Why do Humans Struggle at Picking Stocks?

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Let's go.

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Ryan Nauman Market Strategist Zephyr:

everyone and welcome to zephyr's

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adjusted for Risk Podcast

from the shores of Lake Tahoe.

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I am Ryan Nauman, the market

strategist here at Zephyr.

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Over the past couple years,

the wealth management space

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has been grappling with ai.

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First, it was about

understanding what AI was or is.

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Now it is all about how to implement it

and what are the best ways of using it.

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Well, I have on an industry expert

who's gonna share his thoughts on

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the impact AI is having, on investing

in what his firm is doing to make

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investing more efficient and improve it.

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But first, today's episode is sponsored

by the award-winning Zephyr, which

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helps investment professionals.

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Make more informed investment

decisions on behalf of their clients.

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Alright, I've already talked enough.

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Let's move move on to

the star of the show.

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I'd like to give a very warm

welcome to Jake Ayers Thompson.

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Jake is the founder and chief

executive officer at three ai.

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Jake, thank you so much for coming on.

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It's an honor to have you on.

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I'm really excited

about this conversation.

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When I have conversations about ai, a lot

of it is more about improving processes,

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workflows, and as we all know, I have

a passion for the investment side.

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So really excited about this conversation.

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Can you please tell us a little bit

more about yourself in three ai.

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Jacob Ayres-Thomson Founder & CEO 3AI:

Yeah.

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Thank you very much for having me

on and taking the time to give us

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a chance to talk about what we do.

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So, yeah, three AI basically our

product is Alpha Intelligence.

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We're really focused on,

you mentioned before about

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workflows, that kind of thing.

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We're very much focused

on alpha outperformance,

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predictive insights on equities.

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And, and we deliver that in three

formats, sort of three types of

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users, people, funds and models.

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So for people, we empower them

models, we have various data sets

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that quants use to, to enhance.

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Their own strategies and for funds we

were able to partner with in industry

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to launch sh I powered funds or indices

that they can wrap a fund around.

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So that's basically in short what we do.

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Ryan: Yeah.

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Fantastic.

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I love the fact that you

guys are working on Alpha.

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Generating Alpha said a report

some research on how hard it is for

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financial or for asset managers to

generate alpha in this environment

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because, you know, whether it's

concentration risk or just data is more

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available, markets are more efficient.

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But we'll get into that shortly.

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But why did you start three ai?

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What was your thinking behind starting it?

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Jake: It's that's a 17 year journey

after about five years in the city

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and, and, and studying stats and, and

the markets and that kind of stuff.

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So my own career had I, I've been

in equities trading, I've, I've.

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Been specializing in what we

call stochastic asset modeling.

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Generating asset simulation

systems as an actuary.

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And I'd also worked as a, as as a

quant and basically across those three.

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And also having been been very

interested in kind of Warren Buffet,

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Ben Graham thinking and valuation.

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So should I say, across those

four, I saw a lot of gaps.

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Gaps between how fundamental investors

think versus how your typical PhD,

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who's very statistical thinks.

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Versus what's in data and

what's in information.

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And so my own career was

kind of sidetracked into

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obsessive research in this.

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And I read a couple hundred books

and, and, and maybe 500 plus papers.

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And then I, I recognized really

that the most advanced results

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from papers were really originating

from stuff using machine learning.

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So I went back to university to study

machine learning when they created

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the first master's program in London.

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And from, and, and it

should been on since then.

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So I, I recognize there were lots of

gaps that could be closed and ways

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to improve elements of, of, of the

kind of alpha generation process in

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terms of actually starting through ai.

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At the time I was running

a data science team at a, a

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FTSE listed UK financial firm.

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And we spent maybe 80% of our time looking

at equity forecasting y upload GPUs to,

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to unlock new algorithms back in 2015.

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Before most people knew what

NVIDIA was, unless you're a gamer

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or, or using VR at that time.

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And we had a series of three, kind

of six months to one year live

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forecasting experiments that produced

statistical certainty pretty much that.

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We were generating active

Alpha in our forecast.

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So the decision to leave and, and

to form three AI at that time was

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a, was a statistical decision.

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Also based on understanding

how we're able to get Alpha and

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what's missing in the market.

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I.

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Ryan: it.

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That's fantastic, Jake.

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And I love the the part, you know, about

machine learning and data and it's, it,

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you know, interesting captivating stuff.

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And, you know, I often

say on the show that.

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stocks is really hard.

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I don't do it.

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I leave it up to the smart people to

manage my, you know, I'll pay the, you

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know, 50 basis points or whatever it is

to have an asset manager, manager for me.

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It, I just.

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Takes too much time.

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Struggle with it.

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Why do humans struggle

with picking stocks?

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It's hard.

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It's not easy.

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A lot of information out there,

A lot of stocks situ choose from,

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whether it's individual stocks,

ETFs, mutual funds, you name it.

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But why do you think humans

struggle to pick stocks?

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Jake: It is funny actually, I'm,

I'm pleased you asked that question

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because I've analyzed that.

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And there's, there's, there's

two insights to share there.

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The first is, if we, in observing,

like we see with s and p publish,

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for example, 95% of active managers

are wanting to perform the benchmarks

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that they're trying to beat.

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If we look into that and, and try to

figure out why the, the first question one

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might ask oneself is if humans struggle

to pick stocks, then perhaps they're

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struggling to learn how to pick stocks.

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And I took the s and p 500 stock

data, did a plot, earnings yield

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versus return for the last 20 years.

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Imagine that's your learning,

your experience of professional.

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You work 20 years, you've got

perfect memory of 500 stocks, which

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I would argue most humans don't have.

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And you look at the pattern there

and there's next to no pattern.

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So I think most people

do what Warren Buffet.

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Recommended they don't do, he said,

don't let price be your teacher.

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And the industry tends to be, I

think, too short term and gravitate

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around short term performance.

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Where, where in, in investing even

one year is short term performance.

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So stocks are incredibly noisy and we

can do all the right things and all the

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right research and, and have the right

frameworks for investing, but the market

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will will teach us that we're wrong.

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And if we believe the market, we may then.

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Drop a winning strategy, a

winning way of, of, of, of

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comparing and analyzing companies.

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So I think, and, and then if

you think then within industry.

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It's very hard to do what

Warren Buffett did, which was

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to wonder to perform the.com

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boom for two consecutive years without

looking like an idiot, even though the

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tide went out between 0 0 0 3 and the.com

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bust.

