October 30, 2024 • 24 mins
In Part 2 of Jim Jockle's conversation with Rance Masheck, CEO of iVest+, and Chris Mercer, the companies COO and Head of Business Development, we continue to discuss how AI is shaping trading strategies and enhancing decision-making. Discover the future of AI in finance, success stories from educators using iVest+’s advanced tools, and the growing impact of vertical AI applications.
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(00:06):
Welcome to Trading Tomorrow Navigating Trends in Capital
Markets the podcast where wedeep dive into technologies
reshaping the world of capitalmarkets.
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 back for part two ofour riveting conversation with
(00:59):
Rance Meschick, ceo of IvisPlus,and Christopher Mercer, the
company's chief operatingofficer.
We're back to continue ourconversation on AI's future
potential and the current impact, and how companies in the
finance industry are harnessingAI and embedding it into their
product suites.
Make sure you check out lastweek's episode for part one.
So now just a quick questionhere.
Right, you know sometimes I'mmaking a trade, but I'm not
closing out my position, right?
(01:20):
To what extent are we, is theAI taking?
Speaker 2 (01:32):
To what extent is the AI taking the portfolio into
composition, into consideration,or just strategies and
individual trades at this point?
So what it's going to do isit's going to look at the
overall portfolio and how you'redoing, but, I will say, is
looking at trades that have beenclosed, because if I have
something that I am activelymanaging, I might still be
working that and it doesn't knowwhat that result's going to be
yet.
Now we have talked about addinganother thing for this to look
(01:55):
at just your open positions andmaybe come back to it.
Hey, you know you might want tolook at this stock, or you know
that, like there's an earningsreport coming up on this company
, you've got a lot of exposure.
You might want to look at that.
So we're looking at bringingthat in as well.
So that's a next step on it.
But right now, what we're doingis we're looking at what you've
(02:16):
closed out, because once you'veclosed it, we now have the
results of that trade.
Enough to say, hey, here's whatyou're doing really good at,
here's what you're not doinggood at, what you can improve on
.
And then there's a secondaryphase of this hey, here's what
you have opening that's comingup.
This is not here today.
Here's what you have openingyour portfolio that you might
want to consider, and it's goingto take into consideration on
(02:36):
that, by the way, the trend ofthe stock, what's going on with
the chart, any technicals thatmay come into play, upcoming
events like earnings and thingsthat could impact it, and stuff
like that to let you know aboutthose things.
(02:49):
Yeah, it'd be very cool to see correlation within the
portfolio.
I mean a lot of different coolapplications.
Speaker 2 (02:57):
Yeah, we actually have been expanding this one to
include a little bit of someinformation about how you've
diversified across differentsectors and industry groups.
On this case it told me about,I've done a good job at
diversifying on my differentstrategies, but we want to get
into how you're balancing yourportfolio and how that's playing
(03:18):
out.
We actually are justintroducing economic indicators
to the platform and we want tobring in some of what's going on
with economic indicators andwhat that looks like for the
market and how you'reapproaching the market.
So we're not done.
We have some other ones comingup but these four tools that are
already live in the platformtoday are very powerful pieces
(03:42):
to help you get more effectiveresults in your trading I mean,
that's the whole idea behind itand simplify your effort as a
trader to find the trade andthen to see how you're doing
with those trades.
(03:57):
And it's interesting the approach you're taking of let's
call it four discrete workers,if you will.
That really lays into a broadertrend that we're seeing across
successful AI companies in termsof attacking things through
vertical AI rather than justbroad open applications.
Speaker 2 (04:16):
Right, and that's you know.
It's something that you know,like Adobe Creative Suite
helping you do some edits on aparticular photo, right?
So you're not having aconversation about trading there
.
It's limited to that verticalright.
So that's what we found, youknow.
The second tool we put outthere was the general chatbot.
Even though it was trained onmarket stuff, we found that
(04:36):
people, oh, they thought it wascool, they go play with it, but
it wasn't really impacting theirtrading as much.
It's a great thing is a cheatsheet for a strategy, right?
So I'm about to do a.
I use the bull put spreadexample earlier.
I'm about to do a bull put.
Do I really make sure?
I want to make sure Iunderstand that strategy
properly before I do it?
(04:57):
It's a great thing for that.
But when it comes to honing inon stocks, you want to find.
It's the screener one.
You know how am I doing with mytrading and what could I work
on and improve on.
It's that one.
(05:11):
Got it.
So, chris, let me bring youback in here for a minute.
And Rand, it's fascinating,amazing.
But you know, chris, how arethese tools enhancing the user
experience for your clients?
Are, you know, perhaps somesuccess stories?
Outperformance experience foryour clients, perhaps some?
Speaker 3 (05:27):
success stories, outperformance.
What can you share of thesuccess of that?
We work with a lot of educatorsthat white label our platform,
and so everybody has a differentway of going about trading the
markets and a different set ofscans that they run and what
they're trying to teach theirstudents and stuff like that,
and so what we're seeing withthis is that it's a lot of
flexibility for them to pickwhat they care about and train
(05:49):
their students on it.
So we have a lot of differentsuccess stories, a lot of
different types of successstories, right, because it
depends on who the teacher is.
Who's teaching his group ofpeople how to trade the markets
or do stocks or options.
We've recently added futures tothe platform as well, so we've
got some guys that were reallyinterested in the future stuff,
and this will apply over to thatas well.
