Stig Brodersen  4:50  

This is a fairly new ETF. As you said, it only dates back to October, so a lot of people would say that the art performance that we have seen, that is just due to short volatility or it’s random. This is just some rough numbers but by the time we are recording, the S&P 500 is down by around 7%, and AIEQ 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. I’m curious to hear if this is not just a random occurrence. What’s the narrative behind this performance that we see right now?

Sam Masucci  5:34  

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. So it started to trade every day as an active fund. This fund does trade every day, but pretty much every day.  

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 6000 stocks. It narrows that down to somewhere between 50 and 150 stocks. 

The narrowing process is that it’s looking at many, many billions of bits of information, whether it’s a social media, corporate report, earning economic indicators, and the like. It factors in across the 6000 stocks, and then it ranks them by way of investment opportunities because at the core of the belief is that most managers don’t have the ability to just digest that kind of information on a daily basis.

In addition, they tend to get in late and get out early. So EquBot 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.

Preston Pysh  7:01  

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. However, 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 idea of what we’re talking about here, as far as what’s beneath all of this.

Sam Masucci  7:31  

I can give a cursory view. There’s a great video that we put together with EquBot that’s on the website that goes through what I would call the basics of machine learning, and how the decision process works. 

Though if you think about it, investing in any company and certainly portfolios of 100 or more companies, requires the portfolio managers to look at as much information as possible, that could possibly affect an individual company, its earnings, its management, and the industry, so a great application of deep learning is to be able to funnel and a tremendous amount of this information.

Even though these are US companies, they’re impacted by global events, both within their industries, competitive companies, trade tariffs. Many, many things are impacting these individual names. 

Again, because this machine is able to review the portfolio on a daily basis, digest all this information and do it without any personal biases because that’s the other problem. 

No matter how qualified individuals are in portfolio management, typically they get married names. They tend to, especially in certain market events, tend to drift away from fundamentals. The machine doesn’t do that. 

The machine clearly just looks at the 6000 stocks, ranks them, and picks the top like 100 to 150 stocks. That’s what it’s selling. Even a roomful of analysts would not have the ability to digest this information, properly rank the opportunity and do it without any biases.

Preston Pysh  9:12  

So this information on this deep learning stuff is fascinating. What we’ll do for the audience is we’re going to drop a couple of videos that we find off of YouTube, into our show notes. This way, they can fully understand how complex and how fascinating some of this machine learning is. 

Also, I’m assuming I can embed the video that you were talking about from the EquBot website. We will hopefully be able to drop that into our show notes. If not, we’ll have a link in our show notes to the EquBot page that people can check that out.

Stig Brodersen  9:42  

Sam, I’m curious to hear more about the baseline. Did you go in and backtest, say over 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 ETF?

Sam Masucci  9:58  

What the folks at EquBot did is they first developed the machine learning capabilities and its application to equities. Then they loaded 20 years worth of data back still back into the true back testing. How would this have behaved in various markets with access to this information? What they found was amazing. 

If you think about it 20 years ago, the information that was available is very different from the information that’s available today. So they saw a positive correlation not only with being able to review and benefit from these billions of pieces of information, but also as public access to information has grown, by way of, internet blogs, just publicly available corporate information. They’ve noticed that the machine gets better and it certainly benefits from access to greater information.

Preston Pysh  10:58  

In short, the more data, the better it seems. Even if you would feed it, 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 make better decisions with the more inputs we provide?

Sam Masucci  11:17  

Absolutely, it is taking advantage of data and correlations of different bits of information that you and I would never see as valuable to a particular investment.

Stig Brodersen  11:31  

How did the machine come up with 150 picks? If we look at this in terms of minimizing volatility, which was some of the objectives behind the ETF, typically, you will 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 getting a little less volatility but then perhaps also trade more? You would also pay for more commissions.

Sam Masucci  11:59  

While they are looking to optimize a portfolio that starts with 6000 stocks, I will tell you that when we first launched, we had less stocks in the portfolio. It’s 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.

Preston Pysh  12:28  

 Wow. Yeah, that’s just fascinating. I’m assuming that you’re using the Sharpe ratio to kind of judge that risk reward parameters or does it not even think in those terms?

Sam Masucci  12:39  

Well, I certainly look at Sharpe 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 and while doing that with less vol. So there are a number of specific metrics. 

Preston Pysh  12:56  

One of the things that kind of blew my mind when 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. 

One of the things that I found fascinating early on was it was owning Google, it was owning Amazon. Basically buying up a lot of 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?

Sam Masucci  13:41  

I was most surprised about was the breadth of the industry. So it’s industry agnostic. You look at its largest holding now is Alphabet. It has Texas Instruments in there. It has its second largest folding: Forest City, which is a REIT.Amazon Walmart, it has SCI Investments, a bank service company. 

So it really is not looking at one particular industry, but it’s looking at the individual names. 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 groups. 

