AI Investment Strategies for Retail Investors to Outperform the Market with Andrew Einhorn

In this episode of Future Finance, hosts Paul Barnhurst and Glenn Hopper delve into the world of AI-driven financial analysis with Andrew Einhorn. From anecdotes about early AI experiments to deep insights on leveraging data, Andrew shares how his platform helps investors move beyond opinions and make data-backed decisions.

Andrew Einhorn is an entrepreneur and data scientist with over a decade of experience in creating technology companies. As the CEO and Co-Founder of Level Fields, Andrew's mission is to make sophisticated investment analysis accessible to everyday investors. His background spans epidemiology, regulatory science, and technology, with a focus on using data to simplify complex financial landscapes. He is passionate about solving problems and democratizing investment strategies.

In this episode, you will discover:

• How AI can analyze real-time events to provide actionable investment insights for retail investors

• The role of data in replacing opinion-based decisions with factual, evidence-driven analysis 

• Strategies for successfully integrating AI tools into financial decision-making processes

• Insights into the challenges and rewards of being a startup entrepreneur in the fintech space

• Key factors in building AI models specific to financial services and tailoring models to sector-specific needs
  

The conversation offers valuable perspectives on the transformative role AI plays in the financial industry today. Andrew’s insights emphasized the importance of moving past traditional investment analysis methods and adopting cutting-edge technology to unlock greater market opportunities.

Follow Andrew:

LinkedIn: https://www.linkedin.com/in/einhorn/
Website: https://www.levelfields.ai/

Join hosts Glenn and Paul as they unravel the complexities of AI in finance:

Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii

Follow Paul:
LinkedIn: https://www.linkedin.com/in/thefpandaguy

Follow QFlow.AI:
Website - https://qflow.ai/future-finance

Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.

Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.

In Today’s Episode:

[02:09] – Why your company needs an AI strategy
[09:59] – Navigating the pitfalls of early AI adoption
[16:50] – Introducing Andrew Einhorn and Level Fields
[18:01] – The entrepreneurial drive and startup challenges
[24:02] – From epidemiology to AI in finance
[33:14] – How level fields makes advanced investment analysis accessible
[40:27] – Leveraging proprietary AI models in financial analysis
[44:05] – Quick-fire personal questions with Andrew
[47:12] – Closing remarks and wrap up


Full Show Transcript

[00:01:23] Host1: Paul Barnhurst: Welcome to the future finance show where we talk about.

[00:01:27] Robotic Voice: I for one, welcome our robot overlords.

[00:01:39] Host1: Paul Barnhurst: Future Finance is brought to you by qflow.ai, the strategic finance platform solving the toughest part of planning and analysis. B2B revenue align sells marketing and finance seamlessly. Speed up decision making and lock in accountability with qflow.ai 

[00:02:09] Host2: Glenn Hopper: I think at this point, we're all seeing the incredible potential of generative AI, and a lot of us are starting to experience a little bit of FOMO around not getting it deployed quickly enough in our companies. I want to talk today about how we can implement a generative AI program in our departments or companies. So let's take a few minutes to sort of chart a course for integrating gen AI into your operations. You know, we've at this point, anyone who's gone to a news website, we've all heard about generative AI and the promised benefit, you know, the ideas of automating financial reporting, personalizing investment advice at scale, and detecting fraud with superhuman precision. You know, all these promises that are out there, we want to be using them. But to turn that potential into cold, hard ROI, well, you need a plan. So grab a Fresca, sit back, and let's map out your path to AI driven financial innovation. The first thing you need to do is zoom in on your pain points, like you wouldn't invest in a company without doing your due diligence, right? So the same goes for AI. Before you jump in, take a magnifying glass to your operations and pinpoint exactly where I can move the needle. Maybe it's automating those mind numbing data entry tasks that eat up your analyst time, or using natural language processing to provide instant 24 over seven customer support.

[00:03:23] Host2: Glenn Hopper: Don't just stay in your executive bubble. Get out on the front lines and talk to your teams. They're the ones who are in the trenches, and they'll have invaluable Intel on the daily challenges that are ripe for an AI solution. Plus, involving them early builds builds the buy in that you'll need down the road. So the next thing you need to do is understand what's available now, what the capabilities are that are out there. So, you know, if the AI marketplace today is like a super highway with shiny new tools whizzing by at breakneck speed, it's time to take them for a spin. Consider experimenting with AI solutions like ChatGPT 4.0 for financial statement analysis. Llama for writing technical accounting memos. Claude for building financial dashboards. Maybe there's risk assessment models that enhance your underwriting process, or machine learning algorithms that will give you more accurate fraud detection. This exploration phase allows you to understand the capabilities and limitations of the tools in the context of your specific financial scenarios. You can implement pilot projects to test their performance and gather valuable insights. Additionally, be aware that some of your innovative employees may already be experimenting with AI tools informally, so tap into their experiences and knowledge. And next, you want to pick your battles and your winners. You know, once you've gone through all that, you have a lay of the land and a grasp on the problems you need to tackle.

