Lead-Lag Live

Vuk Vukovic on Elite Network Dynamics, Innovative Market Research, and Strategies for Addressing Economic Inequality

• Michael A. Gayed, CFA

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What if political connections, not just systemic issues, are the real drivers of income inequality? Join us for a compelling conversation with political economist Vuk Vukovic, who challenges conventional wisdom with his insights into the intricate web of elite networks. With his expertise and his book "Elite Networks," Vuk unpacks how these connections significantly amplify the wealth gap, often leaving the middle and lower classes behind. He shares compelling evidence from the US and UK, sparking a critical discussion on the need for decentralizing political power and restructuring incentives to address these disparities.

Our episode also takes an intriguing turn into the world of market research and trading. We explore Vuk's journey from academia to establishing a market research and trading fund, utilizing an innovative approach that combines the wisdom of crowds and social media network analysis. Discover how this methodology, originally designed for accurate election predictions, was cleverly adapted for market trading, achieving notable success by addressing polling biases and capturing nuanced market trends. Vuk’s strategy also highlights the significance of influential observers in providing valuable insights for both political and market forecasts.

We delve into the evolution of trading strategies from 2021 to 2023, with a focus on risk management and maintaining consistent accuracy. Through our discussion, we examine the impact of social media and algorithms on market behavior and consider potential expansions into European equities or cryptocurrencies. As we navigate the complexities of market sentiment and investment trends, we reflect on the dynamic forces shaping our financial landscape, from technological influences to economic shifts. Join us for this thought-provoking episode that bridges political economics and market strategies.

The content in this program is for informational purposes only. You should not construe any information or other material as investment, financial, tax, or other advice. The views expressed by the participants are solely their own. A participant may have taken or recommended any investment position discussed, but may close such position or alter its recommendation at any time without notice. Nothing contained in this program constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments in any jurisdiction. Please consult your own investment or financial advisor for advice related to all investment decisions.

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Speaker 1:

What we have seen for almost four years now of this methodology is incredible consistency in terms of precision. So we get. We were never at any given point in time worse than 60% accuracy right, never better than 70%, but also never worse than 60%. So somewhere in between that and that gives us enough of an edge to know that to place option positions and to make money on the market.

Speaker 2:

What types of limits, if any at all, do you formally put in? How do you think about managing risk? And what if it seems like, for whatever reason, the methodology is just not working?

Speaker 1:

Sure. So that's why it's only 2% exposure per week, no more than that. So if you look at it statistically, obviously we can have like 10 weeks in a row where we lose 2%. That's a minus 20% drawdown in that case, right? But there's never a case where the fund goes down like this, right? Because we don't use leverage, we don't use anything, so the risk of blowing up is virtually zero, right?

Speaker 2:

Unless you know the whole system melts down or something, the reasons for why income inequality happens. I would argue that historically there has never been such a thing as a consistent middle class. It doesn't matter if it's a capitalist system or not. Am I wrong on that? Is it just sort of the nature of economics that you're going to have a wider and wider wealth gap the more an existing system, no matter what guardrails you have in place, persists?

Speaker 1:

You know the problem is at the top of the distribution, not necessarily at the middle or the bottom right. So if you look at the top of the distribution, this is the driver, the main driver of the wealth and income gap, and it's being driven by political connections. That's my whole hypothesis, right? And the response to that is you need to lower political power. So my idea is that billionaires affecting economic policy is a bad thing, whether they're called Sorge and helping the Democrats or they're called Elon Musk and helping the Republicans, right?

Speaker 2:

Let's talk about the opportunity set, because this goes into this point about is the US the only alternative? When you're figuring out your universe, what makes you choose the particular securities that you might go into?

Speaker 1:

I really don't think there's a lot of alternatives to US equities, despite all the things that you might find problematic with Trump.

Speaker 2:

This should be a good conversation. I've got a hedge fund manager who also is an author and also who seems to think there might be some alternatives to US equities, maybe, wow. So, with all that said, my name is Michael Guy, a publisher of the Lead Lag Report. Joining me here is Vuk Vukovic. Vuk, first time you and I are doing this, and I don't know if that many people are familiar with your background, so introduce yourself. Who are you, what's your background, what have you done throughout your career, what do you do now and where are you right now?

Speaker 1:

Sure, thank you, michael, and I appreciate the invitation.

Speaker 1:

I'm happy to be here. So my background is mostly academic. So what I did before having a hedge fund is I did my PhD in political econ at Oxford and from that came what you mentioned the authors of my book, elite Networks, which is out by Oxford University Press. Um, it's kind of part of my page. It's a synthesis of my phd, a more popular version, where I talk about um, the connections between corporate and political elites and how this affects income inequality, distributions of income something that's very topical, I would say, in today's world.

