September 20, 2024 • 32 mins
In this episode of Trading Tomorrow, Serge Houles, the CEO of Tidan Capital, explores the evolving landscape of modern finance. Serge and host Jim Jockle of Numerix discuss the challenges and opportunities of rapid technological advancements in the financial sector.
During this episode, the two also delve into the transformative potential of generative AI. From the initial excitement to the industry's careful approach, Serge provides a thoughtful and unique view of the future of AI in finance. He also shares insights into tokenization, discusses the crucial role of data and expertise, and emphasizes the need for accuracy and reliability as technologies evolve.
Lastly, Serge introduces Nova, a market-neutral volatility strategy, and discusses how technological advancements can help investors navigate volatile markets. Tune in for an in-depth discussion you don't want to miss.
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Episode Transcript
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Speaker 1 (00:06):
Welcome to Trading Tomorrow navigating trends in
capital markets the podcastwhere we deep dive into
technologies reshaping the worldof capital markets.
I'm your host, jim Jockle, aveteran of the finance industry
with a passion for thecomplexities of financial
technologies and market trends.
In each episode, we'll explorethe cutting-edge trends, tools
and strategies driving today'sfinancial landscapes and paving
(00:29):
the way for the future.
With the finance industry at apivotal point, influenced by
groundbreaking innovations, it'smore crucial than ever to
understand how thesetechnological advancements
interact with market dynamics.
Welcome to today's episodewhere we discuss financial
(00:55):
technology's dynamic andever-changing landscape.
Terms like AI, blockchain andcloud computing are not just
buzzwords but the driving forcesreshaping the financial
industry's very core.
Over the years, we've heardpredictions of how these
groundbreaking technologieswould revolutionize everything
from banking to asset management.
But have they lived up to thepromise and what do the experts
(01:15):
in the space think about theirimpact?
Joining us to help explorethese critical questions is
Serge Holtz, ceo and partner atTitan Capital.
Serge not only oversees theday-to-day management of the
firm, but also leads all globalmarketing and business
development efforts.
With a wealth of experiencefrom prominent hedge funds and
fund-to-funds worldwide, serge'srole has spanned quantitative
(01:38):
research, risk management,product development, business
development and marketing.
His strong academic backgroundincludes an MSc in econometrics
and finance from Paris' Dauphineand post-master's degree in
banking and finance from theUniversity of Paris.
So, serge, first and foremost,thank you so much for joining us
today.
Speaker 2 (01:57):
Thank you so much, Yma, for having us and having me
.
Speaker 1 (02:00):
On this show, we've explored a range of cutting-edge
technologies, from AI toblockchain, virtual reality,
cloud computing.
As someone who's deeplyembedded in the industry, how
are these innovations reshapingyour day-to-day work, and to
what extent have any of thesebecome indispensable?
Speaker 2 (02:19):
Yeah.
So if that's okay, jim, I'llstart by sharing my personal
journey of using generative AI.
Mostly, I'll start with that.
In terms of technicalinnovation that I think would
have potentially the most impactin our industry.
I think you also might be thesame to me and many others.
To start, I'm a big fan ofmachine learning, so I've been
(02:40):
using machine learning in myprevious role doing some
interesting optimization.
I was there, you know, beforeJetGPT was released two years
ago.
One of my very good friends,tony Gida, is an expert in
machine learning.
He's read a number of books andhe made me aware of that
release.
I was not aware before that.
So I was there, right there,you know, when it was released
two years ago, in November, andI started using it and I
(03:06):
realized, right there, thepotential, right.
It was like so the, it wascrazy right, the breakthrough,
and it's looking at this,getting all these answers and
even asking crazy complete,complicated questions and all
doing, all doing, alltranslation or summarizing
documents, and so, very quickly,I could see, you know, how this
could be, uh, you know,practically implemented in our
business, right to uh, toautomatize and all the tidy
(03:28):
tasks that you know combinedtogether takes several hours.
But then you know after usingit personally.
First I was like I realized youknow what everybody is talking
about, right, the hallucinations, right, and also that some of
the answers were not veryaccurate for me personally.
So I started learning about,you know, few, uh, few, short,
prompting and customizing and uhand uh.
(03:49):
I think that's where the assetmanagement industry is right,
like in terms of uh, you know,adopting, uh, adopting this uh
technology.
So from what I see, I mean I'mthe ceo of titan capital, so I'm
I talked to a number of peersyou know like ceo and leaders in
that industry and and so I'vebeen using chachiPT a lot and
I'm super excited about thepotential.
(04:09):
So I've been sharing that, youknow, and I meet other people,
other peer, you know CEOs, andthey're like ChatGPT, what you
know, like how do you use it.
So I can see that it's reallynot been adopted yet, not even
you know for people personally.
So, and I think you know, it'snot because the industry is
resistant to change.
I think it's a good thing andit's simply because we have that
(04:32):
fiduciary responsibility.
I think personally there is alot of expectations about that
industry of adopting the newtechnologies, for many reasons.
To start, there is a lot ofbrain power in the industry,
right, in the financial industry.
So then you would expect allthese people, all these brains
combined, you know, to, you knowto customize all these
(04:55):
algorithms and make it you knowand implement in their business
and day-to-day work.
To start, the second is thatthe industry has been, you know,
compiling all this data,structured data, for years and
that make you know and that makethe user obviously easier to
implement machine learning andall these kind of new
technologies.
But then, at the same timebecause this is quite unproven,
right Like I lost account of howmuch data has been created the
(05:20):
last three or five years likethat we created more data
historically the last five yearsthan we have ever before.
So one is a lot of data and allthese technologies, they, you
know.
The pace of evolution also iscrazy, right Like we see,
already two years ago, when youlook at the first, or not even
two years ago, even you know, ayear ago, we had this, you know,
video generated by, you know,generative AI.
(05:43):
We could see how you know allthe, how deficient they were and
all the defects in the video,and then the year after, they
corrected that.
So it's so.
The pace of evolution is soquick that I think it's a good
thing that the industry has notadopted this technology yet
because of this regulatoryresponsibility.
So we have to make sure tostart.
We want to make sure that weprotect our client's capital
(06:05):
before, obviously, I mean theend game for us as a major, and
especially in the apps, you know, in the hedge fund industry, as
we are trading capital.
Obviously, at the end of this,you know, to deliver absolute
return for our clients, but thefirst duty is really to protect
our client assets and I thinknobody expect us to, you know,
to adopt all these technologiesand hope for the best, and I, if
I do, yeah.
So, if I could, I would like todraw a parallel here to people
(06:28):
to understand.
It's like the medical you know.
It's a parallel to the medical,you know industry, where it's
like you would ask a surgeon youknow to use a AI driven scalpel
because, you know, improveprecision by 0, 0.2%, with the
risk of, you know, would seehallucinations and dread, just
to test it out on patients.
It's the same with thefinancial industry.
(06:49):
So I think the potential isgreat in terms of automatizing
all the tidy tasks and,obviously, processing data.
It's obviously great forunstructured data text, video
and so forth but we're not thereyet, I think, in terms of the
pace of adoption.
So that's for GNAI.
Speaker 1 (07:08):
So, you know, one of the key things or trends over
the many years is, when it comesto new technologies, it always
seems the buy side leads thesell side in many ways.
You know why is that ways.
You know why is that?
And you know, I guess.
And the second question is youknow, given hallucinations,
(07:33):
given fiduciary responsibilities, you know what is the
confidence level for things likeAI to become even more
ingrained?
