Trading Tomorrow - Navigating Trends in Capital Markets
Welcome to the fascinating world of 'Trading Tomorrow - Navigating Trends in Capital Markets,' where finance, cutting-edge technology, and foresight intersect. In each episode, we embark on a journey to unravel the latest trends propelling the finance industry into the future. Join us as we dissect how technological advancements and market trends unite, shaping the strategies that businesses, investors, and financial experts rely on.
From the inner workings of AI and ML to the transformative power of blockchain technology, our host, James Jockle of Numerix, will guide you through captivating conversations with visionaries who are not only observing the future but actively shaping it.
Trading Tomorrow - Navigating Trends in Capital Markets
Navigating AI’s Role in Trading: Insights and Possibilities with iVest+ (Part 2)
In Part 2 of Jim Jockle's conversation with Rance Masheck, CEO of iVest+, and Chris Mercer, the companies COO and Head of Business Development, we continue to discuss how AI is shaping trading strategies and enhancing decision-making. Discover the future of AI in finance, success stories from educators using iVest+’s advanced tools, and the growing impact of vertical AI applications.
Welcome to Trading Tomorrow Navigating Trends in Capital Markets the podcast where we deep dive into technologies reshaping the world of capital markets. I'm your host, jim Jockle, a veteran of the finance industry with a passion for the complexities of financial technologies and market trends. In each episode, we'll explore the cutting-edge trends, tools and strategies driving today's financial landscapes and paving the way for the future. With the finance industry at a pivotal point, influenced by groundbreaking innovations, it's more crucial than ever to understand how these technological advancements interact with market dynamics. Welcome back for part two of our riveting conversation with Rance Meschick, ceo of IvisPlus, and Christopher Mercer, the company's chief operating officer. We're back to continue our conversation on AI's future potential and the current impact, and how companies in the finance industry are harnessing AI and embedding it into their product suites. Make sure you check out last week's episode for part one. So now just a quick question here. Right, you know sometimes I'm making a trade, but I'm not closing out my position, right? To what extent are we, is the AI taking?
Speaker 2:To what extent is the AI taking the portfolio into composition, into consideration, or just strategies and individual trades at this point? So what it's going to do is it's going to look at the overall portfolio and how you're doing, but, I will say, is looking at trades that have been closed, because if I have something that I am actively managing, I might still be working that and it doesn't know what that result's going to be yet. Now we have talked about adding another thing for this to look at just your open positions and maybe come back to it. Hey, you know you might want to look at this stock, or you know that, like there's an earnings report coming up on this company, you've got a lot of exposure. You might want to look at that. So we're looking at bringing that in as well. So that's a next step on it.
Speaker 2:But right now, what we're doing is we're looking at what you've closed out, because once you've closed it, we now have the results of that trade. Enough to say, hey, here's what you're doing really good at, here's what you're not doing good at, what you can improve on. And then there's a secondary phase of this hey, here's what you have opening that's coming up. This is not here today. Here's what you have opening your portfolio that you might want to consider, and it's going to take into consideration on that, by the way, the trend of the stock, what's going on with the chart, any technicals that may come into play, upcoming events like earnings and things that could impact it, and stuff like that to let you know about those things.
:Yeah, it'd be very cool to see correlation within the portfolio. I mean a lot of different cool applications.
Speaker 2:Yeah, we actually have been expanding this one to include a little bit of some information about how you've diversified across different sectors and industry groups. On this case it told me about, I've done a good job at diversifying on my different strategies, but we want to get into how you're balancing your portfolio and how that's playing out. We actually are just introducing economic indicators to the platform and we want to bring in some of what's going on with economic indicators and what that looks like for the market and how you're approaching the market. So we're not done. We have some other ones coming up but these four tools that are already live in the platform today are very powerful pieces to help you get more effective results in your trading I mean, that's the whole idea behind it and simplify your effort as a trader to find the trade and then to see how you're doing with those trades.
