Animal Spirits Podcast - Talk Your Book: Invest by Avoiding the Losers

Episode Date: July 19, 2021

On today's Talk Your Book, we spoke with Julian Koski of New Age Alpha about setting baselines and probabilities on current stock prices to help avoid the losers in the future.   Find complete shownotes on our blogs... Ben Carlson’s A Wealth of Common Sense Michael Batnick’s The Irrelevant Investor Like us on Facebook And feel free to shoot us an email at [e-mail] with any feedback, questions, recommendations, or ideas for future topics of conversation. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's Animal Spirits Talkerbook is brought to you by New Age Alpha. Go to New AgeAlpha.com to learn about the human factor. Welcome to Animal Spirits, a show about markets, life, and investing. Join Michael Batnik and Ben Carlson as they talk about what they're reading, writing, and watching. Michael Battenick and Ben Carlson work for Ritt Holt's wealth management. All opinions expressed by Michael and Ben or any podcast guests are solely their own opinions and do not reflect the opinion of Ritthold's wealth management. podcast is for informational purposes only and should not be relied upon for investment decisions. Clients of Ritthold's wealth management may maintain positions in the securities discussed in this podcast. Michael, you wrote about five years ago, which credit to you, you've been blogging for at least five years. That's pretty impressive. Thank you. You wrote 10 things investors can learn from the horse track.

And this is based on a essay by Michael Mubeson. And it's all about setting odds at the horse track. And one of the things that we always talk about, I think one of the conundrums when investing is people always say, think in terms of probabilities, not in certainties. So it's not zero percent or 100 percent. It's put some odds on something. The problem is investing in the markets is not technically like the casino. You don't know the odds when you come to the table. So actually, I need to correct you. Chris on value is where I got the stuff. Michael Mubeson wrote about it. I found it that way. So anyway, it's this guy, Chris, I guess the same. essay by a sports handicapper. And in sports, when you're betting, you see the odds, whether

it's basketball, horse betting, fighting, whatever. You don't really see that in the stock market. But in terms of betting, this guy, Chris said, even a horse with a very high likelihood of winning can be either a very good or a very bad bet. And the difference between the two is determined by only one thing, the odds. So this is like Howard Marxist thing, where like everybody knows the stock is a good company, buy the stock. Yeah, second level thinking. The hard thing to do is actually, I think, get to that first level of, okay, we know that this is a good-looking company and we know the growth rates, but what exactly is priced in. So is that the third level where you start to determine the odds? It's like a negative one level. I don't know.

So we talked today to Julian Koski, who works for a place called New Age Alpha, and they are trying to actually place odds on individual companies. They're a factor. I'm sure there's other quantitative places to do something like this. The way that he explained it was relatively new to me in terms of how they're trying to do this. So they're basically trying to say, here's the baseline based on history and here's what they need to do going forward to justify their current price, which I guess is what a lot of discounted cash flow systems tried to do, but they seem to look at it in a new and interesting way as far as I'm concerned. I've never seemed to frame it as like a probability of success or failure. And then you're in or you're out. And the fact that

that's a relatively concentrated portfolio. I thought this was a pretty interesting conversation. This one kind of threw me for a loop a little bit in terms of what I was expecting. This was interesting. So, all right. So here's our interview with co-founder and chief investment officer of New Age Alpha, Julian Koski. We're joined today by Julian Koski, co-founder and chief investment officer of New Age Alpha. Julian, thank you for being on today. Thanks for having me. I think a good place to start is with a brief introduction. Who are you? Who weighs New Age? Alpha, and what is your company all about?

New Age Alpha is an asset management firm located in New York. We offer a family of exchange traded funds, indexes, separately managed accounts. But that's not really what the story is about. New Age Alpha really is about rethinking the asset management business and how risk is actually managed and therefore, how do you actually generate returns. So the whole focus is on risk management. And to do what we've done, we've departed completely from traditional portfolio management ideas. And we've drawn on the principles of insurance to build this business.

