Our primary goal is to help you to understand FinTech and to become more confident and persuasive in your ability to analyze and make recommendations to executives within the finance industry regarding how to react to these changes. This FIRST MOOC ON FINTECH IN ASIA-PACIFIC offered by HKUST presents the views of several professors from the top business school in Asia as well as perspectives from industry professionals. HKUST has been ranked for many years as the number one EMBA program in the world, as the number one Finance program in Asia, and as the top MBA program in Asia by multiple independent rating and review journals and surveys. This course offers the combined strengths of HKUST in Business, Finance, and Technology as one the world's leading academic institutions on an important area of technology and business innovations. For students from outside Asia, this also gives an insight into changes happening in the most advanced FinTech markets of the world, since Asia leads worldwide in FinTech adoption and creative innovations. This course (FinTech Foundations and Overview) is part of the HKUST FinTech specialization, there will be 3 other courses in the specialization including FinTech Security & Regulation (RegTech), FinTech Risk Management, FinTech Innovative Disruption: Implications for Society. The FinTech specialization will be launched in Fall 2018, stay tuned!
2.8: Equity, Trading, and Investments (Part 1)
Loading...
From the course by The Hong Kong University of Science and Technology
FinTech Foundations and Overview
13 ratings
From the lesson
FinTech Business Applications
The Business Applications of FinTech are essentially the Fin part of the FinTech world, and in this module, we will examine applications across six categories of FinTech, including lending and personal finance, crowd-finding and business financing, payments and retail transactions, equity, trading and investment applications, various types of cryptocurrencies, and banking infrastructure and tools.
Meet the Instructors
Theodore Henry King CLARKAssociate Professor
Information Systems, Business Statistics and Operations Management Department
Welcome back to our course on FinTech Foundations and Overview.
In this module, we're going to be talking about FinTech applications or business,
and specifically, we're going to talk about equity,
investments and trading as part of the FinTech world.
I have with us a guest speaker today again,
CEO of his own company in the FinTech market space, developing apps,
also, working with a number of companies,
in financial services areas related to FinTech opportunities and business challenges.
That's Hilton Chan.
Hilton is an adjunct professor at HKUST.
He's busy running his own company but occasionally,
shows up here to help us out with his course.
My pleasure.
Yes. I am going to briefly introduce the first company,
and then I'm going to turn time over you to talk about the second two.
Robinhood is the B2C space.
Robinhood is a trading platform that allows investors to buy and sell stocks for free.
No commission on their smartphones.
What's cool about Robinhood is,
it is a smartphone app,
and it is very much an app business.
It's easy to use.
It's really good for consumers and it's free.
But it's not necessarily the part
of FinTech you would get excited about as an investor, is it?
Yes, for a professional investor,
what we are concerned is about low latency and also,
we do build our own programs.
We have our logics,
whether we apply in machine learning,
decision tree, AI or whatever.
So, if my son wants to buy a 100 shares of Apple,
Robinhood might be a really good app.
Because you often buy it at commission free.
Yes, that's right.
It's easy to use, it's easy to buy and sell.
Retail stock probably, yes.
But if I'm trying to buy and sell Apple stocks 50 times a day
A, I don't want to use a smartphone app for that and B,
I may be much more concerned about speed of execution,
than I am commission.
Yes exactly.
I mean normally when we use any trading platform as a professional investor,
we have to test whether what we see will be get.
The price on the market,
are we actually getting that price at a low latency level?
And also, the spread of it.
I mean let's say if I make a market order of a particular amount,
would there be a lot of spillage?
That slippage be it builtin,
could be somebody saying that I claim that I don't collect any commission.
But actually, the spread actually collects the amount of it.
So, that's the full professional traders we are concerned about.
So, that could be of concerned concern about
Robinhood as a professional trader or you might say,
"Yes, you're not charging me the commission, but if you're slower,
then some other sites you might be charging me more than what
a relatively low commission fee would be with high transaction velocity.
Right.
And so now, Robinhood says we don't do that.
But, we don't know what the backend they're using,
DOS maybe Robinhood is perfectly authorized to be wonderful.
