We Study Billionaires - The Investor’s Podcast Network - TIP200: AI and Deep Learning with ETFs - Guest Sam Masucci from AIEQ (Business Podcast)

Starting point is 00:00:00 You're listening to TIP. One of the biggest ideas in finance for the coming decade is how will artificial intelligence impact stock selection and investing? Two quarters ago, I recommended a new ETF that selects its positions based on deep machine learning, neural networks, and all that other fun stuff. The fund uses IBM Watson artificial intelligence to read as much data as possible. So whether it's a 10K, 10Q, or even a Twitter feed, it orders all of the data and then determines what is useful and what is irrelevant in making its decisions. Then the logic

Starting point is 00:00:35 selects the stocks that have the highest probability for success. Since inception in October of 2017, the fund has outperformed the S&P 500 by nearly double the yield. Although the fund hasn't been around long, we thought it would be a great conversation to talk to one of the founders about the fund. So on today's show, we have Sam Masucci to talk about how the fund works and what we can expect from artificial intelligence in the future. Additionally, Sam has started a new, new artificial intelligence fund with the legendary investor Jim Rogers, and we talk about that as well on today's show. So without further delay, here's our interview with Sam Masucci. You are listening to The Investors Podcast, where we study the financial markets and read the books

Starting point is 00:01:19 that influence self-made billionaires the most. We keep you informed and prepared for the unexpected. All right. So Sam, welcome to the Investors podcast. We are really excited to. have you on the show today. So thanks for taking time out of your busy day to be with us. Absolutely. Thank you for inviting me. So, Sam, I have a confession. On our show, once a quarter, we assemble what's called a mastermind group, and we get together some of the smartest investors we can think of to come on the show, and we feed around different ideas and different stock picks. And two quarters ago, and I only get one pick during our mastermind discussions, and my pick was AIEQ, which is an ETF that your company has put out there on the market. And at the time,

Starting point is 00:02:15 we really didn't have an idea of what the track record or how things were really going to shape up, but the ETF has been doing fantastic. So I want you to tell the audience a little bit about the ETF, AIEQ, and just kind of give us a general overview of what it is you guys are trying to do. Sure. I'm happy to do that. So we launched AIEQ with our partner Equibot back in mid-October of last year. We had the benefit of being the first fully AI-managed ETF to hit the market. So whatever possible, we'd like to have that first mover advantage. And we had looked at a number of different AI ideas prior to deciding to work with Equibot.

Starting point is 00:03:01 What we really liked with that partner is that the principles, particularly the main principal, Tita, had spent more than 20 years in the artificial intelligence department at Intel. And so he really did have a tremendous background when it comes to machine learning and building models that are machine learning oriented in many, many applications. And his focus for a number of years had been on finance and portfolio management. So we were very excited to discover them, partner with them, and then launch the fund. We were really, looking to hit a very broad spot of the investor public. So most people have at least a portion of their money within S&P 500-like instruments. They're looking for that broad U.S. exposure.

Starting point is 00:03:52 So what Chita and his group at Equabot had done is they looked at developing a portfolio that would offer S&P-like exposure with an outperformance after expenses and less volatility. And it's built on the IBM Watson platform and that it would then be launched and learn every day from different activities within the market. And this is an interesting time to be looking at the market because we really are in uncharted territories when it comes to things like interest rate easing that had gone on and a lot of the stimulus. Now we're really seeing what happens when you start to allow both equity and fixed income markets normalized. and we're happy to say that AIEQ has delivered really on that initial concept. But it is outperforming the S&P, it is doing it with lower volatility, and it's been widely accepted by the investor public. This is a fairly new NCEF.