As you can see from the logo at the back, it says, avoid the losers. Because that's what this is all about. We think there's an epidemic failure within the asset management business and how portfolio managers actually think about returns. They think in terms of picking winning stocks, when really the goal should be to avoid losers. Did you have an insurance background? Obviously, that came through from checking out your website and some of the stuff that you've written, talk about actuaries and that sort of stuff.

Is that the background you had, or is that just kind of the concept that was made from you? So I grew up with actuaries around me, family members, and also my early professional career was that of an accountant, and I so happened to be stationed as an accountant at one of the largest insurance companies in Africa, and I got to learn this methodology. And that's where this was all born from me, seeing how actuaries actually work. We've spoken in the past about avoiding the losers as a strategy for winning because I think the data shows that two out of three stocks underperform the Russell 1000. So not that it's easy to, I mean, picking winners is hard.

It seems like maybe trying to avoid the losers as a more reasonable methodology, obviously easier said than done. But you have something called the human factor. How does that color your investment process? Just building a little bit, Michael, on what you said there. It's even worse than that. Because of the asymmetry of returns, the fact that. that losers get punished a lot more than winners, it really is about managing those losers

and avoiding them. In terms of the human factor, what we say is that the biggest problem out there is losing money because a stock is overpriced. And if you think about overpricing, overpricing comes from one thing only. It comes from human behavior. It's not the fundamentals of a company they're going to overpriced the stock. It's humans and human behavior. We know that the efficient market hypothesis says all information is priced. And that includes what we know about the company, the known financial information, the balance sheet and income statement, but it also includes a lot of this vague and ambiguous information. And what you're seeing investors do, especially today with the gamification and mimification of what's going on, is they're interpreting

this vague and ambiguous information in a systematically incorrect way. So just go back in time. You look, when Steve Jobs resigned and then later, died. A lot of the investors viewed that as a good thing for Apple stock, some sort as a bad thing. But the fact is no one really knew. It's vague. It's ambiguous. You don't know how it's going to come out. But that information is getting impounded into stock prices all the time. There's more vague and ambiguous information in the market today than there is known information, the balance sheets and income statement. But if you look at the insurance industry, so we have a saying, managed risk like an actuary, not like a portfolio manager, because insurance actuaries,

don't underwrite risk based on vague and ambiguous information. The last time you got a life insurance policy and you get a questionnaire, they don't ask you things like, are you going to quick smoking? Are you going to go to the gym? No, they could never underwrite risk based on that kind of vagueness. Well, we believe the same is true about stocks. There's only two things you absolutely know about a stock at any point in time. You know the price it's trading at and you know the financial statements. Now, from those two pieces of information, we calculate the human factor. And maybe I'll stop there and see if there's any other question, then we can go deeper into the human factor. Well, I think one of the hardest questions to answer about any individual stock is what's priced

in. Because you could look at a stock like Amazon over the past 10 years. And every year you could have said, well, based on the valuation, this company is overpriced. And obviously, that hasn't been the case because it's more than growing into some of those valuations. So how do you attempt to solve that problem of what's priced at? Because it's true that there is more information than ever before available to investors. But it seems like this question is becoming harder to answer because of the type of companies we're dealing with now, and especially when it comes to so many technology companies that there are. So let me take you through the math on how we do it.

So we take the stock price and we take the financial statements, and we want to calculate two things. The first thing you want to calculate is what growth rate does that stock price imply. So if you took Amazon and what it was trading at, and we can do it in real time, we look at Amazon stock price, we connected to the financial statements, and we calculate Amazon's implied growth rate for that stock. What do they need to grow by?

Because remember, the more vague and biggest, information being priced into that stock, the higher the growth rate's going to have to be to support that information. Now, the biggest thing from that now, the second thing is, what is the probability that Amazon will fail to deliver that growth rate? Now, to do that, what you do is we take a look back. So we'll look back to the prior 12 quarters to see how many times in the prior 12 quarters Amazon actually delivered that growth rate. And we plot the current growth rate on that distribution of prior growth rates to see the likelihood it will deliver again in the future. Look, you took a simple example.