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And we found out who was

swimming naked, right?

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So that, that's like a five

year cycle where you had to

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kind of hold onto your pants.

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Some people threw the towel in.

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And left industry people's kind of

grandparents were doing BET stock

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investing, buying the latest.com

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stock.

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We've seen a period like

that not too long ago.

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Things like Rivian reaching 140

billion, having never sold a car

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that's now down, I think sub 10

billion or maybe it's gone up again now

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'cause they're finally selling cars.

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So.

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The, the market is kind of crazy.

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Warren Buffet describes it as a

schizophrenic, and you just turn

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up and see if there's opportunity

in the crazy pricing that's there.

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But really, at three ai, we really

think about stocks as companies, and

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you're getting slices of companies.

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So the first part is that people

tend to learn from experience.

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It's a very human thing.

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It's a very animal thing, right?

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It's how our brains work.

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We learn from experience, but

the stock market, the amount of

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data that one gets in a career.

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Is not enough to be

statistically reliable.

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So if you have a very noisy system,

you need to look at hundreds of

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thousands, even millions of data

points to see weak patterns that exist.

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We look at a million years

stock data, that pattern of

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earning shield becomes clear.

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The second point, and I think this

is also probably misunderstood by.

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Certainly the allocators to, to funds

is that there's periods in the stock

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market where stock pickers look bad

or stock pickers look good because

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we tend to benchmark them against the

major indices, the major benchmarks,

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but their market cap weighted.

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So if you look at the last

kind of five or so years.

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The Mag seven have had a

huge weight in the index.

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Arguably hard holding a higher proportion

of the index than the risk management

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framework of a diversified portfolio

would allow a typical professional

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manager to hold well, what does that mean?

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It means even if they're bullish on

those stocks and were right, they may

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have even held less and effectively been

short against them versus the benchmark.

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So one of the interesting

things is if, if you randomly.

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Sample the s and p 500.

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There's been periods in recent times

where 199 out of 200 portfolios

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of equal weight, 30 stocks has

underperformed the s and p 500.

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And you go, well, how the

hell is that happening?

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It's simply because of the, the

weighting difference and the,

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the very largest companies have,

have performed so exceptionally

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well in terms of stock returns.

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During that time period, but

you know, if history teaches us

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anything, these things turn around.

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If we then see a crash, we see if, if we

were to see the mag seven significantly

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decrease in their market cap and

their weighting of the the index.

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Then the average stock picker might

suddenly look like they're producing alpha

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against that benchmark again, dot com.

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Boom.

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If you dig into the data in the.com

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boom and crash, it was really at an index

level, but that was really prominent.

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But if you randomly picked stocks

equally weighted from a wider US stock

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universe, you would've looked like

you're underperforming in the boom and

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you're not seeing as much of the crash.

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So it looked like you were

negative alpha and positive.

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So there's a difference

between stock selection skill.

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And how well you perform

against a benchmark.

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So I think there's two elements there.

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One is learning.

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We're not, I don't think the, we

are naturally the right brain type.

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The way that we learn, I think,

is for a constant physics system.

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We learn how to walk and talk.

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No surprise neural nets.

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Were able to learn to

do this stuff very well.

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You see robots talking and walking

and, and all of this kind of thing.

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And we could see that even 10 years

ago that they were very good at mammal

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like tasks because they're designed

approximately like mammal brain

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structures, albeit over overly simplified.

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But so I think, I think

that's the two key reasons.

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Ryan: No, I love that, Jake.

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And you're right.

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There is maybe that education

gap or it's just hard to learn

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and I feel as if there's.

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Always those periods of, we had it

after, well, during COVID and directly

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after it when money was free and we

had to, you know, FOMO runs rapid

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and meme stocks and those trends or

those periods of time, they kind of

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throw everything that we've learned

fundamentals out the window, and all of

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a sudden we're focusing on fundamentals.

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Those aren't working because

everyone's shooting to the moon.

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So what impact does that have?

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Jake: Yeah.

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I think that the, one of the things

to think about that if you're, if

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you're training systems to learn

how stocks work is to, is to think

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what's the correct way to kind of

approach how you learn from stocks.

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Our approach at three AI really is, is,

is to learn from all stocks, from all

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time, from all regions and all sectors.

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And we, and we take a kind of first

principles based approach to stocks that

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from the perspective of a shareholder,

they're colorless cash printing machines.

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And so if you had infinite time and in,

and therefore had infinite stocks to

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evaluate the averages would be the truth.

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And so that's what we seek to

learn too, which effectively gives

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us a very long-term focus system

that becomes deep on company

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measurement, not on speculation.

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So, so in times when you see like

correlations go to one like COVID or

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Credit Crunch, everything goes down

in, you know, obviously in COVID it

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was worse for airlines than, than

other things, but you get these fear

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spikes or you sometimes also get.

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In reverse these booms

that are speculative.

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I would say we've had a crash

that's speculative somewhat recently

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in, in, in, in software stocks.

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Think people thinking AI

will eat all software.

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It's a hypothesis, but it's getting

priced in as, as probably more

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realistically probable than it is.

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'cause if you're a software

engineer, maybe it means you, you

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need less engineers, but you're

still gonna be developing your

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software just albeit more rapid.

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So.

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There's a, there's so many different

weird and wonderful things that

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happen in the stock market.

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I do think that each company is its own

story and so that we think a bit more

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like that and we seek to deeply measure

the financials of companies knowing that

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the stock market will be consistently

kind of doing the wrong thing.

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But over time it should pay

off if you do the right thing.

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But if you think that doing the right

thing will get the right result.

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Frequently then that's a

misunderstanding of the stock market

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Ryan: Yeah.

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Jake: it, because it

won't forward like that.

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So it is a bit like Warren

Buffet says buy stock.

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I, I I, I buy stocks where I don't mind

if the stock market shuts for 10 years.

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'cause I want to own the

company and sit on the company.

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Ryan: Y That's fantastic.

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I love that you said the stock

market's weird and wonderful.

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Can I can I

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Jake: Yeah.

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Ryan: post that?

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I think that's a fantastic quote.

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I love it because I think that's

why we love it too, right?

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That's why every day it's something

different and why I think so many

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people love the stock market.

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It's weird and wonderful at the same time,

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Jake: Yeah, it is.

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It is.

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It is a, it is a, it is

a very interesting game.

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I think I'm very into game playing

all different types of games,

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playing chess or, or card games.

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And ideally things with

probability in like cards or dice.

