How can this be? So I think that is a great if you ever look for an example to say, CPI is twice as high month over month and expected market increases? 800 points, right. So you say, Whoa, crazy. I hear you on that. Now, I actually, to my opinion, I second everything you said. And to me, it felt like you know, the fourth turning was kind of written almost 20 years ago or so something like that. And raised lady's book really felt to me kind of like, in a sense, confirmation continuation and update, right like to 20 years later, where are we what happened? And how much does it apply? And I think, the birds the house thing about these long term cycles and re picking this up again, it's very good. Now you mentioned that you basically do modeling. And one of the goals I always have with this podcast for our audience is bringing in guests like you that are not necessarily doing exactly the same thing that we're doing with our clients at idealize Pro, helping them to create passive income portfolios, investing in residential real estate, but things that aren't related. So when you talk about modeling, I'm always trying to, you know, at least on a common level, educate myself. And so recently I learned, there's something called Artificial Narrow Intelligence. And then there's Artificial General Intelligence. It's kind of on the spectrum. If you look at what you're doing with your modeling and predictions and stuff like that, can you touch a little bit on what these two are and where you would place your toolset basically, on that continuum?

Alexander Harmsen: 9:15

I think it's much closer to like the AI side of things, right, the idea of neuro artificial intelligence, mostly because it's very focused on, you know, specific types of data. Right? If we're talking about AGI I think, very often we're talking about like multimodal inputs. And so, you know, for humans, for example, if we're talking about AGI that, like mimics humanity, we're talking about being able to take inputs from your vision and hearing and sort of history and tactical feedback and thinking about like, how do we translate all of these different modes together, and then apply that across a number of different domains. Right. And so I guess for us You know, we actually think that the hardest part isn't necessarily the artificial intelligence piece, but it's the infrastructure to collect all the data and set up all the relationships. And so, you know, we're pulling data from 16 different API's, and we're pulling COVID data into our model, we're pulling information about technology trends, you know, sort of standards, markets data, around 50,000 different securities, we're pulling in lots of macro economic data, everything from CPI in South Africa to you know, housing prices in individual states across the US to trade relationships between Ghana and Togo, right. And so these different things feed into the system. And then it's the Knowledge Graph, where I think as they sort of the largest amount of AI and machine learning, where we are, basically, we have this web of potential connections, we can think about this as almost like an explicit neural net that we've set up that takes all these different relationships within the economy. And that keeps getting stronger and stronger, the more data we feed into it, the more people that interact with that, that knowledge graph. And then, you know, at the end of the day, I think it's the application of that data that's also relatively narrow. And so there's hundreds of different applications that we could use this data on these models for, and you know, short term, we basically built a portfolio management tool around it for self directed individual investors. And we figured that was the best possible place to start, you know, people are able to connect their portfolios, import their net worth, pull in real estate, crypto retirement accounts, cash, anything like that, and then connect that to these models to actually help diversify better to think about, you know, how much inflation risk do I have in my portfolio? And so, you know, to sort of tie it back to your AI question, I think we use relatively narrow AI and all these different parts of the system. And then, over the next 10 years, I think that we will start getting closer and closer and closer to AGI as we pull in more nodes. And as we expand the different use cases for this technology, because I can imagine that the same kind of technology could be used eventually, for helping oil and gas companies, you know, understand oil shocks and different parts of the system, you know, helping government set different policy decisions.

Axel Meierhoefer: 12:25

Yeah, I think you're right about that. I mean, one of the things that I would say, as a layman, basically on that spectrum, it would, for me show more and more artificial general intelligence behaviors, when it's not so much being told, look for this kind of stuff, bring it in, and then analyze it in some way. But getting to the point to say, Okay, I'm trying to predict literally, with a word that you use in the name of your organization, I'm trying to predict, let's say, six months out, or eight 910 12 months out for something specific. And to be able to do this, I can, I'm talking like as if I were the computer, either computer or either robot can go out and search for relevant information that I believe I need to actually come up with a large enough for statistical and otherwise data set to increase credibility of the prediction in combination of what is current and what is historic, and then make my prediction, rather than being basically told, bring all this in, crunch it and then give me the result. And then I human interpreting the result. Now, with that being said, in essence, I wonder, and this is really I mean this because I know what both are, but I don't know how this would apply. And if you say hey, don't worry about it is completely irrelevant. But what this system that you use, have to be Australian or Canadian, or any of that, or is it totally irrelevant?