Speaker 1:

Um, but you know that, that aside, after or doing even my academic ventures, uh, with my two colleagues, my partners, uh, we kind of developed this new methodology, a scientific methodology of using um polls, using polling to to kind of make better predictions, and we use it to predict elections, uh, back in 2016,. Both Trump and Brexit with really, really incredible accuracy, and brexit with really, really incredible accuracy. So I'm talking like one percent um for both brexit in terms of errors and and every case swing state for, uh, the trump 2016 elections. We do the same thing in 2020 with the biden elections. We had a company by that time in the uk doing market research and elections and then, as of uh, after the biden election, which is also very successful, very accurate um, then we went for uh. We wanted to test the methodology and markets. So in 2021 and 2022, we were testing this live trading it. I was, it took my own money, took $20,000. Brought it up to over 50,000 in this year and a half Testing, live testing. Uh, everything was kind of published on the newsletter, very transparent, very open, and then people kind of reach out to us you guys should have your own fund.

Speaker 1:

So eventually we started our own fund. So it's a very unusual way of venturing in this space. So I, you know, I have no finance background. I have an academic background. I know some macro, I know some economics. That's my background is my PhDs in economics and political economics. So I know a lot of that stuff. But this is completely different, right? This is our novel methodology, developing something completely different, something new of trying to figure out weekly signals and then playing options off those weekly signals. So it's a pure stats approach and we get the signal based on the wisdom of crowds and network analysis of social media bubbles, and then from that we can get these weekly signals and we make the trades. And ever since we started this in 23, live with the fund and now it's been two years now and we already made about over 60% returns gross, about 50% net.

Speaker 2:

I want to explore a little bit more about the polling side because I think it's interesting and I'm going to approach this from a very novice perspective, but it seems to me the issue with all polling is one thing, one thing only, which is bias, right and maybe being ashamed of what you want to answer with. I know that one of the I forget the name of the effect, but in 2016, all the polls suggested Hillary was going to win by a landslide. All the polls suggested Hillary was going to win by a landslide, and a lot of the arguments around why Trump ultimately won was that the polls were not accurate, because people were afraid of saying they wanted to support Trump Exactly. So how do you even model that type of a dynamic, which seems almost like you can't get to the truth? If people are worried about scorn or embarrassment, well, yeah absolutely.

Speaker 1:

So that's the whole novelty behind our approach, the whole innovation, right. So what we looked at. So this is exactly the issue, right, this is called shy trump voters, right? Or people who are not. You know, they don't want to say, they don't want to admit whatever they, whatever they, uh, whoever they're voting for. So what we did is we looked at this, um, and we gave them a combination of two approaches. One is the wisdom of Krauss, where we don't ask you. So we do ask you who you're going to vote for, but we also ask you who do you think is going to win and what do you think other people around you think is going to? Who's going to win? Right, and based on these questions, you actually get a much more precise answer from people, because you, you encourage them to say to talk about others, right, so you talk about others, right? So you know I'm not voting for Trump, but I noticed that everyone around me is voting for Trump, right? So that way, we don't get your opinion.

Speaker 1:

So we don't use the classic statistical significance that you need in polling, right, you need to be statistically significant, sample, which is almost incredibly difficult to get today From online polling, from telephone polling, et cetera, et cetera. It's very difficult. It used to be, you know, possible in the 90s and the 80s, et cetera, et cetera. It's very difficult. It used to be possible in the 90s and the 80s, et cetera, but not anymore. And so we said forget about that, scratch the statistical significance, we don't even need that. We need purposely biased samples of people who are good observers of their environment, right? So I want you to tell me whether or not the people around you who they're going to vote for, and if you have a good idea of that. That makes you a more valuable user to us, right? So the same logic is translated to markets, but kind of to finish this up, to finish up this logic, so that way I can get you know a lot of people not admitting who they actually want to vote for, but they tell me who around them is voting for whom.

Speaker 1:

Now, in order to figure out whether or not these people are indeed good forecasters, we need their social media bubbles, right? We need to see whether or not you know who they hang out with on social media. The way we do this is we collect the data on your connection, so we don't care about people's ID. You know what they do, who they are online. We don't even collect the names of our individuals. We only want the links of who's connected to whom. So if your connections come into the survey and they tell us what they think, and we can kind of create these clusters, these bubbles, right, and from these clusters we can figure out how okay.

Speaker 1:

So these guys are very bubblish, right. So they're very cognitive echo chambers. So, for example, you know they're like a MAGA all the way, right. Or you know, pro-democrats or whatever, or think of it in markets, perma bears, perma bulls, right.

