RAOUL PAL: Let's dig into this AI because this is a massively misunderstood or just
not understood at all thing.
Took us through how you conceptually see it first then we can talk a little bit more detail
about how it actually works in practice.
TREVOR MOTTL: AI is a really broad catch-all term.
And all AI really is is more advanced analytics and modeling.
So the advantage of AI is we can model things we couldn't model before, and we can analyze
data that we couldn't analyze before.
So a great example is textual data.
AI is made and analyzing large amounts of text data, let's say all of the 10-Ks for
this year.
That text can be analyzed if you ask it very specific questions.
So one could load up all of the 10-Ks into a database, write a program to extract let's
say the length of the risk disclosure section of the 10-K and then see how that's evolved
for a given company over a period of time or compare across industry groups the length
of a risk disclosure section.
That's a very simplistic application, but that's one that a fundamental investor couldn't
do until they had the tools to actually do large-scale textual analysis.
That's an example.
RAOUL PAL: What kind of information does that give you, then?
I believe it's part of a much larger data set that you're looking for something.
Are you spitting out trades based on that, or are you doing analysis and research based
on these kind of things where you would then look in further?
How does this all work in that particular example you go?
TREVOR MOTTL: So that's a simple example.
That's sort of a toy example, but it gives you some context as to how natural language
processing could be helpful.
What we think about is a broad array of these types of signals.
So what we're doing is asking questions of a computer, building systems to go and answer
those questions using AI tools.
And I think this is the key takeaway, which is AI doesn't ask the questions for you.
Investors still have to ask the questions.
And I think good investors ask a lot of questions, some of which we can answer, some of which
we can't.
Doing a discounted cash flow model is a question we can answer.
We know how to do that.
But going and analyzing the sentiment of a earnings report is something that's very challenging
for us because we'd have to go and outsource that and manually score every single earnings
call.
So AI gives us the ability to go and broadly analyze things like earnings call sentiment,
news sentiment, changes to risk disclosure, linkages between companies.
So when we're disclosing competitors in a 10-K or a Q, we can actually go and identify
those competitors, build a graph, and build maps of companies to understand how they compete,
as well as understand how they're interrelated, which right now is really important because
we're talking about potential bankruptcies on the horizon.
And one bankruptcy can lead to another bankruptcy.
If a supplier goes bankrupt, that can impair a producer, and that producer could go bankrupt
because they're not getting the supplies they need in time.
RAOUL PAL: So when we talk about these data sets, I think people are shocked at the amount
of data sets available.
You and I talked last week or so about Orbital Insights and the kind of data sets they've
got.
Talk me through some of the data sets that are available or somewhere that are in private
hands, because I know, for example, JP Morgan has real-time banking data anywhere in the
whole United States, which is an extraordinarily powerful data set.
Talk me through some of these data sets out there because people don't really understand
the magnitude of this.
TREVOR MOTTL: There is an enormous amount of data that's now available.
Much of it's available via API.
So integrating those data sets into a relatively simple Python script is very easy.
So data that's available via API runs the gamut from CIC data, most of the economic
data that you'd get out of the FRED database.
We can scrape or pull down KNQ data, earnings call data.
There's tickerized news data now that's available from both Bloomberg and Reuters.
And while that doesn't sound like a big step forward, it's actually a really valuable tool
and data set because Reuters or Bloomberg have effectively taken all the news related
to a given stock and classified it for you in one specific feed.
So if I want all the news for Microsoft, I can going to get that, and that's true for
any other company.
Obviously, I'm beholden to Reuters' or Bloomberg's classification, but doing that on my own is
rather time consuming, and they've done that for us.
So that's a wonderful addition from a textual perspective.
Additionally, there's sentiment data available, and then there's an entire swath of alternative
data.
I call it the alternative just because it doesn't really fit into the normal data set
that typical investors are-- RAOUL PAL: --Bloomberg Terminal, yeah.
TREVOR MOTTL: Yeah, exactly.
So Orbital Insight is a really interesting company that have produced high-quality geospatial
data, and there's company-specific data.
So they've mapped things like all of the sites of big box stores, all of the locations of
new construction.
They've mapped how high floating top oil storage containers are.
So they've done some really interesting work.
RAOUL PAL: They've even done corn by looking infrared imaging to find out how advanced
the corn planting season is, and this is all done granular satellite data.
It's truly extraordinary, right?
TREVOR MOTTL: It is, and the company really found its roots in doing agricultural research,
looking at patterns, color, infrared, the array of imaging on planting to see how far
along plantings were, how many acres had been planted, et cetera.
So there's an enormous amount of data you can extract from the sky.
And they do a really good job of not only doing data extraction, but they process the
data for you and make it available as an index or as a feed.
So all of the compute-intensive work is outsourced to them.
And there are many more data providers that are effectively outsourcing the compute and
providing prepackaged signal data, which an investor can integrate into their models.
Orbit will be one of them.
RAOUL PAL: And how do you then go about it because there's so many data sets, right?
So first, you're trying to get as much data then you need to answer the questions.
And I'll come into all of this because I've got a ton of questions I want to ask you.
But data sets, so you've got Google, social media, all the newsfeeds, all of the price
feeds, all of the 10-K data, the alternative data, the credit card data.
How do you go about even thinking about what are the data sets available and which ones
you need for what you're trying to achieve?
TREVOR MOTTL: I actually go the other way.
So rather than going and hoovering up a whole bunch of data and looking for some random
signal in it, the approach that we take and one that's less challenging to conceive of
is we ask questions and we test hypotheses using data that's available.
So we'll make a hypothesis about some feature in the market.
We'll go and find the right data.
We'll build a feed.
We'll build an experiment.
We'll test it using a robust set of tools.
We'll look at the output of those tests, and then determine whether or not that's a worthwhile
pursuit.
And I think this focus on questions is something that really is differentiated.
RAOUL PAL: Doesn't that then come down to the human element, right?
The quality of the questioning mind, which is pretty much your team getting together
and saying, OK, these are things that we're observing that may be a hypothesis, and then
we need to test it.
I mean, that requires a lot of intellectual power from the team that doesn't lie within
the AI itself.
It actually is the human element.
TREVOR MOTTL: And I think that that's a key piece.
So that good AI today requires those questions.
We build models to answer questions.
We don't build models specifically to ask questions.
And while there might be some people who are looking at unsupervised AI tools that are
producing really interesting insights, we're really focused on having the AI as something
like an analyst or augmenting the analytical process so that we're building better portfolios
that are potentially more robust, that are more reactive to changes in market environment,
that potentially are more adaptable to regime shifts and also idiosyncratic shifts in individual
companies.