Alexander Harmsen: 13:56

That's a good question. I'll answer this one and comments on what you just said before, right? Because I think over time, the system basically starts breaking down traditional rules of economics. And, you know, I think one of the most fascinating things that has come out of chess computers, for example, and, you know, convolutional neural networks, you know, in how they play these games, is that, you know, the play style has become very different. It's no longer you know, 1520 years ago, when we had this sort of, like historic Kasparov moment, it was basically just a computer that was, you know, had all the rules of expert chess players, but we could just crunch more data and look forward further. And that's how I was able to, you know, that grandmasters of the day, but now we just give it the rules of the game and it's able to find out all its own strategy, and you have these positional sacrifices that a human would never think to make because it's setting up the board better, because it's able to see sort of six, seven moves out that this small disadvantage now ends up having large positional advantages later on and allows for a larger universe of moves and traps delay. And so we don't have an explicit economic school of thought coded into the system. But I do really hope that over the next five years, we'll be actually be able to work with some academic institutions, and maybe even create new schools of thought, to basically dive into what the models are saying, to be able to pull out interesting insights, and actually form new theories around that or point out problems and existing theories for new connections. And so, you know, you, you start with something relatively simple. And you know, a lot of the portfolio management, we a lot of the portfolio management that we do, we like basically connected to these, like prediction models. But the real input into those models is really more coming from the relationships and the inputs. And so for most of our users, it's much more valuable to know that they have an crazy credit exposure, you know, they have a lot of exposure on their portfolio to credit conditions, and they need to fix those exposures rather than a real estate markets about to collapse over the next six months.

Axel Meierhoefer: 16:22

That's actually a good point that I want to kind of introduce you a little bit, because when I was young, which is like a couple of 100 years ago, the saying, went basically data is something called like a piece of metal, right? But when it comes to decision making of human beings, especially, it is very much emotion driven, right, so I'm just throwing out a few things like that have been on bulk lately. For instance, something like FOMO, right fear of missing out, that can go both ways, right? Fear Of Missing Out in selling or fear of missing out in buying, when we, for example, look at equities on the stock market. If I were to go, I just give an example for the audience. If you look at the performance metric, the pure code metal like data for a company like Tesla, right, and you say, Okay, if I categorize it or not, it is on an almost unbelievable exponential growth trajectory, all the data supporting that massively when you then look at the performance of the stock, regardless of the performance of the company, but obviously, the company performance data would go into any kind of analysis, you find out that the stock has lost 50% of its value. Now there's one thing obviously, where there's a relationship to the overall market. But then if you say, Okay, well, I'm analyzing to try to find out which positions would make sense in which kind of a scenario. And you just look at the pure data of the performance of the company that is reported on a quarterly basis in excruciating detail. You see the performance of the stock, and you know that human beings are emotional, how do you actually handle any kind of predictive thing? And I'm asking, basically, if I had asked this question, let's say 12 months ago, right, right. And looking at the performance has, by the way, not changed. If anybody were to plot it is like, the highest predictability on an exponential growth curve you can probably ask for. But the behavior underneath that actually influences people's money, which I'm thinking is kind of what you're helping or trying to help with with your models is completely I would call it at least counter intuitive, if not going all the way too crazy. How does that How can a system how can we as an audience, depending on experts, like you kind of square this, okay, the data says one thing that should actually inform us to be able to say this would make sense, but then the emotional component of humans being applied, can completely contradict anything that you would analyze statistically, or literally factual data or whatever.

