TYLER TUCCI | From SynerAI
TYLER TUCCI | From SynerAI
Researching individual stocks can take a lot of time. In one particular case there were 8,599 news articles since January 1 this year that would have taken 860 hours to read. Add to that some historical data and you would have needed 1.32 years of reading to catch up and see the subtle shift in the way that Netflix was spoken before it's stock price dropped. How much information do you need and how can you put it at your fingertips?
Tyler Tucci is the head of research at SynerAI which runs Foliko an AI-powered platform for retail investors. Foliko's health factor framework evaluates each company as an investment opportunity across six dimensions to pinpoint the specific drivers of a company's growth or decline. Investment decisions have the clarity of a personal advisor with the data support of a team of analysts.
In our discussion Tyler mentioned a couple of his favourite books by Edward Thorp - A Man for all Markets and Fortune's Formula. Edward O. Thorp is an American mathematics professor, hedge fund manager, and blackjack player. To beat roulette, he and the father of information theory, Claude Shannon, invented the first wearable computer. Along with innovative applications of probability theory, Thorp is also the New York Times bestselling author of Beat the Dealer, the first book to mathematically prove that the house advantage in blackjack could be overcome by card-counting. He also pioneered the use of quantitative investment techniques in the financial markets. He lives in Newport Beach, California.
"I think that for a new investor, having a repeatable process is probably one of the most important things that I could recommend because people have had, you know, good 1, 3, 6 months, you know, they got hot, they had great returns, but it wasn't based on, on a process or a framework or anything. They just happened to randomly place the right sequence of trades and they let you keep the money for that. So there's nothing wrong with that. But on a longer term basis, you really need to think about, okay, what is this money need to do for me? You know, am I trying to pay for a new car, short term? Am I trying to pay for somebodies education, longer term? What liquidity needs am I going to have? Can I lock this money up for a while, but having a process and thinking about what you need your money to do is just so imperative."
"There are some people who have pushed back on using ESG as an indicator. I was one of those people, but what Travis found and I think is very interesting is that our ESG measure, I think of it more as a measure of company goodwill, it's able to see basically if a company is very profitable, they will have more money for projects like ESG"
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Stocks for beginners.
I think the last two years have helped people develop some pretty nasty habits in terms of return expectations. I mean, there's been times in history where stocks have just gone sideways or, you know, it's just been, you know, a small return or just the dividend. So making money while investing is certainly not a right. And I'm very concerned about what happens if we just kind of have this soupy macro backdrop, and nobody wants to really help risk assets, the Fed hasn't tightened enough to really hurt them. And we just kind of go sideways.
Hi, and welcome back to Stocks for Beginners I'm Phil Muscatello - researching individual stocks can take a lot of time. In one particular case, there were 8,599 news articles since January one, this year, that would have taken 860 hours to read. Add to that some historical data. And you would have needed 1.32 years of rating to catch up and see the subtle shift in the way that Netflix was spoken of. And that was prior to its fall. How much information do you need and how can you put it at your fingertips? Hello Tyler.
Tyler (1m 8s):
Hey Phil. Thank you for having me.
Phil (1m 9s):
Tyler Tucci is the head of research at Syner AI, do you do a lot of reading.
Tyler (1m 13s):
I certainly don't do as much reading as our AI does. And I found over the last 10 years of my career, the reading list has changed when you come in, you read, you know, your Liar's Pokers your Fortune's Formulas, the, the starter kit, and you may not understand everything, but I think that is a very important place to start. And then you go about your career for about five years and come back to those books and go, oh, this makes a whole lot more sense. Now these days I find myself reading more, more technically based things, as opposed to, you know, I would call it fun finance, reading your Market Wizards or things like that. I just did pick up A Man for all Markets by Edward Thorpe. I loved Fortune's Formula.