Maybe, yep.
But maybe the platform they're using is making
some profit off of a spread or off of latency.
Now, the other thing about Robinhood is they do make money on margin.
They will take the money that people leave in their account that are't using,
Robinhood get to use that for free and they can earn interest on that.
But they can also loan money to people,
who are buying on credit and they can make money that way.
So, it's not like Robinhood doesn't make money.
But, what you said to me about bankers earlier was interesting.
We were talking about this, and you said,
you expressed an opinion about traditional bankers and their fees.
Yes exactly.
Which is what?
In this traditional world,
especially when you turn on the TV in the morning there's a lot of analysts
telling you what is the macro position at the market,
all these sort things, how are you going to invest?
What stoke has the potential? etc.
At the end of the day right I find it quite interesting,
is why would the investor identify a number of good stocks and they say that,
"Oh, I can never invest on any of it.
I declare that I don't invest."
But, if it is a good analytical thing,
I mean if you've done a really good assessment of those stocks,
why would you not invest in it?
That's why I say.
Why wouldn't she?
Why wouldn't? Exactly, yes, why would people.
Warren Buffett says, I like this stock.
He buys it. He's not writing report about it.
He's just saying I often did it.
I like Coca-Cola, I own shares at
Coca-Cola and therefore Warren Buffett is very credible.
Yes.
An investment adviser you know what I mean.
Yes.
Not that he is on that business,
he just runs it at a fund that people love.
Correct.
Okay, but you also said something else related to the fees.
We're talking about Robinhood and how it's a great service,
and a great feature, a great site.
And you said, "But any bank could do that.
They can just cut their fees.
And Robinhood could make money on the margin so can traditional bankers."
But you added something about banking fees today.
Yeah. My concern is for Robinhood,
if suddenly the bank insider also lowering the fee,
then what would be the competitive advantage of Robinhood?
Will it just purely offer commission free?
That's why you see the other potential FinTech company
offering something a little bit more.
Like that's why I love the Algo Developers.
So just being cheaper might worry you?
Yes, I think I am not comfortable.
Then again, my view of this is being free is not a position that bankers like to go to.
Oh, okay.
I mean they do, they offer me free checking,
they offer free bill pay.
But not happily and not usually as a starting gambit.
They're usually making all the money Robinhood is and more,
because they're charging me a lot and they're getting paid a lot.
Your comment earlier to me was getting rich,
they're charging whether they win?
Whether they lose? Whether it's good?
Whether it's bad? Or charging money time.
And this makes banking very profitable.
One of the things we're seeing with Robinhood or other FinTech startups is,
they're not going to be so profitable.
Once these startups either take a lot of market share
or you have to compete with them by
giving things away for free that you're getting paid for now.
This makes investment banking much more stressful because the profits aren't there.
Yeah. Because, I mean, the banking do provide some value added services,
like they have financial advisers.
They help to do your portfolio management.
They would advise you when is the best time to invest.
Right.
When they would consider it's a bit of high risk.
So, that kinda gives us a way from Robinhood and into eToro.
Right.
Because eToro provides you with financial advisers.
Right.
How does eToro do that?
Now, basically, in eToro,
they provide social investment platform.
So that, let's say for example,
if I'm really a good trader,
I claim that I always could help my clients win money.
Then what happens is I could register with eToro.
And eToro is a platform that confirmed that I'm
not just saying something but I'm actually walk my talk.
So, actually I do real trading.
Because some people commit fraud and they say,
buy this stock because it's going to go up and then they say
to another group of people buy the stock because it's going to go down,
the people that you gave the good advice to you
send the next newsletter out to and then the next newsletter.
And before long, whether it's stock trading or game sporting picks or something else,
you're just playing a scam.
And at the end of the day, they only pick those winning selections.
And then you say, now,
I want to charge you because see how good I am.
Yeah, exactly.
People do that in the newsletter business.
Put out 10 newsletters,
one of them is bound to be good.
That one you're really pushing south.
Exactly.