[00:04:39] Host2: Glenn Hopper: So it's time to play kind of like a matchmaker and pair the right AI solutions with the right challenges. But just like your investment portfolio, you need to prioritize. Look for the low hanging fruit that can deliver outsized returns quickly. Maybe it's a chatbot that can handle routine customer queries, freeing up your service reps to focus on high value clients. Or maybe it's an AI powered tool to catch suspicious transactions in real time. Early wins will build momentum and make it easier to secure resources for bigger AI swings down the road. Now in finance and accounting, we are risk averse with good reason, and we need to think about as we're rolling out these new technologies, how are we going to lock down compliance and security? So before deploying your AI solutions, you have to thoroughly address the compliance and security implications.

[00:05:28] Host2: Glenn Hopper: Collaborate closely with your legal and IT teams to ensure adherence to relevant data protection regulations that could be, you know, GDPR, CCPA, or maybe there's industry specific guidelines. You want to carefully audit your AI models to identify and mitigate any potential biases or data privacy risks that could lead to legal or reputational issues down the line. Implementing robust data governance policies and ensuring the transparency of your AI systems is essential for building and maintaining trust with both your clients and the regulators in the financial sector, where trust is paramount, any breach or impropriety can have severe consequences for your organization's reputation and bottom line.

[00:06:07] Host2: Glenn Hopper: So by proactively addressing these concerns and being transparent about your eye practices, you demonstrate a commitment to the responsible and ethical use of these powerful technologies. But to use all of them, you've got to upskill and reskill your workforce. And I talk about this all the time. I have a new book, AI mastery for Finance Professionals, coming out in a couple of weeks. You can preorder now on Amazon. But I think it's very important that if we're going to use these tools, we need to understand the underlying basis for how they work. So, you know, the AI strategy is only as good as the people who are executing it. And to really harness the power of generative AI, you need to invest in leveling up your team's skills. Think multi-pronged training workshops, online courses, hands-on practice. I mean, all these avenues will cater to different learning styles and tech comfort levels, but you also want to anoint some AI ambassadors within each department. So the people who are really leaning in and getting it, maybe the leaders make them the go to gurus who can evangelize best practices and nip any AI anxiety in the bud. I think it's important to create space for employees to experiment, innovate, and yes, even fail forward with AI. A culture of continuous learning will keep you on the cutting edge as AI capabilities evolve at this breakneck speed where they're going right now.

[00:07:25] Host2: Glenn Hopper: Next, I would say measure twice, cut once. Implementing AI isn't a it's not like a set it and forget it proposition. You need a steady finger on the pulse of your AI initiatives. You're constantly measuring the performance against your target KPIs. Are your fraud detection rates improving? Our client satisfaction scores are on the rise. Is your back office running leaner and meaner? Keep tabs on the latest developments in AI, and don't be afraid to course correct as the tools and techniques emerge. You know. Now we're seeing ChatGPT zero one preview that is showing some interesting reasoning abilities, but you can't upload documents to it now. I mean, it's but in a couple of weeks or maybe by the time this airs, that will be a capability that's out there. So you need to be aware of what's going on out there. And if there's a new and better way to do something that you figured out, maybe in ChatGPT 3.5 or in Gemini, and there's a new tool out that's working better. Just foster a culture of knowledge where teams can trade war stories and crowdsource solutions. Iteration is the name of the game with AI, ultimately. Look, we all know generative AI. It's here. It's come on like a storm. It's going to reshape the financial industry and industry on the whole, more than any tool, probably since the internet itself.

[00:08:43] Host2: Glenn Hopper: Embarking on your AI journey, it takes equal parts. You've got to have that strategic vision, and you've got to be willing to roll up your sleeves and execute by surgically targeting pain points, test driving solutions and continually optimizing, you can unlock massive efficiency gains and uncover new revenue streams. But don't forget, I mean right now and this. I say this all the time. Ai is a great way to supercharge human talent today. Not replace it. Ai, all the tools that I've used and everything I've done with AI. We're still in the trust but verify phase. And I think if you invest in your people, cultivate a mindset of innovation and build an AI powered organization that can kind of ride the waves of technological disruption now and in the future, you know, inject some AI adrenaline into your financial institution. I think that really this AI is not going to be top down. You're not going to decide as leader of your company. Here's generative AI. This is how you use it. I think it's a matter of getting the tools into the people's hands, giving them an environment where they can practice with these tools and where it's an open, collaborative environment. I think that that's how we're going to end up implementing this successfully in our companies.

[00:09:59] Host1: Paul Barnhurst: Welcome to this week's edition of Your Job Is Safe for Now. Over the last couple of weeks, we've seen a few big developments in generative AI. Google recently released its notebook LLM. This is a notebook style AI tool where you can upload your sources into different notebooks and have, analyze, and give answers just off your sources. Generate a transcript as well. You can load YouTube videos. I've used it for another podcast just to see how it works. Some others are the new Canva for coding and for other things that released by ChatGPT or OpenAI. This is similar to the notebook, a little bit different use case, some similar ideas, and also Microsoft recently released its new Copilot. So what we're seeing is great advancement in AI. It's amazing to watch how quick it's moving. But lest we forget, it's still making some serious mistakes. There was a recent article earlier this month by CIO magazine, and they called it 12 famous AI disasters. And they share some different situations where AI caused a huge mess. The first was McDonald's. They tried at 100 drive thrus to have AI take your orders. Customers were frustrated. One of the favorite was a customer who kept asking it to stop adding chicken McNuggets to its order. It reached 260 before it finally stopped adding them to the order. Someone shared that TikTok video and there were others like this.