So there's a lot of uses for allthese AI tools, and some of
(06:14):
them care more about one piecethan the other, some of them.
We've got a couple educators.
They really work with theirstudents on their trading
journals, right, so you get tolook at your journal with your
student and talk to them aboutwhat they're doing right and
what they're doing wrong.
Well, now the AI piece canactually give them kind of an
overview and make it easier forthe educator to know what
(06:35):
they're doing without having togo through one by one, through
all the trades or anything likethat.
So there's a lot of use casesfor everything we've built.
(06:43):
Wow, and one of the things I've noted through studies is
you know a lot of financialinstitutions, I think for AI
projects, it's like one out of10 seem to fail.
You know what challenges haveyou had with AI?
Speaker 2 (06:57):
So, you know, you hear this thing oh, ai, let's
sell.
One of the things about this isyou know why AI?
Now, well, one of the thingsthat we I know you didn't ask
that, but it kind of leads tothis piece was, you know, we
felt like, hey, this is outthere.
I mean, you look at theadoption of ChatGPT when it
first came out.
(07:17):
You know now it's kind ofslowed a bit now, but when it
first came out, it was justmassive and we really felt like,
if we didn, which, then we cameinto OK, let's embrace it.
And then you find out that,well, you know, here's a here's
a good analogy with this.
I heard about the.
(07:38):
There was a attorney in front ofthe Supreme Court and in his
brief he mentioned a case lawthat was made up by ChatGPT.
It didn't actually exist, right, and so, you know, it's a
little embarrassing when you'rearguing in front of the Supreme
Court, right, you know?
And, and that's the case.
(08:01):
So in this case, it's people'smoney, right, that's a very
serious thing.
We want to make sure that we'redoing this right.
So we went from, oh, let's makethis available and start
playing with it to OK, wait aminute.
We need to really train this,and the training comes in in
really two forms, and youbrought up one of them it's the
(08:23):
prompt generation.
So what we did was we how do wemake it, how do we set up so
the user doesn't have tounderstand all that and we can
take what they ask and build itinto the prompt that we're
behind the scenes putting out toAI.
So that was one of the reallybig challenges to you know, as
you've said, you know work witha chat GPT.
You had to kind of hone yourskills of how to ask the
(08:45):
question and we don't want usersto have to worry about that.
So we built the in-betweenlayer there that gets us to that
.
And then the other one was howto train it.
On the data, and, just to letyou know, we're sitting on right
now almost two terabytes offinancial fundamental price data
for companies and that isevolving every single day as new
(09:10):
fundamentals come out.
We have the option, we have allthe option prices for every
stock every day back in historyfor like 30 years.
So when you're talking aboutoptions, things having all that
and what the behavior is goingto be, so training it was a
massive amount of work.
That was a big challenge.
(09:31):
Let me ask a follow-up there, just out of my own curiosity,
right?
So it's not just the modelitself.
I mean, you're dealing withthat much data, you're dealing
with real-time data or you knowI'm assuming you're all
SaaS-based cloud-nativearchitecture You're going to
need to scale in terms of yourresponse time, probably
Snowflake around the backend.
(09:51):
I mean, can you give us alittle peek under the hood?
Speaker 2 (09:54):
Yeah, so we have an auto-scaling system that you
know.
We are fully cloud-based onAmazon Web Services, just as a
point on this some of the bestin class in that and they have a
lot of tools in combinationwith what we've done on our side
as to when you need to add andscale up and all that.
(10:15):
We also have some cool thingswhere it's a very strong market
day.
I've seen a few of those in thelast couple of days when you're
seeing a lot of market activity.
We just spin up more serversjust to have it there, because
we know that there's going to bethese waves of people coming in
and out and AI gets hit prettyheavy on those days where you go
.
You know the market just took athree-day tank.
(10:35):
It's a deer in the headlights.
What do I do now?
Right, and those kinds ofquestions coming up.
So we make sure the resourcesare there.
At the moment, one of the thingsthat we do to help with this is
a lot of the data, as I kind ofmentioned earlier on some of
the fundamental data.
You know fundamental datadoesn't change during the market
day.
You know announcements happenpre-market or aftermarket, so
(10:57):
when that happens, we'reprocessing all of that and then
we're feeding that into the AImodels overnight and then all we
have to feed it.
When you ask your question isdepending on the question is
that stock's data or the otherstocks within the industry
group's data for the currentprice data?
So it has that.
There is a fire hose of dataconstantly feeding in and out of
(11:22):
the AI models for that, and youknow one of the things you hear
about about the AI needing somuch power to run and all that
stuff I can tell you that ourserver bills, since we
instituted this, have gone up abit because of the amount of
compute power it takes and theamount of just loading of the
(11:43):
data that happens every singleday and throughout the day to
make sure that it has the latest, up-to-date information so that
when you're getting thoseanswers it's effective.
(11:54):
How do you see AI changing financial technology landscape
in the next you know?
To say 10 years would beridiculous.
I mean, I don't even imaginefive years is challenging, but
I'll go with five years.
So what do you think five yearslooks like?
Speaker 2 (12:08):
So one of the things that to answer that I want to
start with, I want to get ChrisDewey in here too.
So one of the things that toanswer that I want to start with
, I want to get Chris away onhere too.
But one of the things that wewere talking about earlier about
success stories, one of themeasures that we saw with this
was the frequency of trading.