Tyler (1m 54s):
It's one of my favorite favorite books. I highly recommend it to all your listeners,
Phil (1m 59s):
Which one's that? Just repeat the name again so we can, I'll put a link to it in the show notes and blog posts,
Tyler (2m 4s):
Fortunes Formula. I think it's a great read. It's it's very, very interesting
Phil (2m 8s):
Was that what's the key takeaway from it.
Tyler (2m 10s):
It's how to think about and how Edward Thorpe thought about position sizing and risk-taking and you know, blackjack and how it basically was able to go from counting cards, playing blackjack to trading stocks. And basically it is the betting system and the risk management side of investing, which I actually think is more important than, you know, going in there and executing the trades.
Phil (2m 33s):
Yep. Okay, fantastic. So tell us about your background in finance, where, how did you get here? And I believe you started in sales and trading at the Royal Bank of Scotland.
Tyler (2m 42s):
I did, I came through the sales and trading program. So it was, you know, a great opportunity to see all different sides of sales and trading, different asset classes, different roles within those asset classes. By the time I left, I was an interest rate strategist focusing on the end of the interest rate curve among other things. My area of expertise has always kind of been like the Fed and how to trade the front end, basically Euro dollars through five years based on, you know, what they're doing from there. I moved to the real money buy side. I was the trader of basically G10 government paper. And then I also did some FX related cash management stuff.
Tyler (3m 24s):
After that, I launched my own fund in 2019 in 2018. Got it. It's already four years on. And I learned so much, unfortunately my performance was not good enough. I was self-funding. And you know, when people say I launched a fund, I, you know, it's an actual fund three C one, rather. So it was obviously quite expensive to maintain. And the operational costs were pretty intense and the performance was just not good enough. And I had a positive year, but it has to be quite positive, especially when, you know, you're a new manager you're trying to raise money. The easier allocations are for people with track records and people who have, you know, some institutional support.
Tyler (4m 8s):
So it was the greatest learning experience of my life. But unfortunately that came to an end, not long after though, I was introduced through a mutual friend to Syner AI. I, and we've had probably two years of, of really amazing conversations. I think the guys over there are more from a tech background than a markets background. And I, you know, I'm very much from a markets background. So I think the merge of the two has been very, very interesting, Travis and I, our CEO do a show every morning. And I think the value we add is we attack it from both sides. We attack it from here's what a data scientist sees and here's what a markets person sees.
Tyler (4m 50s):
And, you know, let's see if we can test and find some agreement in our thesis. And I think that's exciting as opposed to just kind of making something up, we're really testing whether or not we're delivering alpha or just getting lucky or, you know, what the combination of the two is. So I think that's pretty exciting and that's how I ended up here.
Phil (5m 8s):
I think it's pretty interesting, isn't it? That when you get a multidisciplinary approach to anything, the ideas come from both sides, you know, cause I've seen that it's a big part of FinTech startups is that you've got someone who's really good with data and then someone else who's really good with markets. And I'm sure you're challenged all the time with the kind of stupid questions that they are asking that you wouldn't normally be asking yourself
Tyler (5m 33s):
The other way too. Right. I mean, I, you know, have gotten a, an intermediate statistics degree in the last three months because we do do some pretty rigorous testing and analysis of our model output. So we'll take our model output and then we'll run a whole bunch of different studies on that output. So, you know, we're generating something completely different and adding value, even if you already have access to our data in our platform, we're then taking the next step with it. So I think, yeah, I'm sure him and I both talked past each other at certain points, but the combination of the two I think is what makes this project really, really interesting.
Phil (6m 10s):
The topic of the day is inflation. And you've got a lot of experience with inflation because of inflation's relationship with interest rates, which is where you obviously got a lot of your background and where you've got a lot of experience in it. So what's your view of the Fed's interest rate response? Where are we at the moment
Tyler (6m 26s):
As of today? We're, we're officially at the point where the market fights the fed because the market in general is thinking the Fed's going to go too far and break things and send us into a recession. My thoughts on that is for the last 70 years, every time the fed has had to hike to destroy inflation, they've had to hike through the rate of inflation. I don't actually think that this fed has the stomach, unless inflation comes down meaningfully, unless at the end of the year, we're back at three and a half percent sure they can hike through at three and a half percent inflation rate in a pretty short order. But if we're hanging out around, you know, five, 6% inflation, I'm not sure that they have the guts to really tighten like that to five or 6%, you know, a 5% or 6% funds rate in 2018.