[00:11:50] Host1: Paul Barnhurst: Eventually, McDonald's said this is a failure and ended the experiment because AI wasn't ready and customers were mad because it wasn't working. That's the first. The second is grok. I think it's grok. This is Tesla's AI. Why they named it grok. I'm not sure, but anyone who knows basketball knows. Klay Thompson used to play for the Golden State Warriors, one of the best players in NBA history when it comes to shooting three pointers won multiple championships. Anyway, for some reason, AI accused him of throwing bricks, vandalism, hallucinated there thinking maybe it came from a bad game where a writer had said he threw a lot of bricks, but it accused him of vandalism. So a question like that, when AI makes a false or defamatory statement, who's at fault? Keeps doing that. Our job is probably safe. Another one. This is kind of a really fun one. New York City has an AI chatbot. It was using to help with some of the work it needs to do, called The Markup. It was a Microsoft powered chatbot, my City, and it was giving entrepreneurs information that they could break the law. It said these type of things. It claimed that business owners could take a cut of the workers' tips. Fire workers who complain about sexual harassment and serve food that had been nibbled by rodents.

[00:13:20] Host1: Paul Barnhurst: It also claimed landlords could discriminate based on source of income. Yeah. Those are some real problems. Those are some bad examples. Let's keep going through this article here. I'm reading it as we talk. We'll go through maybe 2 or 3 more today. Air Canada. This is a horrible one. Somebody had a family member pass away. They were flying from Toronto to Vancouver and they wanted a bereavement fare. Well, they asked the chat bot and the chat bot said you buy the tickets and after the fact you can apply for your bereavement. It's exactly what the person did. They bought a one way ticket to Vancouver, and one back spent about 15 $1,600 and went to the funeral, took care of all the family stuff, and then afterwards they filed a claim for a bereavement fare with Air Canada. Air Canada told them, nope, you're not eligible. You have to ask for that ahead of time. It said no. Your chatbot told me I could ask after the fact. They said, that's experimental. We're not responsible. Well, the courts sided with the man and they had to pay him roughly $800. About 600 was damages and the bereavement fare. So you need to be careful what your chatbots say. Yes, you can be eligible.

[00:14:33] Host1: Paul Barnhurst: I've experienced that with Thinkific, with being given wrong information by a chatbot, but it definitely happens. Mine wasn't near this serious, but I can relate. Let's go through a couple more. This is a really bad one. ITutorGroup's recruiting. AI rejects applicants due to age. So the AI was rejecting any woman over 55 years old and anyone who knows age discrimination. That's a no no. Rejecting men over the age of 60. They ended up settling, didn't claim guilt, but settled for about $400,000 for the situation. The article goes on to share several other examples. Another famous one is a lawyer asked about some court cases, and it hallucinated the court cases it used, and the lawyer used them in court. He used them to defend his position. I mean, we'll talk about getting yourself in trouble. Made up court cases in front of a judge. Bad idea. So what we're seeing is that AI still makes mistakes. And sometimes these are really high profile. These are the type of things that are going to have to get better. There are situations where 80% might be good enough and you review it like we've mentioned often. Treat it like an intern, but there are other cases and other jobs where it needs to be 99.9% or even greater. Using AI in battle, those are situations where it can't make a mistake using it in surgery.

[00:16:07] Host1: Paul Barnhurst: People's lives on the line. So as AI continues to develop, it's going to have to get better. And that means some of our jobs are safe. I mean, yes, some may be in danger. We all know AI is getting better, but today the focus is how can we use it? And how can we ensure we're being careful so we don't show up as one of those high profile cases of misusing AI and getting ourselves in trouble? I thought you'd find some of those kind of humorous, a little bit of seriousness there, but I hope you enjoyed this week's. Your job is Safe for now, and as always, go forth. Start using AI. Be more efficient and more effective in your work.

[00:16:50] Host1: Paul Barnhurst: Today on Future Finance. We are excited to welcome Andrew Einhorn to the show. He's the CEO and Co-Founder of Level Fields, an AI driven fintech platform revolutionizing the way investors approach research and market analysis. With over a decade of experience in building technology companies and a track record of delivering innovative solutions, Andrew has made it his mission to level the playing field between retail and institutional investors. His unique journey spans from epidemiology and data science to advising billion dollar hedge funds, and now leading the charge in AI powered investment strategies. Andrew, welcome to the show.

[00:17:34] Guest: Andrew Einhorn: Thanks for having me. Happy to be here. 

