January 2, 2021
The humans vs machines debate continues to thrash and boil in the financial advice arena, almost as though the world at large gave a flying stuff. Old school advisers scuttle for the shadows, knowing their days of poking a moist, signet beringed finger at the yield column in the FT for a slice of someone’s worldly wealth are numbered.
And yet - and yet - robo advice still looks like the awkward kid standing in the corner at a school disco waiting for a slow dance, while the DJ is putting his woofers back in the van. Real people, it seems, aren’t necessarily all hankering after bare bones costs and no-holds-barred automation of the advice function after all. At least, perhaps not in the game-changing numbers envisaged.
I’ve got a young client and he’s about the coolest, most millennial millennial that ever muddled an Old Fashioned. He also happens to be preternaturally wise. A while ago, I explained to him that I’d just been to a platform conference where I was struck by the number of advisers droning on to each other about what their millennial clients want. Automation this, algorithms that, webcams the other.
Except - here’s the thing - this guy, an actual living, breathing millennial, doesn’t recognise that picture. He bristles at the idea that his generation is so passively reactive to technology that they are desperate to abandon the fundamental tenets of human interaction, developed over thousands of years, just because they grew up with smartphones and Netflix binges. I tend to agree with him. As with any age group, some are up for DIY investing and happy to lean on fin tech and some want hands on advice. The market needs to cater for both.
I just read a great book, Garry Kasparov’s Deep Thinking. Notionally an account of his defeat by chess supercomputer Deep Blue in 1997, it’s really a treatise on the dividing lines between machine and human thinking, and a rallying cry for less tension, and more integration, between the two.
Some really interesting parallels between chess and investing emerge. On the one hand, the two things are very different. Investing, for most people, is a loser’s game - in the sense Charles Ellis meant it - in that adequate outcomes can be achieved by sticking to a plan and not doing anything dumb in pursuit of additional returns. Extra risk taking and elaboration are as likely to be punished as rewarded. Investing is also pleasingly democratic. Retail investors with a sensible, patient strategy can, and frequently do, beat clever clogs hedgies with PHDs up the wazoo, just by avoiding the dumb stuff. Cause and effect are murky and poorly understood.
Chess, on the other hand, is a very much a winner’s game. It is in fact a ‘100% information’ game, where both sides know all the information there is to know about the other side, and nothing is left to chance. Cause and effect are laid bare to the well-trained eye. A disparity in skill levels between two players will be mercilessly punished. I couldn’t nick a game off Kasparov if I played him repeatedly for a thousand years.
Where the book gets really interesting is when it talks about the history of computerised chess programming. Kasparov differentiates between type A (‘Brute Force’) programming, where every possible move is considered by the programme, and Type B (selective analysis) which attempts to ape human intuition, using heuristics to narrow the options down to the more plausible moves and only considering these, to reduce inefficient processing time.
In the 1950s, the MIT researcher Claude Shannon declared the type B approach to be the future, assuming that the best way for a computer to win was to ‘out human’ the humans, and that the cruder, less human Type A approach was a technological blind alley. Intuitively, this made complete sense. But it was completely wrong. Eventually, it became clear that Type B programming was the blind alley. It was deceptively hard to get machines to think like humans. The process of replicating human intuition and decision making was a hell of a lot harder than just throwing raw processing power at the problem and letting a computer the size of your living room consider all 1045 possible moves in its monstrous robotic way. And that, essentially, is how Deep Blue beat Kasparov.
It all comes down to Moravec’s paradox. What humans find hard, computers find easy, and vice versa. Let’s agree to stick to what we’re good at.
But what the hell does all that have to do with financial advice?
It is undeniable that technology is changing the nuts and bolts of investing in fundamental ways. Passive funds are stealing market share from stock picking funds in staggering volumes, algos are driving markets in ways that are barely understood. In that context, arguments for trying to benefit from price speculation and all the clever stuff investment management bros do get weaker and weaker. So what are advisers left with? IMHO, it’s all the stuff that computers find hard, of which there is lots.
Financial planning is really all about trade-offs. For example, there’s no such thing as an optimally tax-efficient strategy. What you have is a choice between maximising tax efficiency in your lifetime or for the beneficiaries of your estate. Retirement planning is about finding a suitable trade-off between spending money now and spending money later in your timeline. The most objectively tax efficient approach would leave you miserable in your twenties and thirties, which you probably don’t want. Most people want some fuzzy, slippery middle option that is subjective to them, their family set up and their views on the world. Good luck with that, Deep Blue.
Advisers do, of course, need to understand investing thoroughly, and they need to be smart. But they also need to be good at that uniquely human skill (a point made well in Kasparov’s book) of not just making decisions but deciding when decisions actually need to be made at all. They need to be able to decide which bits of the inexhaustible well of investment data are necessary for good portfolio management and what is surplus, or downright misleading. But they also need to be able to make that data usable to real people with their weird, subjective goals. They need to be navigators at the crossroads where the science of investing meets the art of client goal setting.
As Pablo Picasso, no less, put it, ‘What good are computers? They can only give you answers’.
The value of your investments (and any income from them) can go down as well as up and you may not get back the full amount you invested. Past performance is not a reliable indicator of future performance.
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