Tyler (7m 11s):
Everything went crashing down well, before we got there. So I'm not very sure that they are going to be able to get it there, especially because the connection between US rates in the global economy, Japan is still at zero rates. They're doing a good job insulating their market for now, but they own most of their market. The ECB, I mean, this is pretty much an unadulterated disaster.
Phil (7m 32s):
That's the European Central Bank, isn't it?
Tyler (7m 34s):
Yes. The European Central Bank. I mean, it's not going well. They're trying to fight to get back to a zero policy rate, not even a positive one, just zero and the Euro is headed straight lower, and that's largely a function of the stakeholders of monetary and fiscal policy in Europe are not the same people. Right. So it's very hard to get an agreement when what's good for one is not necessarily good for all
Phil (7m 58s):
Because the different, sorry, I just wanted to break this down a bit. The monetary and the fiscal policy. One is conducted by the central bank and the other is the government and how much money that they're spending. Is that the case?
Tyler (8m 9s):
Yeah. So the ECB controls monetary policy for all of Europe while each country controls their fiscal policy. So generally in the US or countries where it's one central bank for all the policies are generally somewhat in agreement or at least working towards the same goal and working in tandem. In this case, you can have, you know, it's almost like random the juxtaposition between the two and in this case, widening spreads. And by that, I mean the German Ten Year Bund is the, I guess the, the standard bearer or the comp for European interest rates and the spread between that and peripheral debt, IE, Italy, and Portugal is something that is looked at as a way of, of looking at, you know, funding stress.
Tyler (8m 52s):
And the minute the ECB opened their mouth about hiking rates, spreads started to widen out. And they've already had to backstop that they've announced a plan to buy basically periphery debt with the proceeds of the French and German debt. So they already have to act to close these spreads. They're not even at the starting block yet. They've signed up for the race. That's going to be very hard for the fed to meaningfully tighten policy way through them while that's going on. And the other thing is, as the fed tightens policy interest on the debt increases significantly, I mean, from one to 3% is a pretty big move. At some point in the future, you'll have to start issuing debt to service the debt.
Tyler (9m 33s):
And that's when things start to get a little sketchy. And finally, I guess mortgages mortgage rates have almost doubled in the last six months and the way the US property market works. I really don't think that significantly higher mortgage rates will be stood for by the authorities. There's a certain level at which I think they'll decide that it's too painful and have to stop. So those are a whole bunch of headwinds to, you know, a significantly higher funds rate. Personally, I kind of think this ends in stagflationary territory. If I had to put some numbers on it, say CPIs five, the funds rates at four and the next four to five years of for returns and equities and bonds are flat, or you're just getting the carry on the bonds.
Tyler (10m 18s):
But as we were talking before we started, I think the last two years have helped people develop some pretty nasty habits in terms of return expectations. I mean, there's been times in history where stocks have just gone sideways or, you know, it's just been, you know, a small return or just the dividend. So making money while investing is certainly not a right. And I'm, I'm very concerned about, you know, what happens if we just kind of have this soupy macro backdrop and nobody wants to really help risk assets, the fed hasn't hiked high enough to really hurt them. And we just kind of go sideways.
Phil (10m 53s):
Yep. Always like looking historically at what's happened in the past and in the last inflationary period, which was really in the early eighties in the US and I think it was Paul Volcker, who was the Jerome Powell of the day actually had the stomach to put up interest rates to a high enough level where it would crush inflation. But of course it brought along a big, big recession, but I don't think these days anyone has got the stomach for that kind of action anymore.