Alexander Harmsen: 19:05

Yeah, this bothers me to no end that, you know, a lot of investing doesn't really seem to be at least not on the surface, you know, very data driven. And I think a lot of people know about rules and biases, and you still choose to ignore them, you know, because this one is different, or this year is different. But in reality, like I think everything does follow rules, or even last year, you know, 12 months ago, I had many conversations last fall with people where they thought they had questions about how the system was working, you know, why our system was giving certain recommendations. And I remember a number of those recommendations were sell Tesla. It turns out that, you know, a lot of the people on our platform have, at least a year ago had fairly, you know, over exposed tech and us, you know, stock holdings. And Tesla and Ark are very common culprits where, you know, people looked backwards over the last 10 years or five years. And, you know, on many other platforms, you know, they basically, point, you know, they talk about expected returns, as you know, some average over the last 10 years, and we've been in this bull market and things have been mostly up into the right. And, you know, especially our contestants had been, you know, have been huge benefactors from that. But in reality you like when you look at how exposed they are to credit conditions, how exposed they are to your GDP and growth drivers, how influenced they are by liquidity in markets, you know, these sort of underlying macro drivers, you realize that, you know, if you hold them, if you think about your portfolio from a risk standpoint, then, you know, there's this tremendous overexposure. And so one of the interesting things that started happening towards the end of last year on our models is that, you know, we, we had the returns of these different securities drop, but also the risk associated with that, like, basically upper and lower bounds of what the model was predicting, started climbing, you know, across the board. And so, you know, for us, it's not even just about forecasting, or appropriately, it's about being able to measure these underlying exposures properly, and making sure that the risk and the potential volatility associated with all the items in a portfolio are captured properly as well. So basically recommended, you know, find a number of places for people to sell that. And I remember, like, there was conversations I had with people, and they said, you know, what, your rights, I have too much exposure. But, you know, also when it gets to 1500. Like I asked, like, I remember, like, really trying to understand, like, I'm fascinated by the psychology like, I'm trying to understand from these users. Why is 1500 Beside magic number, and it's some analysts that said it at some point where it feels like a round number, or this is, you know, exactly three times when the event like originally bought in, or I don't know, there's all these like, these things that aren't tied to the underlying macro conditions. And I think that the macro drives way more than people really care to admit.

Axel Meierhoefer: 22:30

Yeah, I agree with that last point, for sure. I mean, that there is kind of like a derivative of this time, it's different, where you just exchange the birth time with this company, or this investment, or this house or so forth. And the same argument is being made. But what it also and I don't want to take this too far into the weeds. But one thing that I since I have a rare opportunity, having an expert like you on the show, is to me as being someone who is constantly basically, looking at this not analyzing, not having an AI or AGI or any kind of tool, it's just trying to be informed on behalf of our customers. And the people who asked for our mentoring advice. It appears to me that more often than not, and I've been looking at this now for 20 years, it's not just a quick snapshot, they actually behavior for investments. And it's almost I would go as far as saying, regardless of category, whether you go commodities, or stock market or value assets, like real estate, or COVID, or any of those seem to be overly influenced by what I call the lemmings effect, then the actual data, right, because when you look at the market of growth stocks, or the market of technology companies or stuff like that, the movement seems to be much more coherent. And as a group, then when you go and say, Let's analyze these different companies, right? If you say, okay, Tesla can be categorized as a car company, you analyze all car companies, none of the other car companies, you could take this even in this, if you had to plot that you have to basically do logarithmic scale stuff to even be able to show them together. If you say it's a technology company, and you look at the earnings versus other components of other technology companies that can be an indicator, and the growth in the relationships and stuff like that. It's again, hard to plot them. But then when you go and say forget about the actual data, the actual performance, the actual really what we can gather, and just look at the behavior as it occurs quarter over quarter or month to month or year to year, then I'm finding what I call this lemming effect is it seems like the groups are moving in certain directions as groups, regardless whether one in the group or two or three in the group. The shining example right now they might be at different positions like an apple is obviously different than let's say Nokia or something like Uh, you know, they both make mobile phones, right? But I am fascinated by okay, how is this going? This is also by the way to bring this back to our audience and real estate investing when people obviously asked me, you know, I am not a global predictor like you, but they asked me, Hey, you have been doing 20 years in real estate investing, especially in residential, what do you think is going to happen? Right? And for me, without having these predictive towards I'm basically saying, Okay, I don't think we want to ever get close to one and a half to $2 trillion interest interest payment on national debt. Now, that sounds like a weird argument on first glance, but I'm saying this to say, okay, so this increase in interest rates that we're still in is not going to be able to sustain for very long, because I bought drivers to only pay interest and committed things like welfare and nothing else anymore, no country can survive that no economy can survive them. Now, on the other hand, when I look at real estate, I would say, Okay, one thing that has not changed between 2010 and two, or 12, to 2020, and 22, is we're just not building enough for the size of population that we have. Right? So supply and demand, as silly and simplistic as it might sound is still valid. Right? So yes, you can see