[00:17:36] Host1: Paul Barnhurst: Yeah. Thank you for joining us. We're really excited. We usually touch a lot on corporate finance because that's both Glenn and I's background. So we saw the investment. We thought it'd be fun to talk about something a little different. Another use case within finance for AI. It should be a lot of fun for our audience to go in a little different direction today. But where I want to start is I was looking at your background. I know level fields look like the fourth company you started? If I got that right. What is it that you love about startups and why level fields? How did you end up on your fourth startup already?

[00:18:08] Guest: Andrew Einhorn: I've told some people, I think, that entrepreneurship is sort of a personality disorder, in that you can't really see the world for what it is. You tend to see the world for how you want it to be. And that's just so when I look around and I see a problem without a solution, it drives me to create that solution. It's just there's like an impulse that I have to have it in existence. I just have to build it. And I know ideas are cheap. Everybody has them. But, you know, it's really kind of the follow through and the grit and determination and putting together the right resources that I think is difficult enough for me to keep me out of other trouble. I can stay focused on that and not distract other things in my life. So introducing that kind of element of chaos into my workplace, I enjoy and very much like you, I look at a startup very much like having a child, right? So first it's just the idea, and then the idea becomes a reality, which is sort of your infant is born and it's helpless and you have to do everything for it. And you're the chief everything officer, but then you sort of see it grow into this toddler and eventually a child, and then before long, you know, you're packing bags for college and putting it out for others to be in charge or for it to be in charge of itself.

[00:19:27] Guest: Andrew Einhorn: And that's very much like the stages of a startup. And for me, I don't really like the hands off piece of a company where it gets old enough and more established enough, where it's really you can just kind of farm out all the functional areas and other people are taking care of it. By that point, all the big problems have been addressed and I get bored. So I like kind of the initial spark. Right? How do we get it from 0 to 30 miles an hour, and then someone can figure out how to get it from 30 to 60 miles an hour thereafter. And I just enjoy that process. I like to solve problems that help other people. I tend to create, you know, startups or businesses that eradicate certain dependencies that I find unnecessary or have a lot of red tape or just make life difficult for other people. And so if I can find a solution to that problem, then I have sort of the, you know, double bottom line, right? You have sort of a profit bottom line, but you also have this societal benefit. So, you know, that's what really gets me out of bed in the morning.

[00:20:30] Host1: Paul Barnhurst: And I say, well done. We got personality disorder, children, creativity, sending them off to college. All in. Why we are an entrepreneur I like it. It's good. And solving the world's problems. Great answer. That's what you get from an entrepreneur. That excitement and that willingness to tackle hard things. I appreciate you sharing, and it sounds like you really enjoy one. Just solving problems, but also just that growing and figuring it out. What comes with the challenges of those early days?

[00:21:00] Guest: Andrew Einhorn: That's right. You know, you're sitting there and you're not solving one problem. Usually in a startup you're solving one big problem, and then you have a 100 small problems to solve to include, well, how do you finance it? How do you grow it and how do you get user feedback? How do you determine product market fit? What's the marketing plan going to be? What happens when the rules are changed on you? Right. You know, so someone buys Twitter and all of a sudden you can't access Twitter data to do the things that you used to. So it's a lot of change management very, very rapidly. And so every day is different. I think that I would add to that, like I never have the same day twice ever. It's always something completely different. Sometimes it's a curveball that's thrown at you and you're like, oh my God, why did I sign up for this job? And other times, you know, it's something wonderful that happens that makes you more pathologically optimistic, which is part of the personality disorder of an entrepreneur.

[00:22:00] Host1: Paul Barnhurst: And you never have those days where you're like, wait, what was I thinking, starting my own business? Things are going wrong, right? It's all right. It's all been roses. And it's just up and to the right. Like every forecast for you, Glenn. Is that right?

[00:22:11] Host2: Glenn Hopper: Absolutely, absolutely.

[00:22:13] Guest: Andrew Einhorn: Always a hockey puck stick. Yeah. 

[00:22:16] Host1: Paul Barnhurst: Yes. Always that hockey stick. That's what everybody wants to see, right?

[00:22:20] Guest: Andrew Einhorn: It was, I think, Steve Jobs that said isn't it interesting that overnight successes tend to take ten years? That's what it looks like to everybody else. It's like, oh, you just launched that product and it changed the world. Great. Well, it took eight and a half years of product development to get there. And then a year and a half of, you know, pushing for it to be successful. So yeah, my hats are always off to anybody who starts their own business or even just creates a product. You know, it benefits everybody else. And it's very difficult thing to do. And usually you have to navigate lots of different relationships at home and at work and otherwise to make it happen.

[00:22:58] Host2: Glenn Hopper: You mentioned one thing that stuck out to me too is, you know, the idea that I want to be a founder and start a business. You can't just lead with, okay, I want to do that and then come up with the idea later of what you want to do. So I think that the core to being an entrepreneur and to coming up with these businesses goes back to what you said about being a problem solver. I think about your background as an epidemiologist and the problems you're solving. First off, like I was around so many data science people during the pandemic, everybody wanted to become an amateur, you know, or thought they were an amateur epidemiologist or, you know, biostatistician. Everybody trying to solve for the spread of the Covid, but for you to actually have that background and I think, you know, knowing sort of the way an epidemiologist would look at patterns and trying to find answers and transitioning from that to building AI tools for investors, it you know, maybe to some people it would seem like a leap. But for anyone who's done data science and analytics, it kind of makes sense to make that path. So I'm wondering, though, in your transition from data analysis and event monitoring, moving now to to what you're doing with level fields, can you kind of chart that out for us and talk about some of the challenges along the way and things, and maybe how they tied together, you know? 