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But the stock market for me is the

one where I feel I can just keep going

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deeper, deeper, deeper, deeper, deeper.

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It's like an ion where you never

fully have unraveled all of it.

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The deeper you look in,

the more there is to learn.

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And the interesting thing about

the stock market is it connects

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to nearly all human activities.

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Nearly all of them will ricochet through

the stock market somewhere or other, or

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the financial market somewhere or other.

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So if you're an alien, you come to Earth

and you wanted to kind of see like the,

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the, the activity and behavior of, of

homo sapiens, you might go to the stock

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market to actually see what's really

going on at a kind of fundamental level.

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Both in how we perceive things, but

on actually also what's happening.

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The two, which are not the same thing.

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So.

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Ryan: I love it.

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So let's talk about AI with three ai.

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know, Zephyr was created, you

know, years and years ago,

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technology, and it was based on Dr.

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Bill Sharp's, returns

based style analysis.

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At the time, it was

pretty, you know, moving.

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It was like, wow, we can determine the.

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The style, the behavioral, through

just tracking, you know, re

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returns, regression, and time over.

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A manager for a manager.

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So some great stuff there.

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Now we're advanced to ai.

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What impact can AI have on investment

management space and, and picking stocks?

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Jake: A lot.

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I'll, I'll, I'll, I'll be honest.

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So if, if we, if we think

about the terminals.

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That traders, investors,

the, the financial market

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community have access to today.

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They're really an information layer.

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So they're, they're kind of their

database with a ton of different

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data sets, nice, gooey, and,

and, and very usable analytics.

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It's been really developed over

many, multiple decades four decades

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in case of Bloomberg since 81.

317

: 00:16:36

But they're really an information layer.

318

: 00:16:37

So whilst they, they got rid of the.

319

: 00:16:39

Data bottleneck.

320

: 00:16:40

You go back to ticket tape,

you've got data bottleneck.

321

: 00:16:42

You've gotta go and contact the

company by telephone and, and hope

322

: 00:16:47

that they send you the accounts

and look in the post every day.

323

: 00:16:49

Yay, we've got the accounts.

324

: 00:16:50

Pass it to somebody who's then gonna

write it all up on paper and you

325

: 00:16:54

haven't even got a spreadsheet, right?

326

: 00:16:55

And then you're analyzing

that, et cetera, et cetera.

327

: 00:16:57

So there was a, a data sourcing

bottleneck, just, just probably having

328

: 00:17:00

access to information was the edge.

329

: 00:17:02

Think about Warren Buffet when he started.

330

: 00:17:05

He was buying the investors Almanac

and literally looking at things like

331

: 00:17:08

price to earnings ratio and going,

whoa, this thing looks so cheap.

332

: 00:17:11

It's half book value with a 25%

yield, and profits seem stable.

333

: 00:17:15

So it is a wonderful bond at 25% yield

and or even if it got acquired or

334

: 00:17:20

exited, there's gonna, there, there's

a lot of realizable value too dollars

335

: 00:17:23

for every dollar I'm buying on it,

but she called cigar by investing.

336

: 00:17:27

But the fundamental.

337

: 00:17:28

Thing that he was doing was effectively

stock screening with his own mind,

338

: 00:17:32

looking at basic metrics, right?

339

: 00:17:33

So he was a step ahead of most,

most people don't like looking

340

: 00:17:36

at data, they find it yucky.

341

: 00:17:38

But what we're doing now at three ai,

so if we think of the people part of

342

: 00:17:42

our empowerment, we are essentially

taking all that kind of data that's

343

: 00:17:47

in, in a terminal, that financial data,

the analyst reports, the financial

344

: 00:17:51

forecast of the future, technical price,

behavior risk, et cetera, et cetera.

345

: 00:17:57

And we're using machine learning

methods to analyze the entire

346

: 00:18:01

history of the, of, of, of all

stocks and learn from all of it.

347

: 00:18:05

And then look at all of today's

data for all stocks to be able to

348

: 00:18:09

then forecast them for tomorrow or

in our case, the next 12 months.

349

: 00:18:13

So what that's offering, I think, as a

solution is, is we're going from data

350

: 00:18:18

sourcing being the bottleneck to, and but.

351

: 00:18:22

Being then solved by terminals

so that analysis then becomes

352

: 00:18:26

the bottleneck, right?

353

: 00:18:28

What we are doing is, is essentially

then taking all that data and condensing

354

: 00:18:32

it into a single number that people

care about, which is an alpha forecast.

355

: 00:18:37

And also, most recently we

have effectively quite deep

356

: 00:18:42

stock research reports.

357

: 00:18:43

So about 15 pages long

per stock, 20,000 stocks.

358

: 00:18:46

That's 300,000 pages of equity

research being generated a week.

359

: 00:18:51

Which is readable by PMs

and, and, and, and, and also.

360

: 00:18:54

Then deeper, even analyzes our own AI

to look at what to watch out for, what

361

: 00:19:00

could, what could really move the needle

or flip the view on a particular stock?

362

: 00:19:04

What's driving it, what's not driving it?

363

: 00:19:06

And we found our alpha intelligence

is basically incredibly intelligent.

364

: 00:19:09

It, it's, it's, it's unlike a human

research report, it's incredibly specific.

365

: 00:19:14

You know, think about the future.

366

: 00:19:16

An AI doctor, a human doctor,

human doctor says, Ooh, I think

367

: 00:19:19

we need to give you a scan there.

368

: 00:19:20

There's risk.

369

: 00:19:21

You go, oh my God, I've

definitely got cancer.

370

: 00:19:24

The AI doctor will say, I, I, I

.: 3985

371

: 00:19:28

chance that you have this and

a 4% chance of this, and a 1%

372

: 00:19:31

chance of that type of cancer.

373

: 00:19:33

And, and the rest.

374

: 00:19:34

It's a property of no cancer.

375

: 00:19:36

That's what we find with

our alpha intelligence.

376

: 00:19:38

It's completely specific and

it's able to unlock a, a very

377

: 00:19:42

deep analysis of each stock.

378

: 00:19:43

In our case, we've got over 300 factors

or models that the AI looks at on

379

: 00:19:48

every single stock across over 20,000.

380

: 00:19:51

But at the micro level, we also

have global macro sorry, macro,

381

: 00:19:54

so macroeconomics and a kind of

top down alpha as well as a bottom

382

: 00:19:58

up alpha all combined together.

383

: 00:19:59

So.