Tyler (11m 22s):
And the other thing too, is that when Volcker got to work, things were already pretty bad. The jobless rate was already heading significantly higher. In this instance, the fed slammed the brakes on a smoking hot real estate market and equity market at all time highs, you know, a pretty healthy economy that they're destroying. They're not making bad, worse, they're making good bad, which is, you know, where I kind of base my expectations for flattish forward returns on. Yup. That makes
Phil (11m 52s):
Sense. Yeah. But we were talking off air that really what you're looking at. I mean, what's, your crystal ball is saying to you that we're looking at three or four years of pretty anaemic equity returns.
Tyler (12m 5s):
Yeah. So Syner AI data definitely shows some really significant downside over the next three months. We have Fang trying to find a bottom in October, but that doesn't necessarily mean we see the big snapback that we've traditionally got. COVID being one of those had you bought the, you know, you would have had a great trade quickly. It took 18 years. I think it was for Intel to reach its 2000 high. So, you know, it could take a long time to get back to those levels. And that's my thought is it's not necessarily that for the next three years, I don't think stocks are going to get drilled into line straight lower, but I just think the speed at which they will recover is going to be a bit damper than we're used to
Phil (12m 50s):
Take the long-term view. Okay. So let's talk about Syner AI now. What's really interesting about this is like I was saying at the beginning of the podcast, is that Syner AI is crunching a lot of numbers and looking at a lot of data. Now I talked about one particular aspect of it, which is looking at articles. So that's obviously something to do with sentiment. So there are six health factors that Syner AI is looking at in the data and the numbers that are being crunched here. And I think it's worthwhile looking at these because it's also a way that beginners can start to think about how to look at companies, look at stocks and the kind of factors that affect the price.
Phil (13m 35s):
So over to you, Tyler.
Tyler (13m 37s):
So I'll explain kind of our process broadly and then drill down into each factor. So Syner AI and our platform is Foliko, which is the platform that users can sign up and get our data as well. So Foliko has the six health factors and the processes is basically the following. So our AI reads pretty much anything written on the searchable internet. Plus a few things that, you know, are less searchable.
Phil (14m 6s):
Is that like what journalists are writing about a particular company, for example, across the whole range of media outlets.
Tyler (14m 14s):
Yeah. It can be quantitative or qualitative. So if a CEO comes out and revises a number specifically gives a quantitative measure, our AI will adjust to that quantitative adjustment, but yes, the AI also takes into account who wrote it, how it contributes across our six health factors. And then we standardize all of the articles to basically give a score between negative one and one which shows the overall view of that given health factor. So we have, we have six different health factors. Like I mentioned, if you are a fan of MBA style studies, Porter's five forces, things like this, these principles you'll see some familiar equities in here.
Tyler (15m 2s):
The first one and my favorite one personally, is earnings power. So the health factors called earnings power, but it really is comprised of anything that has to do anywhere that the AI read, having to do with costs, revenue, profitability, new products, partnerships, and returning capital to shareholders. I've also found personally that if you look at where this sits on earnings week versus the price, it actually is generated some interesting observations about outsize moves. So for example, our earnings factor completely turned down months before the last Netflix earnings disaster.
Tyler (15m 44s):
For example, on the bullish side, actually, we were able to catch some upside in KC, which is king soft. That's a Chinese technology company, because on the other hand, since March the earnings power metric had just been going straight up into a sideways equity price. So for me, that's exciting because that's something you can systematize and, you know, I've kind of turned it into my own personal earnings betting system. The second probably, you know, I would argue second most important factor we have is competition. And basically the AI is reading for anything that has to do with competitive forces, changes in pricing, schemes, changing customer relationships. If bargaining power is changing, if market leadership's changing, these are all things that the AI will then synthesize, standardize and then score.
Tyler (16m 33s):
And then there's a couple more here. I won't give everyone everyone, cause I encourage everyone to go check out the website or check out Travis and I show in the morning, I have a look at the details online. Yes. Yeah. But the last one I would give is management. And I think this one's interesting because it's picked up things like our management score turned up for Twitter right before Elon Musk made the buyout offer for example. And likewise, it actually turned down and Tesla. Similarly, if you looked back at the data, we had a very low management score in Amazon when Jeff Bezos stepped down and turned over to new management. So I definitely think separately, they're powerful together.