So we have macro data about howmuch users are using AI and
then can kind of do somecomparative around some things,
(12:30):
and what we found is those thathave engaged with AI number one
are trading more than those thataren't.
It makes sense because throughwhat we talked about here, I can
get to the trade quicker.
Right, I have this 12,000stocks.
What am I going to do?
With a couple of questions, Ican hone it in really quick.
So they're making decisionsquicker, trading more and
talking with them.
(12:52):
They feel like they're makingmore informed decisions because
of what this is giving them.
So I think on my side, part ofwhat I think is going to happen
in the near term the couple yearto maybe five year range is I
think you're going to see moreverticals of this right.
You know, you have Waymo.
I live in San Francisco and youknow now it's very common to
(13:13):
see cars driving around with nodriver in the car.
It's taken a while to get there, but that's a very specific use
case, right and the which is alittle.
It's still a little unnervingto get in a car without a driver
.
(13:27):
I will say yeah, I'm afraid of park assist in my car.
I tried it once and I wasfreaked out.
But please continue.
Speaker 2 (13:35):
Yeah.
So I think you're going to seemore of that.
What happened with, if you lookat the internet, where, all of
a sudden, you know when itlaunched and this stuff was
happening, you had all thesestatic websites out there, but
then it was really internet 2.0where all this data started
coming in.
You know trading platformswe're talking about financial
markets.
(13:55):
You know it wasn't just abrochure about the broker, but
you know, now you had tradingplatforms coming into play and
all that.
I think in AI you're going tosee some of that same kind of
into specific tool sets forspecific outcomes.
You know, in the long term isit going to be?
You know, actually, if we goback a couple of years here
(14:15):
there was a lot onrobo-investing.
You know Morgan Stanley and youknow Fidelity.
These companies were investingin the robo-investing thing,
which was simple algorithms toput you in particular buckets to
do certain things right.
So now you know the holy grailthere is going to have an auto
(14:35):
trading thing all run by AI.
You know, back to your parkassist.
You don't want to hit the carright Back to the courtroom.
You don't want to quote a casethat doesn't actually exist to
the Supreme Court and you don'twant to have that happening in
your trading.
So I think it's going to be awhile before it matures enough
that that's trustable.
So that's going to be, I think,beyond the five-year line.
(14:57):
But in the short term you'llsee some of these siloed
vertical use cases.
Speaker 3 (15:02):
Well, and I think, too, one thing that's you know,
we have certain features builtinto our platform where, if you
pull up a stock, it tells youhow many days until the next
earnings report or how many daysuntil the next dividend or
whatever.
I mean, obviously we have acalendar, but there's very
specific points on the platformthat show you, okay, you've got
this stock up, it's eight daystill earnings or whatever, right
, and maybe how many days tillthese options expire, and that
(15:32):
type of stuff.
Well, you could easily seecombining that with some AI, so
that the minute you buy a stock,you get notified when earnings
are getting close, right, and wewon't have to do anything
anymore.
You won't even have to golooking.
You'll get a pop-upnotification or an email or
whatever it is, to tell youwhat's going on in the stock, so
that you're not stuck in itthrough something you weren't
expecting, right?
So that type of stuff, I think,is going to come fairly quickly
as well.
(15:53):
So just to also complete your example, there Rance around
robo-advisors.
I think for the listeners it'salso important to note firms
like Morgan Stanley maybe 18months to two years before
launching, were quoted as sayinghow they will never have a robo
advisors and there was abacklash, as crazy as that was.
(16:13):
So you know, you know, when wetalk about adoption, you know
it's unfortunately it's not.
If it's when, right and andeverybody can reverse on that.
You know.
So here here and I'm going tostay on robo advisors just for a
second because I think it'sreally interesting the big fear
in algorithmic trading foreveryone was all the algorithms
(16:37):
are going to do the same thing,right?
So how are you guaranteeingthat the whole market is going
to move one way and the AI isgoing to be saying just move the
same way?
How is AI going to be one wayand the AI is going to be saying
that, just move the same way.
You know, you know how.
How is AI going to be smartenough, right?
You know the good trader,instinctually, there's always a
winner.
There's always someone on theother side of the trade.
(16:57):
You know, even you know acouple of days ago, when we had,
you know, those issues in themarket.
You know we're still waiting tosee who the winners and losers
are.
You know the guarantee thatyour AI is smarter than the
other guy's AI and it's going tohelp you make the right
decisions.
Speaker 3 (17:13):
Well, let me say one thing about that real quick.
I can tell you this we focus onthe software, right?
We focus on the platform.
We're not a broker, we'rebroker agnostic.
We connect to a lot ofdifferent brokerage firms.
You can trade and have the sameexperience, trading through us
with any of these brokers thatwe connect to through their APIs
.
I would say you're not going tosee the average brokerage firm
(17:39):
I mean, they're so tied up intheir back end technology and
their clearing and all thatstuff You're not going to see a
lot of these features pop upinto the mainstream platforms
for quite a while.
I don't think it's just goingto be too complicated for them
to do it.
So I feel like that's one waythat we're going to be able to
stay ahead is because all we dois focus on the software side of
this.
Speaker 2 (17:58):
And I think that a lot of times, brokers are a lot
more risk averse.
And I mentioned that TDAmeritrade acquired a company I
had back in.