384

: 00:20:01

What then I think that unlocks for

the investment manager is it gets

385

: 00:20:04

rid of the analysis bottleneck.

386

: 00:20:06

It really turns all of that

information in a way that a human

387

: 00:20:09

would not have the time to do.

388

: 00:20:11

E even a quant would not have

the time to do traditionally.

389

: 00:20:15

So I think for a an active manager,

this is effectively a quant shield.

390

: 00:20:21

So the, the, all that alpha that was being

eaten by quants, we see the massive rise

391

: 00:20:25

in the systematic hedge fund industry.

392

: 00:20:27

There were, there were basically

the evidence-based approach to

393

: 00:20:30

data and using data science and

statistics and, and algorithms.

394

: 00:20:34

It's that on the plate.

395

: 00:20:36

Without needing to necessarily

know why, but but also having the

396

: 00:20:39

information and explainability to how

it relates to that stock and how to

397

: 00:20:43

analyze it and how to think about it.

398

: 00:20:45

That's one aspect.

399

: 00:20:46

The other two are the other two

products that we spoke about.

400

: 00:20:48

So for quants, we're able to give them

our forecast, ingest it into their

401

: 00:20:52

systems to enhance them, and finally,

for the creation of products, like

402

: 00:20:56

wanna create a new ETFA new index,

then it's very easy to use our data.

403

: 00:20:59

It's all stock mapped,

it's ranked it's forecast.

404

: 00:21:02

It's very easy to construct.

405

: 00:21:04

AI enhanced portfolios.

406

: 00:21:06

So I think that's, that, that's

the three that we're immediately

407

: 00:21:09

seeing that we're working on and,

and that we're, we're delivering.

408

: 00:21:13

Ryan: Yeah, that's fantastic.

409

: 00:21:14

And sometimes I feel as if, and you

mentioned it numerous times, or about

410

: 00:21:18

AI just in general, we think of AI in

a broader sense of just making things

411

: 00:21:24

more efficient, processes, you know,

given us some time back in our day.

412

: 00:21:30

But at the end of the day, what

AI best, and I might be probably

413

: 00:21:34

oversimplifying it here, is just taking

huge sums of data, synthesizing it,

414

: 00:21:41

and making it intelligible, correct.

415

: 00:21:43

Like that's at the end of

the day, what AI is doing.

416

: 00:21:46

So like you said, humans aren't

good at just creating, looking

417

: 00:21:50

at millions of lines of data.

418

: 00:21:53

Whereas AI can do that

and make sense of it all,

419

: 00:21:58

and that's really what you guys have done.

420

: 00:22:01

Jake: Yes.

421

: 00:22:02

Yeah, it's a bit more complic.

422

: 00:22:03

I mean, yeah, it's, what I will

say about AI is that AI is,

423

: 00:22:08

is almost as unique as people.

424

: 00:22:10

So people think AI is one

thing, but it's really not.

425

: 00:22:13

It's a, so.

426

: 00:22:16

In, in the financial system.

427

: 00:22:18

We would even today find, I

suspect, thousands of AI systems,

428

: 00:22:23

depending on how we classify ai.

429

: 00:22:24

I, I think of it as machine learning.

430

: 00:22:26

We'd find thousands of them distributed

across the world, primarily in the

431

: 00:22:29

us It's the most advanced sector

it's most advanced region, sorry.

432

: 00:22:33

But, but EE each one is, is

as unique as the designers

433

: 00:22:38

and, and we are, we're quite.

434

: 00:22:42

I think in, in, in, in really

approaching the, the Ben Graham, the

435

: 00:22:46

Warren Buffet kind of learning and

bringing that into the AI world or,

436

: 00:22:50

or, or should we say, beginning with

the hypothesis of is it learnable

437

: 00:22:53

through data, how this stuff works?

438

: 00:22:55

Ryan: Yeah,

439

: 00:22:57

Jake: so.

440

: 00:22:57

Ryan: that's So.

441

: 00:23:00

of touched on it earlier, and

it's part of, you know, the name

442

: 00:23:03

of your scoring system, alpha.

443

: 00:23:06

So if machine learning, if machines all,

whether they are, you know, each ai tool

444

: 00:23:14

is unique and the methodology is unique.

445

: 00:23:17

Doesn't it make markets

much more efficient?

446

: 00:23:20

And if it makes markets more efficient,

how do investment managers, even

447

: 00:23:24

if they're using technology, how do

they generate alpha in such a, you

448

: 00:23:29

know, that's much more efficient?

449

: 00:23:32

Do the technology and data

being more accessible?

450

: 00:23:36

Jake: Okay.

451

: 00:23:37

Okay.

452

: 00:23:37

I'm writing some notes down there.

453

: 00:23:38

There's a couple of questions in there.

454

: 00:23:39

So in terms of efficiency I

think the short answer is yes.

455

: 00:23:44

Artificial al intelligence machine

learning based approaches and, and to

456

: 00:23:48

be honest, anything that, that brings

greater forecasting accuracy, better

457

: 00:23:53

allocation of capital, plays a role.

458

: 00:23:57

In humanity's effort to

build the efficient market.

459

: 00:23:59

A long time ago there was a

hypothesis of an efficient market.

460

: 00:24:02

They called it a hypothesis

'cause it was, it didn't exist.

461

: 00:24:05

It'd be very easy if your progressor to

prove that you'd find in a liquid stock

462

: 00:24:08

call up $5,000 a stock and move its price.

463

: 00:24:11

There you go.

464

: 00:24:11

That wasn't an efficient

idea that, to prove a point.

465

: 00:24:13

You couldn't do that in an

efficient market, could you?

466

: 00:24:15

An efficient market, everything

would be perfectly priced.

467

: 00:24:17

You couldn't move prices.

468

: 00:24:19

So so yes, I definitely believe that.

469

: 00:24:26

Artificial intelligence is going to play

a role in, in enhancing the efficiency.

470

: 00:24:30

I think if you look at your home

markets in the us, we in our data see

471

: 00:24:37

them to be and from our client base

see them to be the most efficient

472

: 00:24:40

stock market in the world today.

473

: 00:24:43

And you have the largest gathering

of highly sophisticated quants,

474

: 00:24:47

armed with tons of compute

and access to tons of data.

475

: 00:24:52

And I think a result of that is that your

stock market actually goes up quicker.

476

: 00:24:56

So pension funds are

larger in the long run.

477

: 00:24:59

If you put the stock market up by

one or 2% a year, every person's

478

: 00:25:01

lifetime, it's a, it makes a huge

impact on the size of their pension.

