February 3, 2025 • 48 mins
Marko Kolanovic was one of the biggest names in markets, earning the nickname 'Gandalf' for some eerily prescient calls over the years. But last July he left his role as JPMorgan's chief global market strategist and co-head of global research, after missing out on a pretty epic rally. Since then, stocks have climbed higher with valuations increasingly stretched. So what does Marko think of the market now? In this episode we talk about his outlook the market, the impact of AI including the new DeepSeek model out of China, plus his own research and analysis techniques.
Read more: Kolanovic on the Canary in the Coal Mine for Higher Energy Prices
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Episode Transcript
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Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of the All Thoughts podcast.
Speaker 3 (00:21):
I'm Tracy Alloway and.
Speaker 4 (00:23):
I'm Joe Wisenthal.
Speaker 2 (00:24):
Joe, in your in your history of being a financial journalist,
what are you like most proud of in terms of
coining phrases? Oh, it gave you a hint just then,
because I assume it's Mint the coin.
Speaker 1 (00:37):
Yeah, Oh, good one, Yes, sure, Mint the coin. That's right, Okay,
thank you.
Speaker 2 (00:41):
Yes, So I have a few. I have China's Great
Ball of Money.
Speaker 4 (00:46):
I like how you're like, what are you most proud of?
Speaker 1 (00:48):
And this is going to be my saying, so what
I'm I keep going?
Speaker 3 (00:52):
Thank you?
Speaker 2 (00:53):
You called me out Europe's sovereign bank loop, although no
one believes that I invented that one, and flows before pros,
which has come up quite a bit on this podcast.
So the idea that you know, when valuations are extremely
high and everyone's buying everything no matter what the price,
that it's kind of momentum that matters more than fundamentals. Today,
(01:13):
I'm very happy to say we are going to be
speaking to a pro who knows flows.
Speaker 1 (01:19):
As so extremely well done, Tracy, Thank you excellent setup.
Of course I appreciate all of the tracy neologisms. Man,
what a time in the market to we talk about flows?
Do we talk about momentum, etc. I feel like I
you know, it's always chaotic, it's always uncertain, you'll never
get an answer. But man, things feel really noisy right now.
Speaker 2 (01:42):
They feel super noisy. So we are recording this on
January thirtieth. It is a week that has seen a
very sharp sell off in tech stocks thanks to anxiety
over deep seak coming out of China. We're going to
talk about that and more generally, we're just going to
talk about what's going on in the market right now,
how investors might be handling it, and how the market
(02:04):
structure might have changed over the years. And as I said,
I'm very excited because we do have the perfect guests,
the pro who knows his flows. He has a lot
of nicknames, actually Gandalf being one of them as well.
Actually I didn't realize one of our colleagues at Bloomberg
kind of coined that name for him. But of course
we are speaking with Marko Kolonovich. He is JP Morgan's
(02:27):
former chief Global market strategist and co head of Global Research,
and now he's with us to talk about the market.
And Marco, thank you so much for coming on.
Speaker 5 (02:38):
O thoughts, thank you so much for having me.
Speaker 2 (02:40):
I'm very excited. I know I've said that three times now,
but I guess we should start with the recent selloff,
like the deep Seek, deep stock selloff. They are all
these superlatives that you can use to describe Monday's action,
like the biggest single stock plunge in history in the
form of Nvidia, and eight of the top ten biggest
(03:01):
one day drops in the S and P five hundred,
et cetera, et cetera. And actually, as we're recording this
on January thirtieth, Microsoft is down six percent after earning,
so maybe the tech selloff isn't over yet. But one
of the interesting things about this week is there hasn't
really been broad contagion. So most stocks in the S
and P five hundred were kind of like, meh, we
(03:23):
don't care. When would you expect some of the anxiety
over Deep Seek to maybe start having a bigger impact
on the broader market.
Speaker 6 (03:33):
So, you know, as you said, there was not much contagion,
you know, and if you look at the different stocks
in SMP. Many of them were actually up, you know,
and many even in the tech sectors sector were up.
You know, if you look for instance, you know, Facebook
yesterday or today, or a bunch of other names that
sort of were perceived that they might be sort of
benefiting from, you know, the sort of open architecture, you know,
(03:55):
type of things that can come at the cheaper price,
you know, and can be still you know, implemented in
their business model when it comes to AI models.
Speaker 5 (04:02):
So it was early contained.
Speaker 7 (04:04):
You know.
Speaker 6 (04:04):
I'm a little bit surprised just because there were like
three or four names that really got hammered, you know,
and that can only be explained with not just with
a panic, but some of the sort of forced selling,
you know, maybe coming from options. You know, if you
are you know, if you're selling in a video puts
for the past few years, you could make a good
living out of it. But then you know, you'll have
a day like we saw this weekend, and basically you
(04:24):
might get you know, forced out of these positions and
maybe have a catastrophic loss.
Speaker 5 (04:28):
So it was fairly limited.
Speaker 6 (04:30):
I'm a little bit surprised, you know, just just because
we didn't really have a meaningful sales since last summer,
you know, sort of when at the back of the
Bank of Japan, you know, so, so I do think
we will we will see one. Perhaps it's a little
bit too early in the year. There's still quite a
bit of an optimism post election. There's a little bit
of seasonality in January. People put money to work, they
(04:50):
get paid, you know, they allocated capital. So maybe it's
a little bit too early. I was somewhat inclined to
see that we will see a bit more, you know.
At the back of it, it's perhaps not over. We
still have a few important earnings to come, so remains
to be seen sort of what happens, you know, maybe
another week of earnings, you know, whether there is any
(05:10):
any sort of follow through, you know, but I do
think that it's gonna be some investors will burn clearly,
and a little bit of a tarnish on the sort
of this thesis that some of these stocks like my video,
just go up, you know every day you can't lose,
you know, Like so I think people will think twice
if something can drop like twenty percent in a day,
you got to also think of it what it does
to you to your risk and.
Speaker 1 (05:31):
Well so Tracy asked about contagion and the anxiety spreading
across the market. But I guess I would flip the question,
which is, as I've said before on the podcast, I'm
a boring index investor. So like I look at a
random American company that is like General Electric, it's doing fine.
I'm not very exposed to General Electric because they're a
(05:51):
small part of the index. I am very exposed to
in Video and Microsoft and so forth. We did a
recent episode about market concentration, but I'm sort of like
curious your take on this fact that, like so many
of these flows, they go into broad market indexes, but
we're really all very exposed to a few concentrated market pads.
Speaker 6 (06:14):
Yeah, so the concentration is the highest, you know, sort
sixties or seventies, so we're looking in fifty years half
of the century history and concentration is sort of at
the highest point. It's been a while for staying there
at this level, like maybe past past year. So it's
a weird market. You know, this concentration came for two reasons.
You know, one is clearly thematic investing in technology. Then
(06:36):
you also have investing in a large company. You have
a theme of momentum sort of that is basically self fulfilling.
You know, more something goes up, more money to tracts,
becomes bigger an index, you know, all the passive flows
into it. So there's a technical aspect, there's thematic aspect.
There's even geopolitical aspect. A lot of money went outside
of the other parts of the world. Europe is doing
(06:56):
worse when it comes to sort of economy. China has
been We've been a sort of with the brink of
this Cold War with China, so money has has left there.
You know, Latime has its own share of sort of issues,
so money has been also geopolitically moving area. So it's
a moving in the US, it's a moving in the indices,
it's a moving in the tech you know, and then
you end up with these give or take ten stocks
(07:17):
that that really sucked up all the all the capital
and evaluations got got very very high. Now, you know,
tech investors, they do have a sort of their rationalization,
So what's going to happen in the future. These things
just grow and grow and grow. And that's when we
when we saw with the deep Seek, we saw a
little bit of a dent in that in that thesis.
But these stocks didn't go up for the sake of
for the thesis. They wound up some of these these
(07:38):
these other flows, you know, so unprecedented concentration is not
going to stay there.
Speaker 5 (07:43):
You know.
Speaker 6 (07:44):
The big question is when will we see that rebalance.
Do we need to see you know, some cyclical downturn
first to purge and to normalize some of these valuations,
you know, because historically these pees were never this high.
I mean, in two thousand they were this high, and
we know how it then, you know, but a lot
of people got burn myself include that with some of
the we were more negative last year, you know. And basically,
(08:06):
you know, market has this tremendous momentum, so the timing
is is going to be challenge.
Speaker 2 (08:11):
What does it take to I guess turn momentum at
this point, Like what what are the catalysts that actually
work here? Because it does feel looking at the market
so much of it is now technical or systematic in
some way. There's a lot of options selling, as you mentioned,
lots of multi strat funds that basically you know, just
(08:31):
have to sell or buy to rebalance their exposure. What
actually changes directions, like how do you get enough to
change a trend.
Speaker 6 (08:41):
So, you know, so there's a technical aspect, sort of
the mechanical aspect of it, and there's a sort of
catalyst or more funda mental angle of things, you know.
So to get things to start moving or to stall,
you usually do need to have some funda mental driver
of it. So perhaps there is concern about economy slowing down,
you know, perhaps there is a concern about something geopolitically,
(09:02):
maybe trade war with China or some sort of blockade
of Taiwan's trads or something like that. Right, so first
you need to have a little bit of a catalyst,
you know. But if the market is technically very strong,
the catalyst is not going to change the momentum. So
what that means is, you know, more specifically. You know,
so when you look at the trend investors, you know,
they have a range of signals, you know, like so
they can look at a one month price momentum, three
(09:24):
month price momentum, six month pricements, or twelve month price momentum,
maybe eighteen months, but that's about it, you know, And
there's some very short term momentum players that look intra
day or on a daily basis but most of these
signals are concentrated around twelve months and two hundred day
moving averages, right. So that's why when people look at
the two hundred mo average, they first when I, you know,
twenty years ago, when someone told me, I said, like,
(09:44):
what is this magic?
Speaker 5 (09:45):
Why would this work? Right?
Speaker 6 (09:46):
But it's reality is that many models, you know, systematic models,
primarily you know computer DRIM, but also psychological investors look
at these things and become self fulfilling. So you need
to actually come close enough to these levels to break them.
Speaker 5 (09:58):
Right.
Speaker 6 (09:58):
So for instance, earlier this week SMP got below around
six thousand or a bit below. We were close to
breaking twenty day moving average and fifty day moving average,
so you can think of it as about one month
and three month price momentum. So that's about the third
of a signal. The big signal is really twelve month
you know, or two hundred day moving average, right. But
(10:19):
when you start moving things you can actually unravel. It's
like a little bit like a snowball. So I thought like,
if the market is going to stay below twenty and
fifty days at the end of the day, you may
get enough selling from CTAs or de risking from cts
that they may get you to another leg lower right,
So it's basically you need to have set up that
you're close enough to these triggers on the downside to
(10:39):
move it. And again I think this week we got
very close. But there was also other flows like rotation.
You saw like selling on a video, but Apple and
Metal went up, you know, like so at the end
now it was like drop but did not drop a lot, Joe.
Speaker 2 (11:08):
It kind of reminds me of like structured credit notes,
where there's that knockoup knockout level and then you get
this massive cliff risk and the whole thing kind of unwindes.
Speaker 1 (11:18):
Right, you have all these rules based investors and something
happens like the model a system sell It is worth
noting that as we are talking ten nineteen am January thirtieth,
and Video is basically right at its two hundred day
moving average. I know this isn't the broader market, but
it's a big part of the broader market, so yeah,
it might as it might as well.
Speaker 2 (11:39):
Be.
Speaker 1 (11:40):
One thing that struck me on Monday, which I was
a bit surprised about in the selloff is that even
on Monday you mentioned that Meta actually closed Green and
they are a maker of a competitor to deep seek.