I like to use Tesla. If Tesla was trading at $100 a share and that implied that Tesla sell $1,000 cars per quarter and you look back over 12 quarters and saw that Tesla has sold 1,000 cars per quarter, well, you pretty much can safely assume in the next quarter, there's about 100% chance they're going to do it again. Now, if that stock was trading at $500 a share and that implied they need to sell 5,000 cars per quarter and they've only done 1,000 cars per quarter, well, then the likelihood of selling 5,000 cars in the next quarter is about 20%. So why would you own that stock? So to answer

your question, Ben, we don't care what's priced into it. Everything can be priced into it. What we want to know is, is the market correct? Do these underlying fundamentals actually reflect Amazon's ability to deliver that growth or Tesla's based on the current stock price? And the only way we know that without having some kind of inside information or we're not going to do the research is to look at it through a probability lens. We're dealing with uncertainty. So much of stock investing is randomness. There's a lot of randomness at work. And if you're dealing with random outcomes, we're going to use a probability to deal with it just like insurance does. It's probability based. How did you come to the conclusion that avoiding losers is a more reasonable

way to place your efforts than picking the winners? Because picking the winner, requires you have knowledge of the future. And the future by definition is not known. And the more you actually forecast this unknown future, actually what you're doing, you're just increasing the odds you're going to be wrong and invest in a loser. In our view, the world of picking winners is almost impossible. You don't know what's going to happen. But when it comes to avoiding a loser, there we can underwrite that risk based on what we know about the company. I don't need to have knowledge of the future. I just need to look to what they need to do. Let me push back on this because I do love the idea of avoiding losers. But to me, it would be one thing if you had

the S&P 500x 100 losers. You're trying to get rid of just the bottom 100 stocks. But that's not what you're doing. You have 50 stocks. So it does seem to me like you are trying to pick the winners. So talk about why am I wrong? Well, I haven't figured out if those remaining 50 stocks are good stocks. I've tried to figure out if those remaining stocks are mispriced. To me, it's not about the fundamental. I'm not looking at the fundamentals. I'm looking at casino odds and I'm basically saying there's a group of investors out there in the marketplace that are using their knowledge of the future to place bets essentially on a set of stocks and I'm looking for the ones that are mispriced to avoid those and buy the ones that are

fairly priced and the only way I can do that again is with that probability so yes you're correct I am picking the winner because it's coming out in the wash but the goal number one is to get rid of the guys that I don't think can deliver. Now, let me be clear. So let's say you've got a probability like, I know Tesla's probability today is about 58% chance that it would fail. Now, am I saying Tesla is going to fail? No, it's not what I'm saying at all. What I'm saying is, you could own Tesla and you can generate a great return. But if Tesla misses its earnings or misses one quarter, that is going to have a huge impact on a person's portfolio. So it's about avoiding that and just buying something else in the interim that can generate that return.

Sorry to break in here. So any easy example here would be every American would have to go see five movies a day for AMC to justify its current stock price, something like that. But how do you do these times when it gets so out of whack? Do you have a longer time horizon for something like this? Is there a turnover? How does that work in terms of how often you're changing the portfolio? So the portfolio is rebalanced quarterly. So every quarter, the way the system works is every day the stock price changes, we get a new probability. But we don't act on that. We only act when the new financial statements come in and then we look at the probability and then rebalance the portfolio. What I'm saying to you, when I tell you a stock has got a

probability, let's say 10% probability of failure, I'm telling you that there's a 90% chance that that that company will deliver the earnings comes the next earning season. That's what I'm telling there's a probability on that. Now, there's still a 10% chances will fail, but I'm telling you there's a 90%. So being with probability, what I'm actually telling is this system is not great at picking individual stocks. It's not good because you can still get a 10% chance of failure, even with a 99% chance. But it's really good at building portfolios. Because based on the law of large numbers, if you're using probability, then your low human factor probabilities are going to outperform your high just over time. Same as mortality tables would work. It's the exact same thing.