:And it's interesting the approach you're taking of let's call it four discrete workers, if you will. That really lays into a broader trend that we're seeing across successful AI companies in terms of attacking things through vertical AI rather than just broad open applications.
Speaker 2:Right, and that's you know. It's something that you know, like Adobe Creative Suite helping you do some edits on a particular photo, right? So you're not having a conversation about trading there. It's limited to that vertical right. So that's what we found, you know. The second tool we put out there was the general chatbot. Even though it was trained on market stuff, we found that people, oh, they thought it was cool, they go play with it, but it wasn't really impacting their trading as much. It's a great thing is a cheat sheet for a strategy, right? So I'm about to do a. I use the bull put spread example earlier. I'm about to do a bull put. Do I really make sure? I want to make sure I understand that strategy properly before I do it? It's a great thing for that. But when it comes to honing in on stocks, you want to find. It's the screener one. You know how am I doing with my trading and what could I work on and improve on. It's that one.
:Got it. So, chris, let me bring you back in here for a minute. And Rand, it's fascinating, amazing. But you know, chris, how are these tools enhancing the user experience for your clients? Are, you know, perhaps some success stories? Outperformance experience for your clients, perhaps some?
Speaker 3:success stories, outperformance. What can you share of the success of that? We work with a lot of educators that white label our platform, and so everybody has a different way of going about trading the markets and a different set of scans that they run and what they're trying to teach their students and stuff like that, and so what we're seeing with this is that it's a lot of flexibility for them to pick what they care about and train their students on it. So we have a lot of different success stories, a lot of different types of success stories, right, because it depends on who the teacher is. Who's teaching his group of people how to trade the markets or do stocks or options. We've recently added futures to the platform as well, so we've got some guys that were really interested in the future stuff, and this will apply over to that as well.
Speaker 3:So there's a lot of uses for all these AI tools, and some of them care more about one piece than the other, some of them. We've got a couple educators. They really work with their students on their trading journals, right, so you get to look at your journal with your student and talk to them about what they're doing right and what they're doing wrong. Well, now the AI piece can actually give them kind of an overview and make it easier for the educator to know what they're doing without having to go through one by one, through all the trades or anything like that. So there's a lot of use cases for everything we've built.
:Wow, and one of the things I've noted through studies is you know a lot of financial institutions, I think for AI projects, it's like one out of 10 seem to fail. You know what challenges have you had with AI?
Speaker 2:So, you know, you hear this thing oh, ai, let's sell. One of the things about this is you know why AI? Now, well, one of the things that we I know you didn't ask that, but it kind of leads to this piece was, you know, we felt like, hey, this is out there. I mean, you look at the adoption of ChatGPT when it first came out. You know now it's kind of slowed a bit now, but when it first came out, it was just massive and we really felt like, if we didn, which, then we came into OK, let's embrace it. And then you find out that, well, you know, here's a here's a good analogy with this. I heard about the.
Speaker 2:There was a attorney in front of the Supreme Court and in his brief he mentioned a case law that was made up by ChatGPT. It didn't actually exist, right, and so, you know, it's a little embarrassing when you're arguing in front of the Supreme Court, right, you know? And, and that's the case. So in this case, it's people's money, right, that's a very serious thing. We want to make sure that we're doing this right. So we went from, oh, let's make this available and start playing with it to OK, wait a minute.
Speaker 2:We need to really train this, and the training comes in in really two forms, and you brought up one of them it's the prompt generation. So what we did was we how do we make it, how do we set up so the user doesn't have to understand all that and we can take what they ask and build it into the prompt that we're behind the scenes putting out to AI. So that was one of the really big challenges to you know, as you've said, you know work with a chat GPT. You had to kind of hone your skills of how to ask the question and we don't want users to have to worry about that. So we built the in-between layer there that gets us to that.