479

: 00:25:05

And so I think the societies' a

beneficiary of a more efficient capital

480

: 00:25:11

allocation system in the stock market.

481

: 00:25:12

If we go to, if we imagine a completely

inefficient stock market it's not going

482

: 00:25:17

to go up as fast because capital's

going to sit, idly being used poorly.

483

: 00:25:21

Rather than being allocated to companies

that use it really, really efficiently,

484

: 00:25:24

and actually in the US you have that

all the way around, starting with VC and

485

: 00:25:26

Angels and the risk tolerance right up

through into the stock market itself.

486

: 00:25:30

The whole thing is more efficient.

487

: 00:25:32

It's why you have, and I think that's

actually why you put those two together,

488

: 00:25:35

why you have the world's biggest and

most successful companies, certainly that

489

: 00:25:38

have been created with new technology,

excluding things like Saudi Aramco,

490

: 00:25:41

which was just sitting on liquid gold.

491

: 00:25:44

In terms of generating alpha.

492

: 00:25:46

So if I talk about two different types,

firstly, hedge funds and systematic, they,

493

: 00:25:51

our clients don't tell us what they do.

494

: 00:25:53

Having been a trader two of our team,

were, we're former traders as well.

495

: 00:25:57

We know certain areas of trading very

different approaches, harnessing and

496

: 00:26:02

working in very different timeframes

often than our own forecasts.

497

: 00:26:06

So they, there's so much

out for in the market.

498

: 00:26:10

That you can have tens and tens

of thousands of people all, all

499

: 00:26:14

becoming specialists in different

areas and exploiting alpha.

500

: 00:26:18

That's orthogonal to our own and ours,

ours is long term and slow moving alpha.

501

: 00:26:22

In terms of active managers, this,

this is a very interesting point.

502

: 00:26:27

So if we think about a,

a portfolio manager, the

503

: 00:26:33

our view is that the Warren

Buffets of this century will,

504

: 00:26:37

will have AI enhancement.

505

: 00:26:39

In the same way that you wouldn't

find a trader on the trading floor

506

: 00:26:42

today without a Bloomberg terminal.

507

: 00:26:43

Imagine giving one just a, a TV screen

and a phone and saying Good luck.

508

: 00:26:47

They're gonna be, they're gonna

be half blind, if not more than

509

: 00:26:50

compared to everybody else.

510

: 00:26:52

The thing for a human active manager that

we've recognized is the limitations of ai.

511

: 00:26:58

It's important to understand them.

512

: 00:27:00

So yes, it's brilliant in the kind

of data such you spoke about before

513

: 00:27:04

where you've got millions and billions

of lines of data and you just can't

514

: 00:27:07

read it, you can't even look at it.

515

: 00:27:09

It's a waste of time.

516

: 00:27:10

You're sitting there and you're

not seeing any pattern, right?

517

: 00:27:12

It's like, okay, great.

518

: 00:27:12

Unless you're brain man, and even

then you wouldn't have the, the.

519

: 00:27:15

Arm strength to, you know, flick

through the 10,000 page volume to see

520

: 00:27:19

all that data anyway, you'd be sitting

there for weeks and weeks and weeks.

521

: 00:27:24

But that works where you have a lot

of data, but there's a lot of areas

522

: 00:27:28

where you don't have a lot of data.

523

: 00:27:29

And so machine learning, particularly

in the stock market where it's

524

: 00:27:33

so noisy, needs lots of data.

525

: 00:27:35

And at three hour we call the, the

data that we work with homogenous data,

526

: 00:27:39

data that is the same for all stocks.

527

: 00:27:41

So we can use it like dollar profit

or, or price moves, analyst ratings,

528

: 00:27:45

that's, that's kind of homogenized

across all stocks has the same meaning.

529

: 00:27:50

But if we think about certain other

companies that, you know, it, maybe

530

: 00:27:53

you're an app company, maybe there's only

five o other listed app companies, you

531

: 00:27:57

haven't got enough, enough price behavior

to get any statistical significance

532

: 00:28:00

on learning the interrelationship

with that data to the stock.

533

: 00:28:03

However, the analyst, the PM who look

at that, can very clearly understand

534

: 00:28:08

its meaning and relate it and feed

it into their financial forecast.

535

: 00:28:11

How they view the stock, they, there's,

there's lots of data sets that just

536

: 00:28:16

aren't yet ready for machine learning.

537

: 00:28:18

And may never be.

538

: 00:28:20

So one of the areas where we work

on with active managers is, is

539

: 00:28:25

this kind of yin yang relationship.

540

: 00:28:27

It's not one or other.

541

: 00:28:28

The optimal is both a

bit like planes today.

542

: 00:28:31

Yeah.

543

: 00:28:31

We can fly planes without a pilot.

544

: 00:28:33

We see it with drones,

but we still have a pilot.

545

: 00:28:35

Why that?

546

: 00:28:36

So.

547

: 00:28:38

So, yes, I, it's I think there's an

education that will come with ai.

548

: 00:28:42

We're certainly invested in that and

working on that with our clients to

549

: 00:28:47

support the, the active managers to, to

realize, okay, there's a whole load of

550

: 00:28:51

lifting that you couldn't do before that

we now do that that was never getting

551

: 00:28:55

done, but really go and focus to these

other areas that are not so suited.

552

: 00:29:00

To, to AI or machine learning that

we're not gonna deal with, we're not

553

: 00:29:03

gonna be able to add value and add

additional value on top of the ai.

554

: 00:29:08

The AI just really forms a

base layer of enhancement.

555

: 00:29:13

Ryan: Yeah, Jake, that's great.

556

: 00:29:16

So let's go to your alpha int

intelligence scoring methodology and tool.

557

: 00:29:24

you've talked a lot about,

benjamin Graham, Warren Buffet, and

558

: 00:29:28

they're, you know, that's, you've

taken a lot from them the years.

559

: 00:29:33

So with your scoring, is it more, got more

of a value tilt to it than value emphasis?

560

: 00:29:40

Does it only work on just than of

value names, or can it, you know, has

561

: 00:29:45

got some growth prospects to it too?

562

: 00:29:47

Or is it all looking for, you know,

good companies at a discounted price?

563

: 00:29:55

Jake: All of the above and in, and

it's a, it is a piece of string answer.

564

: 00:29:59

So one of the things that our alpha

intelligence does is seek to work out to

565

: 00:30:05

what degree those things are important.