Or if I were to go into investment banking, 10 mutual funds,
nine of them are dogs,
one is a superstar,
and I get all the money jumping into the superstar.
And the problem with that also is last year,
superstar is next year's dog.
Yeah.
This is really not a great thing.
Right on the dot.
Yeah, I raise a lot of money because I know how it is funded last year.
That's why the market demand that there is
a platform to ensure the integrity of those traders.
So that I really know what you're really, really doing what you say.
You really put your money and you really do the trade.
And there is a system that is a trust the party that you could really monitor my trade,
not I self-claim that I'm a really good trader for the past 10 years.
So I can have a longer term perspective and really look at this and say,
these people are really, really good and I want to follow them.
Follow them and you can subscribe to it.
So I can subscribe to an investor?
Yes, exactly.
Once you subscribe and follow it and basically copy every action of it,
because you look at its past record and doing it
in real time and there it's a platform that
basically it's like kind of auditing on the trader's performance real time.
And they allow you to copy the other investor easily so that
you could invest your portfolio the same way this guy is on their network.
Right. So, for example,
it's like those online game thing.
If I say that, okay, I have analyzed the market and I showed you how this is performance.
Now, I'm going to buy 1000 stock of Apple.
And then, he click it and buy it.
And you actually see the real actual trade of a successful trader that he's doing.
And if I was really following him,
I could be doing the same trade at the exact same moment.
Exactly. You could trade real time at the same moment.
And so that's interesting.
Now, there's another model,
which is more like your company is Quantopian. What are they doing?
Now, basically, when we look at a trader,
we would look at any successful trader,
we do find out a lot of them,
they have structure. They have logic.
It's not just a gut feeling saying, oh,
I'm going to buy this or buy that and just something out of the blue.
Now, despite that some of them may,
subconsciously, they say, oh yeah,
I have that feeling.
But actually, we try to quantify it.
So, it's something like quantity to finance.
So, we try to qualify it into a mathematical model.
Quantopian is one of the mathematical model.
Mathematical model, statistical model, AI model.
Which contrives your trading.
It could be gut feel.
I feel like buying Walmart today.
Yes.
And why do you feel like that?
I don't know. It's the weather.
It's my mood.
I seem to be really lucky and so I'm betting on black today.
So, Quantopian opening platform.
Quantopian says I've got to have a model in mathematics expressed in code.
Right. And they open the platform for a lot of those graduate students,
graduate from quanfinance, computer finance, math, statistics.
They build their model there.
They build their quant model.
Once they build their quant model,
they can upload a source code on it and they do testing with historical data.
So for example, I mean,
you cannot really pass a real person trader.
Let's say, okay, let's do it, for the past 10 years.
Let's rerun the whole thing and test your mental ability.
Your mental ability.
But if you program it,
then then I could run the computer for two weeks.
And I could put the exact same data in which you would be
using using data sources that were historical.
And then I can look and say-
And then just run it. And then after, let's say,
two weeks computer time or two days computer time,
I have the result. Then, I can see-
I could look and say your model over the past 10 years would have performed this way.
This way, yes. Also,
the good part is when we talk about the past 10 years,
it's not that daily data.
We could even go to hourly.
We could go to even minute.
Even go to second.
We could even go to every tick.
So we are talking about an every day.
But one of my concerns about this is how
do I know that the future is going to be like the past?
Right. Frankly speaking, at the present moment,
nobody will be able to tell you that I know the future.
But basically, we will try to capture all the different patterns in the past.
As an American, I would say,
I've never seen a Donald Trump before as president.
I don't know what to make of this.
Is this going to be good for stocks or bad for stocks?
I don't know, but it's different.
Assuming that Donald Trump is not so unique in
human history that could be someone that is quite similar to his character or behavior.
Are we talking about Nixon? Okay.
Maybe we could find 80 percent, 70 percent.
Now, think about that stock market.
I don't have to win every bet.
As long as it's more than 50 percent.
Look at all the casinos pay off.
This is a good point. That is a good point.
I don't have to win all the time.
Yes. Especially if it's a high frequency trading.
