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 1)
In this episode, Jim Jockle dives into the transformative role of AI in trading. Joining him is Rance Masheck, CEO of iVest+, and Chris Mercer, COO and Head of Business Development for iVest+.
iVest+ uses an innovative AI-powered toolkit to reshape trading for their users with smarter insights and streamlined decision-making. From advanced screeners to personalized trading coaches, explore the AI strategies that are currently redefining the trading landscape.
There was so much information, we had to break it into a two-part episode. This is part one, stay tuned next week as we release part 2 of this insightful conversation!
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. In today's episode, we're covering one of the most talked about innovations of our time artificial intelligence. 2023 was the year much of the world discovered it. 2024 is the year companies are really starting to use it.
Speaker 1:While there's speculation about AI's future potential, we're here to explore its current impact and how companies in the finance industry are harnessing AI and embedding it into their product suites. We'll discuss the benefits, the hurdles, any enduring challenges AI brings to the financial landscape. Joining us to discuss is a company taking big leaps when it comes to AI Rance Meshek, ceo of iVest Plus, and Christopher Mercer, the company's chief operating officer. Ivest is a trading platform designed for educators and retail investors. Founded to reimagine trading platforms, ivest Plus aims to empower self-directed traders with innovative tools and resources. The platform provides comprehensive trading solutions that include stocks and options, and advanced data and insights packaged into user-friendly technology. The company has invested heavily in AI-powered tools, which Rance and Chris will explain further. Gentlemen, welcome to the show.
Speaker 2:Glad to be here. Thanks for having us.
Speaker 1:Well, why don't we just start at the beginning? Why don't you give us a little bit more background on iVest Plus?
Speaker 3:So iVest was started about 10 years ago now. It was in 2013. And the reason I founded this was that I had a company I had sold to TD Ameritrade. Within a very short period of time, I became director of options trading at the firm, and what I found was that, not just at Ameritrade, which is now part of Schwab, but all these brokers, the tools that they give you are I mean, there's some good tools, but it's not as comprehensive as I think it should be Things like journaling, so you know what's working for you and what's not.
Speaker 3:The depth of information it has.
Speaker 3:I mean, their whole goal is get you to the trade.
Speaker 3:Our goal is get you the information you need to be able to trade effectively, and so when I left Ameritrade and founded Ives Plus, we wanted something to do exactly that.
Speaker 3:So we have best in class journaling.
Speaker 3:We have very comprehensive competitive research and fundamental research on companies, great charting and all that that you'd expect, but there is a lot more depth than what a lot of brokers offer to give you a more comprehensive look at things, and one of the things we also did with this was we made it very visual, so people don't want to look at P&Ls and go through all the numbers, but they want to be able to get a snapshot of it in a very visual, very quick way to be able to absorb this. And that's what we really built on and what we founded Ives for was to help people that are not in the markets all day be able to go in, make educated decisions with easy to comprehend information and get them to the trade and then effectively manage that trade and then be able to see their results, and a lot of that is not available at any of the brokerage firms and that's what I wanted to bring to the market and we've done a great job with it and great adoption through those additional tools.
Speaker 1:So your company has one of the most comprehensive AI toolkits I have seen. Why did you decide so heavily to invest in AI?
Speaker 2:Well, I think obviously AI is a big flag right now for everybody, but we saw an opportunity to build that into certain parts of the platform. That would make it a little more conversational for people to use the tools we'd already made, and so we put it on the project list early this year and basically got to work on all these different pieces and kind of integrate them into stuff we already had. So it really helped bring a lot to the forefront that we hadn't really thought through before, make it even easier for people to use.
Speaker 1:So just to give a little bit more context, can you please explain what your four AI tools screener, journal coach and analyst actually do?
Speaker 3:Absolutely so. One of the things that we found that was a problem with AI was, if you made it too broad, you sit down and go okay, I'm going to trade in the markets. What am I going to do with this? Right? So if you just have, like you know, chat GPT, we're just talking to it. We found that people get lost in that.