[00:24:19] Guest: Andrew Einhorn: Sure. Yeah. I've had a traditional career for sure, but the thread that kind of binds it is I've always followed the data and I've always worked with large data sets. Why is that? Really it's the search for truth, the search for an answer and not wanting to, you know, short change that with just opinion. That's subjective right? And so starting early in my career, you know, I would get assignments like, hey, go into the Gulf of Mexico, figure out where the mercury is getting into tuna fish. Right. So you've seen those warnings. You know, if pregnant women shouldn't eat tuna fish during pregnancy because the mercury content. Why is that? So that was an early assignment I was trying to figure out, well, how does it get in? What's the pathway? How does it affect people? How does it get to the canned tuna? And that is, you know, very much a data science way of looking at it. And so that was an early challenge that I had. And we figured it out. It was okay. Yeah. Offshore oil rigs use mercury as a drilling lubricant. And so it goes into the ground in an organic state. They're sort of benthic organisms on the bottom that change that into organic mercury. They have these offshore oil rigs all over the Gulf. They serve as structures for the bait fish. The tuna come in, they eat the bait fish. Bioaccumulates. Fishermen come in, get the tuna from there, and then voila! It gets into the food supply.

[00:25:42] Guest: Andrew Einhorn: So how do you prove that, though, right? With data, we had to prove it and show it. And that's a different set of thinking about the methodology, looking at the conclusions, you know, and poring through that to figure out how is that possible. And then kind of, you know, gut check it, stress test it, make sure it's right. So do sort of like things like that. And you just realize you can let the data drive a lot of your decisions. And so my next kind of six years of my career after that was working really with a lot of government agencies and in some cases large utility companies helping them with regulatory analysis. So early on, as an example, the FAA Office of Commercial Space Transportation was the office that approved SpaceX and others to do the first commercial flights. Before they had to do that, they had to figure out what are the environmental safety, occupational health risks to humans. If we just take this NASA program and outsource it to, you know, all these companies. And I was one of a team of scientists that got selected with a company that I worked for called ICF international, which is publicly traded, to figure it out for the government and say, well, tell us what the threats are projected out for 20 years. So we do kind of mass analysis of data to determine, okay, yeah, there's a chance that, you know, stuff falls out of the sky and lands in our swimming pool, but it's low because usually we'll burn up in orbit.

[00:27:20] Guest: Andrew Einhorn: There's other chances that, you know, these rockets will blow up, you know, at launch. And what's that going to do to the populations and where should we have the launches? What happens to the rocket fuel and so forth. Right. So it's always just data analysis. Data analysis. Didn't know you could build stuff with code. Once I learned that, it was like okay. Aha! Now I can go and build a whole bunch of things that I want. So the next thing we did was quit that job, started a company, and the company was looking at events effectively, but it was looking at four publicly traded companies. So these events would happen. And we built the software program that flagged the events, sent it to the corporate comms folks to let them have an early idea of what was going on, could be positive, could be negative, some market cap, loss of the shares, you know, save $1 billion in losses as they got their talking points out. They said we're not the ones that are going to be sued. This was not our fault. And so you see the share price die 4% on day one. And then the talking points out after we send them, you know, some information about how the market's responding.

[00:28:26] Guest: Andrew Einhorn: The share price goes up. And so if you can imagine doing that for. We had about 100 different logos that were all publicly traded. You start to learn a lot about events. Impact these companies, right? An event happens. Company reacts, the market reacts, the news reacts a certain way. It sort of sets off this chain reaction. And so, you know, after doing this for ten years, we sort of realized, wow, we could take all these patterns that we see. And having ten years of experience doing this and kind of change the looking glass instead of giving it to corporations to use, we can actually just give it to investors. And they would have a decision analytics tool that showed them what was going on and how it normally happens after that. Very much like a weather forecast, right. I said, you can do the same thing with events when they happen. This event occurred. How bad is it going to be? How long is it going to affect the share price? How much is it going to affect the share price. And you start to have those averages give you a better sense of making decisions so that when you get to an event like Jeff Bezos departing Amazon, you don't need to freak out. If you're an Amazon shareholder, you can just say, you know what? Let me look at what the data says.