It was in like 2007.
And one of the things that wasvery frustrating to me is when
it launched, they stripped outseveral really cool tools
because they didn't like that.
So one of the things we have inour current platform is a trade
(18:18):
finder tool that will give youa timeline, a price range, how
to structure an options trade onit, and this has been after
years of research and algobuilding that we did to do that,
and this is not AI, this isjust the old, old fashioned
algorithms and really workingthrough this to do and so we can
(18:39):
adopt that type of thingquicker than a broker is,
because they have to walk thatline very careful when they get
into a recommendation and whatthat means, and we can give you
ideas with background on that tohelp you make that decision and
not fall into thatrecommendation or fiduciary role
.
In this area, I think the samething is going to be the case
(18:59):
where you're going to have somehesitation from brokers and if
they're going to, they're goingto have it be kind of watered
down and now, behind the scenes,they might have some money
managers starting to use some ofthese tools and that that
aren't available to you as anindividual, because the money
manager can use it as a tool.
But still watch out what'sgoing on and making sure that
(19:21):
it's the right thing.
You know, like, like, don'tread the brief that you just
quoted to the Supreme Court.
Make sure it exists, right.
You know, don't just trust it,right.
So you know, keep your hand onthe wheel when it's auto parking
.
Make sure that you caninterrupt, right.
So if you do those things, Ithink it's helpful, but it's
interesting to see.
I really think that, first ofall, not everybody has the same
(19:41):
timeline.
Not everybody has the sameoutcome.
You know, when I was a newinvestor, I was willing to take
a lot more risk than I am today,as my portfolio has gotten
large enough and I've gotten afew more gray hairs, that now
capital preservation is a veryimportant part of my portfolio,
right?
So I think that you're going tosee, you know and time horizons
(20:03):
and you know what people arelooking to do.
You know you're three yearsfrom your kids going to college.
People are looking to do.
You know, your three years,from your kids going to college,
how you're going to approachthings might be different than
if you're now an empty nesterand you're looking toward
retirement in the next, you know, 20 years, right?
So I think there's a little bitof having it mold to what your
specific outcomes are, but thisis an interesting area that we
(20:24):
have to watch, and one big thingabout this is you know, tesla's
had a few cars have an accidentwhen they're on autopilot,
right?
You don't want those things tohappen, so you have to really
watch this, babysit it throughthe process.
One of the great things that wehave going for us is that we
(20:45):
have thousands of users usingthis and we can see the
collation between what they'redoing and what the outcomes are
and move and adjust and workwith us.
And you know, right now, if youlook on the site, it's going to
say this stuff's in beta, andit's probably going to say that
for the next six months or sobecause, well, maybe even longer
than that, because one of thethings that happens is we're
getting.
You know, we launched this andright after we launch it, now
(21:05):
there's new models, right, andare these new models better?
Yeah, you know.
So you have to kind of workwith this and it's going to be
an evolving thing.
Here's the thing.
It's here, it's not going awayand we're working to embrace it
in a way that can help makepeople effective in their
trading without them having toworry about that.
(21:26):
We're doing that effort andpresenting tools to them that'll
help their outcome.
And as those tools get better,you know like, hey, there's
earnings season, you're in astock, what else you know?
Based on what's going on inthese other stocks in the same
sector, what's that likely goingto mean for the one that you
have earnings coming up nextweek for Right and be able to
extrapolate some of those thingsout?
(21:46):
Well, the thing I really like about your approach unlike the
robos it's you're making abetter driver, right?
You're not taking the driverout of the driver's seat, you're
just making a better driver.
Speaker 2 (21:58):
Yes, yes, yes.
(21:59):
So, unfortunately, we have reached the final question of
the podcast and I'll ask it toboth of you.
We call it the trend drop.
It's like a desert islandquestion.
If you could watch only one,only one trend in AI financial
analysis tools, what would it be?
Go ahead, rance, and it'scheating because there's two of
you, so you can pick two trends.
Speaker 2 (22:20):
I would say, competitive analysis, so that
when you go into a particulararea, you're diversifying your
portfolio.
I want some pharmaceuticals.
I want some tech.
I want some this, so that whenyou go into a particular area,
you know you're diversifyingyour portfolio.
I want, you know, somepharmaceuticals, I want some
tech.
I want some this how can I findthe best in class in those
different areas?
I think that that's one rightnow.
There was one area that, as itsits today, that I could use to
(22:45):
improve my results.
It's with those different areasI want to invest in.
What are the best in class togo into in those areas and how
to find those?
Speaker 3 (22:54):
quickly.
I think it would be for me.
Portfolio analysis, justbecause it's so important for
people to be able to.
If you've been trading for sixyears, you've got a P&L
statement from your broker Great, so you know what you made or
lost for the year.
That's it right.
You probably don't know muchelse.
Now, with all this AI stuff andwith something like our trade
journal, you can go back sixyears worth of stuff and really
(23:17):
start to figure out what you didright and what you did wrong,
and I think that's going to becritical for people moving
forward.
(23:22):
Well, rance, chris, I want to thank you so much Eye-opening
fun and I wish you gentlemencontinued success.
And when am I going to get myfree trial?
Speaker 2 (23:36):
We got you covered.
We'll take care of you on thatfor sure, excellent.
(23:38):
Well, thank you so much, and that brings us to the end of
today's pod Thank you, thank you.