They have their open source Lama, but they're seen by
the market as a consumer of AI services because they
(12:00):
don't sell their product. They use it to do things
like better ad targeting, et cetera. Were you surprised that
even on this day in which there is this sort
of exogenous shock to the market, everyone wakes up to
some new thing that actually investors showed a fair amount
of discrimination in terms of what they don't.
Speaker 6 (12:19):
Yeah, so I mean that was that was against you know,
January sentiment is still pretty positive. Economic data are strong,
so people are saying, okay, this is not the beginning
of economic downturn. This is isolated sort of event whereby
some companies will get hit, you know, their sort of
revenues will get hit yea, and some will be able
to do things for cheaper you know, like so we
had the salesforce I believe as well. And so it
(12:41):
ended up not a macro day but more of a
rotational day.
Speaker 7 (12:45):
You know.
Speaker 6 (12:45):
There is also a so called quant factors where you know,
even within technology, some stocks are higher multiples, some stocks
are lower multiple. So you know, some stocks are more
momentum less momentum, you know. Like, so there was a
bit of rotation. So Apple, which was a lagger, it
also kind of quite a bit, although I don't think
there was much fundamental stop going on for Apple. It
was probably just rotation. So so market kind of held up,
(13:08):
you know, and I was sort of at defense whether
whether these like technical levels twenty fifty will will get
broken and will go lower or not. We didn't, you know,
but I do think that sort of you know, evaluation positioning,
and some of these technicals are a bit stretched, you know,
like so I don't have I don't see like a
huge huge ob site from the market. So maybe I'm
switching the topic a little bit.
Speaker 4 (13:28):
But no, No, that makes sense.
Speaker 2 (13:29):
Can I ask you a sort of procedural question, which is,
you know, you were at JP Morgan for twenty years,
and you were in the industry even before that. How
did your sort of research and forecasting process change throughout
those years?
Speaker 6 (13:45):
No, thank you, And it did change, and it's and
it's an interesting good question, you know. So, so I
got my PGE in physics, in theoretical physics, so there
was a lot of coding, There was a lot of modeling.
There was a lot of sort of trying to understand
why one thing leads to another. You know, what is
the cause, what is a concept? What's causality? You know,
and what's the noise? What's statistically important, what's statistically not important?
Speaker 2 (14:07):
You know.
Speaker 6 (14:08):
Like so, so in physics you build these type of models.
You try to understand what's significant, what's not, what you
can neglect, which factors you can you have to take
into account, and most important, how to simplify the complex.
You know, market is an extremely complex system, you know,
in many physical systems, so you need to sort of
move the noise on one side and drivers on the
other side, and try to recognize those patterns, right like So,
(14:28):
although I really never used any formula from the physics
in my almost never really in the finance, but the
way of thinking is similar. So I started Merrilynch in derivatives,
in derivatives research where I started looking, you know, interesting
that we are now in earning seasons. So my first
models were impact sort of earnings on a stock price
and what can you read from options market?
Speaker 5 (14:47):
You know.
Speaker 6 (14:47):
Like so I published some papers, you know, come up
with some formal as, and we were kind of backing out, okay,
what the option market is saying, and then we go
to analysts and say, hey, do you think it makes
sense or doesn't make sense?
Speaker 5 (14:56):
Right?
Speaker 6 (14:57):
And then if you if you think that options are
saying too much of a or too little of a movie,
you could trade these options and stuff like that. So
that was one example of okay, how do you sort
of you know, you have a catalyst, You look at
different markets, you see are these markets aligned? You put
some model together, and then you find the discrepancy with
the model. It's not always going to work, but if
you do it for one hundred stocks, maybe an average
and a portfolio level, you'll you'll be fine, you know.
(15:19):
Like so so so that the relative research and quant research.
I did a lot of quantity research. So you try
to process the data, you try to look at the measures.
And at that time, like you know, twenty twenty five
years ago, it was beginning really of trading of vicks
of volatility, swaps of correlation or dispersion. You know, like
so you kind of try to see, okay, you know,
(15:40):
you look at the vix you look at the market volatility.
What's driving market volatility? While people say, well, it's a
panic or it's not. But let's be more quantitative. You know,
you can how correlated stocks are, what individual volatility of
each stock is, you know, which part is due to
the macro factor the market, what's idiosyncratic, So you can
kind of break these down. Then you can look at
the sector correlation between sector, what's crushed within sector, So
(16:03):
you can kind of quantify these things and analyze and
get some insight, you know, like two thousand and eight,
for instance, we look at the tw thousand and seven
thousand and eight, I look at how the hedging of
options impacts the market, you know, like so you basically
need to look at how many options are out there
in index. Let's say you try to assess what's the
positioning from from the flows from the sort of no
(16:24):
knowing of industry hedging flows, and then you see, okay,
what are the hedging requirements at the end of the
day and two thousand and eight and then twenty eleven,
and like a low hanging fruit, you could see sometimes
these flows would be bigger than market can absorb, you know,
and they would all go stay away from level ETFs
from options, you know, so you see like, okay, there's
like twenty billion dollars to sell and market can't absorb it.
(16:46):
So you know, market towards last ten minutes will drop.
And that's you mentioned sort of Ganda. That's where where
some of these things because people, you know, if you
look from the outside, you say, oh, how can he
get that?
Speaker 7 (16:57):
Right?
Speaker 6 (16:57):
You know, and and but it is really understanding a
bit of technicalities, which is flows, option, convexities, liquidity and
how they how they sort of interfere. But you know,
after twenty fifteen sixteen, people figure it out, you know,
and then people put it in their models.
Speaker 5 (17:12):
They create like.
Speaker 3 (17:14):
A structured product of twenty eighteen.
Speaker 2 (17:16):
Everyone sort of woke up to the vis especially right
Oh yeah, so that's.
Speaker 6 (17:20):
A February em again and yeah, and you know stuff
like that. So you kind of analyze causes and consequences
in the market, something that is new that's not been
yet a look at, you know, and I have focused
on things that we're new in the market, like products, options, futures, CTAs,
those type of things.
Speaker 1 (17:39):
You know, this it's not particularly like technical. But another
thing that seems to be true about the market right
now is, at least maybe up until week ago, and
there's just an incredible amount of optimism in any sort
of measure. So if you ask, you know, there's consumer measures,
do you think stocks are going to be higher in
a year from now?
Speaker 4 (17:58):
Very high levels?
Speaker 1 (18:00):
If you look at things like the Bank of America,
cell side analyst sentiment very close to euphoric levels. If
you look at fund cash levels extremely low right now,
very over everyone is overweight long tech. How do you
ingest this information because on the one hand, you say, oh,
everyone's all in, this is negative. On the other hand,
people have been very optimistic for a while. How should
(18:22):
we as consumers of this information think about what it
says about the fragility of the ball market.
Speaker 6 (18:28):
So you know, clearly in the markets things are mean reverting,
you know, Like so when think reached some very high levels,
you know eventually they will.
Speaker 4 (18:36):
Gone a long time, Yeah, exactly.
Speaker 6 (18:37):
So, so so there is this mean reversion, But there's
also trend, right you know, Like so you know, figuring
out the timing of that is hard, right, you know,
Like I mean if there is a sort of a
limited set of drivers, like in some of these technical markets,
you know, so for instance, ctias, you know, you know,
once when the all the levels are positive, you know,
all the singles are positive, and then volatility drops a
(18:58):
little bit. You know, their mac stout, you know, so
you know they're not going to buy more, right, you know,
like and so you can you that's a that's a
self contained, isolated system where you can say, okay, optimism
is too high. So there's the only downside, right with
the with the entirety of market, right you know, with
you know, you have a crypto, you have a fiscal measures,
you have like monetary stimulus, you have a sentiment shift.
(19:19):
It's hard to handicap all of those, you know. So
it's hard to say that for all of the investors
to be able to know exactly when this thing is
going to stall, right, sure, and and and there are
developments that is hard, like you know, this whole AI,
you know, And I see the I wrote a book
on twenty seventeen about AI with my colleague Rejaesh twenty eighteen.
(19:40):
So we were early on on AI. And you know,
six years ago, right, nobody was talking really about it
at time. So it's not that I don't understand it,
but I'm a little bit cynical about it now. I
think it's it's too hyped up, right, But it's hard
to assess how long people.
Speaker 5 (19:52):
Will be excited about it, right, you know?
Speaker 6 (19:54):
And yeah, so and then you have a change, So
you have political changes that can bring deregulation that can
be change in tax regimes, so you have like a
wild card.
Speaker 5 (20:02):
So it's hard.
Speaker 6 (20:03):
Your question I started like, there is a reversion always,
but where you're going to pick it, it can be very
frustrating and very sort of you can be wrong for
a long time.
Speaker 2 (20:12):
Wait, can I just press you on that point about AI?
Because I think I think the difficulty that investors are
having is AI has a great story right now, and
there's this idea out there that it's this revolutionary technology
that's going to change the world. Joe keeps referring to
it as inventing God when it comes to AGI at least.
(20:34):
And at the same time, there is also a feeling
that people are maybe getting a little too optimistic about it.
There's too much hype in the market. You've started seeing
companies that you know, just put out a press release going, oh,
we're looking into AI, and their stock price goes up.
How should investors handle their exposure to AI? Like, how
(20:55):
do you actually play it at this point in time,
given that you were early to the topic.
Speaker 4 (20:59):
And now cynical.
Speaker 6 (21:00):
You know, I look at it from theoretical side. I
look at it more how to apply it sort of
in finance, in quantitative trading. You know, how to use
a large language model to assess the sentiment, changes in
sentiment and those type of things, right, so you know
how to read quickly things and summarize them and drive
some signals out of it. So there is obviously bigger
question of AI, you know, which you said, it's kind
of philosophical questions like are we going to be replaced?
(21:20):
You know, at which point what's going to be a
role of human once? When when we can you know,
kind of break down our way of thinking and effectively
training and replace it. You know, So their whole host
of other questions, you know, Like so, I'm not skeptical
that this is going to be hugely important, and it
is a hugely important you know, it's not very very
different of what people have been doing, you know, five
(21:43):
years ago or ten years ago or twenty years ago. Obviously,
big progress in computing power, big progress in the models
as well, you know, Like so, so it's it's but
I see it like more as an evolution, you know,
than something that changed with che GDP in twenty twenty three,
like two years ago, as a kind of like a
step function. I see it as an evolution. Always important,
right like ten years ago, when we use our smartphone
(22:05):
to take a pictures, like you know, camera would recognize
the face, it would zoom into face, it would kind
of do the proper focus and stuff like that. So
that's also you know, that's also AI and and things
are advancing, right, and we'll keep on advancing. Now, question
is going to be winners losers? How to monetize? You know,
does that suddenly re rates all of equity market multiple?
(22:27):
You know, like suddenly, okay, people are not gonna work.
These companies go to all the work, so we're just
gonna value them. Like who am I to say that?
And also who am I to say that that's wrong
as well? You know, but there's a lot of speculation
and a trust. You know, people often tell me, well,
imagine just how my way to search Internet has changed,
you know, like like okay, like we were searching Internet
(22:48):
for twenty five years the same way I used to
use like a Netscape like twenty five years ago, right,
and and the same thing you type in a bar
and you and you find something. So for christ sake,
of course it's going to change. Of course, at some
point it's gonna be we're gonna tell something to computer.
Computer will have its own ways of parsing and finding
what's relevant and giving us back information. So I'm not
as excited about that change. I think it's the way
(23:10):
over you change, you know, like, but there's a lot
of optimism.
Speaker 1 (23:13):
Now there's Well, I was wondering because I was looking
at your LinkedIn and you mentioned you have a PhD
in physics graduating from NYU in two thousand and three,
theoretical high energy physics, cosmology, string theory, and finance. I
guess the two part question. A would do you think
there's a world in which if you graduated today you
(23:33):
would have gone into AI instead of going into finance,
because I imagine they would have hired you at those skills.