[00:29:44] Guest: Andrew Einhorn: Well, the data set and double fields or platform says 2% is the move for large cap company that loses CEO. If the company is performing well, 10% is the max drop over a two month period. And then after that the share price is rebound. So you don't have to worry. You don't have to have a gut reaction. You can look at that data set and then go, you know what? I'm going to use this as a buying opportunity. When it drops to 9%, I'm going to pick up more Amazon shares, and then I'm going to be up 9% as it gets back to neutral and then up even more as it goes from there towards all time highs, which is where we're at with Amazon now. And, you know, part of where my background of looking at kind of data is, is trying to parse the BS from reality and looking at the data and saying, you know what, we don't need opinion, we just need facts. And in the market, particularly in financial markets, there's way too much opinion and we sort of want to pull through that cloud of BS and just say, look, this is what usually happens. This is the data. This is how people react. Someone's telling you one thing. They're probably doing something the opposite to manipulate you. So let's just focus on, you know, improving everyone's ability to make money.

[00:31:01] Host1: Paul Barnhurst: Ever feel like your go to market teams and finance speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet qflow.ai, the strategic finance platform purpose built to solve the toughest part of planning and analysis. B2B revenue. Qflow quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go to market stats, make segmented scenario planning a breeze, and closes the planning loop. Create airtight alignment, improve decision latency, and ensure accountability across the teams.

[00:32:09] Host2: Glenn Hopper: That makes sense. And Paul had a question next, but I do want to talk about that information and kind of efficient market hypothesis. And we'll think about, you know, the whole efficient market hypothesis, the idea that eventually and with the speed of information these days, the market reflects all of the available Information that's out there. I think about what retail investors know, what institutional investors know versus what private investors know and when they know it. So first off, you know, institutional investors, they've got analysts who are focused on it. And they're closer to the information. So they know what's going on more. And they have tools that maybe they're reading the Twitter firehose and they get like kind of market sentiment. And they're really tuned into this because this is their full time job. So I think looking at you guys, the goal to make these kind of advanced financial strategies and getting this kind of leveling the playing field, as the name suggests, between institutional and retail investors, has to be a real challenge. Walk me through a little bit of, how, you know, you simplify this for the retail investors by giving them similar information and how you make it actionable to them.

[00:33:27] Guest: Andrew Einhorn: Sure. Yeah. Happy to. So, you know, on our platform, on level fields, we try to make it as easy as possible to create a strategy. Right. And so if you think about the way investing is typically done to date there's really been only two methods. You have fundamental analysis. You're sort of Warren Buffett approach like find companies that have a high margin of safety. You know, they have a low valuation, buy low, sell never or sell high. To do that, you really have to understand profit and loss statements, balance sheets, debt to equity ratios can be complicated, you know for people. The second is technical analysis, which is a lot of looking at sort of momentum of the share price. Is it dropping. Is it rising. How fast is it rising? There are a lot of technical indicators. Some work, most don't. It can be quite confusing. You know you have the MacD and the Mar Maas cross and exponential and whatever that takes a long time to learn. It also takes a long time to learn that most of it doesn't work, and that a lot of it is sort of like laying on your back and looking at clouds and making shapes with them in your mind is sort of the same as looking at these charts and being like, oh, I think I see a pattern.

[00:34:45] Guest: Andrew Einhorn: And so if you back that up and say, okay, for the patterns that do work, why do they work? They work because events just took place that changed the trajectory of the share price. So the technical analysis is responding to an event. You don't know that from looking at the signal. We know that because we track the events behind it. And so you don't need technical analysis if you have the event because it predates the event. So that's the first part. The second part is we look at events in sort of a homogeneous way. So each event can be its own strategy. So for instance we have dividend increases as an event type one level fields. That's a good event, right? It's a bullish event if a company is giving away more cash. Chances are they're in a good financial position. And guess what you'll get as a shareholder. More cash, which is also a good thing for you. So it's very easy to sort of pull that together and say this is a bullish event. And what you can do is just look for, you know, large movements of the dividend increases. So if they're increasing the dividend 10% that's nice 25% really good. You know, 100% incredible. And as the events come in you can look at that as a short term opportunity.

[00:36:03] Guest: Andrew Einhorn: You know a few days. Or you can look at it as an indicator of financial health of the company, which is cutting through. A lot of, you know, the opinion in the market. And for instance, give the example of Dick's Sporting Goods. But the reality was the company was doing quite well and they kept increasing their dividend every quarter and one quarter, they actually increase their dividend by 105%. And you see sort of the difference between what's actually happening with the company and the market perception in the share price movement, but it created those discrepancies between the true value of the company and what it was trading for. And so if you just bought on these signals, very simple signals that they're increasing their dividend large amounts, you'd be up like 150% over that period of time while everyone else was selling their shares to you. And you don't have to look at anything else. So that's one event. That's one trading strategy, one indicator. And it's very helpful. It works short term. It works long term. You don't have to be any kind of market expert to follow that. They must be good at this because they're running these companies. And the company's been in existence for a long time, and they're giving away cash because they don't need it.