Starting point is 00:04:52 As you said, it only dates back to October. So a lot of people would say that the upperformance that we have seen, that is just due to, you know, pure volatility or, you know, it's random. This is just some rough numbers, but by the time recording, the S&P 500 is down by around 7%, and AIQ is up by around 14%. So it's still significant. What is interesting, though, is right out of the gates, it really stumbled the fund, and then it kind of took off. So I'm curious to hear if this is not just a random occurrence, what's the narrative

Starting point is 00:05:31 behind this art performance that we see right now? It's intuitive, right? I mean, the machine and the portfolio was built on historical experience, but it didn't really start to learn in current markets until it started to trade every day. And as an active fund, this fund does trade. Not every day, but pretty much every day. And so it clearly was learning from the prior day's experience, as well as absorbing a lot of information. The fund looks at and the machine looks at, and the machine, looks at 6,000 stocks. And it narrows that down to somewhere between 50 and 150 stocks. And the narrowing process is that it's looking at many, many millions of bits of information, whether it's social media, corporate reports, earning, economic indicators, and the like, it factors that in across the 6,000 stocks, and then it ranks them by way of investment opportunities. Because at the core, the belief is that most managers don't have the ability to just digest that kind of information on a daily basis. And in addition, they tend to get in late and get out early.

Starting point is 00:06:50 So Equibon is designed to be able to digest millions and millions of bits of information, buy in at the right time, sell at the right time. And that did require some market experience. Yeah. So for people listening to this, Sam, they're probably. thinking, especially if they're not familiar with deep learning, they're probably a little skeptical, but for anybody who has studied deep machine learning with these neural networks and things that are really kind of emerging out of Silicon Valley, can you give them a little bit of a background on basically this technology and how it works a little bit to kind of just give them a better

Starting point is 00:07:27 idea of what we're talking about here as far as what's beneath all of this? I can. I can give a cursory view. There's a great thing. video that we put together with Equibot that's on the website that goes through what I would call the basics of machine learning and how the decision process works. But if you think about it, right, investing in any company and certainly portfolios of a hundred or more companies requires the portfolio manager to look at as much information as possible that could possibly affect an individual company, its earnings, its management, and the industry. And so, a great application of deep learning is to be able to funnel in a tremendous amount of this

Starting point is 00:08:11 information. And even though these are U.S. companies, they're impacted by global events, both within their industries, competitive companies, trade tariffs, many, many things are impacting these individual names. And again, because this machine is able to review the portfolio on a daily basis, digest all this information, do it without any personal biases because it's the other problem, no matter how qualified individuals are at portfolio management, they typically get married to games, and they tend to, especially in certain market events, kind of drift away from fundamentals. And the machine doesn't do that. The machine clearly just looks at the 6,000 stocks, ranks them, picks the top, like I said, 100 to 150 stocks, and that's what it's

Starting point is 00:09:01 owning. And even a room full of analysts would not have the ability to digest this information, properly rank the opportunity, and do it without any biases. So this information on this deep learning stuff is fascinating. So what we'll do for the audience is we're going to drop a couple videos that we find off of YouTube into our show notes so they can fully understand how complex and how fascinating some of this machine learning is. We'll also, I'm assuming I can embed the video that you were talking about from the Equibot website. We'll hopefully be able to drop that into our show notes. And if not, we'll have a link in our show notes to the Equibot page so people can check that out.

Starting point is 00:09:42 Sam, I'm curious to hear more about the baseline. Did you go in and back tests, say, all the last 30 or 50 years in terms of training the machine? Or how did you get about really creating that baseline before you release the, Sure. So what the folks at Equibot did is they first developed the machine learning capabilities and its application to equities, and then they loaded 20 years' worth of data. They backfilled back into the true back testing. How would this have behaved in various markets with access to this information? And what they found was amazing. And if you think about it, 20 years ago, the information that was available is very different than the information that's available today.

Starting point is 00:10:29 So they saw a positive correlation, not only with being able to review and benefit from these millions of pieces of information, but also as public access to information has grown by way of, you know, internet, blogs, just publicly available corporate information, they've noticed that the machine gets better. it certainly benefits from access to greater information. So in short, the more data, the better it seems. So even if you would feed it a stream, you'd feed it a stream of data that to maybe you and me wouldn't seem like it's even relevant or important information, sometimes maybe that really helps augment the understanding that the computer has and it's able to actually

Starting point is 00:11:14 make better decisions with the more inputs we provide. Absolutely. It is taking advantage of data. and correlations of different bits of information that you and I would never be as valuable to a particular investment. Let's take a quick break and hear from today's sponsors. All right. I want you guys to imagine spending three days in Oslo at the height of the summer. You've got long days of daylight, incredible food, floating saunas on the Oslo Fjord,