So, if it's a high frequency trading,
every minute there's hundreds of trade.
And I just need to win 51.
Now, if I'm talking about equal bank.
Now not to mention you can vary your bets, as well.
But you're right. I don't have to get everything right.
It's the law of large numbers, right?
This is a principle I learned years ago in business.
Because I started out learning engineering and
math and science and you like to get all the problems right.
There is a right answer to many things in engineering.
But in business, many times we don't know.
We don't know what's going to happen. We don't know who's going to win.
We don't know who's going to lose.
We make bets.
And we look at statistics and the law.
I mean, I teach law.
Good luck figuring out whether a fair use defense is going to work or not.
Students of mine say I have trouble understanding.
I say, so does the judge.
Welcome to real life.
There's a lot of complexities in a real life and we can't always make projections.
But I can look at any set of cracks and say,
I'm 70 percent confident that a judge would rule this way.
I'm 70 percent confident this company is going to fail.
30 percent chance it might win.
There's some weird ones that have won and I've been very wrong before,
I've been wrong as an investor,
I've been wrong as a consultant,
I've been wrong as a professor.
But I'm right often enough to feel pretty good about it and have made pretty good money,
with being right more often than wrong.
So, that's a good feeling.
Yeah.
Quantopian says, if you could do that statistically,
significantly, and write it into code,
and demonstrate over a long period of time that it was
successful and then demo it for the next six months,
and show that at least over the next six months,
this is working and making money.
And sometimes a lot of people consider that history do repeat itself.
So, some of the patterns, right?
Let's say recently there's a drop in the US market.
In fact, statistic shows that 70 percent of it will recover within a week.
Right.
So, 70 percent they are not saying 100 percent, it's 70 percent.
Now do I have to win on every bet? I don't.
If I could win on astrology,
that on 70 percent,
I'm already very comfortable.
Well, I know-.
Especially if it's a high frequency, it's a law [inaudible]
I know people that would say they only have to win on one bet.
All they're going to do is get bitcoin to go back up.
That's a bit close to gambling.
It is.
We're talking about calculated risk [inaudible]
But, if you could really get a better understanding of supply,
demand, drivers, and people are trying.
Yes. And the interesting about e Toro and Quantopian,
they really disrupted the traditional banking.
A lot of these are really black box to the retailer.
Right.
Now. You go to the bank, you walk in right?
You don't know why Fidelity Magellan Fund put their money in these stocks,
we're just asked to trust.
They got a financial advisor or the venture capital partner is making good judgments.
Why? I'm not going to tell you that I'm not writing all this down.
What are you? Teaching a class or something? Forget that.
But with Quantopian, here's what's interesting is,
if you could put 2,000 traders up against each other.
Right.
And Quantopian says for example,
every week will give the top 10 models a cash payout of 500 bucks.
And then you could-.
Yeah. Well, 500 bucks is not much to an investment banker.
You're not going to be in Magellan Funded Fidelity and say,
I want 500 this week.
Whatever, spent that on lunch maybe.
But what is interesting is I might be able to get
a 1,000 young people excited about developing this model coming up with proposals,
and some of them will be good.
This is exactly like the open innovation.
You open up and then you join talents from different areas, different walks of line.
[inaudible] Thousand blossoms blooms some of are going to be good.
Exactly.
And that's going to be powerful,
and that's hard to do with traditional model.
Yeah.
You might get some people getting lucky,
some funds getting lucky but now we can spread it out.
Exactly.
And as an investor,
how does an investor use Quantopian?
Right. So basically, when you called Quantopian,
with all these Algo Trader,
they debug the model.
They test the model then you can decide who you should impress on.
Now what is Algo? I keep hearing this term, Algo Trader.
And I know Alpo is that something you'd feed your go dog.
What is Algo and what does that mean?
Right. Algo is an algorithm.
So basically, you try to-.
All that means is writing it down Mathematically.
[inaudible] exactly.
Any language, any mathematics could be Algo.
I could write it in Fortran. That's fine.
You could write it in Fortran,
you could write it in C, you could write it PR1, the traditional way.