Thank you.
Thanks so much for listening totoday's episode and if you're
enjoying Trading Tomorrow,navigating trends in capital
markets, be sure to like,subscribe and share and we'll
(24:03):
see you next time.
Welcome to Trading Tomorrow Navigating Trends in Capital
Markets the podcast where wedeep dive into technologies
reshaping the world of capitalmarkets.
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 back for part two ofour riveting conversation with
(00:59):
Rance Meschick, ceo of IvisPlus,and Christopher Mercer, the
company's chief operatingofficer.
We're back to continue ourconversation on AI's future
potential and the current impact, and how companies in the
finance industry are harnessingAI and embedding it into their
product suites.
Make sure you check out lastweek's episode for part one.
So now just a quick questionhere.
Right, you know sometimes I'mmaking a trade, but I'm not
closing out my position, right?
(01:20):
To what extent are we, is theAI taking?
Speaker 2 (01:32):
To what extent is the AI taking the portfolio into
composition, into consideration,or just strategies and
individual trades at this point?
So what it's going to do isit's going to look at the
overall portfolio and how you'redoing, but, I will say, is
looking at trades that have beenclosed, because if I have
something that I am activelymanaging, I might still be
working that and it doesn't knowwhat that result's going to be
yet.
Now we have talked about addinganother thing for this to look
(01:55):
at just your open positions andmaybe come back to it.
Hey, you know you might want tolook at this stock, or you know
that, like there's an earningsreport coming up on this company
, you've got a lot of exposure.
You might want to look at that.
So we're looking at bringingthat in as well.
So that's a next step on it.
But right now, what we're doingis we're looking at what you've
(02:16):
closed out, because once you'veclosed it, we now have the
results of that trade.
Enough to say, hey, here's whatyou're doing really good at,
here's what you're not doinggood at, what you can improve on
.
And then there's a secondaryphase of this hey, here's what
you have opening that's comingup.
This is not here today.
Here's what you have openingyour portfolio that you might
want to consider, and it's goingto take into consideration on
(02:36):
that, by the way, the trend ofthe stock, what's going on with
the chart, any technicals thatmay come into play, upcoming
events like earnings and thingsthat could impact it, and stuff
like that to let you know aboutthose things.
(02:49):
Yeah, it'd be very cool to see correlation within the
portfolio.
I mean a lot of different coolapplications.
Speaker 2 (02:57):
Yeah, we actually have been expanding this one to
include a little bit of someinformation about how you've
diversified across differentsectors and industry groups.
On this case it told me about,I've done a good job at
diversifying on my differentstrategies, but we want to get
into how you're balancing yourportfolio and how that's playing
(03:18):
out.
We actually are justintroducing economic indicators
to the platform and we want tobring in some of what's going on
with economic indicators andwhat that looks like for the
market and how you'reapproaching the market.
So we're not done.
We have some other ones comingup but these four tools that are
already live in the platformtoday are very powerful pieces
(03:42):
to help you get more effectiveresults in your trading I mean,
that's the whole idea behind itand simplify your effort as a
trader to find the trade andthen to see how you're doing
with those trades.
(03:57):
And it's interesting the approach you're taking of let's
call it four discrete workers,if you will.
That really lays into a broadertrend that we're seeing across
successful AI companies in termsof attacking things through
vertical AI rather than justbroad open applications.
Speaker 2 (04:16):
Right, and that's you know.
It's something that you know,like Adobe Creative Suite
helping you do some edits on aparticular photo, right?
So you're not having aconversation about trading there
.
It's limited to that verticalright.
So that's what we found, youknow.
The second tool we put outthere was the general chatbot.
Even though it was trained onmarket stuff, we found that
(04:36):
people, oh, they thought it wascool, they go play with it, but
it wasn't really impacting theirtrading as much.
It's a great thing is a cheatsheet for a strategy, right?
So I'm about to do a.
I use the bull put spreadexample earlier.
I'm about to do a bull put.
Do I really make sure?
I want to make sure Iunderstand that strategy
properly before I do it?
(04:57):
It's a great thing for that.
But when it comes to honing inon stocks, you want to find.
It's the screener one.
You know how am I doing with mytrading and what could I work
on and improve on.
It's that one.
(05:11):
Got it.
So, chris, let me bring youback in here for a minute.
And Rand, it's fascinating,amazing.
But you know, chris, how arethese tools enhancing the user
experience for your clients?
Are, you know, perhaps somesuccess stories?
Outperformance experience foryour clients, perhaps some?
Speaker 3 (05:27):
success stories, outperformance.
What can you share of thesuccess of that?
We work with a lot of educatorsthat white label our platform,
and so everybody has a differentway of going about trading the
markets and a different set ofscans that they run and what
they're trying to teach theirstudents and stuff like that,
and so what we're seeing withthis is that it's a lot of
flexibility for them to pickwhat they care about and train
(05:49):
their students on it.
So we have a lot of differentsuccess stories, a lot of
different types of successstories, right, because it
depends on who the teacher is.
Who's teaching his group ofpeople how to trade the markets
or do stocks or options.
We've recently added futures tothe platform as well, so we've
got some guys that were reallyinterested in the future stuff,
and this will apply over to thatas well.
So there's a lot of uses for allthese AI tools, and some of
(06:14):
them care more about one piecethan the other, some of them.
We've got a couple educators.