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Starting point is 00:15:22 sign up for your $1 per month trial today at Shopify.com slash WSB. Go to Shopify.com slash WSB. That's Shopify.com slash WSB. All right. Back to the show. How did the machine come up with 100 to 150 picks? If we look at this in terms of minimizing volatility, which was some of the objectives behind the ETAF, typically you achieve probably 95, 97% of the volatility just by having 15 to 20 stocks. So what was really the discussion behind that in terms of get a little less volatility, but then perhaps also trade more. You would also pay for more

Starting point is 00:16:09 commissions. Well, they are looking to optimize a portfolio that starts with 6,000 stocks. I will tell you that when we first launched, we had less stocks in the portfolio, somewhere between 50 and 75. But as the model has been developed, and as it continues to learn from prior trading experience, we're finding that the optimal holding now is, like I said, 75 and 150 holdings out of this same universe. Wow. Yeah, that's just fascinating. I'm assuming that you're using the Sharp ratio to kind of judge that risk-reward

Starting point is 00:16:46 parameters, or does it not even think in those terms? Well, it's certainly looking at Sharp and other metrics for risk-adjusted returns, but it's most interested in outperformance of the S&P with S&P medium to large cap type returns while doing with less fall. So there are a number of specific metrics. One of the things that kind of blew my mind whenever I was first looking at the picks, because I mean, I was going through the Excel spreadsheet that you can download off the site to see what is it buying and kind of trying to match what I know about the markets, whether something would be a momentum versus a value pick. And one of the things that I found fascinating early on was it was owning Google, it was owning Amazon, it was basically buying up a lot of the stocks that a lot of people talk about. But there were some that weren't on there, call it Tesla. Early on, Apple wasn't on there, but I think it's now a stock that the ETF now owns. I'm kind of curious from your vantage point, was there any surprises that you saw that were either on the list or that weren't on the list and kind of how you interpreted that?

Starting point is 00:17:51 Well, I was most surprised about Pressum was the breadth of the industry. So it's industry agnostic. Yeah. So, you know, you look at its largest holding now is alphabet. It has text and instruments in there. It has its second largest holding is Forest City, which is a REIT. You know, Amazon, Walmart, it has SEI in there. SCI investments is a bank service company.

Starting point is 00:18:15 So it really is not looking at one particular industry, but it's looking at the individual names. And I think that's another advantage that machine learning has because it's very difficult for human portfolio managers to be experts across all industry. What the Equibot model is able to do is just look across the universe and it could have very low buy signals for the majority of names within a particular industry, but it sees one name that it believes is an investment an opportunity because maybe it's down in sympathy with the others and it will invest in it. And I think that's another advantage to applying deep learning to portfolio management. Sam, I'm curious to hear since this is a long only fund, meaning that you can only own

Starting point is 00:19:03 securities, you cannot short them, basically meaning that you're selling them and then you're buying them back later if you think that the market will drop. Say that you have all this information that really signals to you that we're on the market. high back in 2008 and you should be shorting, but you can't do that. What should the fund been doing giving those constraints? So the model was down, but the model was down significantly less than the S&T because while it can't go to cash, it can certainly select for non-correlated assets. And there were assets that were non-correlated within the 6,000 names.

Starting point is 00:19:42 So I guess the next natural question is, do you guys have something in the work? that allows it to hedge or go short or go to cash? You know, we've been talking to Equibot about a number of ideas. We do love the AI space. If you're not familiar with it, we launched our second AI fund a few weeks ago. And our partner there is Jim Rogers from Quantum Fame. That's a brand new fund. It also applies artificial intelligence.

Starting point is 00:20:10 But in that instance, we have worked with Jim Rogers and his team to take what he's learned over a substantial and very successful investment career, looking at global macro opportunities and applying that to their model. And so that's then our next artificial intelligence ETF opportunity, if you will. That one is a little bit different because that can go to cash and in fact has been increasing its cash positions. It's interesting. I was talking with Jim probably two or three weeks ago about this.