Tyler (17m 14s):
They're also very powerful personally. The one I use the most is earnings power, but you know, we have our, I'll call them our core four, which is earnings, power management, reputation and competition. And the other two are systemic and ESG. There are some people who have pushed back on using ESG as an indicator. I was one of those people, but what Travis found and I think is very interesting is that our ESG measure also picks up. I think of it more as a measure of company Goodwill, it's able to see basically if a company is very profitable, they will have more money for projects like ESG and things like that. So it's not just a measure of, is this company green it's, do they have basically free cash to invest in, you know, green initiatives, which has actually been a somewhat positive, positive signal?
Phil (18m 5s):
Well, that's a pretty interesting indicator that a company has the freedom to invest in say some green projects or environmental social projects, which means that they do have the money sitting there.
Tyler (18m 18s):
Yeah. And I thought that was very interesting because as someone who kind of dismissed ESG as a, you know, a market timing factor, let's say that it was descriptive of something else was very interesting. And that's why I share it. So before you uncheck the ESG box, just please keep that in mind. And then the last one is we measure for systemic, which is just like the macro, our overall systemic indicator is pretty poor at the moment. It's pretty negative because we do gross this up and look at it on a market level as well. And yeah, as I was telling you before, you know, as someone who has made a lot of their money on the long side, being bullish, expecting fed policy to shift dovishly when things get a little hairy, our forecasts are really nasty.
Tyler (19m 0s):
You know, we still have significant downside in semi's called. We still think that BABA is going back into double digits. For example, we really do see a good bit of pain here. I think we have Fang in general, bottom in October, but we have some pretty nasty downside targets. In the meantime, before we're ready to make the bullish call. If I had to make a bullish, if I had to use Foliko make a bullish call, the one we're probably most interested here is in biotech,
Phil (19m 29s):
Having this kind of data at your fingertips, I think is so important because for people who are new to investing, they get overwhelmed with emotions because they're seeing the movement of their actual money on a screen going up and down every day. And they're watching CNBC and there's saying so many commentators and talking heads and you're deluged with this kind of information. But what it sounds like you're trying to do is to systemize the approach so that you can take the emotions out of it and just look at the raw data and make investment decisions on that data. But it's really important for investors to understand this, isn't it?
Tyler (20m 4s):
Yes. I think that for a new investor, having a repeatable process is probably one of the most important things that I could recommend because people have had, you know, good 1, 3, 6 months, you know, they got hot, they had great returns, but it wasn't based on, on a process or a framework or anything. They just happened to randomly place the right sequence of trades and they let you keep the money for that. So there's nothing wrong with that. But on a longer term basis, you really need to think about, okay, what is this money need to do for me? You know, am I trying to pay for a new car, short term? Am I trying to pay for somebodies education, longer term? What liquidity needs am I going to have? Can I lock this money up for a while, but having a process and thinking about what you need your money to do is just so imperative.
Tyler (20m 52s):
If you're going to be managing your own money,
Phil (20m 54s):
There's many people that would be listening who are going to be sitting on a lot of paper losses, right at the moment. And your forecast is bleak.. Do you think it's better for people to, I mean, yeah, we're not giving any personal advice here. We can't give personal advice, but so many times people have lost money by selling when markets have turned bad. Do you feel like a longer term approach might be a better way of viewing things despite people's own liquidity issues, as you pointed out?
Tyler (21m 21s):
I think it's important to know whether what you have as an investment or a trade. And you need to know that beforehand. I think there's a lot of people who I'm long for a trade and then it turns into an investment. I mean, how many, you know, I'll raise my own hand on that one, right? Like, yeah. Okay. I'll double down here instead of stopping out. So I think it's important to before you enter, you need to think about, okay, do I think this is a good five-year idea? Is it a good five minute idea? And you need to stick to whatever that is. So if your five minute ideas going wrong, yeah. You're probably going to have to lock in the loss on that. If you bought something because you think, for example, the next 10 years in biotech looks great.