You know, within day-to-dayoperations, you know where's
that confidence level going tobe?
Speaker 2 (07:44):
Yeah, I think that's a very good question, spott.
So I'll start on the first one.
I think, to start, the vastmajority of this industry is
great asset managers.
They are discretionary, right,they use their own judgment to
make investment decisions and Ithink for many of them, like
(08:05):
it's a bit of a leap of faith tostart, you know, to allocate
out like part of your thatprocess to an algorithm and try,
you know and and trust it.
I think that's that startsthere, right, like, um, they,
all these people, they trusttheir judgment first, and that's
also one of the biases that wefind in investing right, like
overconfidence.
Right, and maybe so that couldbe a resistance to adopting all
(08:28):
these technologies.
There are many things thatobviously could be done without
allocating the entire processand that we see already.
I guess you heard about JPMorgan right, creating their own
.
They have their own internaljunior analyst based on AI to
process all the corporateinformation, so all the
unstructured data.
Make use of it for theportfolio managers.
(08:50):
Right To have all thatinformation digested.
You have a huge amount, a hugegain of productivity and time,
and then you can spend value attime in terms of making
meaningful investment decisions.
So I think maybe the resistancecould be there to start.
You know like.
You know like afraid of thechange, afraid of you know
(09:11):
losing their.
You know you know beingcompeted away.
You know also that could bethat you know being competed
away by the algorithm andrecognizing that you know
eventually, potentially theywon't have these biases right.
As human being sorry to jump onthis one but as human beings we
have so many biases thatdoesn't make us a good investor
in the first place.
So obviously using thisalgorithm that should not be
(09:34):
biased could be a way to improveyourself on yourself.
So I think that would be great.
To combine human judgment withall these tools and this
technology, I think would betremendous.
But then again it's slowbecause you know scale of change
.
My take, then, the other one is,I think is a catch-22, right,
like if you start somewhere, youknow first by somehow, you know
(09:58):
collecting all this data, theinfrastructure.
You know all the reports, allthe video, the earning, the
earning calls.
You know for for an analyst.
You know like startingprocessing all that and make
meaning of that and gettingconfidence in.
You know in the lower value add, you know in the chain, the
value chain of a portfoliomanager, then you gain
confidence.
When you gain confidence, youuse it more.
(10:18):
If you use it more, you createmore data for this algorithm to
be trained on and if, if theytrain on that you know like in
terms of being incorporated intoyour value chain all the way to
making actual investmentdecisions, they get better and
then maybe that's when you'restarting a broader pace of
adoption.
But it's again, it's slow and Ithink also I mean what's
(10:41):
important to, I think, tounderstand my take is that you
know technological change arenot deterministic.
It's not because they are therethat they have to dictate.
You know the pace of change andwhen and how you know the
industry will adopt all thesetechnologies.
I think it's I mean, we've doneit before right in terms of
using internet, in terms ofusing mobile phones for our
day-to-day work.
I think it's a good thing thatthings take time.
(11:03):
You know, for the best of ourinvestors, I would say so.
Speaker 1 (11:08):
in today's universe, where is the alpha?
Is it in the data, is it in thestrategy, or is it in the
intuition of the individual.
Speaker 2 (11:18):
That's a very good one.
Actually, I was about to getinto this one also.
At the end of the day, I wouldthink it's the same.
Right, like you know, back many, many years ago, decades ago,
the alpha was in the informationedge and later that became
illegal.
Right, that became, you know,insider trading and all this
kind of thing so like, and thatturned into later to be an
(11:41):
information edge in terms of howmanagers could interpret some
information in a better way orbecause they had access to
information.
You know before, like the macrodata or, you know, building
model and making sense of a lotof data, that was some form of
information edge.
But at the end of the day, youknow, these data quickly have
become available to the vastmajority of managers.
(12:03):
Right, it became.
It became, like, you know,cheaper because many more
managers had access to it, soyou had the economy of scale,
and then it become cheaper andthen, you know, everybody had
access to it.
So today, you know, like, ifyou exclude the new technologies
, today I would argue Any assetmanager in the world has access
to exactly the same amount ofinformation and the same quality
(12:24):
of information.
So then back to your point.
Yes, it's always how you use itand that's the same as
comparing to.
You know.
I think that industry you knowthe hedge fund industry and the
alpha driven industry is thesame, as you know, like
competing in sports.
Competing in sports, like ifyou are a golf player or playing
(12:48):
soccer, you have access toexactly the same tools and
equipment.
So when you're a footballplayer, you have the same shoes
and you use the same ball, andwhen you're a golf player, you
are exactly the same.
You have the same clubs each.
Maybe they could be customized,but the same way.
But at the end of the day,there are only 10 best players
in the world.
It's the same in office.
It's really how you useinformation, how you process,
(13:11):
how you combine you know dataand you combine your own
judgment, how you combinefinancial modeling and your
experience, all that to takeinvestment decisions.
But my take sorry, that was along one and you go right there
in terms of what I'm passionateabout.
You know, at the end of the day, it's all about discipline.
That's why I'm quant originally.
So I really, like you know,quantitative process,
(13:32):
well-researched investmentmodels.
But what I found at our firmsorry to comment, on our side
also is that, like even thepeople in our firm you know
having, you know makingdiscretionary decisions I can
see that through, like you know,very challenging times like the
one we've seen, you know, thelast couple of weeks, you know
we could, you know they've beenquestioning, right With the sale
(13:52):
of like nobody understand andthe VIX, you know skyrocketing
for discipline is and experience, and that I think is hard to At
this stage.
It's hard to train, I think,mike maybe I'm a bit biased-
Well, you know it's funny.
Speaker 1 (14:09):
I know what my VO2 max is, I just haven't figured
out how to optimize it.
It's actually easy.
So you know, clearly,technology has undoubtedly
driven progress, but it also canintroduce new challenges.
Driven progress, but it alsocan introduce new challenges.
So you know in your experiencehow is this rapid evolution of
tech, you know, almostcomplicated your professional
(14:30):
life, like my VO2 Max yeah yeah,maybe we can compare our VO2
Max.
Speaker 2 (14:37):
No so like in many different ways.
Like so, I think you in yourquestion I think that's part of
the answer, right, it's justrapid, right, like so I mean.
Like, I mean, I don't know howmuch you use LinkedIn and, uh,
you know, like, there's not oneday where you can see a post of
okay, these are the new tools todo this and this, and that
(14:58):
changes every week.
A challenge in itself, becauseobviously that challenges you of
doing your job in the best wayand have access to all these
tools.
But it's not like you learnthese tools.
You know.
Like, even if it's, you know,there's a lot of algorithm and
everything, so they process alot of your task, obviously
without any input from your side, but they still require a lot
(15:18):
of customization and humanoversight.
So that has that put, I think,a lot of strain on people of
being on top of, okay, what canyou use?
How can you use it?
How can you stay on top of that?
So that's one challenge.
The other one touch upon yourquestion.
You know, in terms of data,information, everything I think
we went from you know having aninformation edge in terms of
quantity, like you know, havingaccess to that information and
(15:40):
to being now a disadvantagebecause there's so much
information out there, right,there's a lot of noise, there's
so much data that is you know inany way in any shape or form,
like social media news, you know.
And like satellite, you know.
You hear about all the storyabout satellite images, right,
Like, that's a typical exampleabout you know how to use
machine learning to predict theshift in macroeconomic cycle.
(16:02):
I take it doesn't work so well,but anyway.