What the EquBot 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. However, it sees one name that it believes is an investment opportunity, because maybe it’s down in sympathy with the others, then it will invest in it. I think that’s another advantage of applying deep learning to portfolio management.

Stig Brodersen  14:46  

Sam, I’m curious to hear since this is a long-only fund, meaning that you can only own 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 were on the market high back in 2008, and you should be shorting but you can’t do that, what should the fund be doing given those constraints?

Sam Masucci  15:14  

So the model was down, but the model was down significantly less than the S&P, 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 6000 names.

Preston Pysh  15:32  

I guess the next natural question is, do you guys have something in the works that allows it to hedge or go short or go to cash?

Sam Masucci  15:40  

We’ve been talking to EquBot about a number of ideas. We do run the AI space. If you’re not familiar with it, we launched our second AI fund a few weeks ago. Our partner there is Jim Rogers from Quantum fame. That’s a brand new fund. It also applies artificial intelligence, but in any 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. So that’s been 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.

Preston Pysh  16:28  

It’s interesting. I was talking with Jim probably two or three weeks ago about this, and I had just peppered him with questions. I 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 and you can see Jim Rogers ticker for this new AI ETF that they’ve created. You can see the holdings right there on the website which is awesome. 

My eyes popped out of my head when 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 at the yields. We’re talking 50% of the position and I find that really quite fascinating. I’m kind of curious what you guys were thinking when it came up?

Sam Masucci  17:21  

We did speak to Jim and his team about the fund, so that is a monthly rebalanced index, okay. It’s event driven. So 10 days after our initial launch, was its first rebalance period. Cassandra, which is the name of its artificial intelligence bottle, decided to have a significant reduction in some international weighting, because as a global macro with…a… If you think about it, like an MSCI or a world 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 drawdown, it will go to cash and wait for that draw down and use that as an opportunity to buy back in. That’s certainly what the model did when we rebalanced at the end of June. 

Preston Pysh  18:18  

You said it’s rebalancing every 30 days or 10 days, what did you say there? 

Sam Masucci  18:22  

Every 30 days, everything down to 30 at the end of the month. It just so happened that when we launched that was 10 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 and certain concerns about other international events. 

I don’t want to say a cooling off outside the US, but clearly not growing at the rate the US 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 treasures cash and we’re going to look for an opportunity to buy back in.

Preston Pysh  19:02  

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 rebaselining, after so many days, it kind of seems like you’re handicapping the AI at that point?

Sam Masucci  19:16  

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. However, we’re looking at country exposures, not individual company exposure. SO it seems like the right application for the type of exposure, the global macro exposure we’re getting access to, and it certainly was Jim’s approach.

Preston Pysh  19:43  

I was reading in the prospectus for Jim’s ETF. 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 BIKR. If you know anything about Jim Rogers, you’ll completely understand why it’s called BIKR. 

A little backstory, Jim rode around the entire planet on a motorcycle. He did it again in a car. Anyway, the point of me bringing this up was in the perspective, it was saying that it’s buying other ETFs. 

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 buying 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-to-funds thing?

Sam Masucci  20:31  

It’s a combination, so we’ll buy the other ETF, if it’s cost efficient, from an execution standpoint to do that. If not, then we can replicate the country ETF by buying the underline.

Stig Brodersen  20:45  

Is the AI determining what is most cost effective, or this is really a case by case thing here?

Sam Masucci  20:50  

It’s case by case and that’s really being done by our portfolio management. 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 the best execution strategy.

Stig Brodersen  21:08  

For AIEQ you are using Watson’s but you’re not doing that for Jim’s fund. Why is that?

Sam Masucci  21:16  

Well, again, we’re not AI specialists. So our partners in Jim Rogers and his team for BIKR made the selection on how to best develop the machine learning portfolio model. 

In the case of EquBot, same kind of thing. So they’re a licensee of Watson. They’re under their user license program. That’s the way that model has been developed but the model is continually evolving. 

There are other players now. Google DeepMind is another kind of out of the box participant for machine learning basis to build models on and I know that Chita and his team also employ a very large team in India that is constantly improving upon the model. So 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 EquBot then built the home or the portfolio model on top of that Watson foundation.

Preston Pysh  22:25  

I think anybody listening to this will probably quickly draw a conclusion and let’s just fast forward three years into the future here. 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 owning just a regular ETF? Or why am I paying somebody a couple percent to manage my money anymore?”

So there’s a major shift in mindset and just investing in general. 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, 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?

Sam Masucci  23:16  

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 store manager, you’re expecting that store manager was going to outperform the broad market, and that justified sometimes multiple percent or greater expense ratios and it was more blackbox and didn’t offer the kind of intraday liquidity that ETFs now do. 

So I think that ETFs have really changed the investment landscape. They’re highly liquid They’re highly transparent, and they’re highly tax efficient, and they’re very cost efficient. So it’s difficult whether it’s indexing or active and another wrapper. 

