Why AI can help you beat the market


“It’s like thousands of traders working around the clock to help us learn what to invest in and when”

Humans have always welcomed other beings in finance: over twenty years ago, some of the best Wall Street traders were outsmarted by Raven, a chimpanzee who picked stocks by throwing darts.

Her index, called MonkeyDex, became one of the biggest sensations at the turn of the century after delivering a 213% gain.

Perhaps because animals are not so easy to fit in offices, people have turned to other kinds of brains to choose equities.

Big institutions are resorting to artificial intelligence (AI) to analyse stocks collating all sorts of information coming from a plethora of sources.

In fact, while investments could previously be assessed based on financial reports and share price movement – what is called structured data – markets have been heavily influenced by unstructured data over the past few years.

These can be anything from earning calls transcripts, major political events but also social media chatter: in 2021, it appears that a tweet by Elon Musk can potentially make or break a stock.

The AI process is entirely rational as it doesn’t rely on emotional reactions or the investment manager’s gut feeling, while its machine learning skills apply previous experience to new data to continuously improve performance.

Who’s doing it

Some of the big players have already established in-house AI research centres, such as and .

In 2019, led a US$72.5mln investment round in H20.ai, a software that helps companies automate their internal processes using AI.

The investment bank said the results with their investee were “promising” and it was planning to look into the use of AI models across the equity trading floor.

Meanwhile, is investigating how to use AI to crack the usually opaque world of private equity to assess risk.

Last year, PLC () launched the AI Powered US Equity Index (AiPEX) family using technology developed by EquBot and IBM Watson.

AiPEX learns from data points such as a company announcement, a tweet, a satellite image of a store parking lot, or even the tone of language a chief executive uses during an earnings presentation.

The information is used to evaluate the 1,000 largest US public companies and select those whose stock prices are poised for growth, with a portfolio rebalancing occurring monthly.

EquBot, one of the project developers, was also the first one to launch ETFs entirely powered by AI in the US.

AIIQ and AIEQ gather information from quarterly releases, news articles, market activity and social media to select stocks with potential to appreciate, all as they keep learning from previous experience.

Unlike other AI-powered funds, which may require big investments to get access to, they can be bought for as little as the price of one share because of their ETF nature.

“We like to start with the analogy that it basically replicates thousands of research analysts and traders working around the clock to help us learn what to invest in and when,” Equbot chief investment officer and co-founder Chris Natividad told Proactive.

“The reality is, it’s more than that because all these thousands of traders and research analysts speak a dozen different languages and oh, by the way, they know what each other knows all at the same point in time, because these models dynamically move and adjust as new market data is piped in.”

But not all AI is created equal, Natividad noted, so investors can rely on different degrees of technology based on their needs.

It’s only the beginning

Equbot reckons that by 2040, 99% of investment management groups will be using AI in some form and others seem to agree.

In a survey on 100 US wealth managers published last December by , most respondents recognised the benefits of adopting AI, but said they were struggling to scale it across their firms.

They said they would adopt it over the next couple of years but were still stuck in the proof-of-concept stage in late 2020.

Up to 80% of managers reported they were either deploying or scaling both client- and advisor-facing AI-powered technology.

Research may prove them right, as hedge funds using AI delivered gains of 34% in the three years to May 2020, compared to 12% across the global industry.

Justina Deveikyte, associate director of European institutional research at Cerulli, which conducted the study, said the figures showed the technology has advanced enough to adapt to unforeseen scenarios.

“There has long been suspicion of the ability of AI to react to unexpected events, such as the coronavirus pandemic,” she commented.

Humans can stay

As enticing as using an artificial brain sounds, the consensus is that humans will still play a big part when it comes to investment.

“There will be always some people who are better than AI… Investment managers should take all the useful tools out there, research, gut feeling, depending on their investment style,” Spiros Margaris, venture capitalist, influencer and founder-owner of venture capital firm Margaris Ventures, told Proactive.

“For me it’s clear that more AI solutions will come in the market to enhance performance, take out the friction. Because we people cannot deal with this kind of fast information as quickly.”

According to Margaris, AI can help for a short-term investment strategy to speed up the analysis process, while long-term investments can be analysed with more calm by people.

Nonetheless, investors should learn more about AI not only for using it as a tool for investment decisions, but also because the very companies they invest in may be applying the technology to boost their own success.

Despite his company is all about AI, Natividad said that people play a key part, whether it’s about interacting with a client, identifying new sources of information or making operational checks and improvements to the technology.

Going forward, he reckons the investment manager of the future will be a blend with a data scientist.

“The data scientist role is becoming increasingly in demand and having subject matter experts coupled with data scientists, effectively allows them to work on other areas of the business that arguably more critical, or more important or involve a greater amount of human interaction depending on the application,” he told Proactive.