So, yeah, so that creates achallenge in itself.
How do you dissociate from youknow what kind of information is
meaningful for understandinghow that can influence that
company or that country, or youknow, like.
So that's a challenge in itself.
So obviously sorry on this one.
So like the technology is alsohere to help you, right, like,
(16:25):
so sorry on this on this one.
So, like, the technology isalso here to help you, right,
like.
So, yes, in the first hand,that creates a lot of data and
information everywhere, but thatthe technology can obviously
address that in trying to sortout, using this kind of
algorithm to sort out what it'smeaningful, especially if you
supervise learning.
But again, you know, then youneed to understand it, like, and
you need to test it and youknow, in data and information,
you need to understand it, like,and you need to test it.
And you know data andinformation, you need to get
comfortable, I think the thirdone is cybersecurity.
(16:47):
Yeah, so like, yeah, I mean,we've been, I think, on our side
at Thailand, so we've beenusing Gen AI for, like,
marketing and general business,and it's incredible, really,
like what you can do in terms ofproducing marketing material
and getting some template forlegal document or, you know,
summarizing something or getbullet points or something.
(17:09):
But then it also oh, now I sorryI go back to being disciplined,
because it's very difficult tofind, you know, to find like
really, uh, you know verydifferent processes in terms,
okay, you can use it, or eitherthere's some firms have decided
they block it all together.
I don't think it's a good idea,because then obviously you lose
some form of competitive edge ifeverybody else is using it, but
then of course, you have to usethat.
You know, um, you know commonsense like.
(17:30):
Okay, of course, when you useit, you have to take out all the
very sensitive information, allthe client specific information
, all the company specificinformation.
So that requires a lot of, alot of discipline.
So on one hand you knowobviously that you increase
productivity, because it'ssometimes very hard to start
from a blank sheet of paper, buton the other hand you have to
(17:50):
be very disciplined in how youuse it and obviously that human
oversight because of thehallucinations you know, if you
just use it right there, thenyou can have anything that comes
out of it in terms of making upa story about your company that
is, that is not your company.
Speaker 1 (18:04):
I've seen that many times yeah, and and the there
was a us supreme court case, uh,or citing, uh, the attorney was
, uh, in front of the unitedstates supreme court, citing a
case that never happened.
You know, I I'm sure everythingwill improve, but you know it
and it's improving every day.
You know you bring.
You brought up one interestingpoint in terms of you know, and
it's improving every day.
(18:24):
You know you brought up oneinteresting point in terms of
you know, social media and youknow I think back, if I go back
maybe five years, there was theyou know the trend of how can we
gain sentiment analysis, howcan we, you know, utilize and
harness all this data?
You know, albeit unstructured.
You know in terms of investmentdecisions, and I think you know
(18:45):
, for the most part, theconclusion was it's an input,
but it's just an input.
You know the edge isn't there.
But then you know you have thewhole GameStop issue and Reddit
and things of that nature.
You know to what extent hasthat GameStop experience perhaps
changed the way people arethinking about social, in terms
(19:08):
of understanding sentiment?
Speaker 2 (19:11):
I would say, you know it was funny because obviously
you had that episode a couple ofweeks ago, months ago, right,
when he was back trying toinfluence GameStop again.
So, no, that's a great point,because I was about also to get
into, you know, how we couldpotentially at Titan, at our
firm, how we could potentiallyuse, you know, ai, and that's
(19:33):
one area where I can see theclear benefits.
Like, as you say, at the end ofthe day, you know, it's very
difficult to understand, okay,can that create an edge if
everybody can do it, to do this?
I don't think so.
But then, at the same time, howdo you make sense of that noise
, right, like of all the uh, theuh, the reddit and and uh, all
the kind of form.
And obviously now peoplerealize.
(19:54):
So there you have a point.
People realize all these groupof investors, if they gather in
a platform somewhere, they canactually influence, uh, you know
stock prices and it's's.
It's a challenge for us as asactive asset managers, because
we have our risk management andif that goes against your
position, you obviously have tomake decisions, right, like so
so, yeah, I mean that's onething of you know, looking at
(20:17):
this in terms of risk management.
This short, you know the nextgame.
But going beyond that, you knowextreme situation, which
obviously does not happen toooften, right, like it's just
been a few cases and I think itwill remain this way because
that was a combination of heaventhat led to that.
You know craziness, but I thinkin general that's really an
(20:38):
area where using NLP, so naturallanguage processing, to make
sense of unstructured data, solike earnings codes, you know
like, you know after the centralbank policy and you know all
the guidance from central bankpolicy video, audio and process
all that information to makesense of.
(20:59):
Not the information itself,because that's that you can do
on your own right, but like thechange, the nuance of, you know,
the change between one call tothe other or that could provide
some valuable insights intoinvestment processes.
Speaker 1 (21:16):
But you know you bring up a really good point,
especially when it comes down tothe Fed.
I mean, you know I go back tothe 1990s and you know, thinking
about Fed calls, it wassometimes what wasn't said or
the nuance or a head nod of theway something was said that
could move a market.
You know, is that just gettinglost in transcripts?
Speaker 2 (21:36):
Yeah, I think I think this one is a difficult one
because it's so polished.
I think I'm pretty sure I don'tknow how that works, the
process'm pretty sure there aremany people checking you know
how, how the um we did severaltimes, you know how can that be
interpreted outside, right, likeso.
But maybe that one is a harderone, you know, in terms of
trying to make sense of thatinformation.
Uh, but then on the, so yeah,I'm not so sure if, if you can
(22:00):
actually make conclusions aboutthat, because sometimes you know
how it has been the last 10years.
Good news is good news, badnews is bad news, like some,
like I'm not so sure if that'ssignificant in terms of making
meaningful, you know, decisionout of that in terms of the
macroeconomic direction, right,but I think for companies that's
(22:21):
that's super helpful because Imean the.
But I think for companiesthat's super helpful because I
mean management, as and therewas an example a couple of days
ago to see how management andhow people perceive the impact
of management on the company.
I think it was today or twodays ago Starbucks announced
that they would, that they hadyou know that they had the
Chipotle CEO and the stockrallied 25%.
So that's the kind of thingthat you that specific example
(22:45):
was obviously announced.
But I mean these kind of thingsin terms of, as you say, getting
the nuance, the change ofnuance in the narrative for
earning scores, any kind ofunstructured data that could be
very useful to gain insights oncompanies.
But again, sorry, before I Lastone, it's still how you use it.
You cannot just simply say,okay, it's a two-star deviation
(23:08):
from a monotone voice, somethingaway from the average decibel
in the voice that, oh, now theCEO is super excited in using
that model and I should go long.
No, it's just one moreindicator of you know gathering,
like a modality kind of thing,you know where you gather a
different type of information,looking at the financial and
(23:30):
deep down fundamental analysis,and then combine that with this
kind of indicator to fine tuneyour, to fine tune your view on
a company.
Speaker 1 (23:38):
So you know, as you, as we in the beginning of our
conversation you were saying howyou talk to a lot of your peers
and share your enthusiasm, youknow however those conversations
(24:00):
changed?
Has it gone from strategies andmarkets to data lakes and APIs?
And you know proprietaryvertical AI, or you know what,
what, how, what's the?
What's the?
The, the conversation amongyour peer group.
Speaker 2 (24:07):
Actually that's no, that's a good one, jim, but
actually not so much, to behonest, like and when I talk to
people I know in my you know mynetwork people like, oh, here
comes the AI expert again, right, like, because I'm so
passionate about it.
