What costs investors most dearly?
The rogue trader blow-ups, the meme-stock crashes, and the Ponzis may grab the headlines, but for the vast majority of investors – and by extension the institutions that manage their assets – it is the accumulation of minor behavioural mistakes that savage most retail investor returns; their in-the-moment invisibility making them all the more dangerous.
These mistakes are inescapably rooted in individual behaviours, and all the more perilous for their in-the-moment invisibility.
We have estimated the cost over time of poor, emotionally driven investor decisions to be about 3% per year for the average investor.1 This comprises both failing to invest at all, and, when that hurdle has been overcome, investing badly.
This cost should interest everyone because of its sheer size. That’s a huge amount of money! However, what should interest wealth-management firms in particular is that this is money investors are leaving on the table. It is ‘greenfield’ money; it is not money one firm has to compete with another to ‘win’, to ‘redevelop’ under their own banner.
The decisions that lead to this 3% per year are financially poor, but they’re emotionally comfortable.
Which means effective investor engagement – helping investors feel just as emotionally comfortable with the financially optimal decision – can dramatically help reduce this cost.
Our new white paper – Behavioural Engagement Technology: Using technology to understand, map, and improve engagement in personal finance – investigates the nuanced role of engagement in long-term investor returns, and shows how to harness technology to improve an investor’s engagement, their prospects for better long-term returns, and the overall investing experience that dictates both the likelihood of realising those returns, and their enjoyment of the journey.
Not all engagement is good engagement
Failing to invest at all is due to a lack of engagement. Investing badly is due to poor engagement. Which points to a conundrum: isolated tactics to just get investors merely more engaged will only take us so far… and at some point could even backfire.
Because appropriately engaged investors are all alike, but each badly engaged investor struggles in their own individual way, trying to work out what to do, for every investor, every time – to reliably capture more of those lost returns – is impossible without technology.
But not just any technology. Technology built on behavioural expertise, tested on thousands of existing investors, and designed to leverage AI to learn from every investor interaction, to continually refine which prescriptions are promoted for each investor, in each situation.
Oxford Risk’s Behavioural Engagement Technology takes relevant detail about an investor (a range of facts about both their financial situation and financial personality) and uses these to weight possible prescriptions, delivering those that are likely to be most useful for a given investor right now.
What we want to know is how to account for each investor’s financial personality traits, financial circumstances, and their actions (say how frequently they log-in, or trade) in advising on the right next action to take for a specific person at a specific time.
Should we recommend changing the make-up of a portfolio, how decisions are made, the content of a message, the tone, or how that message is sent (e.g. the medium, or the timing)?
Rather than smashing together a hotchpotch of hacks and protocols, we need to start from the understanding that there is no universally ‘best’ engagement protocol. What works for an individual investor is heavily and inescapably dependent upon a complex web of contextual moving parts.
In designing and deploying engagement strategies, it pays to understand the limits of humans to reliably account for these moving parts, and to use tech to overcome them. To understand what both sparks interest, and serves interests. Not least because what engages an investor right now can be at odds with what keeps them engaged over an investment journey.
The understanding of investor engagement is ripe for additional research and refinement, and the use of AI and machine learning to continually fine tune which different types of engagement are most effective for each individual personality signature. Oxford Risk’s Behavioural Engagement Technology is leading the way.
Download our white paper to find out how Behavioural Engagement Technology can help your business get to grips with more robustly, reliably, and repeatedly getting investors appropriately engaged, and keeping them – and their assets – in prime position to prosper.
1 Other studies (e.g. Vanguard, Russell Investments) have come to very similar conclusions. This estimation is necessarily a generalisation, and will differ from investor to investor, and from time to time. It is, however, a generalisation grounded in decades of extensive academic and industry research into the behavioural costs of making bad investing decisions in multiple ways.