Speaker 3:So what we did was we built four very specific tools to really help people be able to do their analysis more effectively and in a quicker way. Analysis more effectively and in a quicker way. So, for example, one of the first things that we added was a competitive analysis on a company. So, for example, let's say that I was going to look at Merck, and when I look at Merck as a pharmaceutical company, I wanted to know how is it doing? What's its strengths and weaknesses. So what we did was we built an AI tool that would and this was our first entry there's no prompting, there's no discussion. It's all us feeding it the questions, the right information and then the user being able to see what that is. So, for example, on something like Merck, it would tell you what its strengths are, things like you know, it's strong market cap and so positive change recently in their pricing, although, looking at right now, it's saying it's down a little bit in the last week, right, stable dividends, things like that. But it also talks about, you know, some of the weaknesses declining prices, you know, high PE ratio, so it's a little overvalued right now. Declining income, which is obviously not good. So then what it would do is look at that, not only give you your strengths and weaknesses there, but then it would tell you other stocks in the industry that you may want to consider instead. So you know, for example, one of them that came up with was Johnson Johnson. That has a better valuation and a stronger investment choice than Merck. You know ABBV has a strong dividend and so on. So you know, then you start to look at some of this and you know, you see that, wow, so Merck may not be performing great, but if I look at some of these others, like, for example, johnson Johnson, and we see that there's some strong price improvement on Johnson Johnson, same thing with ABBV. So what happens is it really helps you, uh, look at a stock, see what its strengths and weaknesses are and maybe find a better fit for you based on what your outcome is in your investing. So that's one of them.
Speaker 3:That was the competitive analysis, and that was our first entry into this, where it wasn't. You know where you're having this dialogue part. It's just. Here's the answer using AI. Now, one of the challenges with this is you want to make sure the information is accurate and the training on these things and how up-to-date they are and all that. So what we do is all of the information is up-to-date as of the moment when you go and ask the question. We actually give it the current fundamental data and from that it will generate that report and give you that information. So not only have we done a lot of training on it throughout the whole process, but we also give it the current information of all of the companies in its industry group for it to be able to do the analysis on that. So that's one of the first things we did was the competitive analysis.
Speaker 1:So, rance, let me just stay right there for a second right. So you know, I'm seeing an incredible engine. Where's the fuel? Where's the data right? You know, when I'm thinking of equity trading, I'm thinking of calendars, I'm thinking of earnings reports, I'm thinking of real-time quotes. You know, how are you powering this to give your users the confidence that the AI has all that right information as of this moment?
Speaker 3:So what happens is, you know, in our platform we have very comprehensive fundamental analysis on companies, all the way down to looking at things like the P&L statements and all that Every day as we pull that information in, process it, you know, come up with a lot of our visuals and that that we do within the platform looking at, you know, key ratios and, you know, comparing it to other companies within the group and all that.
Speaker 3:We take that information every day and we load that into our models. When you ask the question, when you hit the button to say, hey, what's my competitive analysis right now on this, what it's going to do is it's going to then send it the updated price information that it has right now. So it's current, real-time information that it's been given, plus all the fundamental that's loaded every day, right, so that way we can give it overnight this whole comprehensive list and then update it with the real-time price information at the moment, so that what you're looking at is literally today's data, up to real-time information about what's going on with the stocks. And that's one of the challenges with this, because if you look at, like ChatGPT, it's from months ago, right, it's not current now. And what we've done is made sure that it had the information needed right at the moment to be able to do this.
Speaker 1:Wow, all right. Well, take me through the other tools, all right.
Speaker 3:So one of one of the other things that we did then was we thought you know what, let's go with a financial coach, a chat bot kind of thing, but keep it within the bounds of financial. So you know, if you were to ask the question, should I vote red or blue in this election? It's not going to give you that information, but what it's going to do is tell you maybe what industry groups work better under Republican, what industry groups work better under Democrat rule, right? So it gives you information specifically about trading. But in this we did open this to a broader chat capability and so, for example, let's just say you know, if I ask it something like how do I structure a bull put spread and when should I apply this strategy, it will tell me then exactly how I'm going to structure the trade, how to put it together, and then will tell me when I would consider applying that in the market, and then from that I could ask additional questions, have back and forth dialogue with this on how I'd want to be able to do that.
Speaker 3:And what we also found shortly after launching the chatbot was people wanted to go back to previous conversations and be able to go into those and further that particular conversation.
Speaker 3:So if I were to take one you know I asked the other day about, you know, give me 20 good stocks under 50 bucks, and you know it came up with a list of those. And then what we also do with this is we show you a list of what stocks it came up with so you can just click on it and go to further analysis on them if you want to. But the fact that you can then keep those conversations going, we allow you to keep up to the last 28 days of use. So if I had something I did three months ago but I've been going back to that conversation, it's always there for me to be able to continue on. So I can take, let's say, back to that bull put spread right that particular option strategy that I don't quite get. I can ask, let's say, back to that bull put spread right that particular option strategy that I don't quite get. I can ask more questions about it and dive deeper into it. So that was the second one we did, which was a broader chatbot.