I love this line from Michael Mobison. He was talking about horse racing. And he said, fundamentals are how fast the horse runs and expectations are the odds. And I've always thought that when you're looking at Apple, for example, everyone knows it's the best company in the world. Nobody would argue with that. But you don't really know what the odds are until, say, maybe after an earnings report. But it sounds like you would argue that you do have a way of calculating the probabilities. What exactly are you looking at? That's exactly right. We're looking at the forward-looking odds of that. We're saying, based on where that stock price is today. The scariest thing of the market today are people,

are humans and human behavior around the stock price. It's the action around the stock price that's scary. That's the idiosyncratic risk that you have to be aware of. We know about firm-specific risk that you can diversify it away, but you can't diversify away human behavior. That's in every stock price. You can only avoid it. So what you said Michael and what Michael Mubusian also says, that's exactly right. We're looking at the odds that the company will deliver. It's not a perfect science. It's a probability-based science. But on law of averages, again, your low probability stocks, we've seen over 25 years, outperform high probability stocks. It's those guys. The problem is when the world starts talking, they're impounding

this vague and biggest information. Another word for that is gambling. What's the difference between forecasting the future, gambling? And I'm basically saying, look, we've got a system of betting against you. Because I've simply believed that if you're doing that, I'm going to bet against you. That's as simple as that. I don't believe you know. And I think that after regulation fair disclosure that came out in the year 2000, this kind of system really works well. Maybe before that you could argue that somebody had a piece of information I never had access to. Not true anymore. So now, based on that, well, I'll use the probability. I assume that means that this is a fully quantitative strategy. You're not putting any qualitative factors in here.

Yeah, fully quantitative. And then does this end up looking like any other sort of smart beta quantitative strategy? Does it change its stripes in terms of sometimes their momentum stocks, sometimes their growth, sometimes their value? How does that shake out in terms of what this factor actually looks like if it's anything that's comparable to anything else? Every factor has a little bit of everybody else's factor in. But we have very small correlations to the existing Farmer French and we've added volatility and those types of things. The most important thing we see with this factor is that it's dynamic and it's changing over time. So, So today, I know that the H factor is best correlated with quality.

And I know going back a few years, it was best qualitative with the investment factor. And then with quality again, the first time the value factor shows up in a meaningful way is 2012, where we see a little bit of value peeking out in the H factor. So the two things are, it's doing good when other factors are working. It works well when the other factors, but it's also not correlated with them when things start to go wrong. Can you talk about the H factor or is it proprietary? No, I can talk about it.

It is proprietary, but I can still talk about it. What can you tell us? Well, in terms of what, how it's calculated? Yeah, what are we looking at? If I was looking at your whiteboard, what does it show us? Basically, if you think about all the valuation models out there, and there's a whole lot of them, so you can use price earnings models. We use all of them.

Let's take a discounted free cash flow model. Then let's think about that one for a moment. When an analyst uses a DCF model, they generally start with making assumptions. about the growth rates of those companies, they forecast the revenues, they then make assumptions about margins and expenses forecast the cash flows, they put in a cost of capital, and they come up with the stock price and they tell you if it's under or overvalued. Think of the model the exact same way, except start with the stock price as your input. So you start there. Once you start with your stock price, take the financial statements as published. Don't make any assumptions

about expenses and margins. You don't know. My answer is to you, you don't actually know. what they're going to be. It's a formula. It's one simple formula. It's a big formula. Work backwards, calculate the implied revenues. Once you've got those revenues, then you're going to need to want to plot that revenue against a distribution of prior revenue growth rates to see where that likelihood of the current revenues are going to land on the prior. You want to see what's the probability. So think about this. The higher the stock price goes, the higher the implied revenues will go. Now, if they have not delivered that in the prime, a 12 quarters, then the probability is going to stay high. If they have, then it's going to drop and

go low and you're going to want to own it at that point in time. Any other interesting correlations or relationships you've noticed in terms of the type of sectors? Are there any sector explicit controls here that you have certain weights that you won't go over under? Or you just kind of let it shake out where it may? We let it shake out where it's may. The important thing to know is that low H-factor stocks outperform high H-factor stocks 65% of the time on a quarterly basis. So there are moments in time where high H-factor stocks will outperform low. But that adjusts very quickly. Now, if you look at casino odds, which are around just over 50%, and insurance odds, which are almost around 65%, those are very good odds. We're not right all the time. It's