Starting point is 00:20:43 and I had just peppered them with questions. And I just find this so fascinating. And when I went on to your website, and just so people know, the website is ETFMG, you can go on there. You can see Jim Rogers ticker for this new AI ETF that they've created. And you can see the holdings right there on the website, which is awesome. And my eyes about popped out of my head whenever I saw that the bot is picking right now, 50%, it's not cash, it's a three-year duration note, but it might as well be cash, that the yields we're talking.

Starting point is 00:21:22 50% of the position is that. And I find that really quite fascinating. I'm kind of curious what you guys were thinking when that was what it came up with. So we did speak to Jim and his team about it. The fund, so that is a monthly rebalanced index. Okay. And it's event-driven. and so 10 days after our initial launch was its first rebalance period, and Cassandra,

Starting point is 00:21:49 which is the name of its artificial intelligence model, decided to have a significant reduction in some international weighting. Because as a global macro, with kind of a, you know, if you think about like an MSCI or a world type of exposure, this is a model that's looking at optimizing global macro exposures across developed countries, but it's clearly an allocation model. So if it gets concerned about a particular market's drawdown, it will go to cash, wait for that drawdown and use it as an opportunity to buy back in. And that's certainly what the model did when we rebalanced at the end of June.

Starting point is 00:22:29 Yeah, you said it's rebalancing every 30 days or 10 days. What did you say there? Every 30 days. Every 30. At the end of the month, it just so happened that when we lost it, that was 10 days. days after the initial launch. But then when you look at kind of what's been going on internationally, impacts of threats of Washington tariffs, certain concerns about other international events. And what I wouldn't see a cooling off outside the U.S., but clearly not growing at the rate the U.S.

Starting point is 00:23:00 is, which is impacting some of these foreign markets. And that's what this model is saying. So we're going to go to cash, which really pretty much treasurer is cash. And we're going to look for an opportunity to buy back in. Fascinating. Now, it seems like there's a lot, like Jim's model can't be as dynamic. Would you agree with that? Because if it's re-baselining after so many days, it kind of seems like you're handicapping the AI at that point. Well, it's certainly not dynamic in that the frequency or the velocity of the trading is going to be less than a daily actively managed fund.

Starting point is 00:23:35 But we're looking at country exposures, not individual company exposure. And so it seems like the right application for the type of exposure, the global macro exposure we're getting access to. And that certainly was Jim's approach. I was reading in the prospectus for Jim's ETF. And just so everyone knows the ticker on this, it's probably the best ticker name I think I've ever seen for an ETF. It's biker. B-I-K-R. And if you know anything about Jim Rogers, you'll completely understand why it's called biker. A little backstory, Jim wrote around the entire planet on a motorcycle, and he did it again in a car. But anyway, the point of me bringing this up was in the prospectus, it was saying that it's buying other ETFs. But then I read a little bit further, and it seemed like you're looking at other ETFs or you're looking at other indexes, and then you're going in and bond. the underlying assets that are inside of that. Is that what's happening? Are you just buying the other ETFs and it's kind of a fund of funds thing? It's a combination. So we'll buy the other

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Starting point is 00:28:17 Back to the show. Is the AI determining what it's most cost effective or is it really a case-by-case thing here? It's case by case and that's really being done by our performance. portfolio management team. So, you know, we're told on an index rebalance what the weightings and holdings are. We then, with our research and trading team, look at that position and come up with a best execution strategy. For AIQ, you're using Watson's, but you're not doing that for Jim's fund. Why was that? Well, again, we're not AI specialists. So our partners in Jim Rogers and his team for Biker, made the selection on how to best develop the machine learning

Starting point is 00:29:01 portfolio model. In the case of Equabot, same kind of thing. So they're a licensee of Watson. They're under their user license program, and that's the way that model has been developed. But the model is continually evolving. I know that, you know, there are other players now. Google DeepMind is another kind of out-of-the-box participant for machine learning basis. to build models on.