Though I think that applying machine learning to it, I don’t think it will replace indexing. 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 and their non-AI funds again.

Artificial intelligence is already being used across many, many different portfolio managers. In the case of AIEQ, that’s unique in that every day in Boston, in EquBot are sending us a trade case and our portfolio team is executing on those buys and sells. So there really is no human intervention, other than the actual portfolio execution that makes it unique. 

Preston Pysh  24:57  

So Sam on AIEQ, it’s scouring to tons of data points. It’s looking at news feeds, it’s looking at Twitter feeds but that’s almost all English-based text. It’s looking at 10-Ks and Q’s and everything. I’m curious with BIKR. Is it scouring Indian based newspaper or Chinese newspapers? I see one of its biggest holdings is in Brazil right now. I mean, is it able to scour the text of these foreign languages and make some sort of sense from that information? 

Sam Masucci  25:26  

Yes, yes, it is. So there’s a translation component to it. There is the geographic normalities that it needs to pick up as if you had multiple portfolios of managers in each of these jurisdictions. Cassandra is doing so yes, it’s very difficult to replicate that in human form.

Stig Brodersen  25:48  

Sam, you clearly have a lot of knowledge about AI. This fund that we’ve been talking about today, 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 are generally smaller and have more volatility.

Sam Masucci  26:15  

I think that will happen as these markets develop and our whole business is about semantic investing. Semantic ETFs are about narrow concentrated slivers within industry groups. 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 gaming, cyber mobile payments. It is rarely technology.

By their nature, they tend to be smaller portfolios. Portfolios are anywhere from what I would say on the low side, 30 stocks on the high side, maybe 50 stocks and AIEQ is one of the larger. We have socially responsible investing funds. That’s larger. That’s almost 400 stocks in it, but typically we’re looking at very, very narrow exposures. 

I just don’t know the reps or researching opportunity is enough to apply AI to yet. I mean, we’ll get there. Look, we launched in the US the first medical marijuana ETF, MJ. The $400 million fund that we watched at the end of the year. Even that one 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 was about evaluating local laws and regulations, consumer sentiment. I think at some point, there will be an AI application there, but it’s probably premature.

Preston Pysh  27:46  

Well, Sam, we can’t thank you enough for coming on the show. This was just fascinating stuff. If people want to learn more about you or your company, where can they find that out?

Sam Masucci  27:57  

ETFMG.com Find the listing of all of our funds and tickers. We have a dedicated sales and research team that is always happy to speak to anyone interested in learning more about our funds.

Preston Pysh  28:11  

Yeah, we really appreciate it and thanks for talking with us.

Sam Masucci  28:15  

You got it. Thank you.

Stig Brodersen  28:16  

All right, guys. Thank you, 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.

Kais  28:27  

Hello Preston and Stig, 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.

Preston Pysh  28:46  

All right. So you’re talking about the financial ratio here and price to the sales. So for anybody who’s listening to this and they’re not in depth into accounting, sales is your top line number. If I sell a can of coke for $1, my sales number is $1. It’s not when you talk about net income, that’s after you 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 10 cents out of the dollar sale. The 10 cents is your net income, the sales or the revenue, it’s called, is your top line. It’s just the whole number without any expenses subtracted out of it. 

Kais’ 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 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.

Stig Brodersen  29:53  

In terms of using that for your investment approach, the short answer is that you really want to find out companies with the lowest possible price to sales, but it’s a bit more complicated than that. 

The first thing is you need to ask a minimum and compare that  to other stocks within the industry. The reason for this is that if you look at companies like a pharmaceutical business, you will often see gross margins of 90%. You might see an operating margin of 40% for the best companies. 

So the cost structure is just very different in that industry. Then you could compare that to say retail. Walmart, their operating margin is around 4% to 5% and both 4% for Walmart and a 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, “The price to sell should be one or should be three of five,” whatever this you put into your stock screener. It all depends because of course, pharmaceutical companies are priced at a different price sales ratio, like if you would get an operating profit of 4%. Would you rather pay $1 to that company if it was all priced the same? 

Then you have growth companies such as, say, Amazon, and you have long seen investors that base the valuation on the top line and the growth of the top line rather than net profit, partly because there was 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, for Amazon, it was all about doing 10X or 100X, and then they can always mind *inaudible* later.

If you look at even smaller growth companies, that’s before they’re less than for 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. 

In general, in terms of using that in your screener, in terms of looking at the price to sales in your evaluations of the fundamentals for a stock, I don’t think it 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.

Preston Pysh  32:33  

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 will give you access to our intrinsic value course just to say thanks. 

For anybody else out there, if you want to get a question played on our show, go to asktheinvestors.com and you can record your question there and if you get played on the show, you’ll get a free course.

Stig Brodersen  32:58  

Alright guys, that was all that Preston and I had for this week’s episode of The Investor’s Podcast. We will see each other again next week.

Outro  33:05  

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