impossible to be right all the time. There are moments where you get that irrational exuberance and those high H-factor stocks, but at some point, they're going to correct themselves, and this is how it does. Was there ever thought of doing a long, short portfolio with us, or would they have a blowup factor if the bad ones did really good and the good ones did really bad base at your scores. No, no, we have a hedge fund. That's what we do. Oh, okay. So you have the long short as well. We have a long short short. You go long, the low H factor and you short those high H factor stocks. So let's talk more about the business because you just said you've got a hedge fund. You obviously have, or not obviously, you have two ETFs and a billion indexes. That's an

exaggeration, but you have quite a bit of indexes. What does New Age Alpha the business look like? Our goal is to build out a diversified asset management platform geographically as well, so internationally, but also across asset classes. So we're doing everything from fixed income to all the different sized equity classes. So over the next year to two years, we'll launch a family of exchange traded funds. Our whole focus is if you look at the world of indexing, the smart beta, the beta strategies, they answer a fundamental part of an investor's portfolio, but nobody's attempted to build a systematic alpha solution, something that doesn't take an advisor out of its comfort zone. If you want to be in

large-cap blend or you want to be in the S&P 500, that's fine. We'll keep you in there, but we will simply, by removing these high H-factor stocks, remove the losers, give you some additional return that you're not going to get by owning the whole 500. So we have a saying, we take what's good and make it better. So you take the Russell 2000, remove the losers, make it better. We're not asking you to substitute ours with a smart beta, but we are saying complemented with it because we're going to give you something different and we're going to give you an additional return.

So the business is to build a platform of alpha-driven ETFs. This is all about alpha. Just so you know, we can't offer this now, but to our institutional clients, we do offer it. No alpha, no fee. We don't outperform the bench rock. We don't charge. And we've applied to the SEC to ask for that as well for the ETFs. That's great. So what timeframe are you looking at that, like on an annual basis? Three months, quarterly. So if you don't outperform or three months, then there's no fee charge. No fee, no fee charge. I love that. That's great. Love that. That's excellent. Let's say that I was looking at ADVR, the U.S.

Large Cap, there's 50 stocks. Let's say that I was just using a quantitative screen, just to look at the portfolio, had no insight into what was in it. What would I glean from that? Would it look like a value? I'm sorry, I'm not trying to pigeonhole. No, problem. Don't brum. I'm just curious. Does it look cheap relative to the S&P? Does it have high R.O.E? Like, what are some of the characteristics? If the portfolio was just one stock, what would it look like? So the thing to look at is the overall MPT stats. What you're going to see is that a 50 stock portfolio has the same standard deviation of volatility as the overall index. But what you're also going to notice is that it has a much lower beta and a much lower sharp ratio with a higher upside capture

and a much better downside capture. So with 50 names, you're not getting anything different risk-wise from the overall S&P 500, but you're just getting better risk-adjusted returns. You're just getting that better upside capture and that better downside capture with a lower beta. It actually has a lower beta than the overall S&P 500. You also have an ESG version of this same fund. how did you approach the ESG characteristics to this?

Is it the same quantitative factor just slicing off certain companies or industries? How does that work? We're not in the business of providing the ESG scores. So we teamed up with refinative to license their scores. The reason we did is we were never quite sure that ESG on its own can actually add alpha. And it's still unclear to me if it actually can. But if we pay the ESG score up, so to us, 75 and above is really, what we want to invest in. They call a minus and above. We want the best ESG companies and we look for

the lowest H factor scores within that and team that up. So it's got an ESG filter, but it's also got this risk filter that we put on top of it. So it's the best ESG scores with the lowest H factors. And that makes up that ESG platform. I'm sure you've done all the quantitative work on this. Why 50 holdings? And do they start out equally weighted? What does that look like? The more concentrated you get the better. So I can show. show you that monotonically, as I remove losers from this, and I call them losers, but you understand what I'm saying, companies that with a high, vague, and ambiguous information factor in them, as I remove them, 10%, 20%, 30%, I incrementally or monotonically improve the performance.