Alexander Harmsen: 26:13

You can argue the only thing that's valid right at the council supply and demand, that just depends on the level that you're looking at.

Axel Meierhoefer: 26:21

Yeah, exactly. And so in 2008, when everybody basically, a lot of people had bad mortgages, or these properties came on the market. But nowadays, nobody has bad more riches. And there's huge demand and no supply. Now, you could say the demand is subdued due to pricing. But still, as soon as any kind of user price has come down a little bit, or interest rates come down a little bit, or any combination of those kinds of things, or people have higher wages because of inflation, and unions and what have you, as soon as people get even a little breather, the fact that there is basically no supply is still there, didn't go away, just because other things happen. So this, you know, like you see, look in the media, and they say, okay, the real estate market is collapsing. And I'm always saying just shut up your ears, watch some Listen, some music or whatever. Just don't pay attention to it, because it's just wrong. Right. Fundamentals and supply and demand still apply? And I'm glad that you say that's one of the few things that's the true.

Alexander Harmsen: 27:20

I think it's all about these kinds of relationships, right, like, yeah, you know, even the sentiments, I think follows, you know, the supply and demand, people anticipate a certain, you know, a certain amount of time into the future, the expectations change, because of the data. At the end of the day, you know, the supply comes back to you know, a set up different relationships, the demand comes down to a set of different relationships. Right, that depends on unemployment. And it depends on, you know, if your interest rates, and it depends on how people think about job security, it depends on consumption patterns depends on, you know, gas prices.

Axel Meierhoefer: 28:00

I agree with all of that, Alexander. But on the other hand, I also think humans, in general, a little more simplistic than most of them want to admit, right? Like there is this beautiful statistic, I'm sure you've heard that too, where people go around with microphones like downtown and say you have probably heard about, you know, the average person average intelligence, average earners and stuff like that. So what do you think you think you above average, below average, or average 90% of people say I'm at least average or above average, right? So I just had to throw that in there real quick. But what what I'm why I'm bringing this up is basically in this context, the simplicity of human beings is if I'm living, I'm just making an example in a 2000 square foot, four bedroom, two bath house, and I'm paying $2,200, for my mortgage for my insurance, and for my property tax amount. And at some point in the future, somebody says, okay, maybe I want a bigger house or a smaller house, whatever the circumstance might be, it doesn't really matter. But even if you go for a smaller house, and you go out and you say, Okay, well, this one is nice, and it does everything that I want. And I can get it for a price that I'm willing to pay, and you look at your monthly payment, and it's 2700 or 3000, this symptom, mass majority of people would say, I'm not going to a smaller house to pay more every month, right? So when I go back to my supply and demand, it's not what does the media say what the prices are going to do? But it's much more likely that people say, Okay, I have a need in a bigger or smaller place. But the one thing I'm not going to do, because it's just out of human nature is to say I go to a smaller place and pay more, right, or I go to a bigger place and no, I will never be able to afford it. But they will just most likely stay until the environment around them allows them to actually make a change. So and I think that those kinds of things we how people actually tick so to speak, and to put it simplistically,

