Roboinvesting: What it means for investor relations
3 May 2017
By Adam Frederick
Roboinvesting, or passive investing, is not only relegated to the Wall Street elites. Retail investor-focused firms, such as Wealthsimple, are employing passive investing methods at the portfolio level as well. From a corporate and investor relations perspective, traditional thinking has been to ignore passive investments like ETFs in favour of active investors — where people are making the decisions, not machines.
But those days are over. Today, it’s more important than ever to learn how to deal with machines.
There are two types of funds: passive funds, like most ETFs and index funds, and active funds, such as mutual funds, pension funds and hedge funds — with the term “active,” in the traditional sense, meaning there is a human making decisions about what to invest in.
Today it’s important to understand how quickly the industry is turning into a world of AI-driven “active” investing. Smart machines are working with massive sets of data, seeing trends and uncovering meaning that humans can’t. These machines are then either delivering their analysis to fund managers so they can make final investment decisions or, in many cases, they are making the decisions themselves.
Many still think of machines like day traders making fast trades — but this is short sighted. It’s a mistake to confuse the terms “high-frequency trading” and “quant trading” as interchangeable. While all HFTs are quant-driven, not all quant-driven funds are HFTs.
We’re now seeing how algorithms are becoming the next rockstar fund managers — replacing human fund managers.
So how should IR teams deal with this change?
Data: the output you produce
Ask yourself: what types of data are you putting into the market? It’s helpful not only to think of your standard financial disclosures, but rather company- and industry-specific data, such as the types that insurance companies, retailers, aviation companies and auto manufacturers often put out.
Examples of companies who are releasing alternative datasets include the Semiconductor Industry Association’s monthly report on industry revenue trends, FedEx’s “Statistical Books”, UPS’s KPI report, and Salesforce’s “Interactive Analyst Center.”
If you could influence the inputs of an algorithm that was analyzing your company and industry, what would you want to focus on? While you can’t “engineer” data to make your story sound better, you can make it easier for quant investors and robo-analysts to find the types of datasets that best represent your story and improve shareholder engagement.
Do you know how algorithms are viewing your stock right now?
It’s now important to know how algorithms view your stock as it relates to your peers, both direct and indirect. Direct competitors and SIC codes are almost irrelevant in a data-driven world. In today’s world, your peers are those that you trade alongside with. Your traditional peers are meaningless to the machines.
Knowing how the machine-driven market views your stock helps you understand who you are being stacked up against. Based on quantitative analysis, algorithms come up with a view on your company’s stock and trade accordingly. As an IRO you need to put yourself in a position to best understand and interpret these algorithmic views, and the only way to do that is by arming yourself with similar types of datasets and quantitative analysis through surveillance tools.
How can IROs prepare?
Change is upon us, and considerably more is yet to come. By controlling your “data messaging” and leveraging intelligence produced by the same kinds of technology as the quants on the buy-side, IROs today are becoming “stock price strategists.”
To remain relevant, IROs need to have a better understanding of who their true “trading peers” are, how the market views and values their company, and which datasets are driving the quant trading algorithms.
The role of the IRO almost always comes back to shareholder engagement: balancing investor expectations with management’s vision. When armed with the right tools, you can understand the underlying data and how machines view your company and outlook — and that is key in today’s world, and more importantly, tomorrow’s world.