And as I get to removing nearly 85% of those numbers, those high age factor names, I improve the returns the best. But there's at some point where you're now taking on too much volatility. at 50 names, you've got a very well diversified portfolio, but you've also got the highest return. Now, how we weight the names is by the inverse of the H factor. The lower the H factor, the more we want to own of it. So the lower the H factor. So it's weighted because you're almost getting equal weighting. You're getting an incremental benefit, 50 names. It's very marginal the change in waiting, but you are changing it. It is a curve. It's not a straight line. You mentioned that you want to have this family of funds. And when you look at your indexes, you have all over the place,

U.S., Europe, Japan, you have different sectors, you have different sizes between small caps, mid caps, large caps, that sort of thing. Have you found an area where this H factor works better than others where you see a distinct difference going, oh my gosh, in small caps, it really works well? Or is it pretty similar across the board? It's similar across the word, but where it works the best is in large cap S&P 500. The most efficient space there is, is the best return. And that's because that's really where the forecasting of the future and all of the stuff is actually going on. It is the least efficient of all of the indexes. Wait, wait, hold on. I need you to explain because usually it's the opposite.

Correct. Usually we hear the larger the stock, the more efficiently priced, the more coverage, the more assets, obviously. So why are you finding it to be the case that the H factor works best in the Walmarts of the world? Because nobody is looking at risk this way. Nobody. And when you look at risk this way, that's when you see where the inefficiencies actually are. Remember, whom are betting against. I'm betting against analysts who believe they think they know the future. And this probability is undermining that. That's what that's doing.

You're making some big proclamations and more power to you, but are analysts forecast a big part of your equation? Like, what are you doing that's so special. It's what the analysts are doing by leading the market. That's what they do. By leading the market with their models and what they do, we think they're getting the models wrong in the story. We just think the models are wrong because they're impounding their own behavior into those

models. Once you impound their behavior into it, it's not hard for us to look at that and say, it's a simple bet against you that you're not going to be right. You can say to me, I've done all the research in the world and I know something you don't know, but that's not necessarily true anymore. That's not true anymore. So it's safe to say that like you're betting against career risk and the way institutions can be like lemmings and the stuff in the way that is that kind of what you're betting against? Spot on. That's exactly. So what sort of biases do they have that you're systematically exploiting? So I don't know the specific bias I just know that all human behavior should be removed.

So once you've got human behavior at work, our view is to eliminate it. I don't know if it's anchoring. I don't know what the specific behavior is. I also don't know how to arbitrage that behavior because it's not clear that you can arbitrage it. But what I can do is recognize when it's there and avoid it. Is your goal to make the H factor like a thing that people talk about? Yes. To me, what is risk?

Where is risk coming from? Does it come from beta or volatility? Does it come because it's a growth stock or value stock? Do we really care? It comes from behavior. You want to answer one question of a company. Can Amazon deliver the growth implied in its stock price? If the answer to that is yes, you want to own it.

And if the answer to that is no, you don't want to own it. And you need a shortcut to get to that answer. End the story. So to me, that's like the biggest unknown. And to Ben's earlier point, we've been saying for years, at least I have, like Amazon seems fully priced. I don't know how they can keep raising the hurdle. and then jumping over it, but they have.

Amazon's probability is 18% that it will fail. So it's a low probability. We're going to own it. Absolutely. Because again, we have those gut intuitions. I mean, look at Tesla from two years ago. I don't know if you have the time. Tesla two years ago, just before the run up in the stock was trading at $253 a share.

Now, if you did the math on Tesla on that day, you calculated that Tesla needed $6.1 billion in revenue and needed to sell 95,000 new cars per. quarter to support that stock price. Now, the probability, if I looked back over 12 quarters, you saw that Tesla delivered this nearly 80% of the time. But on that date, there were 169 newspaper headlines about Tesla out there. 131 of those newspaper headlines were negative headlines. You had some of the biggest Wall Street guards saying Tesla was going to go out of business. So that's the vague and ambiguous information making its way into Tesla's stock price. And in that case, it didn't overpriced Tesla, underpriced Tesla. If you're