Speaker 4 (23:39):
But B, like when you.
Speaker 1 (23:41):
Think about and Tracy mentioned, you know, like the true AGI,
do you think that the current AI research is on
a path to that sort of AGI inventing God that
a lot of the proponents believe, So you.
Speaker 6 (23:54):
Know, I think eventually it will get there in a
sense that it will sort of address some very important,
you know questions which are kind of deeply what every
person fears or wonders or or sort of seeks, you know,
kind of meaning of our lives, like you know, you know,
future after we die and stuff like that. So they're
they're definitely interesting, interesting things there that can be done.
(24:16):
I mean, people are doing with these like assistants, right
like you train and I believe really this AI will
have to be a lot more personalized, you know, like
so you will train it really on your life experience,
you know, like so if AI can see every image
I saw, if it can read every email, you know,
I believe AI will be able to tell me when
did I make a mistake? When should I do something different?
You know, did you overreact in this live situation?
Speaker 5 (24:37):
Did you not right? And going further?
Speaker 6 (24:39):
Right like that will stay and and my my kids
can after I pass away, they can say, hey, what
would that say in this situation? Right, you know, like
what would maybe I'll be able to in some way
talk to them, right, so you'll blur all these things
which were which were sort of not blurred in the past, right,
you know. Also, you you'll start having these like very
(24:59):
very interest developments, you know, but I would kind of
not look at them from the sort of P and
L perspective, earnings perspective. There's also going to be a
lot of issues as we have already now. I mean,
sometimes I can give you wrong answer. Sometimes it can
be used to do bad things, to impersonate, to deceive,
to manipulate. So there's gonna be a lot of a
lot of interesting I would say, philosophical issues, you know,
technological issues and investing investing issues. But I just don't
(25:22):
think it's going to be as simple as like seven
companies are gonna have P of fifty and everyone else
will have P of ten and it's going to persist
that way. Yeah, I don't think it's going to be
like that in finance, at least him.
Speaker 2 (25:33):
The other thing I wanted to ask you is, you
know you left JP Morgan in July and then pretty
much a month later we had a very sharp sell off.
When you look back at that particular sell off, you know,
we never got to hear from you your thoughts on that.
On that particular week in markets, what did you actually
(25:53):
see and observe there because there are still differing opinions
out there about what the proximate catalyst was for some
of the moves and what was exacerbating what.
Speaker 6 (26:02):
No, So the catalyst was definitely moving rates related to
Japan in the currency, right, that was a sort of catalyst.
But you always have like a spark and a bucket
of fuel, right, and the bucket of fuel will stretched
CTAs stretched vault targeter systematic investors, too much optimism, you know,
and then you start basically hitting the stops across these strategies, right,
(26:22):
ctias hit their cell signals. Volt target is VIX goes up.
What goes up they need to sell, you know, if
you're selling puts on AI names, you suddenly to you know,
you need to kind of close. So so VIX was
very vixed. Behave most phenomenals. So it was a whole
lot of all short wall covering as well, you know.
But again I think it was what was missing for
(26:43):
this to be the turn in the cycle was I
guess you know, GDP employment still fine, right, still hope
that FED is going to cut, right, you.
Speaker 5 (26:52):
Know, So it didn't. It didn't.
Speaker 6 (26:54):
There was a little bit of conflagration but didn't kind
of burn everything down right, So it was a little
bit of satisfaction. But in too long it is.
Speaker 1 (27:01):
Pretty markable because even we got a little sell off,
but it's a very minor sell off. Or basically, the
stock market is more or less at all time highs.
This is despite a pretty big repricing of the expectation
of the short end of the curve, where people were
expecting deep cuts to continue through last year and to
this year. We might not get any cut this year,
and yet still the market is close to.
Speaker 4 (27:22):
All time highs.
Speaker 1 (27:23):
It must be nice on some level to be out
of the game of having to come up with an
end of your price, because that sounds like a job
I would never want to take. But I also wonder,
you know, do you wake up in the morning, it's
still like your time to talk to us about you
know what you have a market outlook for right now?
Like give us some give us what's on your mind?
Speaker 6 (27:45):
Sure like so, so look, it's nice once in a
while that you can be somewhere away and not look
at the fact parst every single world word. Although I
did it yesterday, you know, but a few months ago
I didn't you know, like, so it's nice to make
a break ord. Maybe it's necessary, you know, out markets
are a little bit of a sort of compulsion thing
of compulsion you when you feel like you need to
(28:05):
understand what's going on in the world. You know, So
I think it becomes part of your DNA if you
do it for a long time. So so I do
always think, and I do have an outcome, so give
us so you know, you know, on a sales side,
you kind of need to put a price target. And
I and I think it's a kind of poor way
to summarize everything into one number. It's basically almost you're
(28:26):
telling you're trying to focus probabilities in the world, you know,
because world real world actually works in terms of physics
deep deeply works in terms of probabilities, not just superficially,
you know, in a quantum physics. So you you need
to sort of have a sort of hype proabailistic view
and you're forced to have one view like one hundred
percent or nothing. Right, So so it gets over simplifies.
(28:47):
I think media, you know, and not referring to you,
but media does a bad job. They say, oh, what's
your price?
Speaker 5 (28:52):
Starty?
Speaker 6 (28:52):
They just want to talk about that, and they say, oh,
you're right, you're right. Yeah, so so no, so I
would say, like, you know, if if I can move
away from price, sorry, I do think we'll go back
down in five thousands this year. Sometimes I think at
that point in time, we will we will see whether
the cycle is still strong or it's not. You know,
I think we need to see the whole new political
(29:13):
climate whether it will lead to turmoil, and I believe
more likely than not it will, you know. Like so
those things I think will get us lower, right, you
know at that time, whether whether it becomes an end
of a cycle and we go much lower into four thousand,
that I don't know. I think there's some probability of that,
you know. And then conversely, on the upside, is everything
goes if really this is what they call it Golden age,
(29:36):
the Golden age of America, you know, then market will
stay in six thousands, it can go a bit higher.
I just see, I'm hard pressed to see it going
much much higher, right because evaluations are there, Positioning is
already there. As you said, FED is not cutting, right,
So it's a little bit of a chicken and egg.
I mean, I have been scratching my head, like at
(29:56):
these level of rates, which I do think is that
are restricted rates for now now more than two years
with the commercial real estate here and there, we saw
a few hiccups, you know, like, but you know, I
do think that is sort of under the hood of
economy some damage is being sort of built up and done.
So so I don't think like market really you know,
going to seven thousand or you know, sixty eight hundred
(30:18):
or something like that. So I would say, maybe it
can go sixty five, stay range bound, you know, Like,
so I would sort of formulate the view in terms of, okay,
you perhaps want to sell ups, give yourself a little
bit of a room for some more excitement first few
years of the of the first few months of the year,
but then also be ready to assess once when you
go in, you know, let's say fifty five hundred or
fifty seven hundred, to assess is the cycle potentially ending,
(30:41):
you know, or that's going to be buying opportunity. And
I wouldn't want to sort of say, hey, like it's
going to first stay at sixty one hundred, then it's
going to pull to fifty five, and then you buy
with both hands at fifty five and like that'll be
too predictive, you know, but I think some variation of
that path will be whereby sort of the depth of
a pullback will depends on trade war China, domestic political
(31:02):
situation rates, and like one off things.
Speaker 7 (31:06):
Like we had a Monday.
Speaker 2 (31:23):
I'm glad you mentioned domestic politics because one of the
other weird things about this week when we had the
deep seek cell off was everyone was focused on that,
and you know, tech stalks went down, as we mentioned,
But then on Tuesday everything started rebounding. Even though we
had headlines coming out of the White House about cutting
(31:44):
what amounted to a pretty big chunk of federal spending,
the entire market seemed to look through that, which is
kind of ironic because one of the things we've heard
for the past four years or so is this idea
that you know, deficit spending is driving the entireomy and
stuff like that. I feel like political risk is one
of those things that investors really struggle to price in
(32:07):
because there's so much uncertainty. A lot of it seems
very binary. How do you deal with that?
Speaker 6 (32:13):
So you need to sort of, you know, put some scenarios,
you know, what can happen in terms of taxes, regulation,
you know, tariffs, trade wars, geopolitical conflict you know, and
then see what can how can they impact specific stocks
and industries, countries and maybe overall market sentiment, you know,
and maybe put some scenarios. That's the kind of a blueprint.
And market never goes by that blueprint, but at least
(32:35):
gives you some framework to try to understand if it
doesn't go by your sort of assessment, what have you
missed and what you need to what else you need
to take into count but you put some blueprints sort
of what can happen? So you know, I think you
pointed very well. He was talking about Navidia, Taiwan export sortiz.
Like so those type of things, right. So market that's
(32:55):
then market has its minds of its own, which is
tied to sentiment, you know, and it's tied to momentum.
Speaker 7 (32:59):
You know.
Speaker 6 (33:00):
Most people think momentum, you know, They don't calculate by
the thing. They just feel good about market. They see
good news about market. They their taxi driver or friend
or family feels good about investing, right and and they
choose them to ignore, you know, Like so on Monday,
I was watching CNBC and every single guest was saying, oh,
take your shopping list out, take your shopping list out,
take you buy this, buy this right. So you know,
(33:22):
it creates a little bit of a sentiment. You know,
it creates a sentiment, and people say, okay, you know,
I'll make a punt. I'll buy if it's twenty percent down,
maybe next day is going to that can bounce them,
So people buy right. Some people rotate it say okay,
like I'm getting rid of no video, but look Apple
has been you know, underperformance, so maybe I put my
money there. So the sentiment overall was still pretty strong.
(33:42):
There's this aura of momentum, psychological momentum that it's harder
to break. You know, you don't need to have a
few punches for it to break, for people to sort
of give up.
Speaker 2 (33:50):
So one of the other things I want to ask you,
you know, just again looking back at the sort of
long term changes in the market. And we touched on
this earlier, but we've just seen an hour absolute explosion
in different types of options trading and volatility trading, and
now you even have TikTok influencers who are like pitching
options investing as passive income, like, oh, don't buy a
(34:12):
US Treasury bond, do you an options? But which is
kind of crazy. How has that impacted the market and
how have you seen people, you know, trying to handle
some of that new I guess dynamic that's been introduced.
Speaker 6 (34:28):
So so that's that's a very good question. It started
sort of with around the COVID time. People were locked in,
they got these stimulus checks, they started trading, right, proliferation
of these online brokers, no commission fees, options being traded
as a sort of very short, short and short maturities, right.
You know, options used to be sort of leaps and
(34:48):
then maybe like a monthly options, you know, second and first,
second and third month quarterly options moved to weeklies and
dailies you know, and then in the single names you know,
Like so you had sort of people locked they got money,
and they got these instruments, these extremely powerful instruments with
leverage about one hundred times leverage, you know, like so
you suddenly can make a bets of you know, millions
(35:08):
of dollars even if you have like ten thousand or
five thousand dollars to invest, you know, Like so that changed,
that changed a lot and for most of these people
actually it worked, right because since twenty twenty we had
that pullback when the FED started hiking, But for most
of it it worked, you know, Like so speculative trading activity,
especially on the alongside, it worked. Then you also had
(35:31):
in peril sort of crypto markets growing, right, you know,
Like so if you think of it, like, you know,
a few trillions of dollars of wealth was created there
with probably some of these similar type of investors and
similar type of people, you know, like so so it changed,
you know, so there is less a leverage in terms
of borrowing money with interesting but more a lot more
(35:51):
leverage in terms of option trading activity.
Speaker 7 (35:53):
You know.
Speaker 6 (35:53):
So, as you said, I'm always also surprised. You go
on some of these social media and then you see
all kinds of strategy is that can't lose money, that
making like tens of thousands every day. You just need
to follow him, and it becomes really kind of bizarre.