566

: 00:30:07

Point in time on a stock by stock basis.

567

: 00:30:10

So in 2021, we began

developing explainability.

568

: 00:30:14

This was an original just in data format.

569

: 00:30:17

So it's ugly.

570

: 00:30:18

You know, we're talking over 300 factors.

571

: 00:30:20

So there's like a, a bar chart of

over 300 factors of alpha being

572

: 00:30:23

attributed back to in underlying models.

573

: 00:30:25

Of course, within that would sit

value, would sit, growth, would sit

574

: 00:30:28

momentum, and lots of other data.

575

: 00:30:30

What we, what we source pretty much

straight away was the degree of difference

576

: 00:30:35

that the system was putting onto these

different things, across different

577

: 00:30:40

stocks at the same point in time.

578

: 00:30:42

So.

579

: 00:30:44

Some stocks are more of a value

play and, and when they're more of

580

: 00:30:48

a value play, the pattern that we're

seeing from the alpha intelligence

581

: 00:30:52

is that prior financials are more

predictable, the more reliable companies.

582

: 00:30:57

So you can begin to look

at them a bit like a bond.

583

: 00:30:59

And I think that if we think of Warren

Buffet, he avoids cyclical companies

584

: 00:31:02

and he also avoids banks with, with

derivatives in and things he can't

585

: 00:31:05

understand, or at least he said he used

to, and that that may have changed.

586

: 00:31:09

But, so you have to play to

your circle of competence.

587

: 00:31:12

And the alpha intelligence learns

that again, with, with, with growth.

588

: 00:31:14

We, in recent time could see there was

a lot more weight being put into Tesla.

589

: 00:31:18

If we go back a few years into analyst

reports and what analysts were thinking.

590

: 00:31:23

And that made than, than say Butcher

had the way which was being viewed much

591

: 00:31:26

more as with more importance and weigh

into valuation, value based information.

592

: 00:31:30

Valuation by the way we call valuation

value and growth because I think

593

: 00:31:34

you need both things to value.

594

: 00:31:35

I think so.

595

: 00:31:37

But.

596

: 00:31:38

So it, it, it is very stock specific.

597

: 00:31:41

You mentioned meme stocks earlier

we actually went back and looked at

598

: 00:31:44

some meme stocks and we saw actually,

despite the company being horrible

599

: 00:31:49

one of those particular meme stocks

as it started booming up, became very

600

: 00:31:52

highly rated, but it was really just a

technical volatility trend-based play.

601

: 00:31:57

And, and it was spotting that.

602

: 00:31:58

And, and so the way that.

603

: 00:32:00

I've gotta jump you back The way

that our Alpha intelligence learns

604

: 00:32:03

and how it differs from a model.

605

: 00:32:04

If we think about a model

input equals output, right?

606

: 00:32:07

You put the input in.

607

: 00:32:08

The model is always gonna give

you the same output that is.

608

: 00:32:12

If we think about how our Alpha

intelligence works, it's a bit more like

609

: 00:32:16

a a thing that goes back and reviews

all the movies that have ever been made.

610

: 00:32:20

So it looks at, it looks at today's movie,

you give it an outline, the script, the

611

: 00:32:25

cast, the director, the length of the film

who did the sound, et cetera, et cetera.

612

: 00:32:30

Goes back all through its history, does

pattern recognition, looks at all of

613

: 00:32:34

the movies, works out the relevance

of all the past movies and forms an

614

: 00:32:38

average of how that movie's likely

to go based on all that information.

615

: 00:32:42

And so it's looking at different

past, in the present, depending on

616

: 00:32:45

the stock that it looks at, you see?

617

: 00:32:48

So it's, it's a lot more advanced

than, than the classical kind of

618

: 00:32:51

modeling based approaches or, or

factor tilting based approaches.

619

: 00:32:55

It is really taking a very stock

specific, every stock tells

620

: 00:32:58

a story approach to stocks.

621

: 00:33:01

So yeah, and, and really it's

just learning from the past.

622

: 00:33:03

So we're just getting told

back the truth of the past.

623

: 00:33:07

It's like a super research

mechanism in a way.

624

: 00:33:09

Ryan: Yeah, that's great.

625

: 00:33:11

I was gonna bring up too, like how do you.

626

: 00:33:14

Shift through that noise of like during

COVID with the meme stocks and like you

627

: 00:33:19

said, it was all momentum technical or

you know, just, it, it, it different

628

: 00:33:26

completely forgot about fundamentals.

629

: 00:33:28

Like how do you shift through that and how

does a machine be able to like, kind of.

630

: 00:33:36

And ignore it and focus on

fundamentals, but it's hard to ignore

631

: 00:33:41

when a MC is shooting to the moon

and you know what's driving it.

632

: 00:33:48

Jake: Yeah, so we, we,

we, we don't ignore it.

633

: 00:33:51

We consider that that everything

that ever happened was truthful

634

: 00:33:55

and the key is to try and work out

what that truth was and where you

635

: 00:33:58

can't work out what that truth was.

636

: 00:34:00

It just becomes noise in the system.

637

: 00:34:03

And we accommodate for that.

638

: 00:34:05

So, which gets you to the real core

question of, of, of everything,

639

: 00:34:10

which is trying to search for

causality, not correlation.

640

: 00:34:14

What's the, so, so we think of the

stock market as a, as a highly noisy,

641

: 00:34:19

almost like statistical physics system.

642

: 00:34:22

And our mission really, or our

vision is to solve investing,

643

: 00:34:27

which means solving that system.

644

: 00:34:29

What are all the parts

that move it and why?

645

: 00:34:32

Connecting the dots, but it's very,

very important to be able to have

646

: 00:34:36

methods that recognize they're

not connecting the dots and have

647

: 00:34:40

elements of doubt in what they do.

648

: 00:34:42

I don't think I've answered your question

specifically in the way that it would

649

: 00:34:46

be discussed in, in investing community,

but I don't think the way the investing

650

: 00:34:51

community is to think is the right

way to solve the problem after 20 odd

651

: 00:34:55

years of concentrating on the problem.

652

: 00:34:57

If it was, we, we, we wouldn't

exist today through ai.

653

: 00:35:00

So, but yeah, yeah.

654

: 00:35:01

We're, we're always

gonna have these things.

655

: 00:35:03

Like I said before, I think the stock

market is a bit like, did you play poker?

656

: 00:35:10

Ryan: A little bit.

657

: 00:35:11

Not very good, Jake, not very good.