you had done the math, you would have never been short Tesla. You could see this guy's going to do it. The math was telling you that. But they didn't do that. Everyone's talking about how he's going to go bankrupt. Interesting. On the other hand, so you've given some examples of companies that people obviously misprice in the wrong way. How about some companies that people think are like invincible right now that you see like a high potential failure rate? Or is any that are showing up that way? Boeing, Pfizer. Think about what's going on out there, the reopening of the economy and everybody betting on travel coming back. and airlines growing and Pfizer, people are betting on the fact that this COVID's going to be a

windfall for them. And you know what? Maybe for both stocks, it will be. But we don't know yet. Let them prove to us that they can do it and then we will own it. In the meantime, we'll find something else that's got a lower probability of failure and own that. We're not saying these companies are going to fail. We're just saying hold off until they've proven it can be done in relation to that stock price. How many quarters does it typically take for that to be proven if they're not proven yet. Is it going to take a few quarters then until you get that track record potentially? Okay. At some point, we'll own it. I'll talk about Apple. So Apple back in the year 2000 to 2006,

probability was horrible. It was like 80%, 90% chance of failure. All of a sudden, 2006, that probability goes from the high 80s down to below 10%. All of a sudden, through the iPhone, he stabilizes the revenues, gets a predictive form of revenues, and the probability drops all the way down. And I think quarter after quarter owned Apple all the way through, made lots of money from it. So it's not about trying to be in a rush to find the next growth story. We're not good at that. We don't know when that's going to happen. But at some point, we'll take our time and the probability will tell us when to own it. How long have you been using the H factor? Well, I've been doing this for 20 years. And it's been an evolution, like you've been using

this framework for 20 years? Yeah. So we have never changed the model in 20 years. Never. Wow. We test all the time. I've got PhDs working for us. I've got researchers working for us, and we've tested everything. We look at Bayesian probability. People always give us new ideas. We've never found enough reason to change. Usually you'll find some kind of extraordinary performance in one year, but then all of a sudden there's no consistency.

Consistency for us is key. We're building investors portfolios. I like to compare gambler's portfolio to an investor's portfolio. This is an investor's portfolio. So you want stability and you want consistency. Now, that's not true at the portfolio level. So at the portfolio level, we might make tweaks in that to the portfolio, but never to the H factor itself.

All right. A good place to end, I think, is this. So you've got a table showing the hypothetical outperformance of the H factor. Again, it's hypothetical. So I'm sure you've seen this before. There are these strategies where back tests and the back tests go up, up, up, and all a sudden the things published and all of the alpha miraculously disappears. So the H factor outperformance, I'm just eyeballing.

And I think you've underperformed in only like three years out of 20. So remarkable back test. Why is this going to work in real life? So two things. One, we've got to differentiate between a back test and an investment thesis. Our investment thesis is simple. Low H-factor stocks must outperform high H-factor stocks. And I should be able to take that thesis and apply it to any investment universe.

So I can. So right now on my system, I can't show you, but I've got a Goldman Sachs ETF in front of me, which is the Goldman Sachs hedge industry VIP ETF. and that owns supposedly the biggest conviction names about the long only, I just removed 50% of that portfolio and improved that return by 2.74% on an annualized basis. So a good backtest means taking the thesis and applying it to any universe out there and see if it works. So backtest is different because there you're trying to say, well, small cap stocks combined with pigskin futures, maybe we'll work. Well, who knows if that's going to work. We have a thesis and if the

The thesis doesn't work. We don't apply it. End the story. Julian, this is great. Thank you so much for coming on. No, thank you for having me. Thanks, both. Where can people find your work? New Age Alpha.com. Everything is there. We've got an extensive library. We write a lot. We do a lot of research. We also have a tool that we give away for free where you can actually look up the H-factor scores for all the stocks out there. You can look at all the ETFs out there. And our guys, our portfolio specialists, will show you how to load your own portfolio in

there and see how much human factor you've got in your portfolio and what you could do to remove that human bias and see if you outperform. Excellent. We'll look to all of that in the show notes. Thanks again, Julian. Okay. Thanks, guys. All right.

Thanks, Julian. Thanks, New Age Alpha. Send us an email, Animal Spiritspot at gmail.com. Thank you. Thank you.