They really work with theirstudents on their trading
journals, right, so you get tolook at your journal with your
student and talk to them aboutwhat they're doing right and
what they're doing wrong.
Well, now the AI piece canactually give them kind of an
overview and make it easier forthe educator to know what
(06:35):
they're doing without having togo through one by one, through
all the trades or anything likethat.
So there's a lot of use casesfor everything we've built.
(06:43):
Wow, and one of the things I've noted through studies is
you know a lot of financialinstitutions, I think for AI
projects, it's like one out of10 seem to fail.
You know what challenges haveyou had with AI?
Speaker 2 (06:57):
So, you know, you hear this thing oh, ai, let's
sell.
One of the things about this isyou know why AI?
Now, well, one of the thingsthat we I know you didn't ask
that, but it kind of leads tothis piece was, you know, we
felt like, hey, this is outthere.
I mean, you look at theadoption of ChatGPT when it
first came out.
(07:17):
You know now it's kind ofslowed a bit now, but when it
first came out, it was justmassive and we really felt like,
if we didn, which, then we cameinto OK, let's embrace it.
And then you find out that,well, you know, here's a here's
a good analogy with this.
I heard about the.
(07:38):
There was a attorney in front ofthe Supreme Court and in his
brief he mentioned a case lawthat was made up by ChatGPT.
It didn't actually exist, right, and so, you know, it's a
little embarrassing when you'rearguing in front of the Supreme
Court, right, you know?
And, and that's the case.
(08:01):
So in this case, it's people'smoney, right, that's a very
serious thing.
We want to make sure that we'redoing this right.
So we went from, oh, let's makethis available and start
playing with it to OK, wait aminute.
We need to really train this,and the training comes in in
really two forms, and youbrought up one of them it's the
(08:23):
prompt generation.
So what we did was we how do wemake it, how do we set up so
the user doesn't have tounderstand all that and we can
take what they ask and build itinto the prompt that we're
behind the scenes putting out toAI.
So that was one of the reallybig challenges to you know, as
you've said, you know work witha chat GPT.
You had to kind of hone yourskills of how to ask the
(08:45):
question and we don't want usersto have to worry about that.
So we built the in-betweenlayer there that gets us to that
.
And then the other one was howto train it.
On the data, and, just to letyou know, we're sitting on right
now almost two terabytes offinancial fundamental price data
for companies and that isevolving every single day as new
(09:10):
fundamentals come out.
We have the option, we have allthe option prices for every
stock every day back in historyfor like 30 years.
So when you're talking aboutoptions, things having all that
and what the behavior is goingto be, so training it was a
massive amount of work.
That was a big challenge.
(09:31):
Let me ask a follow-up there, just out of my own curiosity,
right?
So it's not just the modelitself.
I mean, you're dealing withthat much data, you're dealing
with real-time data or you knowI'm assuming you're all
SaaS-based cloud-nativearchitecture You're going to
need to scale in terms of yourresponse time, probably
Snowflake around the backend.
(09:51):
I mean, can you give us alittle peek under the hood?
Speaker 2 (09:54):
Yeah, so we have an auto-scaling system that you
know.
We are fully cloud-based onAmazon Web Services, just as a
point on this some of the bestin class in that and they have a
lot of tools in combinationwith what we've done on our side
as to when you need to add andscale up and all that.
(10:15):
We also have some cool thingswhere it's a very strong market
day.
I've seen a few of those in thelast couple of days when you're
seeing a lot of market activity.
We just spin up more serversjust to have it there, because
we know that there's going to bethese waves of people coming in
and out and AI gets hit prettyheavy on those days where you go
.
You know the market just took athree-day tank.
(10:35):
It's a deer in the headlights.
What do I do now?
Right, and those kinds ofquestions coming up.
So we make sure the resourcesare there.
At the moment, one of the thingsthat we do to help with this is
a lot of the data, as I kind ofmentioned earlier on some of
the fundamental data.
You know fundamental datadoesn't change during the market
day.
You know announcements happenpre-market or aftermarket, so
(10:57):
when that happens, we'reprocessing all of that and then
we're feeding that into the AImodels overnight and then all we
have to feed it.
When you ask your question isdepending on the question is
that stock's data or the otherstocks within the industry
group's data for the currentprice data?
So it has that.
There is a fire hose of dataconstantly feeding in and out of
(11:22):
the AI models for that, and youknow one of the things you hear
about about the AI needing somuch power to run and all that
stuff I can tell you that ourserver bills, since we
instituted this, have gone up abit because of the amount of
compute power it takes and theamount of just loading of the
(11:43):
data that happens every singleday and throughout the day to
make sure that it has the latest, up-to-date information so that
when you're getting thoseanswers it's effective.
(11:54):
How do you see AI changing financial technology landscape
in the next you know?
To say 10 years would beridiculous.
I mean, I don't even imaginefive years is challenging, but
I'll go with five years.
So what do you think five yearslooks like?
Speaker 2 (12:08):
So one of the things that to answer that I want to
start with, I want to get ChrisDewey in here too.
So one of the things that toanswer that I want to start with
, I want to get Chris away onhere too.
But one of the things that wewere talking about earlier about
success stories, one of themeasures that we saw with this
was the frequency of trading.
So we have macro data about howmuch users are using AI and
then can kind of do somecomparative around some things,
(12:30):
and what we found is those thathave engaged with AI number one
are trading more than those thataren't.