[00:37:19] Guest: Andrew Einhorn: Well, if an average person started giving away cash because they don't need it, usually we just call them a billionaire, right? So, like, they've done well, they've already made it. They're okay. They're going to be okay. They have enough billions to do the Giving Pledge or whatever. You know, Zuckerberg and Bezos and those guys do. But it's the same with corporations. And so we have 25 different event types just like that, that are very easy to follow, single event types that make it pretty much simple. I like tech companies and healthcare companies, so I can filter certain types of events, like the dividend increase to just tech companies or healthcare companies. And I also want them to have, you know, positive cash flow so I can create that filter. And forever. Now I have an AI search agent that's going to go looking for that combination of events, that combination of metrics. It's going to tell me when something happens, you know, within minutes of the event occurring, we cover about 6000 US traded equities. And that's orders of magnitude more than an average person could monitor on their own, right. If I asked you guys like, how many stocks could you name? How many do you think you could name by number 20, 30, 50?

[00:38:36] Host2: Glenn Hopper: So I think, I mean, and that's a great point, Andrew. And I think probably why, you know, one of the best benefits of using computers versus an individual human trying to do everything from portfolio balancing to to tracking these stocks and everything. So I could see that. I mean, we're just seeing a much more efficient market when we have access to this kind of information and able to track a lot more than what we could do if we were drawing out our charts and graphs on, on graph paper.

[00:39:08] Guest: Andrew Einhorn: Right. And, and you look at, you know, the market saturation, right. Everybody knows the Magnificent Seven. And we've driven those companies to crazy high valuations. But there's 5000,993. Other companies out there that are some of which are also doing really well, some of which have actually outperformed those companies. If you don't know about them until they're up 1,000%, it doesn't help you very much.

[00:39:36] Host1: Paul Barnhurst: You're saying 1,000% up is not the time to invest.

[00:39:41] Host2: Glenn Hopper: I'm buying. I'm all in on Nvidia today. I'm gonna, I'm diving in for the first time.

[00:39:46] Guest: Andrew Einhorn: Well, it just depends how big it started. You know, if it started with a penny, then it might be.

[00:39:50] Host1: Paul Barnhurst: Sure, sure. Yeah, it's all order of magnitude. But as a general rule, you don't want to buy at a high.

[00:39:55] Guest: Andrew Einhorn: But you know, it's basically looking at there are lots of sectors, lots of industries, lots of companies that nobody talks about, but they do very well. And in particular, they do well at certain times. And that's I think the other aspect of what AI is good at is flagging opportunities that might not be long term. You know, it might not be the next Amazon, but it's crushing it now and crashing it for the next 6 to 12 months. And that could be in a positive economic cycle or a negative one.

[00:40:27] Host1: Paul Barnhurst: So have you built your own models? Are you buying a bunch of data from different sources? How are you bringing that all together to get the real time events kind of using AI? How's that? How's that work for you?

[00:40:39] Guest: Andrew Einhorn: We have different types of data, right? So we have qualitative data. So free text unstructured text for that. You know we're pulling the information ourselves directly from the sources. So if a company puts out a report on their website we get it that way. We have market data. That's just a pricing action, you know, that's commoditized through the exchanges. And then we have the financials, the PNL's the balance sheets of those companies that we do use the vendor for. But then we have to do our own metrics because it comes in raw form. From that, we create our own events. So we have to extract from the text and create an array of events that are proven to move share prices. We're not interested in every single event. We're interested in the ones that are material to the share price movement, and those are the ones that make their way onto the platform. How do we do that? We built our own language model early on in the process that was unique to financial services, which gives us an edge because it didn't have to be everything and everything model like a ChatGPT. We could just focus on, you know, our niche, and that allows you to avoid mistakes in the I would normally make, right. So if you have bluebird bio and a bluebird flying in the sky and you're making an observation because we're in financial services, we know it's not the bluebird flying in the sky. We know it's the biotech company. Things like that. It's simple, you know, example. But that's part of what the AI does. And so our AI is really like a high tech speed reader that's looking for certain things and flagging it and then pulling together the analysis, thereafter, automatically.

[00:42:23] Host1: Paul Barnhurst: Looking for the events, the patterns and pulling it together.

[00:42:26] Guest: Andrew Einhorn: Looking for the events, extracting them, analyzing them, giving metrics and data sets. Yeah. And so it's, you know, copilot, whatever you want to call it. You know, we say AI search agents because you sort of can customize them and just let them run. So, you know, these are the companies I want to look for. I'm going to go play golf now. Let me know, you know, when you find one.

[00:42:48] Host1: Paul Barnhurst: When your phone beeps. Like should I buy it?

[00:42:51] Guest: Andrew Einhorn: Yeah. We're trying to make it that easy, you know. And you know, it's not just for other people. Like, I use it myself for my own investing. And it was my early startup pitch to my wife was like, hey, if this was successful as a business, great. If it's not, I'm going to use it to make money for ourselves. So we'll be okay, you know? And she was like, all right, you better be right. And so, you know. 

[00:43:14] Host1: Paul Barnhurst: They'll be wrong on this one. That's my money.

[00:43:15] Guest: Andrew Einhorn: Yeah, exactly. So, you know, in the mornings I check our own alerts. I'm like, yeah, that's an interesting company. Never heard of it before. Let me look it up and see what they do. They are interesting. You know, it might be a health care company that has some interesting solution. One came through the other day, you know, as an example, I had skin cancer on my face. I had it removed. It was quite painful. It was near my eye. There's a company that actually does a noninvasive surgical procedure for the same thing that they almost tore my eye out for. And it just uses ultrasound technology. No cutting, no sewing. I was so pissed when I learned about this that I went through this for no reason. And there's a little startup. I say startup, it's, you know, a couple billion dollar company based in Florida. And I found out about it because it came through our system.