You have like these people who are at the same
time performer or like women who are like you know,
in the like underwear, suggesting how to trede options, like yeah,
(36:15):
it's all.
Speaker 4 (36:16):
Big fans of mind.
Speaker 1 (36:17):
They follow me on Twitter ADM very flattered.
Speaker 6 (36:20):
So it's it's kind of crazy, you know, like and
we try to handicap it by looking at flows from Robinhood,
see which names are being sort of bold, which names
are being sold, try to see where the retail may
be forced out or something like that. So we did
some quantitative work. We did a lot of the sort
of language large language models sentiment wise, like from Twitter
and those type of other social medias which we could
(36:43):
we could get permission to do. So we're trying to
incorporate it. But I think overall it's hard to one
hundred percent handicap it. But for sure it added leverage
to the market, added speculative element to the market, and
at some point it's not going to probably end up
well right some point, you know, but it's hard to
say when exactly right.
Speaker 2 (37:02):
It's again one of those things that can go on
for longer than you think. Since you mentioned getting data
from robin Hood just then, this is the other thing
I always wanted to ask an equity derivative strategist because
you alluded to this earlier. There's a lot of I guess,
misunderstanding or lack of understanding of what an equity derivative
strategist actually does, and exactly what data they're looking at
(37:26):
in order to make some of their conclusions. Can you
maybe give us like a quick one oh one in
where your data comes from? How much of it is
from official sources like I don't know an EPFR or
someone like that, versus like color that you're getting from
the market question.
Speaker 6 (37:44):
Yeah, no, so sore. They're all kinds of data. So
there are price involume data or kind of technical data
that can be derived from from from those type of things,
which can also be a different time horizons, you know,
like they can be daily. Mostly they are daily, right,
you know, but increasingly you so want to look at
the intra day data, you know, intraday correlations, interramormentum volumes,
large blocks that are traded. So there's also high frequency
(38:07):
one day, but most of it is daily, I would say.
And then there are longer term data, you know, when
you look at the sort of you know, some like
a monthly statistics on positioning or up to the sort
of filings you know, thirteen F filings like holdings and stuff
like that. You know, like so so different sort of
frequencies of positioning volume data price data. Then so directly
(38:30):
market observed data like open interest, you know, options volume
and options those type of things. Right, then you have
sort of fundamental data, you know, and fundamental data. You
have fundamental data for stocks, you know, which are related
to earnings, but increasingly you have a data which are
derived from non traditional sources, you know, so called big
data that can be sort of sentiment measures, but quantitatively
(38:51):
derived measures, objective not sort of a guesswork to some
very specific niche data like you know, satellite you know,
all kinds of like data that you know, we didn't have,
you know.
Speaker 3 (39:01):
How many cars are parkeding parking lots.
Speaker 5 (39:03):
And in front of Walmart and those type of things.
Speaker 6 (39:05):
Right, So you have these stock specific data, earnings derived,
news derived, sentiment derived, and then also non traditional ones
you know, and then you have like micro data, you know,
typically lower frequencies, but increasingly also with some of these
alternative data sets, big data sets, you can try to
figure out like you know, shipping and again sort of
storage and oil tank tanks how full they are and
(39:27):
stuff like that.
Speaker 5 (39:28):
So it's a whole.
Speaker 6 (39:29):
Host of data, you know, like as a quant and
there at this person you probably focus most on the
market data, you know, so open enters, price volumes and
old stuff that is derived from that, but you also
want to want to supplement that with all these other
other data. And then and then some of the data
set you derive it on your own, you know. Like
so so, for instance, got my imbalance in SMP options
put minus call. So I was running that for fifteen
(39:51):
twenty years. And first people tell me to say, you know,
what's that. That's you cannot know what's you know. But
then now everybody has it, actually, you know, and the
same thing with like a CTA stuff, you know, I
and volt targeting exposure. I was getting so much sort
of critique in twenty eleven, twelve thirteen. You know, now
everybody has it, you know, kind of CTA positioning percenta.
So you can derive some on your own based on
(40:12):
understanding with markets.
Speaker 1 (40:14):
All right, I just have one take and I bring
it up a lot, and I sort of feel like
the kool aid man. It's like every conversation I have
to jump it through the wall and interject this. But
you know, there's all sorts of like quant techniques, and
there's the definition of quant and changes over time, and
obviously there's an incredible amount of data that we can
(40:34):
use now. And then there's sort of like old fashioned
quant where you're just like, we're gonna buy you know
that I sort of associate with like AQR from years ago,
where like we're gonna buy the cheap stocks that are
exhibiting momentum, right, and we're going to short the expensive
stocks that are declining momentum. And why doesn't this work anymore?
And all these sort of hand ringing and the traditional
quant industry, why has it?
Speaker 4 (40:55):
Why haven't things mean reverted?
Speaker 1 (40:57):
How much is the fact that, like so many of
these sort of ideas about how the market should work
have been broken by the simple fact that a handful
of American companies that are very big exhibit year over
year earning his growth that are truly remarkable. And this
is a fact not about the market world, but about
(41:17):
the real world that, for whatever reason, these big tech
companies just keep getting bigger despite their size.
Speaker 2 (41:24):
Wait, Joe, you have to end that by saying the
kool aid man catchphrase.
Speaker 5 (41:28):
What did he say?
Speaker 3 (41:29):
Oh yeah, oh yeah.
Speaker 1 (41:31):
As long as the metas and the Googles and the
videos and maybe the Apples of the world just keep
growing earning is like.
Speaker 4 (41:36):
Crazy every year.
Speaker 1 (41:38):
How much does that bust any sort of notion of
mean reversion in market?
Speaker 6 (41:43):
So I think it busts the notion of a value
as a factor. But value is the fact that we've
been straggling for a long time. Yeah, so sort of
probably since you know, decline in interest rates post two
thousand and eight, a lot of these, you know, and
growth of indexation, right, growth of dxation kind of spark
them momentum and change the structure of the market. So
some of these quants, quants models or quant factors work
(42:06):
less and less.
Speaker 7 (42:07):
You know.
Speaker 6 (42:08):
There is also another aspect, which is, you know, once
when you put money to work in these strategies, you
kind of squeeze out the alpha, you know, and and
and and these things are fully priced in so they
stop working. So sort of growth of quant funds, traditional
quant funds, you have quantity tfs, you have like broker
dealers doing quant strategies kind of squeezes out returns, right,
you know, on your question, sort of like these big
(42:30):
companies that keep on delivering what that's that's also a
very good point. You know, quant strategies are designed for
sort of steady state situation when kind of things are
fluctuating around something which is in a steady state. And
we had sort of you know, big sort of big
changes in the world right in technology, Yeah, and also
geopolitically sort of you know, capital moved to us and
(42:51):
then moved into these sectors of innovation, right you know,
And now so you may sort of you know, you
may con simply be out of equilibrium, right, you know,
where some of these mean reversion or quand strategies would work.
A certain type of con strategises have value based strategies.
Speaker 5 (43:07):
Right, question is.
Speaker 6 (43:08):
How long you know, how long can you know, going
back to the concentration, right, how long can it go?
My my question becomes like, let's say, if you have
like a social media company, like a Meta right, I
mean once when they have all the users in the world,
I mean, like you know, they can't go much further, right,
they can go to the.
Speaker 3 (43:24):
Right, there's limits, there's limits.
Speaker 4 (43:26):
You know, but numbers are creating fake ai FA.
Speaker 6 (43:29):
Or go like to Marcel, there's no one there, so
there's some some limitations, right, like and then there's some
sort of also historical when you look at the weight
of stocks in an index, right, so so you're taking
a video percentage rat in SMP, and you know, you
run back history and you see that this basically never happen,
and even if it happens, never lasts forever. Right, But
to your point, it can last. You know, one or
two or three years is enough to ruin a lot
(43:51):
of investment strategies, you know.
Speaker 2 (43:52):
All right, Marco Kolonovitch, thank you so much for coming
on odd lots of real treat for both of us.
Speaker 4 (43:57):
Yeah, that was great, Thank you so much, Thank you.
Speaker 2 (43:59):
So much, Joe, that was really fun. I'm so glad
I finally got to ask him a bunch of questions
about just being an equity derive strategist.
Speaker 4 (44:21):
I've I love those.
Speaker 1 (44:23):
I love those questions so much because they're this sort
of like dark fiber or the dark matter of how
this industry actually works. But everyone just abstracts over them.
It's like, oh, you look at the data and then
you do.
Speaker 3 (44:35):
They but right, like what data?
Speaker 4 (44:37):
Where where do you data actually come from?
Speaker 1 (44:39):
Like I could listen forever to someone and we should
do more of that, just like talk about these aspects
like paying for data and data costs and all that stuff.
I really like Marco, It's a nice conversation.
Speaker 2 (44:51):
The one thing I would say is, you know you
asked that question about like, well, the S and P
five hundred overall didn't do too bad on Monday, and like,
you know, if I'm an induct investor, I have exposure
to GE and so that's good because I don't have
to worry about AI. But I think the question really is,
like how much is GE exposed to AI? In very
indirect way?
Speaker 1 (45:11):
Yeah, that's correct, right, and you do sort of I mean,
you know what, I think. I saw a tweet about this,
so I'm just gonna say it, and it may not
even be true, but if it's not true, it's truth y.
Someone tweeted that apparently there was like a Sherwin Williams
earnings call and someone asked about how much paint data
centers were going to need to paint their walls. I
(45:33):
don't know if it's true, but if it, because I
just saw the tweet. But if it's not true, it
doesn't really matter, because it is true, Like every company
is like are you doing something that could supply do
you sell some product that someone building an AI data
center is going to need at some point? But my
point about GE though, it was kind of the opposite,
which is like GE did fine on that day, but
(45:56):
I wish I had more exposure to GE. What I
really have is a bunch of video Microsoft exposure through
my index.
Speaker 2 (46:01):
Fund, right, But you don't know how much exposure indirect
exposure you have to Microsoft through GE.
Speaker 4 (46:08):
This is because this is true.
Speaker 2 (46:10):
Sconda wrote that excellent column in the au Thought's newsletter
about how more and more of the economy is being
driven by AI. We actually saw on on Monday the
treasury market move a little bit, which you know, okay,
treasuries will go up when there's a market sell off,
but a lot of people were saying, well, this impacts
growth expectations as well, and so that's why you're getting
(46:32):
this reaction.
Speaker 1 (46:33):
No, totally, I think the degree. I mean, especially if
you go back and like Trump announced the half a
trillion dollar Stargate project, and I don't know what's really
going to become of that, but like I believe the
data centers and AI specifically through the data center channel,
is a meaningful important, is a growing important part of
(46:55):
the real economy right now. And if suddenly they're like,
you know what this is a dead end or suddenly
like we don't need this because we can get AGI
so cheaply that it's just like on our laptop.
Speaker 4 (47:07):
That would raise some real econ concerns.
Speaker 3 (47:10):
Yeah, shall we leave it there?
Speaker 4 (47:11):
Let's leave it there.
Speaker 3 (47:12):
Oh yeah, this has.
Speaker 2 (47:14):
Been another episode of the Authoughts podcast. I'm Tracy Alloway.
You can follow me at Tracy Alloway.
Speaker 1 (47:19):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow our guest Marco Kolonovich, He's at Marco in and Why.
Follow our producers Carbon Rodriguez at Carmen Arman, dash Ol
Bennett at Dashbot, and kill Brooks at Kilbrooks. From our
odd Lots content, go to Bloomberg dot com slash odd Lots,
where we have transcripts of blog in the newsletter and
you can chat about all of these topics twenty four
(47:39):
seven in our discord Discord dot gg slash od Loots.