I don't think it's on top ofpeople's mind, from what I can
see, at least to the people Italk to.
Of course I can find examples.
(24:29):
I mean, I know a few managerslike Vaquant having implemented
unsupervised learning, and thereare hedge funds only doing this
, but I think they're just oneof the few.
There was this article in, Ithink, yesterday on Bloomberg
about Baliasni.
You know one of themulti-strategy funds and I think
(24:49):
you know the because thesefunds they have, you know, a
very large asset base and theyobviously can afford to have
larger IT teams and and peopledoing research and everything.
Even then they say you knowthere's a lot of hallucinations.
We have to be careful in how weincorporate.
We can have this replace or notreplace, but at least, you know
, automatize a lot of the workfor doing the junior investment
(25:10):
analyst work.
But there are a lot ofsafeguards everywhere in terms
of you know, okay, how they canuse that information in their
process.
So you know, even people thatare supposed to be leading in
terms of how much investmentthey can put into this people
that are supposed to be leadingin terms of how much investment
they can put into this uh,they're still far away from uh
adopting.
So now the discussions arestill around.
You know, like uh, obviously,the uh election in the U?
S and uh, you know, uh, whereequities go from here.
(25:33):
And uh, you know like uh,because we have that platform
with different teams.
We have one team that is reallyquant and systematic and the
other teams are more likediscretionary.
So there I can see, you know,obviously on both sides, right,
like people being more quant,but even there you know like we
(25:54):
sit back to understanding whatwe're doing.
Right, like again, becausethat's why investors trust you
know and trust us their money.
Speaker 1 (26:03):
Well, I do have to add is there an election coming
up in the United States?
I wasn't sure of that.
Speaker 2 (26:10):
Yeah, that's why it's so undecided.
I'm pretty sure that's whatpeople think that was a good one
.
Speaker 1 (26:15):
So in Q2, you launched Nova.
Perhaps you could share withthe audience what Nova is and
now that we're almost at the endof Q3, how is it performing?
Has it met or exceededexpectations?
You know?
I would love to hear about that.
Speaker 2 (26:32):
Yeah, thank you for asking.
So I mean, we actually launchedtwo strategies last month, not
only Nova, and you know, as wediscussed that was quite a time
to launch a new strategy, right,it was a kind of you say that,
baptism of fire or something.
We say that in French.
Speaker 1 (26:48):
Baptism of fire.
Speaker 2 (26:50):
Yeah, that was kind of that.
So Nova, I start on thestrategy itself and all the two
strategies, what they're tryingto achieve and how they've done
in this environment.
So NOVA stands for NeutralOption Volatility Arbitrage.
It's a market neutral optionslash volatility arbitrage
strategy.
I think it's quite uniquebecause if you look with peer
(27:14):
funds trading volatility andhaving volatility based
strategies, I think what'sunique about the strategy is
it's long, most of the Greeks,so for people listening.
So it's just to be more precisehere what I mean by long the
Greeks.
So it's long VGA, meaning ithas positive sensitivity to
increase in volatility.
It's long convexity, long gamma.
(27:35):
It's also long theta, which isquite hard to achieve.
I mean there's a balancebetween theta and convexity, as
you know, and it has a very lowdelta, so very limited
directional exposures.
That's quite interesting, quiteunique positioning, I think.
And also, you know, back to theidea of Thailand, of being
really fundamentally driven.
That's because the strategyreally in itself exploits an
(27:56):
anomaly in options market thathave exploits in anomaly in
options market.
Uh, that's a very non-anomalybut it still exists and that's
also what I think are the bestalpha opportunities if there is
a phenomenon in markets,financial markets that are there
to stay, because on the otherside you have market
participants which are not, um,you know, which are uneconomical
, which is the case here.
People are ready to pay forprotection more than they're,
(28:17):
you know, they're ready to paymore for protection for input,
and that creates the volatilityskew and the strategy really
exploits that and positions theportfolio in a way to also
harvest that data.
So it's quite interesting.
It's interesting also where itcame from.
So the genesis of the strategycame from the asset manager way
it was managed before.
They had balanced funds, like atypical larger asset manager
(28:38):
for clients having equitiesbonds and a balanced portfolio.
And that strategy was designedoriginally to act as a
diversifier in that, you know,balanced fund.
So that makes it, I think,quite, you know, a very good,
you know allocation tool for,you know, equity slash, balance
portfolios, but also as anabsolute return strategy.
So I really, you know, I thinkit's quite unique.
And then about the uniquenessso in that environment where
(29:01):
obviously it's been quitevolatile, the strategy has done
really well, performed in linewith, you know it's done
historically, it's been positive, you know, in challenging days
for equity markets.
So, like the whole market,neutral, you know,
implementation has played itsrole in this kind of market.
Speaker 1 (29:17):
Okay, you know yeah.
Talk about getting tested rightout of the gate.
Speaker 2 (29:23):
I mean, I can tell you that the strategies were
launched on the 17th of July.
That was the peak of the S&P.
So, yeah, that's exactly that,and we're quite pleased to see
how the world played out anddiversification played out.
Speaker 1 (29:34):
Unfortunately and sadly, we've reached the final
question.
We call this the trend drop.
It's a desert island question.
So if you could only watch ortrack one technology or trend in
the capital markets right now,what would it be?
Speaker 2 (29:48):
That's a good one.
So I think maybe it's apersonal wish on this one also,
it's because I can see there's alot of gain to a lot of
improvement to be made in termsof on the operation side of our
business.
Right, it still requires somuch resources in the whole
settlement process and, you know, like trying to reconcile trade
breaks, so like for me, like adream, I think that's whether
(30:11):
the next potential you know,substantial innovation in
transforming the industry ismore tokenization.
So just to also to be clear onwhat I mean so tokenization is
really converting assets intodigital assets using blockchain,
and there I feel like apotential.
What I see for me, one of thebest implementation of that
(30:32):
technology is to use blockchainfor all the settlements of
transactions, like to make allthese transactions instant, less
effortless, reduce operationalrisk.
You know that's one advantage Isee.
So obviously it has a hugeimpact on the industry, right,
you know, because there are, youknow, so many asset managers,
banks they have hundreds ofpeople trying to reconcile all
(30:55):
these transactions every day.
The second one is, I think,potentially reduce cybercrime,
like if that ledger, thetransparency of the ledger,
makes all these transactionsauditable, then all of a sudden
you solve one of the big issuesin that industry when it comes
to KYC and, obviously, beingable to trace all the funds, so
(31:15):
that also will reduce a lot ofoperational risk.
You know, and for us you know,at the end of the day, you know,
like there've been so many, I'msure there are some
technological issues right, andchallenges in doing this.
It was years ago where you heard, like about all these banks
getting together and trying tofind, you know, like you know,
solutions to find withinthemselves.
(31:36):
It's a system to settletransactions and everything.
There's still no solutions outthere.
Maybe AI will help us to somedegree to accelerate that
innovation.
I would like to see that.
I think that's an area where wedefinitely need improvement,
because obviously there's a lotof money wasted in terms of very
limited value to solve issuesthat could be avoided in the
(31:58):
first place.
So, yeah, that's the one Iwould monitor.
I'm pretty sure it's going tohappen.
It has to happen and that'swhere I see a lot of value in
blockchain.
Speaker 1 (32:06):
Well, serge, I want to thank you so much for your
time, your insights and, as Isaid, I would love to have you
back next season.