Speaker 1:I'm going to dive in with a follow-up. One of the things I noticed as you're kind of going through it, your prompts were very simple Was that by design I mean as a chat, gptt user and co-pilot and whatnot. You know, I've been honing my prompt engineering skills, but this seems a little bit more straightforward.
Speaker 3:Right, you know, like a real simple one here is how do I structure a bullpup spread? Right, you know it's like there's no prompting to that. So here's what we did with this, and I think this is one of the secret sauces to this. You know, it's great that you've been using these and you've learned that how you ask your question is incredibly important, right, and you know how you build that prompt. So what we did for the user is we took that need for that knowledge off of them and we built it into our backend. So when you ask a simple question like you know, how do I structure a bullfoot spread Behind the scenes, the prompt that's actually being asked of.
Speaker 3:It is a much more comprehensive prompt. It's your topic, framed in a way that we know will get you the right type of answer back Right, framed in a way that we know will get you the right type of answer back Right. So it again makes that. Yeah, I've been using ChatGPT for quite a few things. I'm building a closet system right now and how to do certain things I'm asking it questions on and how you. The prompting is so important on it and we found that if we didn't hone in on that it reduced the value to the user, because then they'd have to learn this skill of prompt right, and what we did was we took that away. Let them ask in a simple way. We then take that in our models and how to structure it. So the prompt is asked in a very good way. Plus, we've also done a lot of training of the AI models that we're using to make sure that the combination of the prompt and the training gives them solid answers.
Speaker 1:Got it, wow, okay, what's next?
Speaker 3:All right. So here's one of the things that we found with this. When we went to this, that issue of too open-ended came up. Right. It was. We found that people were. They liked it, but how do I really use this in my trading? And so then what we did was we worked on that and found that, you know, we built it into our screener.
Speaker 3:Now here's the thing about our screener system. If you look at the screener system that we have, we literally have somewhere around 350 different data points to choose from. It's a tremendous amount of data. So somebody that is relatively new to the markets doesn't pour in the numbers all day long and all that stuff. They don't even know what to choose. Right, yeah, I want stocks over between $50 and $100. Okay, but when you start to get into financial strength and what that means and all that, that became a lot more challenging for our users to do. So what we did was we built an AI bot specifically around our screener where you can ask it questions and what it will do is we'll decide what the fields are appropriate for, what your question is and what range it should be. So, for example, you know, let's just go with. You know I'm looking to do covered calls on stocks with solid fundamentals that pay a good dividend. And so then it'll tell you, it'll come back with an answer as to what it's going to look for and why, and then it will come up with a list of the actual fields and the ranges that it should use in those fields. And then what it'll do is display how many stocks fall into those different categories.
Speaker 3:And so when I did this, the first question when I asked that question about covered calls with good fundamentals that could pay a good premium, I had like 450 stocks. That was like too many. So now I'm back to this back and forth dialogue specific to my screener and it's like, hey, this is still too many stocks. Let's add some fundamental filters to make sure it's a good, strong company, but also we don't want something that's too volatile. And then it would come back with what you know, what it thinks it's needed there, and it hones in even more. And then you know I'd say it's still a little bit more. So then I said, hey, one of the things that happened when I did this was there was an $800 stock on the list and I wasn't looking to buy 100 shares of an $800 stock and if you're going to do a covered call, you kind of need to do it in 100s or increments. So I said, hey, let's make sure it's under $150. And so it added that to the list.
Speaker 3:And what started out with? You know, if we look at our whole universe of stocks, we have about 25,000 stocks. We have about 12,000 fully listed stocks. The rest of them are like bulletin board over the counter stuff. So with over 12,000 stocks through this, it shows what the fields were, what the params should be for those fields.
Speaker 3:And now I'm down to like 15 stocks right out of this by those simple questions. And again, notice that to your thing about prompting. I'm not having to know how to construct a prompt. We've taken care of that for the user. We make it a really simple thing. Hey, I'm looking to do covered calls and I want good premium. Boom.