Or right it nowadays,
most of them is in PR1 [inaudible] PR1 [inaudible] that's an old code.
That's an old code, all right.
So, I know how to do that,
but yeah, I'm not sure I'd want to, but yeah.
Yeah, but nowadays, most of them, they use Python.
Because Python got a lot of library access that you could-.
And I'm surprised to find that Python came out of the 1980s.
I think a Python is new, that it's sexy.
It's current. Python has been with us for a long time.
It has for a while.
Why is python so popular?
Because C's been around a long time,
Python's been a long time.
Many of these languages have very deep historical roots.
C is still very useful C, C++, C#.
They are still very useful language at a very low level.
A lot of [inaudible].
They are not efficient.
Yeah.
Operating [inaudible].
At the low level,
definitely, we use that because we're talking about low latency speed and-.
Why do people get so excited about Python?
I think one of the reason is because there's a lot of
library access that you could use the function, the existing function,
there is a lot of owned library,
a lot of scientific model that you
could use Python that easily that you could further develop it.
So, that's why on the [inaudible] -
So, this is why we like Apple because there's a lot of apps.
This is why we like Android because there's a lot of applications.
This is why we like Linux because there's a huge platform of things that we can build on.
And also they are linked with the Open Source Code.
Not Apple?
Not Apple but for Python, yes.
For Python or Linux, yeah?
Yes. So, that means you could be on other people's tablet and then once you improve it,
then a lot of these Open Source Code available.
So, for programmer, we love that,
because it's like knowledge is free,
knowledge sharing, we want to build something that is useful.
Lots of interesting opportunities.
How big would you say this is in terms of its impact in
the finance community or in terms of FinTech?
Is this as big as payment systems or as big as lending or is this much bigger?
No, I think at the present moment,
there are still regulations regarding to giving financial advice. That is a hurdle.
So, RoboTraders or things like that, there's some issues.
Exactly, for Robo-Advisor which just in general,
advice how do you need to put the money? What portion of that?
Probably, the regulations aren't that straight,
especially for retail investor.
If you are providing financial advice exactly what stocks etc.,
they have a very stringent research on it.
Now, I could see that that part is going to be-.
At the current moment,
it's a hurdle, it's a hurdle.
But I could see that once we involve auditing,
we are even more transparent than a bank.
I can see even the regulator to reconsider whether the regulations-.
Well, certainly a struggle for regulators
and regulators from country to country react differently.
Yeah.
When we look at this whole world of regulation,
we've got a whole course dealing with regulation.
So, we'll push that off to another course,
another module on the whole world of regulation but
you can find even in some countries you may say,
investors in Europe can buy this,
but investors in America cannot,
because regulations don't allow Americans to participate in these markets.
Yes.
Or investors in China cannot buy this.
I think Forex is one example.
Yeah.
Investment in Forex stakes a very high margin.
It could be one up to one to 21-200.
In some countries, they restrict it.
At most, your margin rate is one to 20.
And some say, okay it could be upto-
Well, it's also crowdfunding of startup businesses [inaudible].
Yeah, tons of issues like that. [inaudible]
Accredited investors unless you're dealing with us and
35 in these SCC regulations are good,
for protecting small investors,
but they're bad in terms of constraining opportunities and we have to balance that,
as we want to protect investors.
But many investors say,
"Don't protect me from the opportunities of Bitcoin.
Let me play. Let me participate.
Let me in some other opportunity.
Let me trade Forex.
Let me buy startup companies, if I want to do so.
Why is it that it has to be a rich man's club only?
And not small investors,
but it's a tradeoff for regulators because
there's lots of scams and there's lots of problems.
Yes for sure.
And you've got to balance that.
We'll get to that in our Regtech coarse,
and you'll be back with us,
talking about that as well.
Yes.
Okay. Well, thank you very much for your time,
and we welcome you as students to come back and learn more about financial services,
markets and opportunities following up on this discussion of investments equity [inaudible] .
Explore our Catalog
Join for free and get personalized recommendations, updates and offers.
Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.
© 2018 Coursera Inc. All rights reserved.