Speaker 2:And then the other one was how to train it. On the data, and, just to let you know, we're sitting on right now almost two terabytes of financial fundamental price data for companies and that is evolving every single day as new fundamentals come out. We have the option, we have all the option prices for every stock every day back in history for like 30 years. So when you're talking about options, things having all that and what the behavior is going to be, so training it was a massive amount of work. That was a big challenge.
:Let me ask a follow-up there, just out of my own curiosity, right? So it's not just the model itself. I mean, you're dealing with that much data, you're dealing with real-time data or you know I'm assuming you're all SaaS-based cloud-native architecture You're going to need to scale in terms of your response time, probably Snowflake around the backend. I mean, can you give us a little peek under the hood?
Speaker 2:Yeah, so we have an auto-scaling system that you know. We are fully cloud-based on Amazon Web Services, just as a point on this some of the best in class in that and they have a lot of tools in combination with what we've done on our side as to when you need to add and scale up and all that. We also have some cool things where it's a very strong market day. I've seen a few of those in the last couple of days when you're seeing a lot of market activity. We just spin up more servers just to have it there, because we know that there's going to be these waves of people coming in and out and AI gets hit pretty heavy on those days where you go. You know the market just took a three-day tank. It's a deer in the headlights. What do I do now? Right, and those kinds of questions coming up. So we make sure the resources are there.
Speaker 2:At the moment, one of the things that we do to help with this is a lot of the data, as I kind of mentioned earlier on some of the fundamental data.
Speaker 2:You know fundamental data doesn't change during the market day. You know announcements happen pre-market or aftermarket, so when that happens, we're processing all of that and then we're feeding that into the AI models overnight and then all we have to feed it. When you ask your question is depending on the question is that stock's data or the other stocks within the industry group's data for the current price data? So it has that. There is a fire hose of data constantly feeding in and out of the AI models for that, and you know one of the things you hear about about the AI needing so much power to run and all that stuff I can tell you that our server bills, since we instituted this, have gone up a bit because of the amount of compute power it takes and the amount of just loading of the data that happens every single day and throughout the day to make sure that it has the latest, up-to-date information so that when you're getting those answers it's effective.
:How do you see AI changing financial technology landscape in the next you know? To say 10 years would be ridiculous. I mean, I don't even imagine five years is challenging, but I'll go with five years. So what do you think five years looks like?
Speaker 2:So one of the things that to answer that I want to start with, I want to get Chris Dewey in here too. So one of the things that to answer that I want to start with, I want to get Chris away on here too. But one of the things that we were talking about earlier about success stories, one of the measures that we saw with this was the frequency of trading. So we have macro data about how much users are using AI and then can kind of do some comparative around some things, and what we found is those that have engaged with AI number one are trading more than those that aren't. It makes sense because through what we talked about here, I can get to the trade quicker. Right, I have this 12,000 stocks. What am I going to do? With a couple of questions, I can hone it in really quick. So they're making decisions quicker, trading more and talking with them. They feel like they're making more informed decisions because of what this is giving them.
Speaker 2:So I think on my side, part of what I think is going to happen in the near term the couple year to maybe five year range is I think you're going to see more verticals of this right. You know, you have Waymo. I live in San Francisco and you know now it's very common to see cars driving around with no driver in the car. It's taken a while to get there, but that's a very specific use case, right and the which is a little. It's still a little unnerving to get in a car without a driver.
:I will say yeah, I'm afraid of park assist in my car. I tried it once and I was freaked out. But please continue.
Speaker 2:Yeah. So I think you're going to see more of that. What happened with, if you look at the internet, where, all of a sudden, you know when it launched and this stuff was happening, you had all these static websites out there, but then it was really internet 2.0 where all this data started coming in. You know trading platforms we're talking about financial markets. You know it wasn't just a brochure about the broker, but you know, now you had trading platforms coming into play and all that.