Speaker 2 (47:43):
And if you enjoy Oddlots, if you like it when
we discuss the dark magic of equity derivatives, then please
leave us a positive review on your favorite podcast platform.
And remember, if you are a Bloomberg subscriber, you can
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Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of the All Thoughts podcast.
Speaker 3 (00:21):
I'm Tracy Alloway and.
Speaker 4 (00:23):
I'm Joe Wisenthal.
Speaker 2 (00:24):
Joe, in your in your history of being a financial journalist,
what are you like most proud of in terms of
coining phrases? Oh, it gave you a hint just then,
because I assume it's Mint the coin.
Speaker 1 (00:37):
Yeah, Oh, good one, Yes, sure, Mint the coin. That's right, Okay,
thank you.
Speaker 2 (00:41):
Yes, So I have a few. I have China's Great
Ball of Money.
Speaker 4 (00:46):
I like how you're like, what are you most proud of?
Speaker 1 (00:48):
And this is going to be my saying, so what
I'm I keep going?
Speaker 3 (00:52):
Thank you?
Speaker 2 (00:53):
You called me out Europe's sovereign bank loop, although no
one believes that I invented that one, and flows before pros,
which has come up quite a bit on this podcast.
So the idea that you know, when valuations are extremely
high and everyone's buying everything no matter what the price,
that it's kind of momentum that matters more than fundamentals. Today,
(01:13):
I'm very happy to say we are going to be
speaking to a pro who knows flows.
Speaker 1 (01:19):
As so extremely well done, Tracy, Thank you excellent setup.
Of course I appreciate all of the tracy neologisms. Man,
what a time in the market to we talk about flows?
Do we talk about momentum, etc. I feel like I
you know, it's always chaotic, it's always uncertain, you'll never
get an answer. But man, things feel really noisy right now.
Speaker 2 (01:42):
They feel super noisy. So we are recording this on
January thirtieth. It is a week that has seen a
very sharp sell off in tech stocks thanks to anxiety
over deep seak coming out of China. We're going to
talk about that and more generally, we're just going to
talk about what's going on in the market right now,
how investors might be handling it, and how the market
(02:04):
structure might have changed over the years. And as I said,
I'm very excited because we do have the perfect guests,
the pro who knows his flows. He has a lot
of nicknames, actually Gandalf being one of them as well.
Actually I didn't realize one of our colleagues at Bloomberg
kind of coined that name for him. But of course
we are speaking with Marko Kolonovich. He is JP Morgan's
(02:27):
former chief Global market strategist and co head of Global Research,
and now he's with us to talk about the market.
And Marco, thank you so much for coming on.
Speaker 5 (02:38):
O thoughts, thank you so much for having me.
Speaker 2 (02:40):
I'm very excited. I know I've said that three times now,
but I guess we should start with the recent selloff,
like the deep Seek, deep stock selloff. They are all
these superlatives that you can use to describe Monday's action,
like the biggest single stock plunge in history in the
form of Nvidia, and eight of the top ten biggest
(03:01):
one day drops in the S and P five hundred,
et cetera, et cetera. And actually, as we're recording this
on January thirtieth, Microsoft is down six percent after earning,
so maybe the tech selloff isn't over yet. But one
of the interesting things about this week is there hasn't
really been broad contagion. So most stocks in the S
and P five hundred were kind of like, meh, we
(03:23):
don't care. When would you expect some of the anxiety
over Deep Seek to maybe start having a bigger impact
on the broader market.
Speaker 6 (03:33):
So, you know, as you said, there was not much contagion,
you know, and if you look at the different stocks
in SMP. Many of them were actually up, you know,
and many even in the tech sectors sector were up.
You know, if you look for instance, you know, Facebook
yesterday or today, or a bunch of other names that
sort of were perceived that they might be sort of
benefiting from, you know, the sort of open architecture, you know,
(03:55):
type of things that can come at the cheaper price,
you know, and can be still you know, implemented in
their business model when it comes to AI models.
Speaker 5 (04:02):
So it was early contained.
Speaker 7 (04:04):
You know.
Speaker 6 (04:04):
I'm a little bit surprised just because there were like
three or four names that really got hammered, you know,
and that can only be explained with not just with
a panic, but some of the sort of forced selling,
you know, maybe coming from options. You know, if you
are you know, if you're selling in a video puts
for the past few years, you could make a good
living out of it. But then you know, you'll have
a day like we saw this weekend, and basically you
(04:24):
might get you know, forced out of these positions and
maybe have a catastrophic loss.
Speaker 5 (04:28):
So it was fairly limited.
Speaker 6 (04:30):
I'm a little bit surprised, you know, just just because
we didn't really have a meaningful sales since last summer,
you know, sort of when at the back of the
Bank of Japan, you know, so, so I do think
we will we will see one. Perhaps it's a little
bit too early in the year. There's still quite a
bit of an optimism post election. There's a little bit
of seasonality in January. People put money to work, they
(04:50):
get paid, you know, they allocated capital. So maybe it's
a little bit too early. I was somewhat inclined to
see that we will see a bit more, you know.
At the back of it, it's perhaps not over. We
still have a few important earnings to come, so remains
to be seen sort of what happens, you know, maybe
another week of earnings, you know, whether there is any
(05:10):
any sort of follow through, you know, but I do
think that it's gonna be some investors will burn clearly,
and a little bit of a tarnish on the sort
of this thesis that some of these stocks like my video,
just go up, you know every day you can't lose,
you know, Like so I think people will think twice
if something can drop like twenty percent in a day,
you got to also think of it what it does
to you to your risk and.
Speaker 1 (05:31):
Well so Tracy asked about contagion and the anxiety spreading
across the market. But I guess I would flip the question,
which is, as I've said before on the podcast, I'm
a boring index investor. So like I look at a
random American company that is like General Electric, it's doing fine.
I'm not very exposed to General Electric because they're a
(05:51):
small part of the index. I am very exposed to
in Video and Microsoft and so forth. We did a
recent episode about market concentration, but I'm sort of like
curious your take on this fact that, like so many
of these flows, they go into broad market indexes, but
we're really all very exposed to a few concentrated market pads.
Speaker 6 (06:14):
Yeah, so the concentration is the highest, you know, sort
sixties or seventies, so we're looking in fifty years half
of the century history and concentration is sort of at
the highest point. It's been a while for staying there
at this level, like maybe past past year. So it's
a weird market. You know, this concentration came for two reasons.
You know, one is clearly thematic investing in technology. Then
(06:36):
you also have investing in a large company. You have
a theme of momentum sort of that is basically self fulfilling.
You know, more something goes up, more money to tracts,
becomes bigger an index, you know, all the passive flows
into it. So there's a technical aspect, there's thematic aspect.
There's even geopolitical aspect. A lot of money went outside
of the other parts of the world. Europe is doing
(06:56):
worse when it comes to sort of economy. China has
been We've been a sort of with the brink of
this Cold War with China, so money has has left there.
You know, Latime has its own share of sort of issues,
so money has been also geopolitically moving area. So it's
a moving in the US, it's a moving in the indices,
it's a moving in the tech you know, and then
you end up with these give or take ten stocks
(07:17):
that that really sucked up all the all the capital
and evaluations got got very very high. Now, you know,
tech investors, they do have a sort of their rationalization,
So what's going to happen in the future. These things
just grow and grow and grow. And that's when we
when we saw with the deep Seek, we saw a
little bit of a dent in that in that thesis.
But these stocks didn't go up for the sake of
for the thesis. They wound up some of these these
(07:38):
these other flows, you know, so unprecedented concentration is not
going to stay there.
Speaker 5 (07:43):
You know.
Speaker 6 (07:44):
The big question is when will we see that rebalance.
Do we need to see you know, some cyclical downturn
first to purge and to normalize some of these valuations,
you know, because historically these pees were never this high.
I mean, in two thousand they were this high, and
we know how it then, you know, but a lot
of people got burn myself include that with some of
the we were more negative last year, you know. And basically,
(08:06):
you know, market has this tremendous momentum, so the timing
is is going to be challenge.
Speaker 2 (08:11):
What does it take to I guess turn momentum at
this point, Like what what are the catalysts that actually
work here? Because it does feel looking at the market
so much of it is now technical or systematic in
some way. There's a lot of options selling, as you mentioned,
lots of multi strat funds that basically you know, just
(08:31):
have to sell or buy to rebalance their exposure. What
actually changes directions, like how do you get enough to
change a trend.
Speaker 6 (08:41):
So, you know, so there's a technical aspect, sort of
the mechanical aspect of it, and there's a sort of
catalyst or more funda mental angle of things, you know.
So to get things to start moving or to stall,
you usually do need to have some funda mental driver
of it. So perhaps there is concern about economy slowing down,
you know, perhaps there is a concern about something geopolitically,
(09:02):
maybe trade war with China or some sort of blockade
of Taiwan's trads or something like that. Right, so first
you need to have a little bit of a catalyst,
you know. But if the market is technically very strong,
the catalyst is not going to change the momentum. So
what that means is, you know, more specifically. You know,
so when you look at the trend investors, you know,
they have a range of signals, you know, like so
they can look at a one month price momentum, three
(09:24):
month price momentum, six month pricements, or twelve month price momentum,
maybe eighteen months, but that's about it, you know, And
there's some very short term momentum players that look intra
day or on a daily basis but most of these
signals are concentrated around twelve months and two hundred day
moving averages, right. So that's why when people look at
the two hundred mo average, they first when I, you know,
twenty years ago, when someone told me, I said, like,
(09:44):
what is this magic?
Speaker 5 (09:45):
Why would this work? Right?
Speaker 6 (09:46):
But it's reality is that many models, you know, systematic models,
primarily you know computer DRIM, but also psychological investors look
at these things and become self fulfilling. So you need
to actually come close enough to these levels to break them.
Speaker 5 (09:58):
Right.
Speaker 6 (09:58):
So for instance, earlier this week SMP got below around
six thousand or a bit below. We were close to
breaking twenty day moving average and fifty day moving average,
so you can think of it as about one month
and three month price momentum. So that's about the third
of a signal. The big signal is really twelve month
you know, or two hundred day moving average, right. But
(10:19):
when you start moving things you can actually unravel. It's
like a little bit like a snowball. So I thought like,
if the market is going to stay below twenty and
fifty days at the end of the day, you may
get enough selling from CTAs or de risking from cts
that they may get you to another leg lower right,
So it's basically you need to have set up that
you're close enough to these triggers on the downside to
(10:39):
move it. And again I think this week we got
very close. But there was also other flows like rotation.
You saw like selling on a video, but Apple and
Metal went up, you know, like so at the end
now it was like drop but did not drop a lot, Joe.
Speaker 2 (11:08):
It kind of reminds me of like structured credit notes,
where there's that knockoup knockout level and then you get
this massive cliff risk and the whole thing kind of unwindes.
Speaker 1 (11:18):
Right, you have all these rules based investors and something
happens like the model a system sell It is worth
noting that as we are talking ten nineteen am January thirtieth,
and Video is basically right at its two hundred day
moving average. I know this isn't the broader market, but
it's a big part of the broader market, so yeah,
it might as it might as well.
Speaker 2 (11:39):
Be.
Speaker 1 (11:40):
One thing that struck me on Monday, which I was
a bit surprised about in the selloff is that even
on Monday you mentioned that Meta actually closed Green and
they are a maker of a competitor to deep seek.
They have their open source Lama, but they're seen by
the market as a consumer of AI services because they
(12:00):
don't sell their product. They use it to do things
like better ad targeting, et cetera. Were you surprised that
even on this day in which there is this sort
of exogenous shock to the market, everyone wakes up to
some new thing that actually investors showed a fair amount
of discrimination in terms of what they don't.