Alexander Harmsen: 29:58

yeah, one of the things That's, you know, we have a lot of difficulty with right now is like, within the program within the product, you know, this portfolio management tool is basically convincing people to, you know, open their eyes, you know, a lot of people are ready to right now they're down. And, you know, they've, they're either really nervous about checking their portfolios, and so they don't log in as frequently, or they log in, but they're worried about touching anything, or they've heard this notion of like, you know, don't panic sell. And so they, they basically translates to them as like, don't do anything until prices are back up. Yeah, we definitely see people way more, you know, way more likely to buy than sell, like, in general, people are reluctant to sell. And so, you know, I've been listening to these behavioral psychology podcasts recently, basically, to think about, like, the behavior of users, you know, how do we encourage people to really think about, you know, different strategies? How do we think about getting people to take advantage of the situation that the economy is in right now, because there's, you know, many fortunes are made, you know, from down markets. There's so many opportunities out there, you know, many people say that you should, you should balance to take advantage of such situations. And so, you know, I feel like there's small little things that we can do in our product to help people feel more confident, one looking at their portfolio, thinking about the economy, and a lot of that is just explanation, right? Even just very simple, like, very simply connecting some of the dots for them about like, this is why you're down here is these different influences yours, why your concept of you know, what's happening in the world, and why your portfolio was set up, in certain ways disconnected from, you know, 10 months ago at the end of 2021. And I find that that changes people's psychologies, gets them into a different mindset helps them rebalance helps them think about like, Okay, how do I now carry the right amount of risk going forward? And, like you say, there's something fascinating about that psychology that, especially because a lot of people don't think about it very often, right? Like, a lot of our users are professionals, but they're not finance professionals. You know, they're lawyers, and doctors and engineers, and marketers, and product managers. I mean, they are relatively smart people that, you know, have a certain amount of money, and they think about their portfolio intentionally, but are still shutting their eyes and saying, like, oh, like, let me not look at it, you know, this quarter? Let me not do my monthly check in, you know, let me just wait a few more months until, you know, things are back.

Axel Meierhoefer: 32:49

Yeah, I think there is a component there. And I know this is probably ruffling some people's feathers, if they hear me say this, and I want to exclude the majority of the engineering profession. But I have always, as I'm originally from Germany, been completely flabbergasted by the fact that if you meet somebody, and you start talking about numbers, or God forbid, math, right, then the common accepted socially acceptable thing to say is poor. I'm sorry, I'm bad at math, and you get sympathy for that, right. And I find this an egregious failure of the education system, because people don't really seem to realize the consequences of that. And I've always felt I have this huge advantage over a lot of people except in the engineering community, because in the German school system math, to the extent that you needed in daily life, to be able to do some simple computations and have some simpler connections very present and available in your mind has been such a huge advantage, right? If I can look at just the performance data of an organization and say, okay, so what they do what they say, and what they have proven to do, gives me some confidence of predicting this is probably going to happen next. But then as soon as I throw in some percentages, and some performances are some terms like exponential people say, I'm bad at math and go away, which on the one hand, is understandable and honest, but on the other hand, it means you basically robbing yourself of the ability to make informed decisions, or you have to depend on somebody else, which I always find, yes, advice. This is what my business is to businesses. But advice should be combined with a certain level of confidence that you understand something when you outsource that to whether it's an AI system or you to other people, and you really so superficial that you don't even get the basic math or you don't want to because you get away with constantly saying I'm bad at math and everybody is sympathetic, then it's all what I like to call sometimes this 401 K syndrome. People's money is taken out of their paycheck, they don't know where it goes. What it does, how much is it? how it's performing. And then they're disappointed when Alexander runs their numbers and says, holy shit, you're down 40%. And you're overpaying and fees like crazy. And music, didn't you see this, you get a statement every month, you just need to look at it, it's right there in front of you. And people say I'm bad at math.