And, you know, let's get pastthis little election thing
that's going on in the UnitedStates and we'll see where the
markets are then.
Speaker 2 (32:19):
Okay, I appreciate that.
I'd be glad to be back.
Thank you so much, Jim, forhaving me.
Speaker 1 (32:30):
Thanks so much for listening to today's episode and
if you're enjoying TradingTomorrow, navigating trends and
capital markets, be sure to like, subscribe and share, and we'll
see you next time.
Welcome to Trading Tomorrow navigating trends in
capital markets the podcastwhere we deep dive into
technologies reshaping the worldof capital markets.
I'm your host, jim Jockle, aveteran of the finance industry
with a passion for thecomplexities of financial
technologies and market trends.
In each episode, we'll explorethe cutting-edge trends, tools
and strategies driving today'sfinancial landscapes and paving
(00:29):
the way for the future.
With the finance industry at apivotal point, influenced by
groundbreaking innovations, it'smore crucial than ever to
understand how thesetechnological advancements
interact with market dynamics.
Welcome to today's episodewhere we discuss financial
(00:55):
technology's dynamic andever-changing landscape.
Terms like AI, blockchain andcloud computing are not just
buzzwords but the driving forcesreshaping the financial
industry's very core.
Over the years, we've heardpredictions of how these
groundbreaking technologieswould revolutionize everything
from banking to asset management.
But have they lived up to thepromise and what do the experts
(01:15):
in the space think about theirimpact?
Joining us to help explorethese critical questions is
Serge Holtz, ceo and partner atTitan Capital.
Serge not only oversees theday-to-day management of the
firm, but also leads all globalmarketing and business
development efforts.
With a wealth of experiencefrom prominent hedge funds and
fund-to-funds worldwide, serge'srole has spanned quantitative
(01:38):
research, risk management,product development, business
development and marketing.
His strong academic backgroundincludes an MSc in econometrics
and finance from Paris' Dauphineand post-master's degree in
banking and finance from theUniversity of Paris.
So, serge, first and foremost,thank you so much for joining us
today.
Speaker 2 (01:57):
Thank you so much, Yma, for having us and having me
.
Speaker 1 (02:00):
On this show, we've explored a range of cutting-edge
technologies, from AI toblockchain, virtual reality,
cloud computing.
As someone who's deeplyembedded in the industry, how
are these innovations reshapingyour day-to-day work, and to
what extent have any of thesebecome indispensable?
Speaker 2 (02:19):
Yeah.
So if that's okay, jim, I'llstart by sharing my personal
journey of using generative AI.
Mostly, I'll start with that.
In terms of technicalinnovation that I think would
have potentially the most impactin our industry.
I think you also might be thesame to me and many others.
To start, I'm a big fan ofmachine learning, so I've been
(02:40):
using machine learning in myprevious role doing some
interesting optimization.
I was there, you know, beforeJetGPT was released two years
ago.
One of my very good friends,tony Gida, is an expert in
machine learning.
He's read a number of books andhe made me aware of that
release.
I was not aware before that.
So I was there, right there,you know, when it was released
two years ago, in November, andI started using it and I
(03:06):
realized, right there, thepotential, right.
It was like so the, it wascrazy right, the breakthrough,
and it's looking at this,getting all these answers and
even asking crazy complete,complicated questions and all
doing, all doing, alltranslation or summarizing
documents, and so, very quickly,I could see, you know, how this
could be, uh, you know,practically implemented in our
business, right to uh, toautomatize and all the tidy
(03:28):
tasks that you know combinedtogether takes several hours.
But then you know after usingit personally.
First I was like I realized youknow what everybody is talking
about, right, the hallucinations, right, and also that some of
the answers were not veryaccurate for me personally.
So I started learning about,you know, few, uh, few, short,
prompting and customizing and uhand uh.
(03:49):
I think that's where the assetmanagement industry is right,
like in terms of uh, you know,adopting, uh, adopting this uh
technology.
So from what I see, I mean I'mthe ceo of titan capital, so I'm
I talked to a number of peersyou know like ceo and leaders in
that industry and and so I'vebeen using chachiPT a lot and
I'm super excited about thepotential.
(04:09):
So I've been sharing that, youknow, and I meet other people,
other peer, you know CEOs, andthey're like ChatGPT, what you
know, like how do you use it.
So I can see that it's reallynot been adopted yet, not even
you know for people personally.
So, and I think you know, it'snot because the industry is
resistant to change.
I think it's a good thing andit's simply because we have that
(04:32):
fiduciary responsibility.
I think personally there is alot of expectations about that
industry of adopting the newtechnologies, for many reasons.
To start, there is a lot ofbrain power in the industry,
right, in the financial industry.
So then you would expect allthese people, all these brains
combined, you know, to, you knowto customize all these
(04:55):
algorithms and make it you knowand implement in their business
and day-to-day work.
To start, the second is thatthe industry has been, you know,
compiling all this data,structured data, for years and
that make you know and that makethe user obviously easier to
implement machine learning andall these kind of new
technologies.
But then, at the same timebecause this is quite unproven,
right Like I lost account of howmuch data has been created the
(05:20):
last three or five years likethat we created more data
historically the last five yearsthan we have ever before.
So one is a lot of data and allthese technologies, they, you
know.
The pace of evolution also iscrazy, right Like we see,
already two years ago, when youlook at the first, or not even
two years ago, even you know, ayear ago, we had this, you know,
video generated by, you know,generative AI.
(05:43):
We could see how you know allthe, how deficient they were and
all the defects in the video,and then the year after, they
corrected that.
So it's so.
The pace of evolution is soquick that I think it's a good
thing that the industry has notadopted this technology yet
because of this regulatoryresponsibility.
So we have to make sure tostart.
We want to make sure that weprotect our client's capital
(06:05):
before, obviously, I mean theend game for us as a major, and
especially in the apps, you know, in the hedge fund industry, as
we are trading capital.
Obviously, at the end of this,you know, to deliver absolute
return for our clients, but thefirst duty is really to protect
our client assets and I thinknobody expect us to, you know,
to adopt all these technologiesand hope for the best, and I, if
I do, yeah.
So, if I could, I would like todraw a parallel here to people
(06:28):
to understand.
It's like the medical you know.
It's a parallel to the medical,you know industry, where it's
like you would ask a surgeon youknow to use a AI driven scalpel
because, you know, improveprecision by 0, 0.2%, with the
risk of, you know, would seehallucinations and dread, just
to test it out on patients.
It's the same with thefinancial industry.
(06:49):
So I think the potential isgreat in terms of automatizing
all the tidy tasks and,obviously, processing data.
It's obviously great forunstructured data text, video
and so forth but we're not thereyet, I think, in terms of the
pace of adoption.
So that's for GNAI.
Speaker 1 (07:08):
So, you know, one of the key things or trends over
the many years is, when it comesto new technologies, it always
seems the buy side leads thesell side in many ways.
You know why is that ways.
You know why is that?
And you know, I guess.
And the second question is youknow, given hallucinations,
(07:33):
given fiduciary responsibilities, you know what is the
confidence level for things likeAI to become even more
ingrained?
You know, within day-to-dayoperations, you know where's
that confidence level going tobe?
Speaker 2 (07:44):
Yeah, I think that's a very good question, spott.
So I'll start on the first one.
I think, to start, the vastmajority of this industry is
great asset managers.
They are discretionary, right,they use their own judgment to
make investment decisions and Ithink for many of them, like
(08:05):
it's a bit of a leap of faith tostart, you know, to allocate
out like part of your thatprocess to an algorithm and try,
you know and and trust it.