Speaker 1:

So people whose opinions are not that important, we want, or I mean are necessarily biased. We want those people in between, right. So some of your friends in terms of politics, some of them are liberals, some of them are conservatives, some of them are centrist, so you have a higher probability of figuring out who they're going to vote for and what they're going to do, right. So it doesn't necessarily mean that you're going to be right every time, but you have a higher probability. So what we basically do is, with the methodology, we weight every person's opinions based on these probabilities, right. So if you are, for example, someone who is in a more heterogeneous group, you're likely to get a higher weight.

Speaker 1:

Now, with elections, this works great because you have this one point in time and then you know whatever happens with the elections, uh, um, you know, we pull you once and we get that opinion and we see where you are in the bubble. But with markets, even better, because we can see this, we can observe this performance over time, right, so I can see. If so, for example, I all right, you're maybe in a bubble, but your performance keeps improving over time. So that doesn't necessarily mean you know you're a bad predictor, but you're probably going to get. Your score is going to go up and up and we're going to value your opinion more. So the whole point of the methodology is some people's opinions are more valuable than others, right, and we need to figure out who these people are. Not who their identities are, but who they are in terms of how they roll.

Speaker 2:

Right, so you're basically taking out anybody that naturally herds towards the homogeneous group and that, in theory, removes the biases because it's more pure and less influenced by the groupthink dynamic.

Speaker 1:

Absolutely.

Speaker 2:

But how do you do that to markets, I guess is the question.

Speaker 1:

It's the same logic, right? Because with markets it's also. You need very differing opinions. You need very something that's so, for example, something that's polarizing not necessarily polarization in terms of politics, but polarizing in terms of opinions, right? So bulls versus bears, right, left versus right.

Speaker 1:

When we did, like market research, if you do toothpaste, no one cares, right, it's the same thing. Or like shampoos or hair shampoos or stuff. So a regular survey is perfectly, perfectly well for that. But if you want to do things that are very, you know, polar opposites, then you would benefit from having these types of these types of people in there so that you can try to figure out the signal that comes in between them. So with market, the logic is the same. We also ask people. So what do you think the S&P is going to end up by the end of the week? Right? And then we give them a little slider and we ask them about other people. So they give us these opinions, right?

Speaker 1:

This is run through a weekly competition that we have a survey competition. We, through a weekly competition that we have a survey competition, we reward people, we give them money, cash prizes. So nothing fake, real cash, hard cash dollars. This is how we reward them, how we incentivize them to be a part of the game, basically from them. We only take these connections, that we see who they're connected to. We do this through Twitter mostly Twitter or LinkedIn and then that's how we figure out which person has a higher probability of being right or wrong.

Speaker 1:

But then we also, as I said, observe this over time. Right, so if your performance, if you start here but your performance dwindles down, it happens. Right. Then your weight goes down and you matter less and less, so to speak right, and that's the thing right. So usually you're madder. Less and less so to speak, right, and that's the thing right. So usually you're going to have, if you look at the group as a whole, about 100 people are going to be consistently good. No one's going to be good all the time. Right, so you never have the top 10. People are always different, it's never the same people, but in the top 100, it is incredibly consistent. So these are our kind of super forecasters you can think of it that way or best observers.

Speaker 2:

Yeah, I had read Phil Tetlock's book many years ago. I'm vaguely familiar with the concept. Let's talk about a different kind of bias, which is that if you're coming up with a methodology and you happen to be in an environment like the last two years employing that methodology, is it the methodology or is it just the last few years being maybe a bit anomalous? I mean, you can argue a lot of things have been not going according to history. A lot of things are diverging from each other. There are differing opinions on what comes next, but everyone largely agrees, for example, that small caps still suck, while large caps are the only place to be. Does that bias? How do you factor in that cycle dynamic right in the small sample, with a medical?

Speaker 1:

Sure. So just to give you an overview, so we started this in 21, so when there was a strong bull market, like not just a bubble, right. And then we did it also in 22, and these were the years where I made the most money right on my own right, using the same methodology. And then this fund was launched 23, 24. And, if you remember, 23 was also very kind of jumpy. Initially.

Speaker 1:

You had that March situation with the banks and then you had a kind of a shift from there. You had the AI bubble on the other hand. So there was, there was a lot of things happening. It's, you know, thinking as last year, a lot of different incentives, a lot of different issues. You know, thinking as last year, a lot of different um incentives, a lot of different issues, um, but yeah, I mean so. So thus far, I would say that we went through like one or three, one, two or even three different regimes, maybe micro regimes, right, maybe not like big huge macro regimes, um, and and the methodology was consistent. Obviously, I cannot, you know, say how we would have done back in the 2000s, 2010s. I don't, don't have the data for that. We only have the data from 2021 until today.