Speaker 2:I think in AI you're going to see some of that same kind of into specific tool sets for specific outcomes. You know, in the long term is it going to be? You know, actually, if we go back a couple of years here there was a lot on robo-investing. You know Morgan Stanley and you know Fidelity. These companies were investing in the robo-investing thing, which was simple algorithms to put you in particular buckets to do certain things right. So now you know the holy grail there is going to have an auto trading thing all run by AI. You know, back to your park assist. You don't want to hit the car right Back to the courtroom. You don't want to quote a case that doesn't actually exist to the Supreme Court and you don't want to have that happening in your trading. So I think it's going to be a while before it matures enough that that's trustable. So that's going to be, I think, beyond the five-year line. But in the short term you'll see some of these siloed vertical use cases.
Speaker 3:Well, and I think, too, one thing that's you know, we have certain features built into our platform where, if you pull up a stock, it tells you how many days until the next earnings report or how many days until the next dividend or whatever. I mean, obviously we have a calendar, but there's very specific points on the platform that show you, okay, you've got this stock up, it's eight days till earnings or whatever, right, and maybe how many days till these options expire, and that type of stuff. Well, you could easily see combining that with some AI, so that the minute you buy a stock, you get notified when earnings are getting close, right, and we won't have to do anything anymore. You won't even have to go looking. You'll get a pop-up notification or an email or whatever it is, to tell you what's going on in the stock, so that you're not stuck in it through something you weren't expecting, right? So that type of stuff, I think, is going to come fairly quickly as well.
:So just to also complete your example, there Rance around robo-advisors. I think for the listeners it's also important to note firms like Morgan Stanley maybe 18 months to two years before launching, were quoted as saying how they will never have a robo advisors and there was a backlash, as crazy as that was. So you know, you know, when we talk about adoption, you know it's unfortunately it's not. If it's when, right and and everybody can reverse on that. You know.
:So here here and I'm going to stay on robo advisors just for a second because I think it's really interesting the big fear in algorithmic trading for everyone was all the algorithms are going to do the same thing, right? So how are you guaranteeing that the whole market is going to move one way and the AI is going to be saying just move the same way? How is AI going to be one way and the AI is going to be saying that, just move the same way. You know, you know how. How is AI going to be smart enough, right? You know the good trader, instinctually, there's always a winner. There's always someone on the other side of the trade. You know, even you know a couple of days ago, when we had, you know, those issues in the market. You know we're still waiting to see who the winners and losers are. You know the guarantee that your AI is smarter than the other guy's AI and it's going to help you make the right decisions.
Speaker 3:Well, let me say one thing about that real quick. I can tell you this we focus on the software, right? We focus on the platform. We're not a broker, we're broker agnostic. We connect to a lot of different brokerage firms. You can trade and have the same experience, trading through us with any of these brokers that we connect to through their APIs. I would say you're not going to see the average brokerage firm I mean, they're so tied up in their back end technology and their clearing and all that stuff You're not going to see a lot of these features pop up into the mainstream platforms for quite a while. I don't think it's just going to be too complicated for them to do it. So I feel like that's one way that we're going to be able to stay ahead is because all we do is focus on the software side of this.
Speaker 2:And I think that a lot of times, brokers are a lot more risk averse. And I mentioned that TD Ameritrade acquired a company I had back in. It was in like 2007. And one of the things that was very frustrating to me is when it launched, they stripped out several really cool tools because they didn't like that. So one of the things we have in our current platform is a trade finder tool that will give you a timeline, a price range, how to structure an options trade on it, and this has been after years of research and algo building that we did to do that, and this is not AI, this is just the old, old fashioned algorithms and really working through this to do and so we can adopt that type of thing quicker than a broker is, because they have to walk that line very careful when they get into a recommendation and what that means, and we can give you ideas with background on that to help you make that decision and not fall into that recommendation or fiduciary role. In this area, I think the same thing is going to be the case where you're going to have some hesitation from brokers and if they're going to, they're going to have it be kind of watered down and now, behind the scenes, they might have some money managers starting to use some of these tools and that that aren't available to you as an individual, because the money manager can use it as a tool. But still watch out what's going on and making sure that it's the right thing. You know, like, like, don't read the brief that you just quoted to the Supreme Court. Make sure it exists, right. You know, don't just trust it, right. So you know, keep your hand on the wheel when it's auto parking. Make sure that you can interrupt, right. So if you do those things, I think it's helpful, but it's interesting to see.