I think that's that startsthere, right, like, um, they,
all these people, they trusttheir judgment first, and that's
also one of the biases that wefind in investing right, like
overconfidence.
Right, and maybe so that couldbe a resistance to adopting all
(08:28):
these technologies.
There are many things thatobviously could be done without
allocating the entire processand that we see already.
I guess you heard about JPMorgan right, creating their own
.
They have their own internaljunior analyst based on AI to
process all the corporateinformation, so all the
unstructured data.
Make use of it for theportfolio managers.
(08:50):
Right To have all thatinformation digested.
You have a huge amount, a hugegain of productivity and time,
and then you can spend value attime in terms of making
meaningful investment decisions.
So I think maybe the resistancecould be there to start.
You know like.
You know like afraid of thechange, afraid of you know
(09:11):
losing their.
You know you know beingcompeted away.
You know also that could bethat you know being competed
away by the algorithm andrecognizing that you know
eventually, potentially theywon't have these biases right.
As human being sorry to jump onthis one but as human beings we
have so many biases thatdoesn't make us a good investor
in the first place.
So obviously using thisalgorithm that should not be
(09:34):
biased could be a way to improveyourself on yourself.
So I think that would be great.
To combine human judgment withall these tools and this
technology, I think would betremendous.
But then again it's slowbecause you know scale of change
.
My take, then, the other one is,I think is a catch-22, right,
like if you start somewhere, youknow first by somehow, you know
(09:58):
collecting all this data, theinfrastructure.
You know all the reports, allthe video, the earning, the
earning calls.
You know for for an analyst.
You know like startingprocessing all that and make
meaning of that and gettingconfidence in.
You know in the lower value add, you know in the chain, the
value chain of a portfoliomanager, then you gain
confidence.
When you gain confidence, youuse it more.
(10:18):
If you use it more, you createmore data for this algorithm to
be trained on and if, if theytrain on that you know like in
terms of being incorporated intoyour value chain all the way to
making actual investmentdecisions, they get better and
then maybe that's when you'restarting a broader pace of
adoption.
But it's again, it's slow and Ithink also I mean what's
(10:41):
important to, I think, tounderstand my take is that you
know technological change arenot deterministic.
It's not because they are therethat they have to dictate.
You know the pace of change andwhen and how you know the
industry will adopt all thesetechnologies.
I think it's I mean, we've doneit before right in terms of
using internet, in terms ofusing mobile phones for our
day-to-day work.
I think it's a good thing thatthings take time.
(11:03):
You know, for the best of ourinvestors, I would say so.
Speaker 1 (11:08):
in today's universe, where is the alpha?
Is it in the data, is it in thestrategy, or is it in the
intuition of the individual.
Speaker 2 (11:18):
That's a very good one.
Actually, I was about to getinto this one also.
At the end of the day, I wouldthink it's the same.
Right, like you know, back many, many years ago, decades ago,
the alpha was in the informationedge and later that became
illegal.
Right, that became, you know,insider trading and all this
kind of thing so like, and thatturned into later to be an
(11:41):
information edge in terms of howmanagers could interpret some
information in a better way orbecause they had access to
information.
You know before, like the macrodata or, you know, building
model and making sense of a lotof data, that was some form of
information edge.
But at the end of the day, youknow, these data quickly have
become available to the vastmajority of managers.
(12:03):
Right, it became.
It became, like, you know,cheaper because many more
managers had access to it, soyou had the economy of scale,
and then it become cheaper andthen, you know, everybody had
access to it.
So today, you know, like, ifyou exclude the new technologies
, today I would argue Any assetmanager in the world has access
to exactly the same amount ofinformation and the same quality
(12:24):
of information.
So then back to your point.
Yes, it's always how you use itand that's the same as
comparing to.
You know.
I think that industry you knowthe hedge fund industry and the
alpha driven industry is thesame, as you know, like
competing in sports.
Competing in sports, like ifyou are a golf player or playing
(12:48):
soccer, you have access toexactly the same tools and
equipment.
So when you're a footballplayer, you have the same shoes
and you use the same ball, andwhen you're a golf player, you
are exactly the same.
You have the same clubs each.
Maybe they could be customized,but the same way.
But at the end of the day,there are only 10 best players
in the world.
It's the same in office.
It's really how you useinformation, how you process,
(13:11):
how you combine you know dataand you combine your own
judgment, how you combinefinancial modeling and your
experience, all that to takeinvestment decisions.
But my take sorry, that was along one and you go right there
in terms of what I'm passionateabout.
You know, at the end of the day, it's all about discipline.
That's why I'm quant originally.
So I really, like you know,quantitative process,
(13:32):
well-researched investmentmodels.
But what I found at our firmsorry to comment, on our side
also is that, like even thepeople in our firm you know
having, you know makingdiscretionary decisions I can
see that through, like you know,very challenging times like the
one we've seen, you know, thelast couple of weeks, you know
we could, you know they've beenquestioning, right With the sale
(13:52):
of like nobody understand andthe VIX, you know skyrocketing
for discipline is and experience, and that I think is hard to At
this stage.
It's hard to train, I think,mike maybe I'm a bit biased-
Well, you know it's funny.
Speaker 1 (14:09):
I know what my VO2 max is, I just haven't figured
out how to optimize it.
It's actually easy.
So you know, clearly,technology has undoubtedly
driven progress, but it also canintroduce new challenges.
Driven progress, but it alsocan introduce new challenges.
So you know in your experiencehow is this rapid evolution of
tech, you know, almostcomplicated your professional
(14:30):
life, like my VO2 Max yeah yeah,maybe we can compare our VO2
Max.
Speaker 2 (14:37):
No so like in many different ways.
Like so, I think you in yourquestion I think that's part of
the answer, right, it's justrapid, right, like so I mean.
Like, I mean, I don't know howmuch you use LinkedIn and, uh,
you know, like, there's not oneday where you can see a post of
okay, these are the new tools todo this and this, and that
(14:58):
changes every week.
A challenge in itself, becauseobviously that challenges you of
doing your job in the best wayand have access to all these
tools.
But it's not like you learnthese tools.
You know.
Like, even if it's, you know,there's a lot of algorithm and
everything, so they process alot of your task, obviously
without any input from your side, but they still require a lot
(15:18):
of customization and humanoversight.
So that has that put, I think,a lot of strain on people of
being on top of, okay, what canyou use?
How can you use it?
How can you stay on top of that?
So that's one challenge.
The other one touch upon yourquestion.
You know, in terms of data,information, everything I think
we went from you know having aninformation edge in terms of
quantity, like you know, havingaccess to that information and
(15:40):
to being now a disadvantagebecause there's so much
information out there, right,there's a lot of noise, there's
so much data that is you know inany way in any shape or form,
like social media news, you know.
And like satellite, you know.
You hear about all the storyabout satellite images, right,
Like, that's a typical exampleabout you know how to use
machine learning to predict theshift in macroeconomic cycle.
(16:02):
I take it doesn't work so well,but anyway.
So, yeah, so that creates achallenge in itself.
How do you dissociate from youknow what kind of information is
meaningful for understandinghow that can influence that
company or that country, or youknow, like.
So that's a challenge in itself.
So obviously sorry on this one.
So like the technology is alsohere to help you, right, like,
(16:25):
so sorry on this on this one.
So, like, the technology isalso here to help you, right,
like.