Tyler (22m 3s):
If you bought a biotech company and you're down 15% on it, I don't really think you've let the story play out. So if you bought it for five years, then hold it and that's fine. And that's the intention you had. So as long as all the positions you have are intentional, I think that's, that's, what's important. So if you have dead weight in your portfolio and you don't think it's coming back, yeah. I would recommend thinking about decreasing the size of that position and allocating to something else, a better idea you have, but it's important to know what you own and how long you own it for and why you own it.
Phil (22m 35s):
Okay. Well, let's get to biotech that you referred to a moment ago. And then that's one of the things I like. I got a little bit of a shock when he said biotech, because I know how long the stories in biotech take to play out. If you don't understand biotech, you don't understand the amount of time that's involved, the regulatory requirements, the testing, and we're talking, you know, up to a decade and more for a story to play out. So tell us about biotech and why you see it as an area that might possibly be something to be bullish about.
Tyler (23m 7s):
I mean, it's not really up to me. And that's the interesting thing is it's Foliko data. That's just so unbelievably bullish and we are more bullish on certain components of the XBI. The XBI is the US-based biotech, ETF. Every holding in there is like 1% or less. So you do get a broad diversification, but that's sometimes a problem. Sometimes if you have a view on a single stock, you want to be anti diversified. You want to be just long or short that one, if your conviction is high. So it's really, you know, a function of Foliko data. That's just flagged a outsize number of companies as for potential upsides. And if I could be more specific without giving tickers, it is not the acquirers of the technology.
Tyler (23m 51s):
It is the innovators. So it's the smaller companies that we think we've had a decent bear market in biotech. The balance sheets are in decent shape. So we should see there's enough fresh cash there for some acquisitions. So I think that's what Foliko is picking up and has in mind. But you know, like we said, at the outset that AI reads a lot more than I do. So I leave the big decisions to add on stuff like this.
Phil (24m 12s):
Well, let's have a talk about Foliko and Syner AI a new user coming to have a look at the website, what are they presented with and what is your offering?
Tyler (24m 21s):
So when they log in, they'll see ranked by timeframe, some of our top ideas by highest upside. That doesn't mean highest conviction, but it's an interesting starting point because personally I'll go through and I'll look and see if there's a theme. For example, on, on fed day, we had that large squeeze higher. The list of buyers were all stocks that I would consider about as speculative as you can get, you know, squeeze candidates, things that short covering rally, as opposed to real buying. So you can start to form a view once you step back. And that's my favorite way to use it is by looking at all the data. Sure. You can go in and point and click on some of our best ideas and do them as one-offs.
Tyler (25m 5s):
But when you're taking a mathematical approach, like we are in a, you know, a stats based approach, I think you want as many cracks as possible. So I like using more data, not less data from the platform. You'll be able to look at any ticker, our six health scores, the top and bottom five contributors to the health scores. So basically what the company is doing, right, what they're doing wrong. We also have time-based price forecasts. So, you know, you can look at anywhere between one and 12 weeks and we'll tell you for, you know, I think it's the 1500 stocks on our coverage list. We'll tell you where we think those are going to be. And we've definitely seen some success in, in some outsized calls.
Tyler (25m 47s):
You know, I think that's pretty exciting. And really our platform is for everybody. If you are a day trader, there are ways to use it. If you are, somebody's registered investment advisor and you need to kind of put a larger quantitative view together on your portfolio, you can look at all the health factors and kind of get a feel for that. If you like to trade earnings, like I do, we have something for that. It can be a platform that is used for trading. It can be a platform that's used for research. That's why we're excited about it. I think it's got institutional applications. We've talked to some hedge funds about using our data. It's got retail applications. We want people to sign up on Foliko.