Speaker 1:

But what we have seen for almost four years now of this methodology is incredible consistency in terms of precision. So we get we were never at any given point in time worse than 60% accuracy right, and never better than 70%, but also never worse than 60%. So somewhere in between that and that gives us enough of an edge to know that to place option positions and to make money on the market right. So when I say option positions, we only risk basically 2% of the portfolio. Uh, for the underlying, that's the value of the underlying that we buy, uh, and if we're wrong, that's all, that's the only thing that we use. That we lose, right, so it expires worthless, we're down to zero and that's it. So we lose a two percent.

Speaker 1:

But if we're right, uh, we can, we can earn multiples of that and the thing is over a given year, right, we don't need to be super right that often we can. Only, you know, most of the time it's going to be either a small gain or a very small loss, something around 1% plus, 1% minus. But then you have like six, seven, eight weeks per year when we could make five, four, five, six, even 7%, which was kind of the best result of last year, 7% in a week. So you know, in the end the methodology is one thing, but the strategy, the trading strategies, is another thing. Right, the methodology stays the same. It always gives us the same precision. It's very consistent. But the methodology changes depending on, among other things, market regimes in terms of how much we want to be exposed to, etc. But it also kind of factors in these differences.

Speaker 2:

So to your point about the strategy implementation on the hedge fund using the methodology. What types of limits, if any at all, do you formally put in? How do you think about managing risk? And what if it seems like, for whatever reason, methodology is just not working?

Speaker 1:

Sure. So that's why it's only 2% exposure per week, no more than that. So, if you can, if you look at it statistically, obviously we can have like 10 weeks in a row where we lose two percent. That's a minus 20 percent drawdown. In that case, right, but there's never a case where the fund goes down like this, right, because we don't use leverage, we don't use anything that so there's the risk of, of blowing up, is virtually zero, right, unless you know the whole system melts down or something. Um, but.

Speaker 1:

But the whole point is, you know, with these very limited risk on a weekly basis, we, you know, we have this um very good idea of of you know how to do risk management and how to basically preserve the funds of our investors.

Speaker 1:

That's why we're basically gradually trending up most of the time, right, um. So I'm saying so two percent is the, the options exposure, and then 90 is basically bonds, or or um, 90 is bonds, short duration t-bills, and eight percent is cash, which is basically a cash buffer. So when I lose the two percent, if I lose it like five times in a row, then I get down to my, my ten percent limit. But even if that happens, we would still probably, you know, go, and it never happened like five times in a row. But even if it does, what we would do is simply lower risk exposure at that point, reduce it from two to 1%, maybe even less, just to see if maybe there's a market regime shift or something happening. Maybe there's something with the strategy, something with the methodology, but that's the case where we react and see what's happening and potentially change the strategy.

Speaker 2:

In your research. Are there likely to be an increased, is there likely to be an increased acceleration in terms of the wisdom of crowds being wrong because the crowd tends to get together faster than ever because of social media, because of algorithms that are getting people to think the same way. I wonder if that sort of point about the homogeneous versus heterogeneous that becomes more and more challenging because of technology, about the homogeneous versus heterogeneous that becomes more and more challenges because of technology and connectivity.

Speaker 1:

Well, yeah, I mean definitely right, but that's the thing that we saw with the elections, perfectly right. So you have this homogeneity. It's immense. Right, the clusters are insane. When we did the Trump-Hillary election, you could see these two very strong clusters where these two groups one was saying you know, trump is killing the election. The other was saying killer is killing the action, without even you know, realizing what's happening on the other side.

Speaker 1:

But that's why we kind of discount those people, right, I mean, they exist, they always exist. Of course they do. But you know, it's the point is to try to figure out the people in between that are better at this. That's why there's the money incentive, right, so we want you to be right, so that you can earn some money. And with this monetary incentive. Before, when we did elections, we didn't have any money, so we just gave them a bragging rights Beat your friends, be better than your friends. It worked to a certain extent, but once you introduce money, monetary incentive, it's much better. So you kind of force people to be more accurate. So that's one thing, right, the monetary incentive, but it's also the questions themselves. So when I ask you what you think who's going to win, that's one type of question. But when I ask you, what do you think other people around you think who's going to win, it's necessarily convoluted, because we want you to spend, at least you know, 30 to 45 seconds, 60 seconds, thinking about this. And what this does is and this is based, rooted in psychological research from kahneman and Gretzky forces you to switch from like system one to system two, thinking right. So you have your system one brain which is, like you know, I'm left or I'm right, and I want my candidate to win. So I think they're going to win. But then I asked you, what about other people? You know, think about, put yourselves in their, put yourselves, put yourself in their shoes and think about what they would say. And then this forces you not only to kind of think about this answer, but go back to the previous answer and correct that.