So, yes, in the first hand,that creates a lot of data and
information everywhere, but thatthe technology can obviously
address that in trying to sortout, using this kind of
algorithm to sort out what it'smeaningful, especially if you
supervise learning.
But again, you know, then youneed to understand it, like, and
you need to test it and youknow, in data and information,
you need to understand it, like,and you need to test it.
And you know data andinformation, you need to get
comfortable, I think the thirdone is cybersecurity.
(16:47):
Yeah, so like, yeah, I mean,we've been, I think, on our side
at Thailand, so we've beenusing Gen AI for, like,
marketing and general business,and it's incredible, really,
like what you can do in terms ofproducing marketing material
and getting some template forlegal document or, you know,
summarizing something or getbullet points or something.
(17:09):
But then it also oh, now I sorryI go back to being disciplined,
because it's very difficult tofind, you know, to find like
really, uh, you know verydifferent processes in terms,
okay, you can use it, or eitherthere's some firms have decided
they block it all together.
I don't think it's a good idea,because then obviously you lose
some form of competitive edge ifeverybody else is using it, but
then of course, you have to usethat.
You know, um, you know commonsense like.
(17:30):
Okay, of course, when you useit, you have to take out all the
very sensitive information, allthe client specific information
, all the company specificinformation.
So that requires a lot of, alot of discipline.
So on one hand you knowobviously that you increase
productivity, because it'ssometimes very hard to start
from a blank sheet of paper, buton the other hand you have to
(17:50):
be very disciplined in how youuse it and obviously that human
oversight because of thehallucinations you know, if you
just use it right there, thenyou can have anything that comes
out of it in terms of making upa story about your company that
is, that is not your company.
Speaker 1 (18:04):
I've seen that many times yeah, and and the there
was a us supreme court case, uh,or citing, uh, the attorney was
, uh, in front of the unitedstates supreme court, citing a
case that never happened.
You know, I I'm sure everythingwill improve, but you know it
and it's improving every day.
You know you bring.
You brought up one interestingpoint in terms of you know, and
it's improving every day.
(18:24):
You know you brought up oneinteresting point in terms of
you know, social media and youknow I think back, if I go back
maybe five years, there was theyou know the trend of how can we
gain sentiment analysis, howcan we, you know, utilize and
harness all this data?
You know, albeit unstructured.
You know in terms of investmentdecisions, and I think you know
(18:45):
, for the most part, theconclusion was it's an input,
but it's just an input.
You know the edge isn't there.
But then you know you have thewhole GameStop issue and Reddit
and things of that nature.
You know to what extent hasthat GameStop experience perhaps
changed the way people arethinking about social, in terms
(19:08):
of understanding sentiment?
Speaker 2 (19:11):
I would say, you know it was funny because obviously
you had that episode a couple ofweeks ago, months ago, right,
when he was back trying toinfluence GameStop again.
So, no, that's a great point,because I was about also to get
into, you know, how we couldpotentially at Titan, at our
firm, how we could potentiallyuse, you know, ai, and that's
(19:33):
one area where I can see theclear benefits.
Like, as you say, at the end ofthe day, you know, it's very
difficult to understand, okay,can that create an edge if
everybody can do it, to do this?
I don't think so.
But then, at the same time, howdo you make sense of that noise
, right, like of all the uh, theuh, the reddit and and uh, all
the kind of form.
And obviously now peoplerealize.
(19:54):
So there you have a point.
People realize all these groupof investors, if they gather in
a platform somewhere, they canactually influence, uh, you know
stock prices and it's's.
It's a challenge for us as asactive asset managers, because
we have our risk management andif that goes against your
position, you obviously have tomake decisions, right, like so
so, yeah, I mean that's onething of you know, looking at
(20:17):
this in terms of risk management.
This short, you know the nextgame.
But going beyond that, you knowextreme situation, which
obviously does not happen toooften, right, like it's just
been a few cases and I think itwill remain this way because
that was a combination of heaventhat led to that.
You know craziness, but I thinkin general that's really an
(20:38):
area where using NLP, so naturallanguage processing, to make
sense of unstructured data, solike earnings codes, you know
like, you know after the centralbank policy and you know all
the guidance from central bankpolicy video, audio and process
all that information to makesense of.
(20:59):
Not the information itself,because that's that you can do
on your own right, but like thechange, the nuance of, you know,
the change between one call tothe other or that could provide
some valuable insights intoinvestment processes.
Speaker 1 (21:16):
But you know you bring up a really good point,
especially when it comes down tothe Fed.
I mean, you know I go back tothe 1990s and you know, thinking
about Fed calls, it wassometimes what wasn't said or
the nuance or a head nod of theway something was said that
could move a market.
You know, is that just gettinglost in transcripts?
Speaker 2 (21:36):
Yeah, I think I think this one is a difficult one
because it's so polished.
I think I'm pretty sure I don'tknow how that works, the
process'm pretty sure there aremany people checking you know
how, how the um we did severaltimes, you know how can that be
interpreted outside, right, likeso.
But maybe that one is a harderone, you know, in terms of
trying to make sense of thatinformation.
Uh, but then on the, so yeah,I'm not so sure if, if you can
(22:00):
actually make conclusions aboutthat, because sometimes you know
how it has been the last 10years.
Good news is good news, badnews is bad news, like some,
like I'm not so sure if that'ssignificant in terms of making
meaningful, you know, decisionout of that in terms of the
macroeconomic direction, right,but I think for companies that's
(22:21):
that's super helpful because Imean the.
But I think for companiesthat's super helpful because I
mean management, as and therewas an example a couple of days
ago to see how management andhow people perceive the impact
of management on the company.
I think it was today or twodays ago Starbucks announced
that they would, that they hadyou know that they had the
Chipotle CEO and the stockrallied 25%.
So that's the kind of thingthat you that specific example
(22:45):
was obviously announced.
But I mean these kind of thingsin terms of, as you say, getting
the nuance, the change ofnuance in the narrative for
earning scores, any kind ofunstructured data that could be
very useful to gain insights oncompanies.
But again, sorry, before I Lastone, it's still how you use it.
You cannot just simply say,okay, it's a two-star deviation
(23:08):
from a monotone voice, somethingaway from the average decibel
in the voice that, oh, now theCEO is super excited in using
that model and I should go long.
No, it's just one moreindicator of you know gathering,
like a modality kind of thing,you know where you gather a
different type of information,looking at the financial and
(23:30):
deep down fundamental analysis,and then combine that with this
kind of indicator to fine tuneyour, to fine tune your view on
a company.
Speaker 1 (23:38):
So you know, as you, as we in the beginning of our
conversation you were saying howyou talk to a lot of your peers
and share your enthusiasm, youknow however those conversations
(24:00):
changed?
Has it gone from strategies andmarkets to data lakes and APIs?
And you know proprietaryvertical AI, or you know what,
what, how, what's the?
What's the?
The, the conversation amongyour peer group.
Speaker 2 (24:07):
Actually that's no, that's a good one, jim, but
actually not so much, to behonest, like and when I talk to
people I know in my you know mynetwork people like, oh, here
comes the AI expert again, right, like, because I'm so
passionate about it.
I don't think it's on top ofpeople's mind, from what I can
see, at least to the people Italk to.
Of course I can find examples.
(24:29):
I mean, I know a few managerslike Vaquant having implemented
unsupervised learning, and thereare hedge funds only doing this
, but I think they're just oneof the few.
There was this article in, Ithink, yesterday on Bloomberg
about Baliasni.