Starting point is 00:29:27 And I know that Cheetah and his team also employ a very large team in India that is constantly improving upon the model. So, you know, it's not inconceivable at some point that they migrate from a IBM Watson model to their own. But right now, that has been the base that they have operated off of. I compare it to, say, a house. So Watson serves as the foundation and the folks at Equibat then built the whole. home or the portfolio model on top of that Watson Foundation.

Starting point is 00:29:58 So I think anybody listening to this will probably quickly draw a conclusion. And let's just fast forward three years into the future here. And let's say that AIEQ continues to outperform the market by the margins that it's doing it already. I think there's a lot of people that start saying, hey, what the heck am I doing? own in just a regular ETF or why am I paying somebody a couple percent

Starting point is 00:30:25 to manage my money anymore and there's a major shift in mindset and just investing in general. So I'm kind of curious. I would assume you're very positive on that outlook or you wouldn't be doing the things that you're doing.

Starting point is 00:30:41 But how do you see the future of finance playing out if what you're doing right now turns out to be as successful as I think a lot of people expect it to be? Well, look, I've been in the ETF space since 2004. I've been in the structured financial product space since the late 80s. And there has been a seismic shift in the way that people think about how they want to invest. It used to be that you paid up for a star manager. You were expecting that star manager was going to outperform the broad market. And that justified

Starting point is 00:31:15 the sometimes multiple percent or greater expense ratios. It was more black box and it didn't offer the kind of intraday liquidity that ETS now do. So I think that ETS have really changed the investment landscape. They're highly liquid. They're highly transparent. They're highly tax efficient. And they're very cost efficient. And so it's difficult whether it's indexing or active in another wrapper.

Starting point is 00:31:45 When I think about then applying machine learning to it, I don't think it will replace indexing, and I don't think it will replace all active managers, but it certainly is going to be a standard and a benchmark that investors are going to use to measure the performance of their funds, their non-AI funds again, and artificial intelligence is already being used across many, many different portfolio managers. In the case of AIEQ, that's unique in that every day, Watson and Equibot are sending us a trade tape, and our portfolio team is executing on those buys and sales. So there really is no human intervention other than the actual portfolio execution.

Starting point is 00:32:28 That makes it unique. So Sam, on AIEQ, it's scouring tons of data points. It's looking at news feeds. It's looking at Twitter feeds. But that's almost all English-based text. that's looking at 10Ks and cues and everything. I'm curious with Biker, is it scouring Indian-based newspaper or Chinese newspapers? I see one of its biggest holdings is in Brazil right now.

Starting point is 00:32:52 So, I mean, is it able to scour the text of these foreign languages and make some sort of sense from that information? Yes. Yes, it is. So there's a translation component to it. There is the geographic normality that it needs to pick up, as if you have. multiple portfolios and managers in each of these jurisdictions Cassandra is doing. So yeah, it's very, very, again, it'd be very, very difficult to replicate that in human form. Sam, you clearly have a lot of knowledge about AI.

Starting point is 00:33:25 This fund that we've been talking about today, you know, they're looking at the S&P 500, so these huge companies that it also seems like everyone else is trying to beat. I'm curious to hear if you have thought about applying your knowledge. into specific industries, perhaps small portfolios, and companies that generally smaller and has more volatility? I think that will happen as these markets develop. I mean, our whole business is about thematic investing, and thematic ETFs are about narrow concentrated slivers within industry groups.

Starting point is 00:34:01 So we don't look at all technology. We look at those sectors of technology, we think are the most interesting, fastest growing, whether it be, you know, gaming, cyber, mobile payments, Israeli technology. So by their nature, they tend to be smaller portfolios. Those portfolios are anywhere from what I would say on the low side. 30 stocks on the high side, maybe 50 stocks. AIEQ is one of the larger. We have a socially responsible investing fund.

Starting point is 00:34:31 That's larger. That's almost 400 stocks in it. But typically we're looking at very, very narrow, exposures, and I just don't know the breadth of research and opportunity is enough to apply AI to yet. I mean, we'll get there. Look, we launched in the U.S. The first medical marijuana EF, MJ, the $400 million fund that we launched at the end of the year. Even that's one.