It makes sense because throughwhat we talked about here, I can
get to the trade quicker.
Right, I have this 12,000stocks.
What am I going to do?
With a couple of questions, Ican hone it in really quick.
So they're making decisionsquicker, trading more and
talking with them.
(12:52):
They feel like they're makingmore informed decisions because
of what this is giving them.
So I think on my side, part ofwhat I think is going to happen
in the near term the couple yearto maybe five year range is I
think you're going to see moreverticals of this right.
You know, you have Waymo.
I live in San Francisco and youknow now it's very common to
(13:13):
see cars driving around with nodriver in the car.
It's taken a while to get there, but that's a very specific use
case, right and the which is alittle.
It's still a little unnervingto get in a car without a driver
.
(13:27):
I will say yeah, I'm afraid of park assist in my car.
I tried it once and I wasfreaked out.
But please continue.
Speaker 2 (13:35):
Yeah.
So I think you're going to seemore of that.
What happened with, if you lookat the internet, where, all of
a sudden, you know when itlaunched and this stuff was
happening, you had all thesestatic websites out there, but
then it was really internet 2.0where all this data started
coming in.
You know trading platformswe're talking about financial
markets.
(13:55):
You know it wasn't just abrochure about the broker, but
you know, now you had tradingplatforms coming into play and
all that.
I think in AI you're going tosee some of that same kind of
into specific tool sets forspecific outcomes.
You know, in the long term isit going to be?
You know, actually, if we goback a couple of years here
(14:15):
there was a lot onrobo-investing.
You know Morgan Stanley and youknow Fidelity.
These companies were investingin the robo-investing thing,
which was simple algorithms toput you in particular buckets to
do certain things right.
So now you know the holy grailthere is going to have an auto
(14:35):
trading thing all run by AI.
You know, back to your parkassist.
You don't want to hit the carright Back to the courtroom.
You don't want to quote a casethat doesn't actually exist to
the Supreme Court and you don'twant to have that happening in
your trading.
So I think it's going to be awhile before it matures enough
that that's trustable.
So that's going to be, I think,beyond the five-year line.
(14:57):
But in the short term you'llsee some of these siloed
vertical use cases.
Speaker 3 (15:02):
Well, and I think, too, one thing that's you know,
we have certain features builtinto our platform where, if you
pull up a stock, it tells youhow many days until the next
earnings report or how many daysuntil the next dividend or
whatever.
I mean, obviously we have acalendar, but there's very
specific points on the platformthat show you, okay, you've got
this stock up, it's eight daystill earnings or whatever, right
, and maybe how many days tillthese options expire, and that
(15:32):
type of stuff.
Well, you could easily seecombining that with some AI, so
that the minute you buy a stock,you get notified when earnings
are getting close, right, and wewon't have to do anything
anymore.
You won't even have to golooking.
You'll get a pop-upnotification or an email or
whatever it is, to tell youwhat's going on in the stock, so
that you're not stuck in itthrough something you weren't
expecting, right?
So that type of stuff, I think,is going to come fairly quickly
as well.
(15:53):
So just to also complete your example, there Rance around
robo-advisors.
I think for the listeners it'salso important to note firms
like Morgan Stanley maybe 18months to two years before
launching, were quoted as sayinghow they will never have a robo
advisors and there was abacklash, as crazy as that was.
(16:13):
So you know, you know, when wetalk about adoption, you know
it's unfortunately it's not.
If it's when, right and andeverybody can reverse on that.
You know.
So here here and I'm going tostay on robo advisors just for a
second because I think it'sreally interesting the big fear
in algorithmic trading foreveryone was all the algorithms
(16:37):
are going to do the same thing,right?
So how are you guaranteeingthat the whole market is going
to move one way and the AI isgoing to be saying just move the
same way?
How is AI going to be one wayand the AI is going to be saying
that, just move the same way.
You know, you know how.
How is AI going to be smartenough, right?
You know the good trader,instinctually, there's always a
winner.
There's always someone on theother side of the trade.
(16:57):
You know, even you know acouple of days ago, when we had,
you know, those issues in themarket.
You know we're still waiting tosee who the winners and losers
are.
You know the guarantee thatyour AI is smarter than the
other guy's AI and it's going tohelp you make the right
decisions.
Speaker 3 (17:13):
Well, let me say one thing about that real quick.
I can tell you this we focus onthe software, right?
We focus on the platform.
We're not a broker, we'rebroker agnostic.
We connect to a lot ofdifferent brokerage firms.
You can trade and have the sameexperience, trading through us
with any of these brokers thatwe connect to through their APIs
.
I would say you're not going tosee the average brokerage firm
(17:39):
I mean, they're so tied up intheir back end technology and
their clearing and all thatstuff You're not going to see a
lot of these features pop upinto the mainstream platforms
for quite a while.
I don't think it's just goingto be too complicated for them
to do it.
So I feel like that's one waythat we're going to be able to
stay ahead is because all we dois focus on the software side of
this.
Speaker 2 (17:58):
And I think that a lot of times, brokers are a lot
more risk averse.
And I mentioned that TDAmeritrade acquired a company I
had back in.
It was in like 2007.
And one of the things that wasvery frustrating to me is when
it launched, they stripped outseveral really cool tools
because they didn't like that.