You know one of themulti-strategy funds and I think
(24:49):
you know the because thesefunds they have, you know, a
very large asset base and theyobviously can afford to have
larger IT teams and and peopledoing research and everything.
Even then they say you knowthere's a lot of hallucinations.
We have to be careful in how weincorporate.
We can have this replace or notreplace, but at least, you know
, automatize a lot of the workfor doing the junior investment
(25:10):
analyst work.
But there are a lot ofsafeguards everywhere in terms
of you know, okay, how they canuse that information in their
process.
So you know, even people thatare supposed to be leading in
terms of how much investmentthey can put into this people
that are supposed to be leadingin terms of how much investment
they can put into this uh,they're still far away from uh
adopting.
So now the discussions arestill around.
You know, like uh, obviously,the uh election in the U?
S and uh, you know, uh, whereequities go from here.
(25:33):
And uh, you know like uh,because we have that platform
with different teams.
We have one team that is reallyquant and systematic and the
other teams are more likediscretionary.
So there I can see, you know,obviously on both sides, right,
like people being more quant,but even there you know like we
(25:54):
sit back to understanding whatwe're doing.
Right, like again, becausethat's why investors trust you
know and trust us their money.
Speaker 1 (26:03):
Well, I do have to add is there an election coming
up in the United States?
I wasn't sure of that.
Speaker 2 (26:10):
Yeah, that's why it's so undecided.
I'm pretty sure that's whatpeople think that was a good one
.
Speaker 1 (26:15):
So in Q2, you launched Nova.
Perhaps you could share withthe audience what Nova is and
now that we're almost at the endof Q3, how is it performing?
Has it met or exceededexpectations?
You know?
I would love to hear about that.
Speaker 2 (26:32):
Yeah, thank you for asking.
So I mean, we actually launchedtwo strategies last month, not
only Nova, and you know, as wediscussed that was quite a time
to launch a new strategy, right,it was a kind of you say that,
baptism of fire or something.
We say that in French.
Speaker 1 (26:48):
Baptism of fire.
Speaker 2 (26:50):
Yeah, that was kind of that.
So Nova, I start on thestrategy itself and all the two
strategies, what they're tryingto achieve and how they've done
in this environment.
So NOVA stands for NeutralOption Volatility Arbitrage.
It's a market neutral optionslash volatility arbitrage
strategy.
I think it's quite uniquebecause if you look with peer
(27:14):
funds trading volatility andhaving volatility based
strategies, I think what'sunique about the strategy is
it's long, most of the Greeks,so for people listening.
So it's just to be more precisehere what I mean by long the
Greeks.
So it's long VGA, meaning ithas positive sensitivity to
increase in volatility.
It's long convexity, long gamma.
(27:35):
It's also long theta, which isquite hard to achieve.
I mean there's a balancebetween theta and convexity, as
you know, and it has a very lowdelta, so very limited
directional exposures.
That's quite interesting, quiteunique positioning, I think.
And also, you know, back to theidea of Thailand, of being
really fundamentally driven.
That's because the strategyreally in itself exploits an
(27:56):
anomaly in options market thathave exploits in anomaly in
options market.
Uh, that's a very non-anomalybut it still exists and that's
also what I think are the bestalpha opportunities if there is
a phenomenon in markets,financial markets that are there
to stay, because on the otherside you have market
participants which are not, um,you know, which are uneconomical
, which is the case here.
People are ready to pay forprotection more than they're,
(28:17):
you know, they're ready to paymore for protection for input,
and that creates the volatilityskew and the strategy really
exploits that and positions theportfolio in a way to also
harvest that data.
So it's quite interesting.
It's interesting also where itcame from.
So the genesis of the strategycame from the asset manager way
it was managed before.
They had balanced funds, like atypical larger asset manager
(28:38):
for clients having equitiesbonds and a balanced portfolio.
And that strategy was designedoriginally to act as a
diversifier in that, you know,balanced fund.
So that makes it, I think,quite, you know, a very good,
you know allocation tool for,you know, equity slash, balance
portfolios, but also as anabsolute return strategy.
So I really, you know, I thinkit's quite unique.
And then about the uniquenessso in that environment where
(29:01):
obviously it's been quitevolatile, the strategy has done
really well, performed in linewith, you know it's done
historically, it's been positive, you know, in challenging days
for equity markets.
So, like the whole market,neutral, you know,
implementation has played itsrole in this kind of market.
Speaker 1 (29:17):
Okay, you know yeah.
Talk about getting tested rightout of the gate.
Speaker 2 (29:23):
I mean, I can tell you that the strategies were
launched on the 17th of July.
That was the peak of the S&P.
So, yeah, that's exactly that,and we're quite pleased to see
how the world played out anddiversification played out.
Speaker 1 (29:34):
Unfortunately and sadly, we've reached the final
question.
We call this the trend drop.
It's a desert island question.
So if you could only watch ortrack one technology or trend in
the capital markets right now,what would it be?
Speaker 2 (29:48):
That's a good one.
So I think maybe it's apersonal wish on this one also,
it's because I can see there's alot of gain to a lot of
improvement to be made in termsof on the operation side of our
business.
Right, it still requires somuch resources in the whole
settlement process and, you know, like trying to reconcile trade
breaks, so like for me, like adream, I think that's whether
(30:11):
the next potential you know,substantial innovation in
transforming the industry ismore tokenization.
So just to also to be clear onwhat I mean so tokenization is
really converting assets intodigital assets using blockchain,
and there I feel like apotential.
What I see for me, one of thebest implementation of that
(30:32):
technology is to use blockchainfor all the settlements of
transactions, like to make allthese transactions instant, less
effortless, reduce operationalrisk.
You know that's one advantage Isee.
So obviously it has a hugeimpact on the industry, right,
you know, because there are, youknow, so many asset managers,
banks they have hundreds ofpeople trying to reconcile all
(30:55):
these transactions every day.
The second one is, I think,potentially reduce cybercrime,
like if that ledger, thetransparency of the ledger,
makes all these transactionsauditable, then all of a sudden
you solve one of the big issuesin that industry when it comes
to KYC and, obviously, beingable to trace all the funds, so
(31:15):
that also will reduce a lot ofoperational risk.
You know, and for us you know,at the end of the day, you know,
like there've been so many, I'msure there are some
technological issues right, andchallenges in doing this.
It was years ago where you heard, like about all these banks
getting together and trying tofind, you know, like you know,
solutions to find withinthemselves.
(31:36):
It's a system to settletransactions and everything.
There's still no solutions outthere.
Maybe AI will help us to somedegree to accelerate that
innovation.
I would like to see that.
I think that's an area where wedefinitely need improvement,
because obviously there's a lotof money wasted in terms of very
limited value to solve issuesthat could be avoided in the
(31:58):
first place.
So, yeah, that's the one Iwould monitor.
I'm pretty sure it's going tohappen.
It has to happen and that'swhere I see a lot of value in
blockchain.
Speaker 1 (32:06):
Well, serge, I want to thank you so much for your
time, your insights and, as Isaid, I would love to have you
back next season.
And, you know, let's get pastthis little election thing
that's going on in the UnitedStates and we'll see where the
markets are then.
Speaker 2 (32:19):
Okay, I appreciate that.
I'd be glad to be back.
Thank you so much, Jim, forhaving me.
Speaker 1 (32:30):
Thanks so much for listening to today's episode and
if you're enjoying TradingTomorrow, navigating trends and
capital markets, be sure to like, subscribe and share, and we'll
see you next time.
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