Tyler (26m 28s):
We want them to use the data and we want them to do their own studies on our data like we do. And I think that's the real value is when you take our output and then you can start using it in your investment framework, you know, you don't have to just blindly blindly follow Foliko completely. It's just another great tool to have. So when you're looking at, you know, I look at positioning, I look at pure sentiment, I would say. And then, you know, I kind of look at this, I'm a technical analyst as well. I kind of think about these as, as technical indicators almost, you know, it's an oscillator between minus one and one, and based on how those move against price, you actually can generate tradable signals.
Tyler (27m 12s):
So, I mean, I think, you know, we've almost given too much because there's so much on the platform. You can go by single name. You can go by sector, you can back it up and look at, you know, how we view the whole market. So that's why I work with these guys. And I'm here to tell you guys about it is I think this is different than your general. Here's a one factor of sentiment, indicator writer, you know, something like that. And those definitely have their place, but this is a bit different. And our CEO, he's a Quant, right? And not just that he built this AI, not for markets. He just realized that it could trade markets as well. I mean, there's lots of applications for this. It is a tech product that just happens to have markets applications.
Tyler (27m 54s):
And that's what I think is interesting. I think there's so many instances of people want to beat the market. So they just goal seek something. This is not goal seek. This is AI that learns, it could be doing a bunch of different things, but we just happen to realize that it's a pretty good trader too, on top of everything else.
Phil (28m 12s):
Okay. So here's a question without notice then when you're talking to the tech guys, what's the question say that they've asked you, that's made you think back to your own first principles as an investment analyst,
Tyler (28m 25s):
Something that's made me challenge my own beliefs.
Phil (28m 27s):
Yeah. I'd say that. Yeah. That's a good way of looking at it.
Tyler (28m 30s):
I mean, first and foremost, it's nice that when he runs his analysis, it says what it says. So the biggest thing for me is this helps eliminate the human bias and my human bias. You know, the biggest takeaway has been, there's been so many instances where we're testing our output. So I'll trade it in each trade account, like pretty small size, just because we have a whole bunch of other grand plans for this stuff. We have some other financial services products. We think we can, you know, our data lens too. So we're very much actively testing this. We want to trade this. We're not just trying to hand this to somebody else and say, here you go, you trade it. We think we can trade it.
Tyler (29m 10s):
We think we can make money. We're just sharing it. So I think that's the biggest thing is just to have faith in the model. I came into this just like anybody else would excited, but skeptical our CEO, you know, about as good as it gets in terms of as a data scientist. So that's what really excited me is that, you know, it's not just a bunch of macro tourists sitting around and talking about whatever it is. We have real data, we do real statistical analysis. And like I just told you with biotech, like that thing overrules me. Right. And that's the thing I've learned is, you know, I've started to really trust this thing and I've seen some, some crazy red degree in moves when I thought things were left for dead.
Tyler (29m 51s):
Like all kinds of things that have really made me believe in this.
Phil (29m 54s):
That's great. And so we'll put all the links in the episode notes in the blog post, and we'll also put some links to a couple of your videos as well, because I've watched a couple of the videos as well. They're great viewing and a lot to be learned from there. So Tyler Tucci, thank you very much for joining me today.
Tyler (30m 10s):
Thank you for having me.
Phil (30m 12s):
If you found this podcast helpful, please tell a friend, especially if it's someone who needs to start thinking about investing for their future, you'll be helping them and helping me to keep this show on the road.
Chloe (30m 22s):
Stocks for beginners is for information and educational purposes, only it isn't financial advice and you shouldn't buy or sell any investments based on what you've heard here. Any opinion or commentary is the view of the speaker only not stocks for beginners. This podcast doesn't replace professional advice regarding your personal financial needs circumstances or current situation.
Stocks for Beginners is for information and educational purposes only. It isn’t financial advice, and you shouldn’t buy or sell any investments based on what you’ve heard here. Any opinion or commentary is the view of the speaker only not Stocks for Beginners. This podcast doesn’t replace professional advice regarding your personal financial needs, circumstances or current situation.