658

: 00:35:14

Jake: Okay.

659

: 00:35:15

Yeah.

660

: 00:35:15

So, so if you think about playing

poker, a bad poker player, when

661

: 00:35:19

the flop comes, will say, oh, crap,

I've, or, or they go all in or

662

: 00:35:23

something, and then the flop comes.

663

: 00:35:25

It wasn't how they had hoped it might be.

664

: 00:35:27

They lose, they think,

oh, I made a mistake.

665

: 00:35:30

A really good play player might win a

hand and think they made a mistake because

666

: 00:35:35

they didn't play the distribution right.

667

: 00:35:37

I think stock returns are as

noisy or, or even perhaps, noisier

668

: 00:35:42

than, than, than, than the flop.

669

: 00:35:44

In, I say they are noisier

than the Flopping Poker.

670

: 00:35:48

And so the important thing is to really

learn to, or what we seek through AI is to

671

: 00:35:53

learn to the long-term truth of of stocks.

672

: 00:35:56

So that means that we see the full

lifecycle of over 150,000 stocks.

673

: 00:36:02

And through enough data, if we see a

stock from birth through to death, and

674

: 00:36:06

the average stock life, by the way,

is seven years, if you go through all

675

: 00:36:09

the data, the data that we've been

through, which is not long, but at

676

: 00:36:12

the same time, it takes all the atoms

in our body apparently to replace.

677

: 00:36:14

Although I've looked that up online.

678

: 00:36:15

I'm not sure if that's actually

true, but, but it's a commonly

679

: 00:36:18

assumed thing that we say.

680

: 00:36:20

Then it's not really that long, but

the, our, our simple principle is that.

681

: 00:36:28

Like I said at the beginning,

companies are owe us colorless

682

: 00:36:31

cash printing machines.

683

: 00:36:32

And from the perspective of a shareholder,

if I could know the rate at which

684

: 00:36:35

it prints cash or needs to eat it to

keep going the rate of change in the

685

: 00:36:40

printing of that cash and or whether

or not that cash printing machine was

686

: 00:36:43

vibrating with smoke coming out of it,

IE defaulting not gonna exist tomorrow.

687

: 00:36:48

And what you want, you want, and if

the price of it is how much floor

688

: 00:36:50

space it takes and your floor space

is your capital, you really want small

689

: 00:36:54

printers that print cash rapidly are

increasing the rate at which they

690

: 00:36:56

print cash and look sturdy as hell.

691

: 00:36:58

They're gonna be there a hundred

years and, and it's, and it's that

692

: 00:37:01

simple and everything else is noise.

693

: 00:37:04

You see, if you think mathematically,

the Warren Buffet talks about this

694

: 00:37:10

as well, you know, when talks about

things like Clayton Holmes who didn't.

695

: 00:37:14

Visit management.

696

: 00:37:15

When we bought them, we looked at the

record to see what management was like.

697

: 00:37:19

We, the focus here is, is toward

not just measurement of the past and

698

: 00:37:25

Warren Buffet, which use his wonderful

mind to estimate into the future.

699

: 00:37:30

We, we are money balling that in a

way, using all the data possible to

700

: 00:37:33

estimate the future in the present.

701

: 00:37:36

In the past, focus much

more to measurement.

702

: 00:37:38

And then comparing all the world

stocks and, and, and that's how we're

703

: 00:37:41

sourcing and generating Alpha, which

is, which is kind of money balling.

704

: 00:37:45

And it is, and actually it's like the

film Moneyball, if you've seen that.

705

: 00:37:48

Because in that film, when I watched it

back, my jaw almost dropped because it was

706

: 00:37:52

almost a direct parallel to what we do.

707

: 00:37:54

We cover 20,000 stocks in that film,

they took about 20,000 baseball players.

708

: 00:37:58

In that film, they find the baseball

players that maybe aren't gonna sell

709

: 00:38:02

t-shirts or, or, or, or trading cards.

710

: 00:38:06

They're not necessarily gonna be on the

cover of a magazine, but statistically

711

: 00:38:09

we're good and certainly undervalued.

712

: 00:38:11

A lot of the world's best stocks

are hiding in plain sight.

713

: 00:38:13

They're just not sexy.

714

: 00:38:15

People are not interested in them.

715

: 00:38:16

Might be like a lift company

out in Germany that sells lifts.

716

: 00:38:19

Might be some industrial

chemical manufacturing

717

: 00:38:22

company you've never heard of.

718

: 00:38:24

That are serious cash machines and trading

at a decent price and, and growing profit

719

: 00:38:29

nicely, and looking after shareholders

doing buybacks, you're ending up with more

720

: 00:38:33

of the same stock, et cetera, et cetera.

721

: 00:38:35

So we've sought to go much, much deeper

into what we make visible to our systems

722

: 00:38:41

than, than what we see out there in the

systematic investing space, enabling AI

723

: 00:38:46

to answer and ask, or ask and answer.

724

: 00:38:48

Lots and lots and lots of questions.

725

: 00:38:50

So effectively our systems

will ask different questions

726

: 00:38:53

depending on what they get.

727

: 00:38:53

The answers back they, it

is investigative as well.

728

: 00:38:57

Again, you wouldn't get

that in classical modeling.

729

: 00:38:59

You wouldn't have that level of

intelligence in how it thinks.

730

: 00:39:03

Ryan: I love your comparison

to the movie in sports.

731

: 00:39:07

I love it.

732

: 00:39:07

It is a great comparison.

733

: 00:39:10

Let's.

734

: 00:39:10

Jake: It is really the, it is,

it is a very much a a similar,

735

: 00:39:13

similar thing, really similar thing.

736

: 00:39:16

Ryan: Let's finish.

737

: 00:39:17

Great conversation.

738

: 00:39:19

I learned a ton.

739

: 00:39:19

I'll probably go back and

watch this a couple times.

740

: 00:39:22

Has taken it all in.

741

: 00:39:24

All the great insight.

742

: 00:39:25

Let's just finish.

743

: 00:39:26

So financial advisors out there

that are watching, how can they

744

: 00:39:29

implement strategies like this?

745

: 00:39:31

Like they find it really interesting.

746

: 00:39:34

They wanna implement some AI and more

quant into their client portfolios.

747

: 00:39:39

Where can they start?

748

: 00:39:43

Jake: So are we talking about a

financial advisor that's actually

749

: 00:39:46

creating strategies for their client

or allocating their clients into funds?

