00:00It's time for today's Drill Down, where we focus on one ETF. Eric, what do we have?
00:04Scarlett, today we're looking at HAPI, which is the Harbor Human Capital Factor U.S. Large Cap ETF.
00:09Okay, what does this do? Basically, it takes the market and it pulls out stocks where employees
00:17aren't quite as happy or, you know, interested, happy to work where they're working, basically.
00:23So it's mostly going to track the S&P, but it's going to look at the factor of happiness where
00:28you work. So they think this factor is correlated to better returns, right? So let's look at this
00:3435 basis point fee, which is good because it does have low tracking error. The fees tend to come down
00:39when you have low tracking error because people use it in bigger chunks in the portfolio. It's a
00:43couple years old. It's got $500 million, so half a billion in this thing since it came out. Let's
00:49look at the performance here. And you can see it's based on an index, so it's not active, but the
00:54index has rules that make it tweak from or differ from the S&P 500. So you can see here,
00:58you got
00:59most of it looks like the S&P, right? What's not here? Tesla. We will go over why Tesla is
01:03not in
01:03there, but according to their study, employees aren't quite as happy there, and they think that
01:08will differentiate and do better than the S&P because of that. Let's look at this versus the S&P
01:13just to get an idea of how it's performed. And it actually has outperformed. So given, you can see
01:19from the line here, it's not taking much more risk. It moves just like the S&P. So if you
01:22can eke out
01:2311 percentage points over the S&P with not really much more risk at all, that is some old school
01:30outperformance, Scarlett, and that's probably why it has half a billion. All right, Eric, thank you so
01:34much. And joining us now to talk about this ETF is David Van Adelsberg. He is founder and partner at
01:39Irrational Capital. So just explain to us, David, when Eric says it measures employees' happiness
01:45happiness with their company, these are emotions. I mean, this is something kind of squishy. It's not,
01:51I mean, how tangible is it really? I mean, how can you put numbers to it?
01:55Yeah, so this is the magic. We're basically taking soft and putting a very hard edge on it. So we're
02:01doing this with data. We have a very large data set. It goes back for 20 years. And the ability
02:08to
02:08process this data is really the magic. We measure two things. We measure intrinsic and extrinsic
02:15motivational characteristics. Social scientists sort of bifurcate. The external stuff is the usual
02:21suspects. It's compensation and benefits. That actually has a pretty modest outperformance.
02:29But stuff that's internal, so this is, you know, human and personal and emotive. And you feel it here
02:36in Bloomberg and probably everywhere else. This is the energy where we're leaning in. We're helping
02:41a colleague. We have a sense of purpose, a feeling of appreciation. You know, we have a level of trust
02:47in the organization. That really lights up the board. We use 100% of the data. We rank each company
02:53on an annual basis, and we use it to select stocks. You know, it's interesting. This reminds me almost
02:57of the inverse of the founders ETF, which is just stocks run by the founders who are usually like a
03:03little, you know, sprinkle their DNA all over the company. Yeah, it can be hard driving. You know,
03:07Steve Jobs at Apple, Elon Musk at Tesla. But those have done pretty well. But it's interesting that
03:14they're, you, Amazon and Tesla are two stocks in here that are, would be underweight or not at all.
03:20Yep. And they are obviously prominent companies. So what would you say to somebody who's like,
03:24look, I like the idea, but I just, I don't know if I can live without having all Mag 7
03:28in there.
03:29Like I just, these companies are too earth changing. Yeah. Well, you shouldn't buy it. And
03:34I mean, but the bottom line is you shouldn't buy it and you won't get the outperformance that you
03:39talked about a few minutes ago. But other than that, it's all good. So you do this for big cap
03:44stocks. That's HAPI happy. You also do this for small cap. Yeah, we do it. We have, our data set
03:50is very substantial. We have about three quarters of the Russell 2000. So we do it for small cap as
03:55well.
03:55I mean, is this ESG? It almost makes me think of a more practical version of ESG. We did,
04:04I remember, I think it was Tudor. Somebody had this survey of what people really care about.
04:10And just capital, I think it was. Yeah. And I think like things that you hear in the media a
04:15lot
04:15were down the list, but the top things were like, treat your employees well in terms of how people
04:19think companies should behave. And this sort of, you know, kind of jives with that.
04:23Completely. You know, the question about ESG will let the people who buy it decide, you know,
04:28whether it is or it isn't. At the end of the day, if we're not producing outperformance in our indexes,
04:34then there's really no reason to, you know, reason to call us. If you think about it, what could be
04:39more fun? I mean, look, every manager that I've come across or almost every manager says something
04:45to the effect of people are our most important asset. Yes. Most important means we should be able
04:49to measure it. Asset means we should be able to get access from an investment perspective. And so
04:55what could be more fundamental than investing in people? How do you think about AI then? Because AI
05:00threatens to replace a lot of jobs, displace a lot of jobs as well. And it may color the way
05:06that employees
05:07think about their company, just even in terms of how the company says that they're investing in AI. Yeah,
05:12it's a great question. So we think that the job apocalypse is slightly overblown. It's not that there
05:20won't be any impact. But there are two types of projects with AI. One is automation. We're trying to find
05:27a
05:27way to do things without people. And the other is augmentation, right? So we're all using augmentation. I
05:33don't know if you used it to prepare for today's show. I did, by the way. And it makes it
05:37for full disclosure. It
05:39makes us more productive. It makes us have more information. It makes us be able to do more. And so
05:45the through line, what we've seen and others as well, is that the levels of trust that an organization
05:51has with its employees really matters whether you're augmenting or you're automating. And it's not just our
05:58view. But McKinsey, for example, did a study which basically said trust is highly correlated to the success of
06:04AI implementation. And how did you stumble upon this? I mean, you're in the industry. What did you read or
06:12find that
06:12made you think, I'm going to face my whole career around this? Yeah. So my background is technology, a stint
06:18in
06:18HR consulting, and then financial services. And this sits in the middle of those concentric circles. And you also have
06:26JP
06:26Morgan using your human capital factors as well in their own research. Just explain to us how they've done that,
06:32how they've
06:32incorporated it. So JP Morgan found us, Coram Chowdhury from the quant research team found us. They've published six
06:41detailed research reports over the last five and a half years. We have no commercial relationship with
06:47them. The reason that they're so fascinated about this is that they're bringing something really unique to
06:52their clients. If I could, could I read one quote from one of the research papers? So in the first
07:01research
07:01paper, they said the human capital factor strictly dominates all styles across all metrics. It has
07:07the highest returns, lowest volatility, highest sharp ratio, highest hit rate, and lowest maximum drawdown.
07:13Not our words, theirs.
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