This is the Anderson Files on PodClips. The Anderson Files is a look at commerce, investment, economics, and retirement issues that affect each and every one of you. Your host is Mike Anderson, Executive Vice President of the Warner Companies, a Foundation Risk Partners Company. The Warner Companies is a Registered Investment Advisor, with securities offered through M Holding Security Incorporated, member of FINRA and SIPC. And now, your host, friend, and colleague Mike Anderson.

Hello everyone. My guest today is Derek Elphick. Derek is Chief Investment Officer of the investment management division of the L. Warner Companies, a Registered Investment Advisor. Derek is a certified financial analyst with 10-plus years experience in the investment management industry. Today, we’ll be looking at the Liquid Leaders Equity Strategy that he manages and exploring the various aspects of this quant-based investment strategy. Derek, welcome.

Thank you very much.

And to get started, can you share a little bit of your background with us?

Yeah, of course you talked about I’m co-Chief Investment Officer of the Warner Companies. I’ve been managing large-cap equities for individuals and institutions for over a decade. And before I was doing that, I was in the healthcare space, doing mergers and acquisitions. So pretty traditional background, but most people I meet on Zoom, they don’t know I’m actually 6’8″. And so before that, I was a professional basketball player. And I think that that helps a lot in the investment world. Quant in particular, because the same kind of bug that gets you out there, shooting 500 jump shots a day is the same kind of thing. You need to sit there for hours and quant, right? Working with algorithms, going through it, so I think it’s pretty relevant, and yeah, that’s the background.

Yeah, it’s interesting you reference your basketball career and the repetition, and I’m out of UCLA and coach John Wooden. His practice was the core to winning – the game was the game, but you win the game by what you do in practice.

Absolutely.

Oh gosh, yes. And with that, can you explain what is the Liquid Leaders strategy?

Yeah, sure, Liquid Leaders is a quantitative strategy. You mentioned that. And it’s focused on large-cap blue-chip stocks. It’s powered entirely by machine learning and proprietary algorithms we’ve been developing over the last seven years and we’ve got this thesis. We believe quantitative, AI-powered investment strategy is going to dominate the investment industry over the next 15 years. So we think we’re very early. I think we’re just past year one of a tech super-cycle that’s going to last for the next 15 years.

In developing the strategy and the fund, how long did it take? How long have you been working on before it was even, the fund was launched? How many? Was this years in developing?

Years, yeah. You know, if you look over time, most managers with staying power have a distinct process. And a lot of the best have all been quantitative funds. You know, Renaissance, firms like that, and so you need to be able to change with the times. And humans tend to have too many biases and lack the ability to change, where machine learning or quant has none of these biases and can change on a whim. And so the staying power of firms over time has been based in quantitative finance, as opposed to active management, passive management. Those are great, but those long-term firms like Renaissance, that’s what you see.

How has the strategy done since the launch, and when was the launch?

We did a soft launch, which we typically do, but our composite started at the end of 2023 and we’ve done very well. We’ve compounded at 28% net return through the end of February ’25, which is better than all the major benchmarks over that same time period. We’re beating the S&P by over 5% net of fees, so that’s done very well. When we launched, we felt very confident in what we were doing, so we don’t just track ourselves against benchmarks. We track ourselves against our competition, and if you look at ETFs, so we’re tracking our performance versus domestic ETFs. We’ve outperformed 97% of the 1,000 or so that are on the domestic market. And you talk about Vanguard, which specializes in passive, same numbers. We’ve outperformed 97% of their funds. And when you look at this dip in gross stocks this year, that’s going to make our numbers look even better. That small, slimmer of funds that were doing better than Liquid Leaders were really just loaded with gross stocks. And so our relative numbers are getting better.

Great. The population of the 25 stocks held long at any one time – how often do you remix those 25? Is there a certain interval in which a reallocation is looked at where they take place?

Yes, that’s actually one of the things people really like about Liquid Leaders, is when people hear quant, they think funds that trade every day or significant turnover, but we’ve optimized Liquid Leaders, we only trade once a month, so that’s really different. For quantitative-based funds, we rebalance on the first day of the month, and so you don’t see that insanely high turnover like you see at other quant funds. You’re not seeing daily trading, it’s once a month. And so we’ve optimized things that way, it’s worked really well.

You are listening to The Anderson Files, with Mike Anderson and guest Derek Elphick. To continue, Derek, what are some of the things that you’ve learned working with algorithms and the machine learning to invest that you referenced?