Starting point is 00:34:59 When I think about what's being studied, there aren't that many global players in the space. It's going to grow because regulation continues to improve around it. But there it's about evaluating kind of local laws, regulations, consumer sentiment. I think at some point there will be an AI application there, but it's probably premature. Well, Sam, we can't thank you enough for coming on the show. This is just fascinating stuff. If people want to learn more about you or your company, where can they find that out? ETFMG.com.

Starting point is 00:35:33 Find a listing of all of our funds and pickers. We have a dedicated sales and research team that is always happy to speak to anyone interested in learning more about our funds. Yeah, no, we really appreciate it and thanks for talking with us. You got it. Thank you. All right, guys, thank you to Sam. So at this point of time in the episode, Preston and I would like to respond to a question from the audience. And this question comes from Kais.

Starting point is 00:36:00 Hello, Preston and Stick. This is Kais from Atlanta. I'm a committed listener of the show. My question is about the price to sales ratio. What does it really indicate and how can we use it in our stock valuation? Thank you for all the great work you do and thanks for spreading the knowledge. Thank you very much. All right.

Starting point is 00:36:20 So you're talking financial ratio here, the price to the sales. So for anybody who's listening to us and they're not in depth into accounting, sales is your top line number. So if I sell a can of Coke for $1, my sales number is $1. It's not when you talk net income, that's after you'd subtract out what it costs to pay all the employees, how much it costs for the sugar, how much it costs for the can. And maybe at the end you'd be left with $0.10 out of the dollar sale. The 10 cents is your net income. The sales or the revenue is it's called is your top line.

Starting point is 00:36:57 And it's just the whole number without any expenses subtracted out of it. Kice's question here is saying, if the stock is trading for $10 and the company had $10 in sales, that price to sales ratio would be 1.0. He's saying, what's the significance of this? So now that I've kind of described, you know, what those numbers mean for the audience, I'm going to let Stig answer this question. I want to hear what he has to say. So in terms of using that for your investment approach, the show to answer this,

Starting point is 00:37:32 that you really want to find companies with the lowest possible price to sales. But it's a bit more complicated than that. The first thing is you need to, as a minimum, compare that to other stocks within the industry. And the reason for this is that if you look at companies like in a pharmaceutical business, you will often see gross margins of 90%. And you might see operating margin of 40% for the best companies. So the cost structure is just very different in that industry. And then you can compare that to, say, retail, Walmart.

Starting point is 00:38:08 Their operation marketing is around 45%. And both 4% for Walmart and 40% for some pharmaceutical companies, it's good. But it's all within that industry. So it's not a fixed number you can just put in and say, you know, the price to sell should be one or it should be three or five, whatever it is, you put into your stock screener. It really all depends because, of course, pharmaceutical companies, they are, they're priced at a different price to sales ratio.

Starting point is 00:38:41 Like if you would get an operating profit of 40%, would you rather pay $1 into that company if it was all priced the same? And then you have growth companies such as, say, Amazon, and you had long seen investors that base the valuation on the top line and the growth of the top line rather than the net profit, partly because there were no net profit. So it was really hard to base your valuation based on that. But because there was this assumption in the market that, you know, for Amazon, it was all about doing 10x or 100x, and then they can always monetize later.

Starting point is 00:39:19 And if you look at even smaller growth companies, that's before they're, before they're, before their list and before anything like that, say that it will go for venture capital funding. That's really when you only look at the top line, because the top line works as a proof of concept that people are willing to pay for the product. So the price to sales you would pay on something like this would be very, very different. So in general, in terms of using that in your screener, in terms of looking at the price to sales in your valuations of the fundamentals for a stock, I don't think is a good valuation metric. I don't think any key metrics can stand alone, but price to sales is definitely more like a supporting metric.

Starting point is 00:40:07 Thank you so much for leaving your question here for us. And for doing so, we're going to give you a free access to one of our paid courses on our TIP Academy site. We'll give you access to our intrinsic value course. just to say thanks. And for anybody else out there, if you want to get a question played on our show, go to AsktheInvesters.com and you can record your question there. And if you get it played on the show, you'll get a free course. All right, guys, that was all that Presta and I had for this week's episode of The Investors

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