AI/ML in investment management: Current applications

Now that we have clarified the meaning

of the key concepts such as AI, deep learning, unstructured data. Let's have an overview of

how they're actually applied in the business of investment management. The investment industry is

undergoing a major transformation with advances in information and

data analytics. And this transformation is probably even

more pronounced than in the other sectors that we discussed in this fintech

series like payment and lending. This is because instead of outsiders like

tech startups trying to disrupt the game, investors themselves particularly

institutions such as mutual, pension, and

hedge funds are actively chasing and adopting these new technologies in the

competition for market bidding returns. Consequently, we're at a stage where

even tasks that are traditionally thought of as requiring a high cognitive

level such as asset allocation, security selection, trading and

economic forecasting are increasingly being outsourced fully to advanced

machine learning algorithms. Think about a traditional stock trader,

what image pops into your head? Well, maybe someone who's busy

analyzing stock charts for things like trends, tops,

bottoms, etc, right? Well, some of these

tasks like chart reading can be done much more accurately and

efficiently by computers. New machine learning tools like

signal processing and neural networks can spot much more nuanced patterns in

the data that humans cannot detect. And consequently, these high-level tasks

are increasingly being done by algorithms. This is already happening

in a lot of institutions like BlackRock which is going to replace

more than half of its human traders with machine learning algorithms. More adventurous entities like Sentient

would have hedge funds entirely run by machine learning algorithms

end-to-end from stock selection to portfolio construction and

to trade execution. For individuals,

now they're even online, quote unquote, crowdfunding platforms like Quantopian and

Numerai that aim to crowdsource new investment algorithms directly

from the investing public. Like other funding platforms, they

directly connect the individual traders on one side with capital providing

investors on the other side. These platforms will often get

their financial databases for free to the individual investors

to test their algorithms on. Then, they will pull investor

money to fund the algorithms that perform well in the back test. So overall in the past decade,

we saw AI and machine learning being firmly

established as a powerful and necessary decision support system that help us

make more informed investment decisions. And the corollary is that

data both structured and unstructured are becoming more and

more important and valuable. More complex models need more and better

data to properly train and estimate. And consequently, any new data that

can potentially provide extra insights are increasingly being sought after. And there are even now specialists

like RavenPack that gathers data in real-time such as news and

social media as soon as they came out, process these data in real-time and sell

the outputs to investment institutions. Now to the downside, there are some

common issues with using so much AI and machine learning in our

investment decisions. Perhaps the most important issue to be

aware of is the lack of transparency. A lot of the machine learning

models are well quite complex and are very difficult to understand. Even if you spend the time

to understand them, the results from a lot of these algorithms

are often very hard to replicate. And most investors are not

willing to share their secrets. This again leads to an information

asymmetry problem where you can have an institution claiming to

use advanced algorithms, but in reality are doing none of that or

worse. These types of AI scums

are on the rise and might undermine investor

confidence in AI based investing. A related point is that because

these algorithms are so complex and many are claiming to be highly automated,

many investors might actually be reluctant to turn over their trading accounts

completely over to these algorithms. Research has indicated that

many people have an aversion to too much automation

when it comes to money. I will say, well, are you really want

my money to be managed by a robot? Robots like the ones that we saw in

the movies are either dumb or evil. And I like to have some good

old-fashioned human touch to it. Rational or not, these aversions might pose some roadblocks in the future

growth of AI based strategies. Finally, even if you're

not afraid of AI and you can understand these algorithms, they

still have a really short track record. Most of the strategies launched really in

the latter half of the last decade and we're not sure if their

performance can be persistent yet. To illustrate this, let's compare the

performance of AI based hedge funds with human-based funds and the broader market. This graph uses data from

Eureka Hedges hedge fund performance indices from December

2010 to December 2019. The blue line is the cumulative

performance index of AI based hedge funds. As you can see, indeed, the AI funds

have significantly outperformed the overall hedge fund index,

which is the orange line below. However, the picture is a lot less rosy if

we superimpose the S&P 500 Index to it. As you can see, in fact, both types of funds have

underperformed the broader market. Granted, the AI fund index

is a lot less volatile but depending on which period

you choose is really hard to say which strategies have

a consistently higher sharpe ratio. And this brings us to some predictions

on what might happen in the future for the investment industry in the age

of AI and machine learning. Based on the adoption trends and

limitations, the near future will probably be a period of continued transformation

instead of truly revolutionary changes. Barring any quantum leaps in computing,

pun intended. We'll just continue our current trend. There will be more data available

every day as it has been and we'll have faster computers to

analyze them more efficiently. And we'll continue to bring more

cutting-edge algorithms that are more adapt at analyzing

large-scale financial data. By the same token,

analytics and automation will continue to play a more and more

important role in the investment industry. And the trend of automating away more

cognitive tasks will continue and probably will expand to other

areas in the financial sector. Traditional jobs that are considered,

quote unquote, human centric and safe from automation like sales and

marketing in banks, for example, are no longer that safe. New advancements in natural

language processing for instance, are rapidly enhancing computer's

ability to generate human speech. This has already been applied in many

chatterbox customer service applications and some of you might have

seen those in action. But they're now spreading to even more

advanced areas like writing contracts and generating analysts' reports, threatening

the jobs of some securities lawyers and research analysts alike. And this trend will keep expanding and

we'll see a much bigger role for data analytics, AI, and

machine learning in other investment related peripheral fields such

as regulatory compliance, fraud detection, market research,

and risk management.