That’s a great question, and some of the things we learned are actually really cool. If I had to sum it up, I’d say risk management and just how key that is, as well as technical analysis. And so I learned something very powerful early on. Because if you think about it, you have two sets of processes. You have algorithms, and then you have optimization, you’re optimizing a data set. And what’s really interesting, all of the optimizations that prioritize risk management tend to do better than the algorithms prioritizing return. And so that was pretty eye-opening, so weighting the importance of risk management was key. So that’s one thing, and the other is the importance of technical analysis, which is funny because I’ve always been more of a fundamentals investor, right? I think most people like to think of themselves that way. When you do the CFA, it’s so focused on fundamentals, but I didn’t have enough respect for technical analysis. And our algorithms can look at anything earnings, valuation, momentum, value, anything like that. But the technical analysis is preferred, kind of like risk management by the algorithms. And so I just think I didn’t understand technical analysis well enough myself over the years. But it’s funny when you work with these algorithms.

I’m sure you, like me, have read so many investment books over the years, but when you see how these things prioritize different strategies, some of the quotes I used to read from years ago that didn’t make sense to me then, now make sense. And so when it comes to technical, there’s a Stan Druckenmiller quote from the 80s where they asked him, Do you use technical analysis? And he described it as simply a picture of liquidity. And that never really rang home to me. But now it’s clear as day, when the liquidity dries up for a financial instrument stock, anything, you really want to avoid that instrument. And so technical analysis gives you this great picture of liquidity, and you could almost think of Liquid Leaders as the liquidity leaders, and so liquidity is so key in the markets. Technical analysis has taught me that, and our process, the algorithms and how much they leaned on this type of thing really just brought it home for me, so I thought that was interesting.

Was there a moment in time where you looked at, OK, here’s the fundamental analysis that has been kind of the bedrock of building up your investment analysis and building portfolios? And now, with technical analysis so solidly coming into the picture, was there some epiphany moment that took place where, yeah, you know this really to apply this, it makes sense and there’s value in utilizing this approach? Was there anything in particular?

Yeah, absolutely so, if you think of it. And I’m big on making these chess analogies. But when this thing was standalone, I used to compete against it myself. In early ’22, it sold out of Microsoft, which has always been my biggest position. I’ve been a long-term lover of Microsoft. And sold out of it and bought Merck. And I thought to myself, huh, this is in 2022, I thought, you know, that’s kind of a silly move. From that point forward, I think Microsoft was down 20% and Merck went up 40%, and so both are great companies. Both have great fundamental dividend, all that type of thing, but there’s no way that human investor, fundamental investor can look at the liquidity picture, the sector flows, all these types of things that are really moving markets, the way that computer-based investing can do it. And so 2022 was when I said, Hey, this is. There’s no way I’m ever going to be able to compete against this type of thing again. And that was an aha moment.

For you.

Yes.

Something in addition that I’ve been wanting to ask you. And that is, in light of the machine-learning aspect of the fund, does the fund over time, does the strategy, and the machine learning, does it learn from itself? It starts to, okay, here’s the experience of the last two quarters, the last three quarters, four quarters. Does it begin to learn from itself as well, or is that an aspect that’s built into the strategy?

Yeah, it’s kind of what I was describing before, is if you have algorithms, right, running on their own, that’s one thing, so if you put that over here on the right and on the left, you have optimizers which are looking at the set of algorithms and optimizing what’s working. And so you’re constantly optimizing the set of algorithms over periods of time, so you’re always getting what’s working. The next step, I think, is optimizing the optimizer, right? And that’s in the pipeline. And I think when you look at what’s happening, there’s so much happening, tech and investing. But AI and large language models, because right now we’re looking at a lot of structured data and unstructured data, which is what large language models are very good at looking at. These models aren’t very useful right now, but if we start to incorporate unstructured data, and as we try to optimize the optimizers, I think that these types of models are going to be huge and they become more available, cheaper to use. I think everyone knows the DeepSeek story. It’s coming fast, right? So we are optimizing, so that should provide the effect I think you’re kind of asking, but we can do better from an optimization standpoint. I think that’s what everybody’s looking at right now. That’s the big question.

I’m talking with Derek Elphick on the Liquid Leaders Equity strategy, and this is The Anderson Files on Podclips.io. Derek. Turning to AI itself, How do you think AI will shape the investment landscape in 10 years?

That’s a great question. Again, I’m going to lean on this chess analogy. I’m an outlier. I believe the investment management industry is low-hanging fruit for AI, and a lot of folks think heavily regulated industries are somewhat protected. I’m an outlier there. But the way I like to think of it is in the late 90s when Grandmasters at chess started playing against the supercomputers. IBM’s Deep Blue, and that in ’97, Deep Blue beats Kasparov. And the world kind of changes. Eight years later, that was it. That was the last time even a computer-assisted human had a win. Now, it’s almost silly to think about, that a human could beat a fully optimized chess program. I think if you look at all these types of strategies humans have devised, passive and active ETFs mutual funds, I think in eight years, I think that that will seem archaic. Like I said, I’m an outlier. But once this technology becomes more widespread, I think it’s just low-hanging fruit. I think all industries are in this transition mode over the next eight years. But I do think that this AI technology will disrupt most parts of our daily lives.

