The IR Society Conference 2018 included much talk of disruption and innovation, and how these terms map to changes in the IR world, particularly with reference to the introduction of “new technologies” to the sector. We heard how AI and deep data mining are being applied to activities such as targeting, for example, leveraging off the back of the structural changes to the market brought about by MiFID II.
It is worth considering how useful really is the model of disruptive innovation, the concept of the moment in our sector, but which actually started life in the 1997 book, The Innovator’s Dilemma, by Harvard Business School professor, Clayton M Christensen. The theory proposed by Christensen was that traditional businesses risked being undermined by start-up competitors with new ideas. The old would always give way to the new.
The rocks of hubris
Aside from marvelling at the twenty-year time lag between the publication of this theory and its arrival in IR Land (clearly, disruption is not always a speedy process), we should also consider whether the theory really has legs or whether this is another case of old wine in new bottles.
The theory of disruptive innovation, simply put, is that incumbent firms develop innovation slowly and surely but there comes a point when this development goes further than customer requirements. The next stage is that the disruptive firm arrives with an inferior but cheaper product or service, targeting the lower end of the market. The incumbent firms fail to recognise or tackle this perceived low-grade threat, so eventually head to the rocks of hubris.
This model is somewhat different from what we normally perceive as “disruption”, which is that large creaking dinosaur businesses are undermined by new flexible smaller companies with new technologies, effectively disrupting the existing supply value chain.
Christensen’s theory has largely been discredited in subsequent research studies because, as so often in economic theories, the reality does not quite match the model. Incumbent companies have often proven themselves adept at responding to potential disruption by amending their business model and/or developing resistant technology; client loyalty to businesses or brands trumped the disruptors; low-end low-value propositions often end up being stuck at the bottom of the market while incumbents up-scale their offering to greater profitability; new low-cost technologies are themselves often overtaken by the development of more efficient processes by incumbents; and larger companies buy smaller companies which present a threat to market share.
AI and machine-learning
It has been a constant refrain of technological innovation, and indeed innovation in general, that the new will replace the old but this is rarely the case in entirety. Augmentation is more likely than substitution.
Mapping this back to the Investor Relations space, we can see that there have been major moments of change in the last 60 years in this market. Big Bang was one of them and arguably MiFID2 was another. Both introduced a wave of technological change related to their structural impacts.
The development of artificial intelligence and machine-learning will no doubt have an impact on us all, and hopefully in a rather more beneficial way than some would imagine (if Hitchcock were alive today, he would no doubt re-shoot The Birds with a sky full of drones). We have already seen major developments in algorithmic trading and asset management, alongside the dramatic increase in the proportion of public equity held by passive, quantitative and index investors. It was inevitable that there would be osmosis of these concepts to the IR world, especially at this time post MiFID II when the old canard of IR being as much about marketing as information supply has become a lived reality for issuers. Now, more than ever, the IRO’s job is to hunt down active asset managers and they need better tools to do so.
Technology is only part of the answer, however. Technology remains data-blind for the most part, so even the most sophisticated algorithms or data mining programmes crash on the rocks of poor underpinning data. There is limited benefit in building castles of analytical complexity to guide your IR strategy when you do not really know who are your shareholders or who really owns your peers.
At least in the UK we can get a reasonable understanding of who owns our domestic peers through the public ownership data, but this is a luxury not available to many of our European cousins.
In all markets, and certainly in the UK retail sector at the time of writing, stock-lending further significantly muddies the waters of transparency of ownership. It is difficult to tell from the public ownership data what investors really own when 15% of the share register is effectively missing from the lists.
The proto-fashionable model of predicting future behaviour of funds and fund managers based on past behaviour may ignore the considerable biases of these two factor, leading to misleading data outputs and poor IR strategy. In any case, we should be thinking about the impact of human decision- making processes on active funds. There is a complex relationship between portfolio managers, internal investment management processes, stock selection, asset allocation and risk by sector, market, currency and global politics. The lesson of 2008 is that markets often do not run to plan. Many asset management firms and funds are very different beasts from even just a few years ago due to ongoing restructuring of the buy-side, and the risk appetite of a 60 year old portfolio manager may be very different from their 50 year old self.
Quant only takes you so far in Investor Relations. Active investors are human beings who want to meet another set of human beings, corporate senior management, before they invest, in order that they can get comfortable with them as stewards of their capital. Some portfolio managers are now trained in reading body language to identify if their potential or current investee company senior manager is telling the truth. Conference and video calls have not yet supplanted physical investor meetings, though perhaps the long-heralded holographic technology of the future will eventually suffice.
Building the relationship between the equity issuer and the investor takes time. Trust is usually not built in one meeting alone. It is important that the issuer understands the investor as much as the other way around. The role of IR is to widen and deepen the traction between the two parties through ensuring adequate contextualisation of the equity story. Technology can play an important part of this process by way of use of audio-visual elements such as video and virtual reality.
Innovative thinking around how you communicate your equity story is as important as the use of technology for either market analysis or message delivery. Getting your equity story right in the first place is paramount. No technology, disruptive or otherwise, is going to help if you have no clear strategy or if you lack transparency in your reporting.
Active asset managers’ demands remain simple: they want a clear explanation from companies of how they make their money and how they will keep on making their money. This essential requirement is not going to be disrupted for some time yet.