Speaker 2:I really think that, first of all, not everybody has the same timeline. Not everybody has the same outcome. You know, when I was a new investor, I was willing to take a lot more risk than I am today, as my portfolio has gotten large enough and I've gotten a few more gray hairs, that now capital preservation is a very important part of my portfolio, right? So I think that you're going to see, you know and time horizons and you know what people are looking to do. You know you're three years from your kids going to college. People are looking to do. You know, your three years, from your kids going to college, how you're going to approach things might be different than if you're now an empty nester and you're looking toward retirement in the next, you know, 20 years, right?
Speaker 2:So I think there's a little bit of having it mold to what your specific outcomes are, but this is an interesting area that we have to watch, and one big thing about this is you know, tesla's had a few cars have an accident when they're on autopilot, right? You don't want those things to happen, so you have to really watch this, babysit it through the process. One of the great things that we have going for us is that we have thousands of users using this and we can see the collation between what they're doing and what the outcomes are and move and adjust and work with us. And you know, right now, if you look on the site, it's going to say this stuff's in beta, and it's probably going to say that for the next six months or so because, well, maybe even longer than that, because one of the things that happens is we're getting.
Speaker 2:You know, we launched this and right after we launch it, now there's new models, right, and are these new models better? Yeah, you know. So you have to kind of work with this and it's going to be an evolving thing. Here's the thing. It's here, it's not going away and we're working to embrace it in a way that can help make people effective in their trading without them having to worry about that. We're doing that effort and presenting tools to them that'll help their outcome. And as those tools get better, you know like, hey, there's earnings season, you're in a stock, what else you know? Based on what's going on in these other stocks in the same sector, what's that likely going to mean for the one that you have earnings coming up next week for Right and be able to extrapolate some of those things out?
:Well, the thing I really like about your approach unlike the robos it's you're making a better driver, right? You're not taking the driver out of the driver's seat, you're just making a better driver.
Speaker 2:Yes, yes, yes.
:So, unfortunately, we have reached the final question of the podcast and I'll ask it to both of you. We call it the trend drop. It's like a desert island question. If you could watch only one, only one trend in AI financial analysis tools, what would it be? Go ahead, rance, and it's cheating because there's two of you, so you can pick two trends.
Speaker 2:I would say, competitive analysis, so that when you go into a particular area, you're diversifying your portfolio. I want some pharmaceuticals. I want some tech. I want some this, so that when you go into a particular area, you know you're diversifying your portfolio. I want, you know, some pharmaceuticals, I want some tech. I want some this how can I find the best in class in those different areas? I think that that's one right now. There was one area that, as it sits today, that I could use to improve my results. It's with those different areas I want to invest in. What are the best in class to go into in those areas and how to find those?
Speaker 3:quickly. I think it would be for me. Portfolio analysis, just because it's so important for people to be able to. If you've been trading for six years, you've got a P&L statement from your broker Great, so you know what you made or lost for the year. That's it right. You probably don't know much else. Now, with all this AI stuff and with something like our trade journal, you can go back six years worth of stuff and really start to figure out what you did right and what you did wrong, and I think that's going to be critical for people moving forward.
:Well, rance, chris, I want to thank you so much Eye-opening fun and I wish you gentlemen continued success. And when am I going to get my free trial?
Speaker 2:We got you covered. We'll take care of you on that for sure, excellent.
:Well, thank you so much, and that brings us to the end of today's pod Thank you, thank you. Thank you. Thanks so much for listening to today's episode and if you're enjoying Trading Tomorrow, navigating trends in capital markets, be sure to like, subscribe and share and we'll see you next time.