And disrupting, hopefully in a positive way, or just in a different way, and not necessarily negative?

I think that there’s a huge way to look at it, both negative and positive. We all know the negative where it’s, Oh, the jobs all go away, right? That’s one thing, but I think people ignore what could be coming with robotics, right? And so these companies spending $80, $90 billion on AI Capex spend, they’re pretty smart managers running these companies and their job’s not to manage quarter to quarter. But I think they’re looking down the road to robotics, where we talked about large-language models. I think that all these applications are going to. The world will have an aha moment when we get to that point. Oh, this is why we built all that infrastructure. It’s not so your kid can cheat on his essay with downloading having them write essays for you. So just like the internet, there’s going to be a pause and then a breakthrough, kind of like the iPhone. And then I think it will be apparent to everybody. But that’s my opinion.

Why do you think, on the risk management side, why that is such an important topic right now?

Apollo did a great piece on this, where a lot of people, advisors and investors, everyone has this recency bias. And what’s worked, passive has worked. Passive is kind of aiding the world, in my opinion, passive always works great. If you get in at 14 times earnings on the S&P and then ride it to 22 times earnings, which is kind of where we are. Apollo looked a layer deeper and said, Hey, newsflash. If you take the top 10 components of the S&P 500, they’re trading at 49 times earnings. And so I think anyone a step up from, kind of the generalist, sees that and says, Hey, this is kind of a red flag. I think sitting here at these valuations is where you say, Hey, maybe there’s a better mouse trap, or maybe it’s time for me to think about risk management. And I think it’s that time, and so I think it’ll be a huge topic over the next couple of years. Is folks who’ve saved for retirement their whole lives should they have $0.50 of every dollar in eight stocks? That’s what we were taught years ago in school not to do, and now it’s commonplace. And so I think it’s going to be a big topic. I think risk management is going to be huge over the next five years.

What are the next steps for liquid Leaders, for the strategy? What do you see as the next step?

Yes, I kind of just touched on that a second ago, but it’s moving from structured data to unstructured data. So prices and data from financial exchanges, all that data is structured. But these large-language models. They can look at pictures, listen to earnings calls, listen to press conferences, picking up on the tone of people’s voice. The types of things that are coming are unreal and so we haven’t had, there’s not much efficacy now when you try to use these types of things, but as the tech gets better, it’s going to get cheaper. The processing is getting more powerful, I think it’s going to be huge, so incorporating unstructured data is kind of the frontier in what we’re trying to do.

Is the Liquid Leader strategy currently taking on new clients?

Yes, we are. And so when we launched this, we went out and met with a lot of our colleagues, other funds, and everyone kind of gave us the same answer is to kind of do it in a hedge fund wrapper or something like that. I was kind of upset with that because that’s not the way I think, I can’t stand giant barriers to entry like that. And so we took a different strategy, we launched it with the intention of having it go viral. So, keep the fees low relative to hedge funds, keep the investment limits low. Because we think if you try this and you’re going to tell folks about it, we want that almost internet effect where you can go viral like this. So we have a very low minimum $100,000. We’re custodying assets at Schwab, so it’s very user-friendly, most people already know the platform, and so that’s our strategy. We want to go viral with this, not put up gates. That’s been our big thing. It’s like that for now. I do think if we hit a certain cap on AUM, then we might increase the minimums or do something like that. But we’re not there yet. So anyone who’d like to try this strategy, we’re open now for new clients.

And in the future, with AUM building? And if it then makes sense, to raise the minimum, as we say, that’s a nice problem to have. If you’re even faced with that consideration.

Exactly right.

Is there anything that you’d like to add?

No, I think we kind of touched on everything. I think it’s building, everyone is kind of aware of AI, unlike a year and a half ago, two years ago, where it was kind of behind the scenes. And so I think it’s got a huge, everyone’s aware and I think people want these types of products and so that’s been great for us. And we’re continuing to try to innovate, which is really what this is about.

And Derek, how can listeners contact you? What’s the best way, most direct way for listeners to contact you or contact the firm to make inquiry on Liquid Leaders?

I can be emailed. It’s delphick@lwarner.com. That is my email address, so it’s d -e -l -p -h -i -c -k @lwarner.com. Mike, I’ll give you all the information someone can use to contact us or invest if you’re interested in opening an account.

That would be great. Derek, thank you so much for this session today. And great success with the Liquid Leaders Equity Strategy.

Thank you Mike, I appreciate it.

You bet. Thank you Darrell Wayne, recording engineer, Mark Alyn, producer. I’m Mike Anderson and this is The Anderson Files on Podclips.io. Keep calm and keep listening.

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