750

: 00:39:50

Ryan: If

751

: 00:39:51

Jake: Both.

752

: 00:39:52

Ryan: that are are creating investment

portfolios, whether it's mutual funds,

753

: 00:39:56

ETFs, individual stocks for their clients.

754

: 00:40:00

Jake: Okay, so I mean, I'm

working for three ai so I'll

755

: 00:40:03

tell you about three AI side.

756

: 00:40:05

We can effectively provide

an alpha intelligence.

757

: 00:40:09

Interface with pretty much a global

coverage of every stock in the

758

: 00:40:13

world, over $50 million market cap.

759

: 00:40:15

It is instantly searchable

to find the world our highest

760

: 00:40:19

rated, lowest rated stocks.

761

: 00:40:21

We have.

762

: 00:40:23

And deep explainability, like

I said, about 300,000 pages of

763

: 00:40:26

equity research updated weekly.

764

: 00:40:29

So it's, I think the average analyst

covers about: 2020

765

: 00:40:33

So I guess you'd say it's about a thousand

analysts, but we tested it against the

766

: 00:40:38

institutional brokers estimate system and.

767

: 00:40:41

The, which covers 18,000 sales side and

our forecasting accuracy, we're explaining

768

: 00:40:45

alpha for the following year, circa

20 times higher than analyst ratings.

769

: 00:40:50

In the, in the, since we've been

live with our stuff, which is: 2020

770

: 00:40:55

If the for funds we're, we're

launching AI powered funds with s and

771

: 00:40:59

p Global, so should we say indices?

772

: 00:41:02

So we, we, we, we, we provide our

forecast data to s and p Global.

773

: 00:41:05

They're creating indices with it to

create enhanced versions, alpha seeking

774

: 00:41:10

versions of the s and p 500 s and p world.

775

: 00:41:14

And there'll be more to come.

776

: 00:41:16

Actually, we've got.

777

: 00:41:17

Clients in the Middle East and, and,

and Asia now are showing interest.

778

: 00:41:20

And I think we'll be global

with those products in the

779

: 00:41:22

not, not too distant future.

780

: 00:41:24

Should we speak again?

781

: 00:41:25

I think we might well be so, but

I, I, I would recommend to, to, to

782

: 00:41:30

do due diligence if, if for example

people are using AI systems for Alpha.

783

: 00:41:38

I think it's really important to

statistically analyze the performance.

784

: 00:41:43

Of the of the forecasts.

785

: 00:41:46

And if the, and if the statistical

performance of the forecast is robust,

786

: 00:41:50

then, and you understand why the apples

and pears of it, there's actually

787

: 00:41:54

fundamental reasoning, grounded in

reality, not in data, but in the

788

: 00:41:58

real world that we can see with our

eyes, then, then, then sit with it.

789

: 00:42:03

Like all good, like all proper

systematic approaches or, and stuff.

790

: 00:42:07

You can sit with that and you

should get paid out in time and

791

: 00:42:11

outperforming in time, but obviously

nothing's a guarantee on anything.

792

: 00:42:16

The market can be, you know, the

old saying the market can be crazier

793

: 00:42:20

than we've got the wallet to kind

of stomach, but which is true.

794

: 00:42:24

Ryan: Wow, Jake, fantastic conversation.

795

: 00:42:27

I, like I said earlier, I learned

a ton of really interesting stuff.

796

: 00:42:33

Thank you so much for coming on.

797

: 00:42:34

Really an honor to have you on.

798

: 00:42:36

Where can our audience get more

information about three ai.

799

: 00:42:41

Jake: We've got a website like everyone.

800

: 00:42:42

So you can go to our

website, www.threeai.co.

801

: 00:42:47

And see us there.

802

: 00:42:48

I must be honest with you, we have

very limited information on our website

803

: 00:42:52

because our clients are on the buy side.

804

: 00:42:55

We're an alpha unit, right?

805

: 00:42:56

Like, like we see ourselves as an, as

a kind of a team with our, with, with

806

: 00:43:01

our clients that we're, we're almost

like an, you could imagine like an out

807

: 00:43:05

outsourced like research alpha r and d

center and trying and supplying that and

808

: 00:43:10

distributing that back into industry.

809

: 00:43:12

So our website is, doesn't contain a

great deal of information on there, but

810

: 00:43:18

if, but if you have institutional clients

that wish to work with us, then they

811

: 00:43:22

could reach out to us via the website.

812

: 00:43:25

There's videos on there too, but

you can go right back to: 2019

813

: 00:43:28

I did a talk at when we were

earlier, first formed at: 2019

814

: 00:43:32

the university at Harvard in I

think 22 or UCL, the same year.

815

: 00:43:37

As well.

816

: 00:43:38

And you can see in those, actually some

of the things I spoke about today, what

817

: 00:43:42

we, what we were seeing in forecast,

how, how we were seeing an scur in

818

: 00:43:45

the future of stock returns with our

lowest rated, highest rated, looking

819

: 00:43:50

somewhat like an S with significant

alpha being generated across a

820

: 00:43:54

hundred trillion dollars of stocks.

821

: 00:43:56

So if you have, you have clients

that have active managers they

822

: 00:44:00

wish to, to produce new, innovative

strategies that seek to beat the

823

: 00:44:03

market, not conventional, thematic.

824

: 00:44:05

You come up with thematic,

we'll turn up and power it.

825

: 00:44:08

We'll put the engine in it.

826

: 00:44:10

And and if you've got quants, you

listen, by all means reach out as well.

827

: 00:44:13

'cause we have unique and

proprietary data sets.

828

: 00:44:15

So our quant customers have told

us that roughly 50% of the alpha

829

: 00:44:19

are unlocking is on scene to them.

830

: 00:44:21

And I think, I think we're really

unlocking the company behind the stock in

831

: 00:44:25

a way for, for the kind of quant AI era.

832

: 00:44:30

Ryan: I love it.

833

: 00:44:30

Jake, thank you so much.

834

: 00:44:32

Like I said, thank you.

835

: 00:44:33

It was an honor.

836

: 00:44:34

thank you so much for listening

to this episode of zephyr's

837

: 00:44:38

Adjusted for Risk Podcast.

838

: 00:44:40

You can watch all of our other episodes

on the Zephyr YouTube channel and Spotify.

839

: 00:44:44

Please be sure to like and

subscribe to those channels

840

: 00:44:47

and give us follow on LinkedIn.

841

: 00:44:49

you very much and have a

great rest of your week.