Speaker 6 (12:19):
Yeah, so I mean that was that was against you know,
January sentiment is still pretty positive. Economic data are strong,
so people are saying, okay, this is not the beginning
of economic downturn. This is isolated sort of event whereby
some companies will get hit, you know, their sort of
revenues will get hit yea, and some will be able
to do things for cheaper you know, like so we
had the salesforce I believe as well. And so it
(12:41):
ended up not a macro day but more of a
rotational day.
Speaker 7 (12:45):
You know.
Speaker 6 (12:45):
There is also a so called quant factors where you know,
even within technology, some stocks are higher multiples, some stocks
are lower multiple. So you know, some stocks are more
momentum less momentum, you know. Like, so there was a
bit of rotation. So Apple, which was a lagger, it
also kind of quite a bit, although I don't think
there was much fundamental stop going on for Apple. It
was probably just rotation. So so market kind of held up,
(13:08):
you know, and I was sort of at defense whether
whether these like technical levels twenty fifty will will get
broken and will go lower or not. We didn't, you know,
but I do think that sort of you know, evaluation positioning,
and some of these technicals are a bit stretched, you know,
like so I don't have I don't see like a
huge huge ob site from the market. So maybe I'm
switching the topic a little bit.
Speaker 4 (13:28):
But no, No, that makes sense.
Speaker 2 (13:29):
Can I ask you a sort of procedural question, which is,
you know, you were at JP Morgan for twenty years,
and you were in the industry even before that. How
did your sort of research and forecasting process change throughout
those years?
Speaker 6 (13:45):
No, thank you, And it did change, and it's and
it's an interesting good question, you know. So, so I
got my PGE in physics, in theoretical physics, so there
was a lot of coding, There was a lot of modeling.
There was a lot of sort of trying to understand
why one thing leads to another. You know, what is
the cause, what is a concept? What's causality? You know,
and what's the noise? What's statistically important, what's statistically not important?
Speaker 2 (14:07):
You know.
Speaker 6 (14:08):
Like so, so in physics you build these type of models.
You try to understand what's significant, what's not, what you
can neglect, which factors you can you have to take
into account, and most important, how to simplify the complex.
You know, market is an extremely complex system, you know,
in many physical systems, so you need to sort of
move the noise on one side and drivers on the
other side, and try to recognize those patterns, right like So,
(14:28):
although I really never used any formula from the physics
in my almost never really in the finance, but the
way of thinking is similar. So I started Merrilynch in derivatives,
in derivatives research where I started looking, you know, interesting
that we are now in earning seasons. So my first
models were impact sort of earnings on a stock price
and what can you read from options market?
Speaker 5 (14:47):
You know.
Speaker 6 (14:47):
Like so I published some papers, you know, come up
with some formal as, and we were kind of backing out, okay,
what the option market is saying, and then we go
to analysts and say, hey, do you think it makes
sense or doesn't make sense?
Speaker 5 (14:56):
Right?
Speaker 6 (14:57):
And then if you if you think that options are
saying too much of a or too little of a movie,
you could trade these options and stuff like that. So
that was one example of okay, how do you sort
of you know, you have a catalyst, You look at
different markets, you see are these markets aligned? You put
some model together, and then you find the discrepancy with
the model. It's not always going to work, but if
you do it for one hundred stocks, maybe an average
and a portfolio level, you'll you'll be fine, you know.
(15:19):
Like so so so that the relative research and quant research.
I did a lot of quantity research. So you try
to process the data, you try to look at the measures.
And at that time, like you know, twenty twenty five
years ago, it was beginning really of trading of vicks
of volatility, swaps of correlation or dispersion. You know, like
so you kind of try to see, okay, you know,
(15:40):
you look at the vix you look at the market volatility.
What's driving market volatility? While people say, well, it's a
panic or it's not. But let's be more quantitative. You know,
you can how correlated stocks are, what individual volatility of
each stock is, you know, which part is due to
the macro factor the market, what's idiosyncratic, So you can
kind of break these down. Then you can look at
the sector correlation between sector, what's crushed within sector, So
(16:03):
you can kind of quantify these things and analyze and
get some insight, you know, like two thousand and eight,
for instance, we look at the tw thousand and seven
thousand and eight, I look at how the hedging of
options impacts the market, you know, like so you basically
need to look at how many options are out there
in index. Let's say you try to assess what's the
positioning from from the flows from the sort of no
(16:24):
knowing of industry hedging flows, and then you see, okay,
what are the hedging requirements at the end of the
day and two thousand and eight and then twenty eleven,
and like a low hanging fruit, you could see sometimes
these flows would be bigger than market can absorb, you know,
and they would all go stay away from level ETFs
from options, you know, so you see like, okay, there's
like twenty billion dollars to sell and market can't absorb it.
(16:46):
So you know, market towards last ten minutes will drop.
And that's you mentioned sort of Ganda. That's where where
some of these things because people, you know, if you
look from the outside, you say, oh, how can he
get that?
Speaker 7 (16:57):
Right?
Speaker 6 (16:57):
You know, and and but it is really understanding a
bit of technicalities, which is flows, option, convexities, liquidity and
how they how they sort of interfere. But you know,
after twenty fifteen sixteen, people figure it out, you know,
and then people put it in their models.
Speaker 5 (17:12):
They create like.
Speaker 3 (17:14):
A structured product of twenty eighteen.
Speaker 2 (17:16):
Everyone sort of woke up to the vis especially right
Oh yeah, so that's.
Speaker 6 (17:20):
A February em again and yeah, and you know stuff
like that. So you kind of analyze causes and consequences
in the market, something that is new that's not been
yet a look at, you know, and I have focused
on things that we're new in the market, like products, options, futures, CTAs,
those type of things.
Speaker 1 (17:39):
You know, this it's not particularly like technical. But another
thing that seems to be true about the market right
now is, at least maybe up until week ago, and
there's just an incredible amount of optimism in any sort
of measure. So if you ask, you know, there's consumer measures,
do you think stocks are going to be higher in
a year from now?
Speaker 4 (17:58):
Very high levels?
Speaker 1 (18:00):
If you look at things like the Bank of America,
cell side analyst sentiment very close to euphoric levels. If
you look at fund cash levels extremely low right now,
very over everyone is overweight long tech. How do you
ingest this information because on the one hand, you say, oh,
everyone's all in, this is negative. On the other hand,
people have been very optimistic for a while. How should
(18:22):
we as consumers of this information think about what it
says about the fragility of the ball market.
Speaker 6 (18:28):
So you know, clearly in the markets things are mean reverting,
you know, Like so when think reached some very high levels,
you know eventually they will.
Speaker 4 (18:36):
Gone a long time, Yeah, exactly.
Speaker 6 (18:37):
So, so so there is this mean reversion, But there's
also trend, right you know, Like so you know, figuring
out the timing of that is hard, right, you know,
Like I mean if there is a sort of a
limited set of drivers, like in some of these technical markets,
you know, so for instance, ctias, you know, you know,
once when the all the levels are positive, you know,
all the singles are positive, and then volatility drops a
(18:58):
little bit. You know, their mac stout, you know, so
you know they're not going to buy more, right, you know,
like and so you can you that's a that's a
self contained, isolated system where you can say, okay, optimism
is too high. So there's the only downside, right with
the with the entirety of market, right you know, with
you know, you have a crypto, you have a fiscal measures,
you have like monetary stimulus, you have a sentiment shift.
(19:19):
It's hard to handicap all of those, you know. So
it's hard to say that for all of the investors
to be able to know exactly when this thing is
going to stall, right, sure, and and and there are
developments that is hard, like you know, this whole AI,
you know, And I see the I wrote a book
on twenty seventeen about AI with my colleague Rejaesh twenty eighteen.
(19:40):
So we were early on on AI. And you know,
six years ago, right, nobody was talking really about it
at time. So it's not that I don't understand it,
but I'm a little bit cynical about it now. I
think it's it's too hyped up, right, But it's hard
to assess how long people.
Speaker 5 (19:52):
Will be excited about it, right, you know?
Speaker 6 (19:54):
And yeah, so and then you have a change, So
you have political changes that can bring deregulation that can
be change in tax regimes, so you have like a
wild card.
Speaker 5 (20:02):
So it's hard.
Speaker 6 (20:03):
Your question I started like, there is a reversion always,
but where you're going to pick it, it can be very
frustrating and very sort of you can be wrong for
a long time.
Speaker 2 (20:12):
Wait, can I just press you on that point about AI?
Because I think I think the difficulty that investors are
having is AI has a great story right now, and
there's this idea out there that it's this revolutionary technology
that's going to change the world. Joe keeps referring to
it as inventing God when it comes to AGI at least.
(20:34):
And at the same time, there is also a feeling
that people are maybe getting a little too optimistic about it.
There's too much hype in the market. You've started seeing
companies that you know, just put out a press release going, oh,
we're looking into AI, and their stock price goes up.
How should investors handle their exposure to AI? Like, how
(20:55):
do you actually play it at this point in time,
given that you were early to the topic.
Speaker 4 (20:59):
And now cynical.
Speaker 6 (21:00):
You know, I look at it from theoretical side. I
look at it more how to apply it sort of
in finance, in quantitative trading. You know, how to use
a large language model to assess the sentiment, changes in
sentiment and those type of things, right, so you know
how to read quickly things and summarize them and drive
some signals out of it. So there is obviously bigger
question of AI, you know, which you said, it's kind
of philosophical questions like are we going to be replaced?
(21:20):
You know, at which point what's going to be a
role of human once? When when we can you know,
kind of break down our way of thinking and effectively
training and replace it. You know, So their whole host
of other questions, you know, Like so, I'm not skeptical
that this is going to be hugely important, and it
is a hugely important you know, it's not very very
different of what people have been doing, you know, five
(21:43):
years ago or ten years ago or twenty years ago. Obviously,
big progress in computing power, big progress in the models
as well, you know, Like so, so it's it's but
I see it like more as an evolution, you know,
than something that changed with che GDP in twenty twenty three,
like two years ago, as a kind of like a
step function. I see it as an evolution. Always important,
right like ten years ago, when we use our smartphone
(22:05):
to take a pictures, like you know, camera would recognize
the face, it would zoom into face, it would kind
of do the proper focus and stuff like that. So
that's also you know, that's also AI and and things
are advancing, right, and we'll keep on advancing. Now, question
is going to be winners losers? How to monetize? You know,
does that suddenly re rates all of equity market multiple?
(22:27):
You know, like suddenly, okay, people are not gonna work.
These companies go to all the work, so we're just
gonna value them. Like who am I to say that?
And also who am I to say that that's wrong
as well? You know, but there's a lot of speculation
and a trust. You know, people often tell me, well,
imagine just how my way to search Internet has changed,
you know, like like okay, like we were searching Internet
(22:48):
for twenty five years the same way I used to
use like a Netscape like twenty five years ago, right,
and and the same thing you type in a bar
and you and you find something. So for christ sake,
of course it's going to change. Of course, at some
point it's gonna be we're gonna tell something to computer.
Computer will have its own ways of parsing and finding
what's relevant and giving us back information. So I'm not
as excited about that change. I think it's the way
(23:10):
over you change, you know, like, but there's a lot
of optimism.
Speaker 1 (23:13):
Now there's Well, I was wondering because I was looking
at your LinkedIn and you mentioned you have a PhD
in physics graduating from NYU in two thousand and three,
theoretical high energy physics, cosmology, string theory, and finance. I
guess the two part question. A would do you think
there's a world in which if you graduated today you
(23:33):
would have gone into AI instead of going into finance,
because I imagine they would have hired you at those skills.
Speaker 4 (23:39):
But B, like when you.
Speaker 1 (23:41):
Think about and Tracy mentioned, you know, like the true AGI,
do you think that the current AI research is on
a path to that sort of AGI inventing God that
a lot of the proponents believe, So you.