Alexander Harmsen: 35:20

I agree with you. I think it's, it makes me sad. When I hear people say I'm bad at math, I think it does feel like a failure of the education system. And I think there's tremendous opportunity in earlier ages for teachers to be able to tie lessons and math to real world examples. I mean, at the end of the day, it's underpinning everything that we do. But then like, at the same time, I'm trying to reconcile that with, you know, most of the people that we talked to most users in the system, you know, most people I meet at parties, or think they truly want to control their finances, they want to get on top of their investing, they feel like they don't really have the confidence and don't have their arms around the problem the way they're supposed to. And I think there's something systemic about those Well, robo advisors, wealth managers, I think, in general, they are intentionally trying to make this seem more difficult than it has to be technical terms of describing things in a complicated way. And, you know, we like to think that with our portfolio management system, we specifically Jung made it simple to use, but then also, like, allow anyone to basically double click and double click and double click to get deeper and deeper and deeper into the models and the data and the explanations, because, you know, we want to believe that, you know, our users our intelligence, and we basically let them choose the level of depth that they're comfortable with. Because I I honestly think there's like some obligation on us to educate as well, you know, read this kind of information. There's something about, you know, democratizing access to these, you know, hedge fund level tools that like seems like a very compelling mission.

Axel Meierhoefer: 37:09

Yeah. And that's why we brought you on, because we want to get the message out that people know there are tools that are reasonably simple to use, but they also giving you the opportunity to dig deeper. And as you educate yourself, you increase your understanding. I mean, in a nutshell, if I had to say, what is the biggest culprit for this whole math phobia that people have, and it might sound simplistic, but I believe it's the lack of base 10. If we in the United States and other places in the British Commonwealth community, keep doing feet and pound and inches and that kind of stuff? Not honestly, I mean, it sound potentially trivia. But if you think about the calculation that somebody would have to be able to do in their mind to be good with handling all these kinds of variables, and you compare that to base 10. It's like making it artificially hard. When base 10. Couldn't be easier, right, like so. I think that whoever made that decision not to switch to base 10, in some countries around the world did a huge and probably unintentional disfavor that leads to this, okay, this is too hard for me, you help me how much is it three point like 2.51 makes the conversion from this category of what is weighed into this length into this. It's just crazy, right? I mean, when I came here, I it took me forever to understand what font height and speeds and converting everything. And when I really need it, I'm still doing everything in base 10. But I think we beat that horse. So let's go to something completely unrelated, but maybe kind of connected. We always asked at the end of an interview every guest, if you had a time machine and you could go forward and backward. You know what, you know, you just aren't allowed to change the time space continuum. Where would you go and why?

Alexander Harmsen: 38:51

Yeah, fascinating. I saw one of these behavioral psychology episodes, like these podcasts that I've been listening to. And this lady ends the podcast, each one of our episodes with this question to her guests. Basically, the question I think is fascinating, like, definitely ties to this time sink Time Machine question. Basically, she talks about, like, the early days of film, beyond movies, you know, basically, they would, you know, put the video camera in a specific location. And then they would have kind of like a stage production, or they would have all the actors going in and out and stage left and stage right. And then at a certain point, someone started moving the video camera during the movie, and it was this, like, very obvious scenario, or very obvious insight that completely changed film and movies. And now, you know, it's taken for granted. And so I actually think that, you know, if I were to go into the future, there's not even like a specific moment I would look for, I think, I would love to just Like, live for a week or two in the future, just to observe, like, what are these obvious things that society has learned? And that is, you know, around us, and what do we take for granted 100 years into the future, just because I think that, you know, there's so many things that we could change about society right now, there's so many inefficiencies, there's so many obvious, you know, small changes that we need to make that, you know, this future trip could tell us and inform us of, and it's like these small little inefficiencies of the small things that are just out of sight and hidden from us that I think could, you know, could radically change or do radically change economies and societies. And, you know, there's even small things within portfolio management and economics that like, you know, we take for granted now, but 20 years ago, were completely novel. And it doesn't take very long to make those things obvious.