So one of the things we have inour current platform is a trade
(18:18):
finder tool that will give youa timeline, a price range, how
to structure an options trade onit, and this has been after
years of research and algobuilding that we did to do that,
and this is not AI, this isjust the old, old fashioned
algorithms and really workingthrough this to do and so we can
(18:39):
adopt that type of thingquicker than a broker is,
because they have to walk thatline very careful when they get
into a recommendation and whatthat means, and we can give you
ideas with background on that tohelp you make that decision and
not fall into thatrecommendation or fiduciary role
.
In this area, I think the samething is going to be the case
(18:59):
where you're going to have somehesitation from brokers and if
they're going to, they're goingto have it be kind of watered
down and now, behind the scenes,they might have some money
managers starting to use some ofthese tools and that that
aren't available to you as anindividual, because the money
manager can use it as a tool.
But still watch out what'sgoing on and making sure that
(19:21):
it's the right thing.
You know, like, like, don'tread the brief that you just
quoted to the Supreme Court.
Make sure it exists, right.
You know, don't just trust it,right.
So you know, keep your hand onthe wheel when it's auto parking
.
Make sure that you caninterrupt, right.
So if you do those things, Ithink it's helpful, but it's
interesting to see.
I really think that, first ofall, not everybody has the same
(19:41):
timeline.
Not everybody has the sameoutcome.
You know, when I was a newinvestor, I was willing to take
a lot more risk than I am today,as my portfolio has gotten
large enough and I've gotten afew more gray hairs, that now
capital preservation is a veryimportant part of my portfolio,
right?
So I think that you're going tosee, you know and time horizons
(20:03):
and you know what people arelooking to do.
You know you're three yearsfrom your kids going to college.
People are looking to do.
You know, your three years,from your kids going to college,
how you're going to approachthings might be different than
if you're now an empty nesterand you're looking toward
retirement in the next, you know, 20 years, right?
So I think there's a little bitof having it mold to what your
specific outcomes are, but thisis an interesting area that we
(20:24):
have to watch, and one big thingabout this is you know, tesla's
had a few cars have an accidentwhen they're on autopilot,
right?
You don't want those things tohappen, so you have to really
watch this, babysit it throughthe process.
One of the great things that wehave going for us is that we
(20:45):
have thousands of users usingthis and we can see the
collation between what they'redoing and what the outcomes are
and move and adjust and workwith us.
And you know, right now, if youlook on the site, it's going to
say this stuff's in beta, andit's probably going to say that
for the next six months or sobecause, well, maybe even longer
than that, because one of thethings that happens is we're
getting.
You know, we launched this andright after we launch it, now
(21:05):
there's new models, right, andare these new models better?
Yeah, you know.
So you have to kind of workwith this and it's going to be
an evolving thing.
Here's the thing.
It's here, it's not going awayand we're working to embrace it
in a way that can help makepeople effective in their
trading without them having toworry about that.
(21:26):
We're doing that effort andpresenting tools to them that'll
help their outcome.
And as those tools get better,you know like, hey, there's
earnings season, you're in astock, what else you know?
Based on what's going on inthese other stocks in the same
sector, what's that likely goingto mean for the one that you
have earnings coming up nextweek for Right and be able to
extrapolate some of those thingsout?
(21:46):
Well, the thing I really like about your approach unlike the
robos it's you're making abetter driver, right?
You're not taking the driverout of the driver's seat, you're
just making a better driver.
Speaker 2 (21:58):
Yes, yes, yes.
(21:59):
So, unfortunately, we have reached the final question of
the podcast and I'll ask it toboth of you.
We call it the trend drop.
It's like a desert islandquestion.
If you could watch only one,only one trend in AI financial
analysis tools, what would it be?
Go ahead, rance, and it'scheating because there's two of
you, so you can pick two trends.
Speaker 2 (22:20):
I would say, competitive analysis, so that
when you go into a particulararea, you're diversifying your
portfolio.
I want some pharmaceuticals.
I want some tech.
I want some this, so that whenyou go into a particular area,
you know you're diversifyingyour portfolio.
I want, you know, somepharmaceuticals, I want some
tech.
I want some this how can I findthe best in class in those
different areas?
I think that that's one rightnow.
There was one area that, as itsits today, that I could use to
(22:45):
improve my results.
It's with those different areasI want to invest in.
What are the best in class togo into in those areas and how
to find those?
Speaker 3 (22:54):
quickly.
I think it would be for me.
Portfolio analysis, justbecause it's so important for
people to be able to.
If you've been trading for sixyears, you've got a P&L
statement from your broker Great, so you know what you made or
lost for the year.
That's it right.
You probably don't know muchelse.
Now, with all this AI stuff andwith something like our trade
journal, you can go back sixyears worth of stuff and really
(23:17):
start to figure out what you didright and what you did wrong,
and I think that's going to becritical for people moving
forward.
(23:22):
Well, rance, chris, I want to thank you so much Eye-opening
fun and I wish you gentlemencontinued success.
And when am I going to get myfree trial?
Speaker 2 (23:36):
We got you covered.
We'll take care of you on thatfor sure, excellent.
(23:38):
Well, thank you so much, and that brings us to the end of
today's pod Thank you, thank you.
Thank you.
Thanks so much for listening totoday's episode and if you're
enjoying Trading Tomorrow,navigating trends in capital
markets, be sure to like,subscribe and share and we'll
(24:03):
see you next time.
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