Speaker 6 (23:54):
Know, I think eventually it will get there in a
sense that it will sort of address some very important,
you know questions which are kind of deeply what every
person fears or wonders or or sort of seeks, you know,
kind of meaning of our lives, like you know, you know,
future after we die and stuff like that. So they're
they're definitely interesting, interesting things there that can be done.
(24:16):
I mean, people are doing with these like assistants, right
like you train and I believe really this AI will
have to be a lot more personalized, you know, like
so you will train it really on your life experience,
you know, like so if AI can see every image
I saw, if it can read every email, you know,
I believe AI will be able to tell me when
did I make a mistake? When should I do something different?
You know, did you overreact in this live situation?
Speaker 5 (24:37):
Did you not right? And going further?
Speaker 6 (24:39):
Right like that will stay and and my my kids
can after I pass away, they can say, hey, what
would that say in this situation? Right, you know, like
what would maybe I'll be able to in some way
talk to them, right, so you'll blur all these things
which were which were sort of not blurred in the past, right,
you know. Also, you you'll start having these like very
(24:59):
very interest developments, you know, but I would kind of
not look at them from the sort of P and
L perspective, earnings perspective. There's also going to be a
lot of issues as we have already now. I mean,
sometimes I can give you wrong answer. Sometimes it can
be used to do bad things, to impersonate, to deceive,
to manipulate. So there's gonna be a lot of a
lot of interesting I would say, philosophical issues, you know,
technological issues and investing investing issues. But I just don't
(25:22):
think it's going to be as simple as like seven
companies are gonna have P of fifty and everyone else
will have P of ten and it's going to persist
that way. Yeah, I don't think it's going to be
like that in finance, at least him.
Speaker 2 (25:33):
The other thing I wanted to ask you is, you
know you left JP Morgan in July and then pretty
much a month later we had a very sharp sell off.
When you look back at that particular sell off, you know,
we never got to hear from you your thoughts on that.
On that particular week in markets, what did you actually
(25:53):
see and observe there because there are still differing opinions
out there about what the proximate catalyst was for some
of the moves and what was exacerbating what.
Speaker 6 (26:02):
No, So the catalyst was definitely moving rates related to
Japan in the currency, right, that was a sort of catalyst.
But you always have like a spark and a bucket
of fuel, right, and the bucket of fuel will stretched
CTAs stretched vault targeter systematic investors, too much optimism, you know,
and then you start basically hitting the stops across these strategies, right,
(26:22):
ctias hit their cell signals. Volt target is VIX goes up.
What goes up they need to sell, you know, if
you're selling puts on AI names, you suddenly to you know,
you need to kind of close. So so VIX was
very vixed. Behave most phenomenals. So it was a whole
lot of all short wall covering as well, you know.
But again I think it was what was missing for
(26:43):
this to be the turn in the cycle was I
guess you know, GDP employment still fine, right, still hope
that FED is going to cut, right, you.
Speaker 5 (26:52):
Know, So it didn't. It didn't.
Speaker 6 (26:54):
There was a little bit of conflagration but didn't kind
of burn everything down right, So it was a little
bit of satisfaction. But in too long it is.
Speaker 1 (27:01):
Pretty markable because even we got a little sell off,
but it's a very minor sell off. Or basically, the
stock market is more or less at all time highs.
This is despite a pretty big repricing of the expectation
of the short end of the curve, where people were
expecting deep cuts to continue through last year and to
this year. We might not get any cut this year,
and yet still the market is close to.
Speaker 4 (27:22):
All time highs.
Speaker 1 (27:23):
It must be nice on some level to be out
of the game of having to come up with an
end of your price, because that sounds like a job
I would never want to take. But I also wonder,
you know, do you wake up in the morning, it's
still like your time to talk to us about you
know what you have a market outlook for right now?
Like give us some give us what's on your mind?
Speaker 6 (27:45):
Sure like so, so look, it's nice once in a
while that you can be somewhere away and not look
at the fact parst every single world word. Although I
did it yesterday, you know, but a few months ago
I didn't you know, like, so it's nice to make
a break ord. Maybe it's necessary, you know, out markets
are a little bit of a sort of compulsion thing
of compulsion you when you feel like you need to
(28:05):
understand what's going on in the world. You know, So
I think it becomes part of your DNA if you
do it for a long time. So so I do
always think, and I do have an outcome, so give
us so you know, you know, on a sales side,
you kind of need to put a price target. And
I and I think it's a kind of poor way
to summarize everything into one number. It's basically almost you're
(28:26):
telling you're trying to focus probabilities in the world, you know,
because world real world actually works in terms of physics
deep deeply works in terms of probabilities, not just superficially,
you know, in a quantum physics. So you you need
to sort of have a sort of hype proabailistic view
and you're forced to have one view like one hundred
percent or nothing. Right, So so it gets over simplifies.
(28:47):
I think media, you know, and not referring to you,
but media does a bad job. They say, oh, what's
your price?
Speaker 5 (28:52):
Starty?
Speaker 6 (28:52):
They just want to talk about that, and they say, oh,
you're right, you're right. Yeah, so so no, so I
would say, like, you know, if if I can move
away from price, sorry, I do think we'll go back
down in five thousands this year. Sometimes I think at
that point in time, we will we will see whether
the cycle is still strong or it's not. You know,
I think we need to see the whole new political
(29:13):
climate whether it will lead to turmoil, and I believe
more likely than not it will, you know. Like so
those things I think will get us lower, right, you
know at that time, whether whether it becomes an end
of a cycle and we go much lower into four thousand,
that I don't know. I think there's some probability of that,
you know. And then conversely, on the upside, is everything
goes if really this is what they call it Golden age,
(29:36):
the Golden age of America, you know, then market will
stay in six thousands, it can go a bit higher.
I just see, I'm hard pressed to see it going
much much higher, right because evaluations are there, Positioning is
already there. As you said, FED is not cutting, right,
So it's a little bit of a chicken and egg.
I mean, I have been scratching my head, like at
(29:56):
these level of rates, which I do think is that
are restricted rates for now now more than two years
with the commercial real estate here and there, we saw
a few hiccups, you know, like, but you know, I
do think that is sort of under the hood of
economy some damage is being sort of built up and done.
So so I don't think like market really you know,
going to seven thousand or you know, sixty eight hundred
(30:18):
or something like that. So I would say, maybe it
can go sixty five, stay range bound, you know, Like,
so I would sort of formulate the view in terms of, okay,
you perhaps want to sell ups, give yourself a little
bit of a room for some more excitement first few
years of the of the first few months of the year,
but then also be ready to assess once when you
go in, you know, let's say fifty five hundred or
fifty seven hundred, to assess is the cycle potentially ending,
(30:41):
you know, or that's going to be buying opportunity. And
I wouldn't want to sort of say, hey, like it's
going to first stay at sixty one hundred, then it's
going to pull to fifty five, and then you buy
with both hands at fifty five and like that'll be
too predictive, you know, but I think some variation of
that path will be whereby sort of the depth of
a pullback will depends on trade war China, domestic political
(31:02):
situation rates, and like one off things.
Speaker 7 (31:06):
Like we had a Monday.
Speaker 2 (31:23):
I'm glad you mentioned domestic politics because one of the
other weird things about this week when we had the
deep seek cell off was everyone was focused on that,
and you know, tech stalks went down, as we mentioned,
But then on Tuesday everything started rebounding. Even though we
had headlines coming out of the White House about cutting
(31:44):
what amounted to a pretty big chunk of federal spending,
the entire market seemed to look through that, which is
kind of ironic because one of the things we've heard
for the past four years or so is this idea
that you know, deficit spending is driving the entireomy and
stuff like that. I feel like political risk is one
of those things that investors really struggle to price in
(32:07):
because there's so much uncertainty. A lot of it seems
very binary. How do you deal with that?
Speaker 6 (32:13):
So you need to sort of, you know, put some scenarios,
you know, what can happen in terms of taxes, regulation,
you know, tariffs, trade wars, geopolitical conflict you know, and
then see what can how can they impact specific stocks
and industries, countries and maybe overall market sentiment, you know,
and maybe put some scenarios. That's the kind of a blueprint.
And market never goes by that blueprint, but at least
(32:35):
gives you some framework to try to understand if it
doesn't go by your sort of assessment, what have you
missed and what you need to what else you need
to take into count but you put some blueprints sort
of what can happen? So you know, I think you
pointed very well. He was talking about Navidia, Taiwan export sortiz.
Like so those type of things, right. So market that's
(32:55):
then market has its minds of its own, which is
tied to sentiment, you know, and it's tied to momentum.
Speaker 7 (32:59):
You know.
Speaker 6 (33:00):
Most people think momentum, you know, They don't calculate by
the thing. They just feel good about market. They see
good news about market. They their taxi driver or friend
or family feels good about investing, right and and they
choose them to ignore, you know, Like so on Monday,
I was watching CNBC and every single guest was saying, oh,
take your shopping list out, take your shopping list out,
take you buy this, buy this right. So you know,
(33:22):
it creates a little bit of a sentiment. You know,
it creates a sentiment, and people say, okay, you know,
I'll make a punt. I'll buy if it's twenty percent down,
maybe next day is going to that can bounce them,
So people buy right. Some people rotate it say okay,
like I'm getting rid of no video, but look Apple
has been you know, underperformance, so maybe I put my
money there. So the sentiment overall was still pretty strong.
(33:42):
There's this aura of momentum, psychological momentum that it's harder
to break. You know, you don't need to have a
few punches for it to break, for people to sort
of give up.
Speaker 2 (33:50):
So one of the other things I want to ask you,
you know, just again looking back at the sort of
long term changes in the market. And we touched on
this earlier, but we've just seen an hour absolute explosion
in different types of options trading and volatility trading, and
now you even have TikTok influencers who are like pitching
options investing as passive income, like, oh, don't buy a
(34:12):
US Treasury bond, do you an options? But which is
kind of crazy. How has that impacted the market and
how have you seen people, you know, trying to handle
some of that new I guess dynamic that's been introduced.
Speaker 6 (34:28):
So so that's that's a very good question. It started
sort of with around the COVID time. People were locked in,
they got these stimulus checks, they started trading, right, proliferation
of these online brokers, no commission fees, options being traded
as a sort of very short, short and short maturities, right.
You know, options used to be sort of leaps and
(34:48):
then maybe like a monthly options, you know, second and first,
second and third month quarterly options moved to weeklies and
dailies you know, and then in the single names you know,
Like so you had sort of people locked they got money,
and they got these instruments, these extremely powerful instruments with
leverage about one hundred times leverage, you know, like so
you suddenly can make a bets of you know, millions
(35:08):
of dollars even if you have like ten thousand or
five thousand dollars to invest, you know, Like so that changed,
that changed a lot and for most of these people
actually it worked, right because since twenty twenty we had
that pullback when the FED started hiking, But for most
of it it worked, you know, Like so speculative trading activity,
especially on the alongside, it worked. Then you also had
(35:31):
in peril sort of crypto markets growing, right, you know,
Like so if you think of it, like, you know,
a few trillions of dollars of wealth was created there
with probably some of these similar type of investors and
similar type of people, you know, like so so it changed,
you know, so there is less a leverage in terms
of borrowing money with interesting but more a lot more
(35:51):
leverage in terms of option trading activity.
Speaker 7 (35:53):
You know.
Speaker 6 (35:53):
So, as you said, I'm always also surprised. You go
on some of these social media and then you see
all kinds of strategy is that can't lose money, that
making like tens of thousands every day. You just need
to follow him, and it becomes really kind of bizarre.
You have like these people who are at the same
time performer or like women who are like you know,
in the like underwear, suggesting how to trede options, like yeah,
(36:15):
it's all.
Speaker 4 (36:16):
Big fans of mind.
Speaker 1 (36:17):
They follow me on Twitter ADM very flattered.