Axel Meierhoefer: 40:58

Yeah, that's a great point. I mean, I've been reminded, and people probably have an aha moment here, When access is something weird again, but I've seen a couple of years ago for the first time, and it was probably just me observing it for the first time and has been probably out there, way longer than that, where people went to the gym. And they got a little kind of thing where you could put your your mobile phone into a little sleeve that was basically attached to your arm. And I looked at them while they were like on a treadmill. I'm like, say, you slide this down a little further. You actually got to be Spock in enterprise. When I was young, and we watched all these enter Star Trek Enterprise series. And everywhere they went, Spock had that little thing on his arm that he basically typed in to measure the temperature and the air quality and stuff like that. And that is, what, 30 years, 35 years ago, maybe 40 years when that was out there. And everybody said, Oh, look at this, this will never happen. And now

Alexander Harmsen: 41:55

read science fiction.

Axel Meierhoefer: 41:57

And you know, it was I mean, not everybody has it, obviously. I mean, you have an epic watch, maybe. But I mean, the thing that looks like a tricorder we are carrying a tricorder around that is probably as capable as what Spock had in the first few episodes of Star Trek. So I think it's fascinating when you say I wonder what kind of things will be solved, what kind of things will change, you know, will we get those 100 per 100 mile solar panel array that sort of provides like renewable energy to the whole globe that Elon Musk talks about? So yeah, cool. When you get there, take a good look, come back and then tell all of us how it was and what you found.

Alexander Harmsen: 42:33

I'll be back on the next podcast all.

Axel Meierhoefer: 42:38

Being said, Alexander was such a great opportunity to talk a little bit differently about things that we normally not, so often address. If people say, Okay, this predictive model and using artificial intelligence and all these inputs that you describe, I'm fascinated, I would like to see where I stand with my investments with my money. What should they do? How can they get in touch? How can they go about it?

Alexander Harmsen: 43:01

Yeah, thanks so much for having me on, simply go to global predictions.com. We have this free individual portfolio management tool there for self directed investors. Like I've mentioned before, you know, import your net worth connect accounts, retirement, crypto, cash, private equity, real estate, you know, it spits out your portfolio score, automated, personalized recommendations helps connect that to the economy shows out your your downside protection, how closely your risk and portfolio actually matches your actual risk profile that you're comfortable investing with, and a risk adjusted return that sort of underpinned by a Sharpe ratio. And it breaks all of that down, you can go through multiple different levels of that to understand what's actually going on, run different simulations for your portfolio, create draft portfolios to be able to move to something better, whether times are good or whether times are bad, you know, we'd like to think that it's much more important to be able to stay on top of the portfolio management and make sure that you're rebalancing appropriately and make sure that you're getting the highest possible portfolio score. In the show notes. We will also share a referral code to get access to the gold membership program, just use axils referral code for that. And

Axel Meierhoefer: 44:24

thank you, Alexandra. That's great. One quick follow up question. I know that some people in our community are not always very comfortable to permanently or even temporarily connect accounts. Is it possible in your system to just populate it manually?

Alexander Harmsen: 44:39

Absolutely. You can upload a CSV file with different securities information, or, you know, put it in manually. We have some tutorials and yeah, we timed it takes about two minutes to input our portfolio 40 different securities. We try to make that process as easy as possible. I think probably 80% of Our users connect their accounts and 20% are a little bit more skeptical about connecting using services like Plata Yodlee, and instead choose to import their securities. No problem with that at all these update over time, you know, we track markets and so

Axel Meierhoefer: 45:17

yeah, that's good. That's very good. I'm sure that 20% Alright 1984. Alex, and it was absolutely a pleasure, thank you for being on the show and sharing your wisdom and your ability to describe how you actually make sense of all this enormous amount of data. So thank you for being here.

Alexander Harmsen: 45:36

Great to meet you. Thank you so much for having me on the show. Until next time.

Axel Meierhoefer: 45:42

Thanks for listening. And I hope you enjoyed today's episode of the The IDEAL Investor Show more info and the links we mentioned during the show in the show notes or you can go to our website at idea where to go a.com and sign up for the Apple podcast link. And if you'd like to talk to me sign up for a strategy call. Hopefully you want to share what you learned with your network and bring more people in we're really eager to hear your comments and until next time, be well stay safe and chop