Breakfast Briefing with Q4
Technology continues to transform the world of capital markets as we know it. But it would appear that IR professionals have to catch-up with the buy-side. While investors are sparing no expense at collecting big data on the corporates they back, most IR professionals are hardly exploiting the potential for this kind of advanced intelligence to understand and predict investor decisions.
Hosted atop London’s iconic Gherkin building, Q4’s latest “Breakfast Briefing” (here’s the first talk in the series) focused on the impact of big data on the capital markets and what this means for the role of the modern IRO. The majority of the audience confessed that they weren’t yet using big data analytics to guide their IR programs or investor-targeting strategies. But they also agreed that big data (and its associated technologies) aren’t just a passing fad. The panel of IR experts featured Chief Data Evangelist at DPA, Julian Schwarzenbach, Managing Director for Europe at Q4, Amit Sanghvi, and Head of IR at RWE, Gunhild Grieve.
According to Julian Schwarzenbach, many IR professionals are skeptical about trusting big data analytics. At the heart of it, this skepticism stems from the sheer volume of information and not knowing how big data can effectively be collected and put to good use. That essentially is the job of algorithms and machine learning. “People sometimes lose sight of the fact that the business logic they’re trying to codify is actually very complex,” he explained. “What these algorithms really do is codify the intelligence you already have.”
For Amit Sanghvi, gathering and processing intelligence is becoming a real game-changer for getting an in-depth look into how and why investors behave the way they do. He pointed to Q4’s new AI Targeting tool, which digests more than five million data points on each company it covers. These include fundamental and economic data on an issuer (such as last quarter’s dividend yields and returns, versus its associated sector benchmarks) – over an unprecedented period of the last ten years. Combined with data on each fund or institution’s portfolio of holdings, Q4’s proprietary algorithms can process all of these moving parts, to form meaningful insights about decisions money managers make.
“AI Targeting ingests all of the data of a given fund manager’s portfolio and what he/she bought and sold at different points in time,” said Sanghvi. “It then combines that data with information about each company and the historical macroeconomic environment, to determine the underlying decision-making process for that fund manager, when he/she bought or sold a particular stock.” At present, AI Targeting can generate a detailed list of potential investor targets, rated between one and 100, along with the five top factors that drive their investment decisions.
This not only represents a next-generation method to formulate investor targets, but also redresses some of the balance lost, in an age where investors fuel their decisions by exploiting non-financial disclosures and other personal information. In fact, your company may not even be aware of disclosing this kind of big data. Sanghvi cited the example of satellite photos of supermarket parking lots, which offer clear indications about how a business is doing; and ultimately, how likely a company is to meet its valuations.
Gunhild Grieve stressed the importance of being aware of these ‘alternative data’ sources to understand how investors perceive your company. “There might even be other data points you’re not aware of that analysts and investors will know about,” she explained. “Other people will draw their own conclusions and even publish on it.” Grieve cited an incident in which RWE’s shares were trading at a premium of up to 40 percent, ahead of some strong Q3 results. This obviously seemed like good news at the time, but on the day the results were published, the firm’s stock price nose-dived. “Talking to investors, we found out that although we had performed well, it was mid-November and many investors didn’t see the potential for much more upside. Many made an exit to sure-up accounts for year-end (with the intention to buy back in the new year).”
AI tools have the potential to predict these types of investor behaviours. Ultimately, IR teams will be able to prepare for the risk of share price momentum slowing down, or for the worst case scenario, a bubble bursting. This is particularly crucial in our post-MiFID II environment. With the sell-side’s new limits in offering corporates investor targets (thanks to fewer direct conversations with investors), we truly need advanced big data tools to fill the notable gap in investor outreach – especially at investor meetings.
But while big data analysis is becoming ever more sophisticated and practical, IROs are still integral in making sense of the data and turning insights into action. For Grieve, it’s absolutely crucial to integrate big data with your own input. She cites the example of turning the tables on investors, to gain additional insight into how her stock is held. “You can turn to investors and ask, is this correct? How much do you own? Why have you held, bought or sold?” Grieve explained. “You’ll be surprised how much information you can obtain.”
The buy-side may have their robo-advisers, but IR professionals can breathe a sigh of relief that they won’t be replaced by robo-IROs. “Modern IROs need to combine advanced intelligence with their own insight.” Schwarzenbach added, “data is a very powerful enabler for what you want to achieve, but it’s not the whole answer. An IRO’s skill set, experience and expertise are still required to turn these powerful sources of intelligence into effective action.” Sanghvi concluded the conversation, “the key to optimizing big data analytics is saving yourself time and freeing up your ‘mental load’, so you can put the relationship back into investor relations and be the best at your job.”
Marla Hurov is the Content Marketing Manager at Q4 Inc and blogs regularly about trends in IR and digital communications.