Speaker 1:

And we saw this because we did this. First. We tested this on students, student populations, because my two colleagues were professors at the time One is from physics, one was a computer scientist and we tested in classes of students. We were asking them to predict their test scores and then later, with elections as well. So we noticed that people, when they did these tests we did, like A-B testing groups, et cetera. You know, control treatment. If you give them the second question, you make them, you force them to become more precise because they go back to the previous answer and correct it right. So that's why we made the method, the Bayesian method, right. So you get, like. You know, the idea is you get your information and then you kind of work on your priors right, you try to change your priors. That's the whole logic.

Speaker 2:

Let's talk about, maybe a higher level view of where we are now.

Speaker 2:

Obviously, everyone loves to talk about Trump and loves to talk about how he's going to make the economy go faster and all this stuff, and it seems to me that there's a lot of very conflicting opinions by the market itself on what trump is going to do, and that's probably because trump is conflicting himself from a lot of perspectives.

Speaker 2:

Um, I'm I'm struck by the fact that, for example, small caps really rallied hard, initially outperformed on the election, but then I have given it back and then some right, because you know the initial reaction was trump is going to be, uh, is a catalyst for small caps, maybe because of deregulation, maybe because of, you know, lower taxes, but at the same time, trump might be inflationary, which makes higher for longer still a thing which works small caps. So when you see these narratives and I understand it doesn't necessarily impact the methodology what do you do with it? You just say to yourself, well, it is, and I understand it doesn't necessarily impact the methodology what do you do with it? Do you just say to yourself, well, it is what it is and we kind of stick to it? I mean, do you place any kind of weighting on. You know being contrarian to a certain narrative, that's out there.

Speaker 1:

No, I mean, I like to read about these things, I like to talk about these things and I certainly have an opinion about them, but we don't let it affect the methodology at all. So the methodology itself is kind of, you know, agnostic towards whatever is happening. Because, from the people's opinions, for example, if I ask you, you're going to think about, like small caps, you're going to think about all this. Our questions are mostly on like the main indices and we trade only S&P 500. So only options in the S&P 500. So, basically, by getting to all these people, I'm embedding all their opinions within the survey itself. So I don't need to add my own right, I don't need to add my own bias, because I have this bias correction mechanism. Plus the methodology, you know, on average is much more precise than I am. I'm 50-50, like everyone, right, maybe even worse than that. So that's, you know the whole point. I can obviously have an opinion and, as you said, right, I fully agree.

Speaker 1:

Right, you have these two diverging forces. On one hand, low taxes, deregulation, all these things are potentially great for SMEs and for small caps small caps specifically, but also the SME sector but then, on the other hand, you know, remember what happened during the inflationary period, what happened with small caps? They were, you know, destroyed, right? So that you have these two things and especially, I think, their reaction. So you had this post-selection bump and then, after the Fed, pivot, basically the whole thing reversed.

Speaker 1:

And, you know, the market, from what I can see, is still struggling to understand this. Now, obviously, these things in general are important to us, right? Um, you know, is the market in an upside move or a downside move? Uh, um, but it's all about, you know, whether our participants are going to figure this out in time. Even if they don't, even if we're late for a week or two, doesn't matter, right?

Speaker 1:

As long as we capture the trend. So, for us, trend is the friend, right? So think of this methodology as kind of a combination of sentiment and momentum, right? So, if there's a strong uptrend or a strong downtrend, we can make a lot of money. But if it's more sideways, if it's more kangarooish, then we're probably going to be flat, right? Not much is going to happen. Or, you know, even worse, if it's very jumpy, then we're going to lose because the, you know, the option premium is going to expire, um, you know, because the vol is either becoming too expensive and then it kills you, um, and you don't make much. But you know you, just, those weeks happen and we, just, you know, uh, uh, survive and and embrace through.

Speaker 2:

So I've I've said many times before that every investment trading strategy has three factors, right? So what's your signal or signals? What's your look back period on the signals? And then what is the opportunity set that you're executing on the signals Last several years? If your expression of risk off was long duration treasuries, you got your ass handed to you. However, if your expression of risk off the last several years was, instead of long duration treasuries, gold or utilities or the dollar, even with the exact same timing of a signal, you probably would have done okay, probably even outperformed, given how strong gold and the dollar has been.

Speaker 2:

I mention that because it seems to me that people often confuse signal with the expression of the trade around the signal. They think a signal is broken, they think a methodology is broken. Maybe what's broken for a moment of time is just what your limited opportunity set, absolutely. So let's talk about the opportunity set, because this goes into into this point about. Is the US the only alternative? When you're figuring out your universe, what makes you choose the particular securities that you might go into?