Speaker 6 (36:20):
So it's it's kind of crazy, you know, like and
we try to handicap it by looking at flows from Robinhood,
see which names are being sort of bold, which names
are being sold, try to see where the retail may
be forced out or something like that. So we did
some quantitative work. We did a lot of the sort
of language large language models sentiment wise, like from Twitter
and those type of other social medias which we could
(36:43):
we could get permission to do. So we're trying to
incorporate it. But I think overall it's hard to one
hundred percent handicap it. But for sure it added leverage
to the market, added speculative element to the market, and
at some point it's not going to probably end up
well right some point, you know, but it's hard to
say when exactly right.
Speaker 2 (37:02):
It's again one of those things that can go on
for longer than you think. Since you mentioned getting data
from robin Hood just then, this is the other thing
I always wanted to ask an equity derivative strategist because
you alluded to this earlier. There's a lot of I guess,
misunderstanding or lack of understanding of what an equity derivative
strategist actually does, and exactly what data they're looking at
(37:26):
in order to make some of their conclusions. Can you
maybe give us like a quick one oh one in
where your data comes from? How much of it is
from official sources like I don't know an EPFR or
someone like that, versus like color that you're getting from
the market question.
Speaker 6 (37:44):
Yeah, no, so sore. They're all kinds of data. So
there are price involume data or kind of technical data
that can be derived from from from those type of things,
which can also be a different time horizons, you know,
like they can be daily. Mostly they are daily, right,
you know, but increasingly you so want to look at
the intra day data, you know, intraday correlations, interramormentum volumes,
large blocks that are traded. So there's also high frequency
(38:07):
one day, but most of it is daily, I would say.
And then there are longer term data, you know, when
you look at the sort of you know, some like
a monthly statistics on positioning or up to the sort
of filings you know, thirteen F filings like holdings and stuff
like that. You know, like so so different sort of
frequencies of positioning volume data price data. Then so directly
(38:30):
market observed data like open interest, you know, options volume
and options those type of things. Right, then you have
sort of fundamental data, you know, and fundamental data. You
have fundamental data for stocks, you know, which are related
to earnings, but increasingly you have a data which are
derived from non traditional sources, you know, so called big
data that can be sort of sentiment measures, but quantitatively
(38:51):
derived measures, objective not sort of a guesswork to some
very specific niche data like you know, satellite you know,
all kinds of like data that you know, we didn't have,
you know.
Speaker 3 (39:01):
How many cars are parkeding parking lots.
Speaker 5 (39:03):
And in front of Walmart and those type of things.
Speaker 6 (39:05):
Right, So you have these stock specific data, earnings derived,
news derived, sentiment derived, and then also non traditional ones
you know, and then you have like micro data, you know,
typically lower frequencies, but increasingly also with some of these
alternative data sets, big data sets, you can try to
figure out like you know, shipping and again sort of
storage and oil tank tanks how full they are and
(39:27):
stuff like that.
Speaker 5 (39:28):
So it's a whole.
Speaker 6 (39:29):
Host of data, you know, like as a quant and
there at this person you probably focus most on the
market data, you know, so open enters, price volumes and
old stuff that is derived from that, but you also
want to want to supplement that with all these other
other data. And then and then some of the data
set you derive it on your own, you know. Like
so so, for instance, got my imbalance in SMP options
put minus call. So I was running that for fifteen
(39:51):
twenty years. And first people tell me to say, you know,
what's that. That's you cannot know what's you know. But
then now everybody has it, actually, you know, and the
same thing with like a CTA stuff, you know, I
and volt targeting exposure. I was getting so much sort
of critique in twenty eleven, twelve thirteen. You know, now
everybody has it, you know, kind of CTA positioning percenta.
So you can derive some on your own based on
(40:12):
understanding with markets.
Speaker 1 (40:14):
All right, I just have one take and I bring
it up a lot, and I sort of feel like
the kool aid man. It's like every conversation I have
to jump it through the wall and interject this. But
you know, there's all sorts of like quant techniques, and
there's the definition of quant and changes over time, and
obviously there's an incredible amount of data that we can
(40:34):
use now. And then there's sort of like old fashioned
quant where you're just like, we're gonna buy you know
that I sort of associate with like AQR from years ago,
where like we're gonna buy the cheap stocks that are
exhibiting momentum, right, and we're going to short the expensive
stocks that are declining momentum. And why doesn't this work anymore?
And all these sort of hand ringing and the traditional
quant industry, why has it?
Speaker 4 (40:55):
Why haven't things mean reverted?
Speaker 1 (40:57):
How much is the fact that, like so many of
these sort of ideas about how the market should work
have been broken by the simple fact that a handful
of American companies that are very big exhibit year over
year earning his growth that are truly remarkable. And this
is a fact not about the market world, but about
(41:17):
the real world that, for whatever reason, these big tech
companies just keep getting bigger despite their size.
Speaker 2 (41:24):
Wait, Joe, you have to end that by saying the
kool aid man catchphrase.
Speaker 5 (41:28):
What did he say?
Speaker 3 (41:29):
Oh yeah, oh yeah.
Speaker 1 (41:31):
As long as the metas and the Googles and the
videos and maybe the Apples of the world just keep
growing earning is like.
Speaker 4 (41:36):
Crazy every year.
Speaker 1 (41:38):
How much does that bust any sort of notion of
mean reversion in market?
Speaker 6 (41:43):
So I think it busts the notion of a value
as a factor. But value is the fact that we've
been straggling for a long time. Yeah, so sort of
probably since you know, decline in interest rates post two
thousand and eight, a lot of these, you know, and
growth of indexation, right, growth of dxation kind of spark
them momentum and change the structure of the market. So
some of these quants, quants models or quant factors work
(42:06):
less and less.
Speaker 7 (42:07):
You know.
Speaker 6 (42:08):
There is also another aspect, which is, you know, once
when you put money to work in these strategies, you
kind of squeeze out the alpha, you know, and and
and and these things are fully priced in so they
stop working. So sort of growth of quant funds, traditional
quant funds, you have quantity tfs, you have like broker
dealers doing quant strategies kind of squeezes out returns, right,
you know, on your question, sort of like these big
(42:30):
companies that keep on delivering what that's that's also a
very good point. You know, quant strategies are designed for
sort of steady state situation when kind of things are
fluctuating around something which is in a steady state. And
we had sort of you know, big sort of big
changes in the world right in technology, Yeah, and also
geopolitically sort of you know, capital moved to us and
(42:51):
then moved into these sectors of innovation, right you know,
And now so you may sort of you know, you
may con simply be out of equilibrium, right, you know,
where some of these mean reversion or quand strategies would work.
A certain type of con strategises have value based strategies.
Speaker 5 (43:07):
Right, question is.
Speaker 6 (43:08):
How long you know, how long can you know, going
back to the concentration, right, how long can it go?
My my question becomes like, let's say, if you have
like a social media company, like a Meta right, I
mean once when they have all the users in the world,
I mean, like you know, they can't go much further, right,
they can go to the.
Speaker 3 (43:24):
Right, there's limits, there's limits.
Speaker 4 (43:26):
You know, but numbers are creating fake ai FA.
Speaker 6 (43:29):
Or go like to Marcel, there's no one there, so
there's some some limitations, right, like and then there's some
sort of also historical when you look at the weight
of stocks in an index, right, so so you're taking
a video percentage rat in SMP, and you know, you
run back history and you see that this basically never happen,
and even if it happens, never lasts forever. Right, But
to your point, it can last. You know, one or
two or three years is enough to ruin a lot
(43:51):
of investment strategies, you know.
Speaker 2 (43:52):
All right, Marco Kolonovitch, thank you so much for coming
on odd lots of real treat for both of us.
Speaker 4 (43:57):
Yeah, that was great, Thank you so much, Thank you.
Speaker 2 (43:59):
So much, Joe, that was really fun. I'm so glad
I finally got to ask him a bunch of questions
about just being an equity derive strategist.
Speaker 4 (44:21):
I've I love those.
Speaker 1 (44:23):
I love those questions so much because they're this sort
of like dark fiber or the dark matter of how
this industry actually works. But everyone just abstracts over them.
It's like, oh, you look at the data and then
you do.
Speaker 3 (44:35):
They but right, like what data?
Speaker 4 (44:37):
Where where do you data actually come from?
Speaker 1 (44:39):
Like I could listen forever to someone and we should
do more of that, just like talk about these aspects
like paying for data and data costs and all that stuff.
I really like Marco, It's a nice conversation.
Speaker 2 (44:51):
The one thing I would say is, you know you
asked that question about like, well, the S and P
five hundred overall didn't do too bad on Monday, and like,
you know, if I'm an induct investor, I have exposure
to GE and so that's good because I don't have
to worry about AI. But I think the question really is,
like how much is GE exposed to AI? In very
indirect way?
Speaker 1 (45:11):
Yeah, that's correct, right, and you do sort of I mean,
you know what, I think. I saw a tweet about this,
so I'm just gonna say it, and it may not
even be true, but if it's not true, it's truth y.
Someone tweeted that apparently there was like a Sherwin Williams
earnings call and someone asked about how much paint data
centers were going to need to paint their walls. I
(45:33):
don't know if it's true, but if it, because I
just saw the tweet. But if it's not true, it
doesn't really matter, because it is true, Like every company
is like are you doing something that could supply do
you sell some product that someone building an AI data
center is going to need at some point? But my
point about GE though, it was kind of the opposite,
which is like GE did fine on that day, but
(45:56):
I wish I had more exposure to GE. What I
really have is a bunch of video Microsoft exposure through
my index.
Speaker 2 (46:01):
Fund, right, But you don't know how much exposure indirect
exposure you have to Microsoft through GE.
Speaker 4 (46:08):
This is because this is true.
Speaker 2 (46:10):
Sconda wrote that excellent column in the au Thought's newsletter
about how more and more of the economy is being
driven by AI. We actually saw on on Monday the
treasury market move a little bit, which you know, okay,
treasuries will go up when there's a market sell off,
but a lot of people were saying, well, this impacts
growth expectations as well, and so that's why you're getting
(46:32):
this reaction.
Speaker 1 (46:33):
No, totally, I think the degree. I mean, especially if
you go back and like Trump announced the half a
trillion dollar Stargate project, and I don't know what's really
going to become of that, but like I believe the
data centers and AI specifically through the data center channel,
is a meaningful important, is a growing important part of
(46:55):
the real economy right now. And if suddenly they're like,
you know what this is a dead end or suddenly
like we don't need this because we can get AGI
so cheaply that it's just like on our laptop.
Speaker 4 (47:07):
That would raise some real econ concerns.
Speaker 3 (47:10):
Yeah, shall we leave it there?
Speaker 4 (47:11):
Let's leave it there.
Speaker 3 (47:12):
Oh yeah, this has.
Speaker 2 (47:14):
Been another episode of the Authoughts podcast. I'm Tracy Alloway.
You can follow me at Tracy Alloway.
Speaker 1 (47:19):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow our guest Marco Kolonovich, He's at Marco in and Why.
Follow our producers Carbon Rodriguez at Carmen Arman, dash Ol
Bennett at Dashbot, and kill Brooks at Kilbrooks. From our
odd Lots content, go to Bloomberg dot com slash odd Lots,
where we have transcripts of blog in the newsletter and
you can chat about all of these topics twenty four
(47:39):
seven in our discord Discord dot gg slash od Loots.
Speaker 2 (47:43):
And if you enjoy Oddlots, if you like it when
we discuss the dark magic of equity derivatives, then please
leave us a positive review on your favorite podcast platform.
And remember, if you are a Bloomberg subscriber, you can
listen to all of our episodes absolutely ad All you
need to do is find the Bloomberg channel on Apple
podcast and follow the instructions there. Thanks for listening.
Hosts And Creators
Joe Weisenthal
Tracy Alloway
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