Machines transform shareholder targeting with highly accurate trading predictions

19 June 2018

By Adam Frederick

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Over the past several months, I’ve spent a lot of time thinking and writing about the impact of Artificial Intelligence (AI) and machine learning on IR and the capital markets. Working in the industry for nearly 20 years, I’ve seen some pretty significant developments, including: decimalization, Reg NMS, Dodd-Frank, Reg SHO, the advent of HFT, dark pools, and MiFID II. Since the late 1990s, the markets have been evolving, slowly but steadily. In 2018, we now find ourselves on the precipice of a game-changing revolution. AI-driven technology is transforming how corporates target, connect with and nurture institutional investors.

 

Old school qualitative targeting based on wishful thinking

Up until recently, IR professionals typically had to match their marketing story with the proper institutional investors, using only qualitative data to consider: are they a GARP name, do they prefer dividends, are they invested in my peers but not me, what’s their potential buying power? The process has lacked any real rigor; typically presenting the same old “top tier” mutual funds and money managers.

Essentially, there’s been no hard data to back-up old-line targeting. IRO’s haven’t been able to actually determine how likely a prospect is to make an investment after meetings with senior management. This means that heading into an NDR or Investor Day, there’s been no reliable way to estimate ROI. Strategies have basically been based on “hope.”

The marketing and corporate access process has also relied too heavily on sell-side input and influence. And having seen NDR agendas for the better part of two decades, it’s clear that the best interests of issuers aren’t always top priority (no offence to my sell-side friends).

The whole point of shareholder targeting and corporate access is to strategically place the right institutional investors in front of the right management teams, at the right time. This is where qualitative analysis falls short. It involves too many subjective and biased perspectives, from client-broker relationships to reliance on legacy processes.

 

AI and machine learning is changing the game

While AI may sound like science fiction, smartphones are already acting like personal assistants, autonomous cars are self-driving, and smart grids are controlling traffic and energy usage. Machines are becoming increasingly sophisticated at dissecting huge data sets, to accurately predict human behaviour. A great example is “smart search.” Have you ever noticed, when you start typing a search command in Google, it starts to autofill suggested phrases (beyond just your search history)? Google’s search algorithms string together keywords and phrases, interpreting what you’re likely thinking.

AI Targeting isn’t much different. Think of it as computers ‘reading the minds’ of PMs. By understanding past behaviours of investors in individual stocks, AI identifies and interprets repeatable patterns and tendencies. In this way, machines can predict, with an unprecedented level of certainty, which funds and investors are likely to buy which stocks, at which points in time. This is a real game-changer, for both issuers and investors alike.

 

Applying AI technology for next-gen targeting and stronger ROI

As an IRO, how do you actually leverage AI technology? An obvious application is marketing. Just imagine presenting your senior management with an NDR agenda comprised of investor names you quantitatively know are most likely to purchase your shares in the near-term. Now, picture each of these target list names accompanied by quantitatively-generated confidence scores, predicted position size, and the underlying key factors that are most crucial to them (including the most engaging parts of your story to pique their interest).

Armed with this next-level intelligence, you can lead the conversation to ensure that you hit the most relevant points for your audience. Ultimately, this means that the issuer also saves time and money, and management can focus on more capital-efficient tasks. For the IRO, this not only frees up valuable budget dollars, but more critically, improves ROI on marketing efforts.

 

Staying on top of the changing tide in shareholder targeting and corporate access

While AI and machine learning are still in their infancy, when it comes to IR and the capital markets, we’ve seen some tremendous progress over the last couple of years. AI is no longer some sort of futuristic sci-fi talked about in Hollywood movies, but practical cutting-edge technology backed by years of research and rigorous backtesting. In fact, it’s AI and machine learning that powers Q4’s latest and greatest breakthrough in shareholder targeting. You can see AI Targeting in action in this 60 second video, identifying potential investors with unprecedented accuracy and speed. As the IR industry and capital markets increasingly adopt these kinds of machine-driven innovations, it’s essential to stay ahead of the game, by starting to leverage them now.

 

Adam Frederick is the Senior Vice President of Intelligence at Q4 Inc and blogs regularly about targeting and corporate access, as well as artificial intelligence and machine learning.

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