Speaker 1:

Okay, so for us it was when we started the survey that was back in 2021, I was mostly kind of focused. I was doing it and promoting it on Twitter, on LinkedIn, and mostly focused towards the US crowd and traders on the S&P 500. So I was really really focused on the US specifically for a number of reasons, but the main one was so there's two. Right, the main one was the traders that we have in there. The day traders are typically trading US equities, trading SPY, spy, s&p 500. But it's also liquidative, right, because we you know that, you know trading the s&p 500 is immense liquidity and as we scale up the fund, I have no problem going from, you know, 5 million, 30 million, 200 million to a billion. Right, there's not really an issue of scale. Uh, because you know whether I'm trade, for right now I'm trading about 2,000, 2,500 contracts, whereas you know the average daily liquidity is about a million contracts. So there's no problem for me to kind of get into this and trade, even with the scale up. You know you move to other instruments, you move to SPX cash or you move to futures or whatever. But, yeah, I mean kind of to go back to your question in terms of the alternatives. So the reason we chose this is because that's the group that we have.

Speaker 1:

If we can potentially get people that are following European equities or commodities, or Bitcoin or crypto, right, then that's another potential having you have, let's say, unqualified returns if it happens right. But we would have to have like at least a year or two year or whatever testing period. But at this point, you know, we will probably test it at one level, but yeah, but even if you look at, you know, globally now, going to the macro side of things, I really don't think there's a lot of alternatives to US equities, despite all the things that you might, you know, might find problematic with Trump. There's obviously positive impacts, like we mentioned, tax deregulation, potentially higher earnings, etc. There's also the inflation problem On the other hand, are we going back to higher for longer, etc. But then, on the other hand, if you look at Europe with its political economic crisis, if you look at China's slowing down, if you look at Japan, it's a political economic crisis. If you look at China, it's slowing down. If you look at Japan, it's probably going to be raising rates this year, which is being expected already, probably priced in to a certain extent, but not entirely, even emerging markets, there's not a lot of alternatives to basically the US, and one of the reasons is the super strong market skew. You have seven stocks, the max seven, driving most of the S&P returns. I even saw somewhere that if you like, excluded NVIDIA from the S&P, you would have. S&p performance would be similar to European equities. Right, so it's not big of a difference. But you know, a couple of these AI companies make all the difference.

Speaker 1:

So right now, if you look at the macro cycle, if you look at the macro cycle, we're definitely, I think the bubble, the AI bubble, is brewing. When will it burst? No idea, right, obviously it could last for a decade, it could last a couple of years. But you know, going back to the whole macro regimes of the past few years, if you look at 23, 23, 24, this to me seems very, it looks very close to the 95 environment where you had the Fed raising rates. There was not a crisis immediately, right, right, so there was no, no impact there. But then you know, it brewed. It created another bubble that was, that was kind of brewing until 99, 2000, dot com bubble, right, and then it burst spectacularly.

Speaker 1:

Again, no idea when this is going to happen, but I do expect it to happen. Maybe it's this year, maybe it's in the five years, no idea. But at one point, when that happens, by the way, in terms of the fund, we'll probably make a killing. Not that I'm kind of looking forward to it or want it to happen, it's just I think it's inevitable personally, but no idea when. So at this point, I'm more likely to lean bullish rather than bearish, just because it's. You know, as long as these AI companies keep performing and they are performing pretty well which some of Trump's policies they could provide a boost. But on the other hand, let's look at what happens with inflation.

Speaker 2:

I want to hit on what you mentioned at the start of the conversation the book and the income inequality point that you mentioned at the start of the conversation the book and the income inequality point that you mentioned. I'm always fascinated by the reasons for why income inequality happens. I would argue that historically there has never been such a thing as a consistent middle class. It doesn't matter if it's a capitalist system or not. Am I wrong on that? Is it just sort of the nature of economics that you're going to have a wider and wider or not? Am I wrong on that? Is it just sort of the nature of economics that you're going to have a wider and wider wealth gap the more an existing system, no matter what guardrails you have in place, persists?

Speaker 1:

Well, I mean, it's a difficult question. Let's say yes and no. What I'm looking at specifically? One of my root causes of inequality that I tried to find out was these political connections, because if you look at the old inequality literature, their main findings are inequality, the gap is widening because those at the top, so at the top of the distribution, so 1% of income earners or top 0.1% of income earners tend to earn outsized returns. Now, there's a lot of explanations for this, from the globalization hypothesis, the skills hypothesis, et cetera, et cetera, and there's also tax policies, regulations, et cetera. But what I'm specifically focusing on, I try to look at whether or not you are, as a corporate executive, so people that are in the top 0.1%, people in the members of the corporate boards and I had a data set of about 1 million corporate board members of all publicly listed US companies and UK companies and their connections to politicians.