[00:44:03] Host1: Paul Barnhurst: That's cool. We're coming near the end of our time. I know we're kind of up against our time allotment we have here. So we're going to move into our personal questions and I'll go first. Let Glenn give you one and then we'll move on from there. So that works as we've used ChatGPT 4.0. We fed it some information, and it came up with a list of questions that we could ask to get to know you better. And I'm going to give you one of two options. You can randomly pick a number between 1 and 25. Or the random number generator can pick the number for you, and there'll be a corresponding question with that. 12, all right. Now this is a pretty good one. What's a guilty pleasure that you indulge in when no one's looking?

[00:44:46] Guest: Andrew Einhorn: I ate a lot of dark chocolate.

[00:44:49] Host1: Paul Barnhurst: I like it.

[00:44:50] Host2: Glenn Hopper: I love how quickly that came to you. It's like, all right.

[00:44:53] Host1: Paul Barnhurst: You didn't even have to hesitate on that one. It's like I'm a chocolate guy.

[00:44:58] Guest: Andrew Einhorn: Well, I have kids, so I have to do it when no one's looking, so, you know, otherwise they got their hands out and waiting for it.

[00:45:04] Host1: Paul Barnhurst: I understand that one. I have a good two.

[00:45:07] Host2: Glenn Hopper: This the personal questions is Paul and I trying to figure out how we can eventually outsource our entire jobs to the AI's. And when we first started doing this, I was just telling and we, you know, we kind of bounce around and use the different LLMs to see what they come up with. And I would just say, come up with 25 questions for a podcast guest. But I'm trying to refine these now and saying, so we're actually like loading in your bio and the questions that we had for you and saying, all right, come up with some sort of relative questions to that. And I'm not seeing really any difference, but I'm thinking we're going to keep refining this, Paul, and we're eventually going to just have the AI create the guest questions. We're going to have 11 lab do our, you know, mimic our voices. And we're going to and we're just we're going to be sitting on the beach drinking margaritas and have our robots selves.

[00:45:55] Host1: Paul Barnhurst: A few questions that are unique in here. Got a little better. We'll make progress.

[00:45:58] Host2: Glenn Hopper: Yeah. So the way I do it is instead of just going through the list and doing the random numbers, I just after it creates the question, I say pull one right now for me to ask the guest. So we will do that.

[00:46:12] Guest: Andrew Einhorn: I'm curious now what the other questions are. I'm curious what? The list. Anything else interesting on the list?

[00:46:17] Host2: Glenn Hopper: Let's see. Well, okay. Here we go. I like this one. If you could switch lives with any fictional character, who would it be and why?

[00:46:27] Guest: Andrew Einhorn: That's a difficult one. I don't know, maybe some one of the Dwayne Johnson movies? Probably.

[00:46:32] Host1: Paul Barnhurst: I was going to say Scrooge Mcduck. If you ever watch that as a kid. Ducktales.

[00:46:37] Guest: Andrew Einhorn: Ducktales. Scrooge. Yeah.

[00:46:41] Host2: Glenn Hopper: I was going through superheroes. I was trying to figure out which superhero power would be the cool. Actually, Tony Stark from Marvel World would be pretty. Because you got the no superpowers, but enough money to build your own? Yes.

[00:46:52] Guest: Andrew Einhorn: Superpowers, I guess. Yeah. Although he takes a beating in most of those movies.

[00:46:56] Host2: Glenn Hopper: That's true. Yeah. Whereas the Rock does not.

[00:46:59] Host1: Paul Barnhurst: The rock can hold his own, I've heard. Yeah.

[00:47:04] Guest: Andrew Einhorn: Fictional or reality? 

[00:47:06] Host1: Paul Barnhurst: Yeah. I think in both cases, he does. All right. Yeah. I wouldn't want to mess with him in either realm.

[00:47:10] Guest: Andrew Einhorn: Right, exactly.

[00:47:12] Host1: Paul Barnhurst: Thank you for joining us, Andrew. Really enjoyed getting to chat with you a little bit, learn about what you're doing at Level Fields, how you're kind of thinking about AI and trying to level that playing field, pun intended, right? Hence the name level that playing field between retail and industrial investors. I can't think of the term. It's one of those moments, but really excited to see how you guys continue to scale and grow and appreciate you joining us for a few minutes today. So thank you, Andrew.

[00:47:39] Guest: Andrew Einhorn: Thanks for having me. I appreciate it. Nice to meet you.

[00:47:41] Host2: Glenn Hopper: Thanks, Andrew. Yeah, likewise.

[00:47:43] Host1: Paul Barnhurst: Thanks.

[00:47:45] Host1: Paul Barnhurst: Thanks for listening to the Future finance show. And thanks to our sponsor, qflow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice and may your robot overlords be with you.