RAOUL PAL: And so typically the kind of strategies that you're looking at, again, you're running
a range of strategies, what kind of vol are they, and have you experimented with high-vol
strategies using AI, which tend to be longer-term time horizon strategies?
Because one of my beliefs is that it's much harder to compete with humans on a longer
time horizon where you've got pattern recognition and that perceived living in the future and
too many expected paths to get there or machines to deal with it yet.
So have you looked at A, what kind of vols you have now, and have you ever looked at
kind of longer time horizon and higher-vol strategies to see what kind of return profiles
you get out of that?
TREVOR MOTTL: We have looked at higher-vol strategies.
Our strategies tend to be lower-vol strategies, both in the long only and in the long-short
world.
RAOUL PAL: Why is that?
Is that the kind of five to eight vol or whereabouts in the vol?
TREVOR MOTTL: So in the long-only world, somewhere between 75% to 90% of the S&P type vol.
And in long-short, typically in the four to eight vol range.
But obviously, that's regime dependent.
The longer time horizon is actually pretty tricky.
As you extend the time horizon, the signal degrades.
The noise tends to take over.
And we haven't built strategies that do forecasting on very long-term basis using AI.
We're far more focused on that one week to one month period where there's the potential
for the algorithm to have a variant perception, versus some of the other investors out there.
RAOUL PAL: And that's one of the points I've raised to people who've been concerned that
how do we compete with guys like you who have A, huge experience, but are asking all the
questions, have the tools, have the deep understanding of all of the makeup of the markets from a
very granular level.
It's very difficult for people to feel that they can compete in a marketplace.
And one of my answers to people like that is be in the longer-term time horizon because
then the only competition is humans.
So you have edge there, apart from people's brains against brains and luck.
TREVOR MOTTL: I think that's good advice, and I'll elaborate a little bit on that answer.
When you think of investing, I really break investing into three axes.
There's a pricing axis.
You can be better at pricing an asset better than other people.
Can be better at pricing optionality better.
You can be better at pricing yield better.
It runs the whole gamut.
You can be better at picking people for a venture capitalist, picking founders.
The second axis I focus on is holding period, which echoes your point.
And that goes from the very, very short term, the milliseconds in the high-frequency world,
to the multi-year horizon investors in the venture capital and private equity world to
the Warren Buffetts of the world who are sitting there holding things forever.
And then the third axis I think about is liquidity.
And it's distinct from holding period because there are some assets that are relatively
cheap to get into and get out of.
There are other assets that have enormous transaction costs, as well as you might not
be able to find a buyer.
It could take years to find a buyer for very bespoke asset classes.
So if you look across those three axes, there are plenty of places to find an edge.
There are amazing investors in every corner of that three-dimensional space.
And I think investors need to pick their area in that three-dimensional space, understand
their core strengths, and look to exploit those core strengths.
And I know that's a very generic description, but the great investors that are featured
all over the world have typically done that.
They're focused on a specific area of that space and done that well.
RAOUL PAL: Yeah, it's like Stan Druckenmiller did an hour and a half interview on Real Vision
a while back.
And one of the things he talked about was when he's taking a big position, he will only
use basically bond futures, bonds, or currencies.
Because in the end, he said I can get out in milliseconds 24 hours a day, and nothing
else gives you that ability to manage risk.
And his expertise is knowing when to apply massive leverage and into what is normally
a lowball asset.
It's super interesting.
So just to finish off, I'd love to get your thoughts, because I'm a macro guy and that
means I live in the future, where is this all going in five years?
How do you see this space evolving?
Because it's a very interesting, rapidly-developing space.
And it'll obviously splinter and morph as it goes, but where do you think it's all going?
TREVOR MOTTL: I think we're going to see a convergence of the quantitative and the fundamental.
And there's going to be shades of gray throughout that.
There'll be firms that are purely quantitative.
They'll always be firms that are truly fundamental.
And in the middle, I expect to see the adoption of more quantitative tools that influence
the fundamental discretionary investment process where you see the investment process potentially
becoming more repeatable, less exposed to negative emotional biases.
You're able to integrate more granular data.
I think our understanding of investing in markets will increase over the next five years
because we are able to use these models, use this data, use this compute to answer questions.
We never were able to answer before.
RAOUL PAL: So that brings me up to one thing is right now, there's the world of human behavioral
investing, which is what basically you're exploiting, the alpha from understanding that,
how people react around earnings numbers, whatever it may be, right?
Now, as AI comes up and becomes a more dominant force, where does the signal come from?
Because you're then competing against each other.
TREVOR MOTTL: I think that's back to the crowding point.
RAOUL PAL: The Silicon Valley thing about AI is you get to a point where it takes over
from human emotion.
Not we're saying that AI runs the portfolios, but they're observing each other now and not
observing the humans because there's less of that around this activity, which we've
already seen with index funds, for example, have been very prominent in the market, versus
the old value investors.
TREVOR MOTTL: I think that crowding point is true for many of the innovations we've
seen in finance over the years.
If we went back to the '20s, we could go in, document all of the strategies that got crowded.
if we started in the early '80s, block trading, at some point, got commoditized and got crowded.
Stat arb got crowded then you saw quantitative strategies get crowded.
Longshort market neutral had periods where it was crowded and became very aware of crowding
to evolve.
So there's not a right answer.
There's so many degrees of freedom in investing that as an area gets crowded, it potentially
creates other opportunities in other areas.
RAOUL PAL: But do machines just trade against machines, I guess is the point?
As we've seen retail volumes and other volumes falling, do we get to a point where it's betting
against somebody else's machine, just philosophically?
TREVOR MOTTL: Sure.
In the high-frequency world, I think you see that today.
And in order execution in trading volume, effectively, it's algorithm versus algorithm
to effect trade.
For a longer time horizon, I think that becomes a harder limit to expect because there will
be variant perception amongst investors for many companies.
If we all sat around the table to evaluate one company with all of the computers in the
world and all of the data, we still could come up with two different answers or three
different answers or four different answers.
And I don't expect that to change.
I always expect markets to evolve.
And the emergence of AI as a tool will foster the evolution of markets.
RAOUL PAL: Fascinating.
All of this is fascinating to me, hence I drilled you with a million questions because
there's so much learning for everybody on what it is.
Because not only is it a whole different development going on, but what it also does is ask questions
of us as investors, how we run our own processes.
Are we asking the right questions?
Are we doing the same thing?
Are we observing the risk-adjusted returns in the right way within our portfolios?
Because these are real questions.
Just because it's a machine doesn't mean you shouldn't be asking of yourselves if you're
an individual investor, too.
I just think the whole thing is super fascinating, and I really appreciate you coming spending
some time to explain it all.
And I wish you the best of luck with it because it's an amazing space to be in.
And it'd just be very interesting to see how it develops over time.
TREVOR MOTTL: Thank you very much.
This has been a lot of fun Raoul.