Speaker 1:

Connections being defined as so I know, quoted myself connections being defined as if you are did you work together or did you go to school together at the same point in time, the same generation, same university, obviously. Or did you work together or are you a member of the same club, country club, golf club, charity organizations, et cetera. This doesn't necessarily mean that you're friends, but you have a higher probability of reaching someone within that network if you need them, right, if you need a favor for them. And when you look at it that way, when you kind of again compare in terms of two groups, then those that are politically connected compared to those that aren't, tend to have a much higher wage premium because of this element of connection, right. That's something that I find for both the US and the UK.

Speaker 1:

So, in that perspective, going back to your question, the middle classes it's not necessarily so. The middle classes, you know you have people moving out of poverty, for example, in China and India and all these countries In Europe and the United States. This happened much in a time period that was different and in the context was worse. We have to go back to history in a long period of time. But you know, the problem is at the top of the distribution, not necessarily at the middle or the bottom right. So if you look at the top of the distribution, this is the driver, the main driver of the wealth and income gap and it's being driven by political connections.

Speaker 1:

That's my whole hypothesis. Right, driven by political connections. That's my whole hypothesis, right? And the response to that is you need to lower political power. So my idea is that billionaires affecting economic policy is a bad thing, whether they're called Soros and helping the Democrats or they're called Elon Musk and helping the Republicans, right, I think neither of these two scenarios are good. What I think the solution is now again, maybe not an ideal, uh. Uh, the ideal place for discussion because it's a long discussion, um is to basically lower political power, decentralize it more and make sure that you know, probably good allocations is not done on a centralized political level, but on more decentralized, uh, let's say, state or local level. Again, it doesn't solve all problems, but it's kind of a start where you think of not hinging so many decisions and so many events on who's going to be in power. But again, maybe it's a little bit utopistic, maybe it's a little bit kind of it's a point of discussion, right, but there's something there.

Speaker 2:

Well, it sounds like you're basically advocating for a structural shift of incentives, as opposed to just a band-aid of, you know, transfer wealth yeah, yeah, exactly, exactly that's right which we should horse paid sim.

Speaker 2:

That's totally logical um thank you um on the as a thought experiment and I wanted to take it back to the methodology and the strategy. It's interesting, right, because what's happened to society in the wealth gap is happening in markets. It's the large get larger, the small are unable to keep up. You're seeing it Whatever's happening in real time, but then it can happen with asset inequality. A market can happen at one. What's needed to at least somewhat correct this Meaning is the simplest answer. Just, you need to have some nasty recession. But can you have a recession when there's so much leverage in the system where the Fed would just let it happen? I mean, it seems to me like we've kind of gone away with the idea of traditional bear markets and recessions now.

Speaker 1:

That's true. You don't have the classical punishment mechanism, right? I mean, you had something in 2022, but obviously it didn't. It didn't complete the job. So, to answer your question specifically, so, yeah, I'm fully aware of you, know these, these let's, let's switch and focus on the max seven. Obviously, a lot of investors are buying their stock. These companies are fantastic. Their performance is incredible, right. On the other hand, you know they're being allowed to do this because they're monopolies. So are we going back to the? Well, not monopolies, but de facto monopolies. Not the euro, but there's de facto meaning that, um, so, so you're always going to have google saying, but we're saying, but we're not dominating the advertising space because you have Facebook, but it's not really the same space.

Speaker 1:

Maybe it is right I mean, the trust regulators certainly think it is so one of the solutions to something like that, because it's also the effect of superstar firms and people that are hired by so-called superstar firms. This is not my research. This is based on research from other economists. They were looking at this effect as something that drives inequality, and this is what you mentioned that you have these top companies that are able to afford and pay their top employees the highest salaries, and even the median salary in that kind of company is much, much higher than to any other company in the country, and that's also a certain driver of inequality. Is any other company in the country is, and that's also a certain driver of inequality. Is it a bad thing? Is it a good thing? That's something that's a different type of philosophical discussion, but, in terms of their corporate power, is definitely something that should be limited, right?

Speaker 1:

So I'm always a proponent of you know, wherever there's a concentration of power, you need to reduce it, right? Whether it's in politics, whether it's in politics, whether it's in the corporate sector, especially in this case, because both of these axes are so powerful. You need to basically reduce this concentration of power, right. One way would be entry trust laws to make them more strict, make them more lenient, make them more kind of applicable to modern times, like you know what basically Teddy Roosevelt did in the beginning of the 20th century also breaking up the trust. Different era, different types of companies, different types of economies of scale, whatever, but it's the same underlying logic, right?