Speaker 3:You see, you still have too many stocks. What do you want to hone it in on? You know you still have too many stocks. What do you want to hone it in on? You know, hey, let's make sure they're good. You know solid fundamentals, but I don't want something too volatile. Right, that's what I did in my example here and it hones it in. And then I've got my 15 stocks listed here and, by the way, once I've done that, once I can also save that particular screener for use anytime I want to right.
Speaker 3:But so then I've got my stocks on here that I'm working on and just as a quick little example here, if I went with I don't know, let's just go with one of these here we have, aem was one of the first ones on our list and if I looked at this and went to do a covered call on that particular stock, it would go in. Our system will do the analysis on it, come up with this what the structure should be. And here I have a stock that's around $72. If I were to do a $75, and if your users know that covered callers are selling the right for somebody to buy away from you at 75 when the stock's at 72, you know I'm set up for about a 7% return in about six weeks, right. And you know, if I want a little bit more room for it to move up, I can, you know, adjust that accordingly. But you know now I'm up to about a 12% return in about six weeks if it goes up to $80, right.
Speaker 3:So what it does is, you know, honed in on the list. Then I go start to do my research on that list of stocks and see how it plays out. But the fact that you can now do this with very simple, just little conversation, without having to build big prompts, all that stuff or have any clue what all these different things even mean. In this example, we have over a dozen criteria that it came up with and you didn't have to know what that stuff even meant. You just look at hey, I got 15 stocks. Okay, great, that's a good number. Let me go look at this and see how they play out. And, and you know, just substantially short circuits or shortens the length of time it takes you to get to your answers, and reduces the amount of information and understanding you need about all this different data to be able to do it. The AI decides that for you, so it just simplifies the process on this substantially to be able to really kind of hone in on finding what you want to find. And then there's one more that I got to share with you, because this is such a powerful piece of this In our platform in general, one of the things that's really, really powerful about it is our journaling capability, and if anybody is doing you know for anybody your listeners that might do options trading.
Speaker 3:Options trading in journaling is a real challenging thing and we've done a lot to build out our journaling capability around stock and around futures and also around options. But one of the things that happened so you get to see what's working, what's not, what your returns are on different things and all that but what we found was that you know you still have to go pour through this and say, ok, why did this work? Why did this not work, whatever? Well, what we did was we now built in this was our latest one where we'll take your trading history of, and you can do this, you can hone it in, you can look at a particular sector, you could look at certain date ranges. However, you want to hone in the journal, right? So you use the filters we have for the journal, you get at what you want to look at, then you send it over to AI and then what will happen is it will analyze your trading not general, your specific behavior and tell you what your strengths are, what your areas for improvement are, and so, for example, one I've just run here is I'm doing really good with bull call spread strategies. I'm doing really good on profits when I do my stock trades not just opposition, but stock itself.
Speaker 3:One of the things I happen to do, though, in my areas for improvement is sometimes I let things that aren't working out real well run too long. I don't cut my losses, I don't close a trade out and I'm hoping it. You know that old saying that nothing turns a short-term trade into a long-term investment quicker than the stock going in the wrong direction, right, and I find myself sometimes having that issue. And so it's telling me hey, you know, I'm letting some things run through options, you know through options expiration, and it's biting me and I'm having some trades that you know that I didn't close them out in time, right, and so it gives me, it tells me areas of improvement, tells what I'm doing well, what my strengths are, areas for improvement, some general observation. So, just as an example, here in my portfolio, it's telling me hey, I've got a good mix of successful trades, profitable outcomes through this. I'm using diversification across different strategies and so on, so I'm doing well in that area.
Speaker 3:But it's telling me that, hey, you need to focus on three key areas to improve your trading. You need to focus on completed trades, closing out trades that aren't working for you. Don't let them run to the end. Maybe you can do a little bit better job at active trade management. And if something's not working, you know, review it, maybe adjust it or whatever, but don't just let it hang out there. Right? So it's telling me areas that I can focus on to improve my trading and but but even if you don't, if you just say, hey, what's working well and you do more of what's working well and stop doing what's not working well, you're going to do better in your trading. But it goes beyond that. It not only tells you what you're doing well and what you're not doing well. It also will tell you what areas you should focus on to improve on your trading results.
Speaker 1:Thank you for listening to part one of this episode on AI and investing with Rance Meschick, the CEO of iVest Plus, and COO Chris Mercer. Stay tuned next week as we release part two of this exciting conversation.