Speaker 1:

You don't want to allow whatever company there is, even if it's like oh, these tech companies are creating so much value for the average consumer. Absolutely true, right, you know, lowering prices for some things, uh, and creating these mass opportunities online on social media, et cetera. But still, the question is, you know, should they be allowed to have that much power? And the answers in an economy, purely utilitarian, economic sense, no um. In terms of, you know, investor betting on these companies, well, absolutely, you know, if, if I'm an owner of the stock of these companies, I would want them to perform as best as they can. If I'm the owner of the S&P 500, I want them to perform as best as they can. But it's a question of what's good for society at this point, I think, allowing concentration of power, even if it's fantastic companies, but it's not something that we should look like. We were talking about this briefly before the show started something that we should look like.

Speaker 2:

David Pérez we were talking about this briefly before the show started. For you, as somebody who's you know out there starting to do these podcasts, you know what's your end game, what's your objective. You got the hedge fund. You've got your more academic work, your book, try to build a name, obviously, and yeah, I think you communicate very well, but like what's sort of your end goal personally, Well, the short term, one is coming to New York with my family, right, I'm moving this year a couple of months.

Speaker 1:

I'm still in Zagreb, in Croatia, where I'm from I'm Croatian, by the way Um, but yeah, the longterm, obviously, developing the fund, um, and and kind of, you know, offering this type of product, that's well, we at least don't think it's out there, right, it's a different type of a fund, it's. You know, I don't have the classical, the standard finance background. I don't have this. I have an academic background and we're doing things a little bit differently. Maybe we're not, you know, in the long run, maybe it doesn't work, but you know, we think we have a pretty decent shot just because of the statistics, and we know we know statistics. But you know the overall goal. I mean, I would like to, you know, in the long run, to promote ideas that I've developed as a scientist.

Speaker 1:

Now, I always think that, you know, when I was before, when I was in academia, you know you write these papers, you write these great, do this great research, write these great books, but no one cares, right. Or even if they do care, they're to promote an agenda. I think you know, if you want to influence things and change things. You need to do it with money, but again, not to the level where you know you want to infiltrate yourselves in these elite networks, which is, I love, my book and my PhD. By doing that, by infiltrating, you have the highest probability of changing things, but not necessarily in a good way. Right, then you're still concentrating power. You're part of the problem, not part of the solution, even if you want to be part of the solution. If you're concentrating power, it's not good. You need to decentralize power.

Speaker 1:

I'm not sure exactly how I would do this, even if I'm very successful with the fund and we make a lot of money, whether it's promoting NGOs, whether it's promoting the ideas itself, but at least being out there making sure that this is part of the debate. Let's think about reducing power. Let's think about decentralizing power. Let's think about alternative solutions and changing incentives rather than, you know, changing economic policies. Policies are fine, they're good, but if they're rooted in incentives, they're much more. If we're rooted in the idea of changing incentives, they're much more. If we're rooted in the idea of changing incentives, they're much more likely to be successful, rather than just, you know, let's implement punishments or let's implement whatever, you know our ideological worldview thinks is the better idea. I hope I gave you a decent response to that question.

Speaker 2:

No, no, no. I'll tell you why I said it, because I've interviewed like 800 different guests. Yeah.

Speaker 1:

I noticed You're very busy.

Speaker 2:

And sometimes it's like everything else, right. There's guests that say things I agree with that I don't agree with, but I'm always respectful, I always try to get interesting nuggets from things, but I never quite know the underlying incentives of the guests. And most of the time, incentives of the guests, I mean, and most of them, the incentive is what it's to do marketing and promote and build awareness for whatever problem, which makes it and there's nothing wrong with that, it's totally, totally logical. Um, you know, when I, when I, when I hear what you've accomplished, it's all very impressive and it seems to me that you, you know, probably have a a bright future in terms of just being out there and communicating and with the kind of a unique way of looking at markets in the world. Um, so I give you credit, right, because I think you're doing the right thing by doing these kind of conversations. So, to that end, uh, but for those who want to track more your thoughts, for your work, or kind of just learn more, uh, where, which point?

Speaker 1:

sure, so my twitter handle is wolf underscore vukovic, so wolf. My name, vuk, actually means wolf, so wolf underscore Vukovic. Or my website, vukvukovicorg. Or my Substack newsletter, which is Oracle's Predictions. This is where we run the survey competition every week, and yeah, I mean, I think that's plenty so far. Linkedin, as well, is a good place to reach me if you want.

Speaker 2:

I enjoyed the conversation, folks. Hopefully those that watched this did as well. I have, I believe, another podcast later today with the good folks at Simplify, so stay tuned for that, and, vuk, I appreciate it and we'll be in touch. Thank you very much, michael. Cheers everybody.

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Michael A. Gayed, CFA

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