The so-called march of the robots has totally captivated the public’s mind, especially over the last year. Technologies like Big Data and AI have taken the worlds of industry and finance by storm: their potential is no longer just the talk of tech-savvy experts, but of global leaders and business moguls too.
But there’s something about this year that seems to be making people lose their heads over these disrupting technologies. There is a well-established cycle that many of these mini-revolutions go through: a concept gets dreamt up, then developed, then it soars into the limelight as ‘the next big thing’ before being deflated as reality fails to live up to expectations. Gartner’s Hype Cycle for Emerging Technologies perfectly captures this trend in the business world:
It wouldn’t be fair to call Big Data or AI ‘buzzwords’ any more – but could the finance industry be in the midst of a hype cycle when it comes to their potential to revolutionise the world of investment and research? It seems so, and this is especially down to how automation is breaking out of the factory and into the office, whipping up traditionally high-skilled sectors into a frenzy as algorithms seem to be outpacing and undercutting the human mind.
Getting Carried Away
The World Economic Forum in Davos, for example, hosted hours of discussions on how to handle unprecedented rates of automation. Kai-fu Lee, the influential tech VC with 50 million social media followers in China, was confident that “pretty much anything that requires 10 seconds of thinking or less can soon be done by AI or other algorithms.” Elsewhere, WEF founder Klaus Schwab told Sergei Brin how a prime minister of “quite an important country” told him “there are three powers left in the world – one is the US, one is China and one is Alphabet.”
These world leaders aren’t getting excited because they’re tech geeks, though. Anyone with a passing interest in the matter knows that Big Data’s potential has been well-appreciated since the Noughties, and automation has been replacing people for decades. The two fields were always on a collision course, but it’s only now that the sparks are flying.
Big Data used to be seen as a headache by both the science and business communities: the only question anyone had time to ask was how to handle it, let alone what the best use of it was. AI was for a long time confined to replicating processes that humans already fully grasped, with machine learning too raw to start taking advantage of information that we don’t already know how to handle.
In fairness, we still haven’t got very far. The difference now is that, in many industries, information technology has tipped the balance between opportunity and cost enough to spur large-scale changes in strategy. No sector is a better example of this than banking and finance.
Improvements in data processing and trading algorithms, for example, seem to be turning the industry on its head, making quantitative trading, investment and research strategies look more worthwhile. Investors have started thinking more critically about the fees they pay to fund managers.
This has plunged companies with human-intensive approaches into difficulties – Paul Tudor Jones’ hedge fund, for example, had to lay off about 15% of its workforce last year amidst $2bn of investor withdrawals – whilst firms that can afford to are shifting from active to passive management, as exemplified by BlackRock’s massive overhaul announced in March, adding nine quantitative-strategy (quant) funds and phasing out some traditional stock-pickers, in a move that affects some $30bn of assets.
Man vs Machine
But it’s too simplistic to say that the quantitative is winning out over the qualitative. BlackRock’s quant funds are arguably yet to prove themselves. Almost two thirds of their quant lineup underperformed last year, with four of their five main quant hedge fund strategies seeing losses. They were obviously confident enough to start their robo-revamp last month, but the emphasis was on the value proposition of active managers – whether their high fees were worth it.
Hedge funds are still getting returns, after all – posting a collective 2.63% in the first quarter of this year – they’re just not doing as well as the indices, which passive funds track. The S&P 500, for example, saw a 6.07% return in the same period.
It’s simply too early to call a winner in the fight between man and machine. Indeed, in many areas of finance, there probably won’t ever be a clear victor. Data is powerful, but it also has limitations. Humans have their limitations, but in other areas have power beyond even the biggest and most sophisticated mainframes.
The Limits of Data
Out of all the information that’s indexed, we only know how to process some of it. And it will always come down to human decision-making how that information is processed, and what gets done with it.
This is why even the most ‘robo’ of robo-advisers still have finance experts leading their algorithms’ developers. The same goes for other sectors: JPMorgan’s famous COIN machine, for example, still requires the oversight of lawyers and loan experts.
Roles like that are valuable, but the debate over things like AI and Big Data often gets too carried away with them as well. World leaders are worried about the returns from technological advances all going to those who own and run these machines – but this ignores all the information that can’t be indexed by computers, or that can only be made sense of by people.
After all, out of all the information out there, only some of it is indexable. Out of all that’s indexable, only some of it is indexed. This limitation is exactly why clients have come to Third Bridge looking for answers on investment opportunities ranging from a Kenyan oil field left abandoned since 1974, to the publishing industry in Kazakhstan, to the market for instant noodles in Nigeria.
It’s in that information space that there’s still a role for almost entirely human-driven approaches. Again, the AI debate gets carried away with this: the typical argument goes that only emotion-based roles are truly safe from automation in the long run, and that doesn’t sound very relevant to the world of investment. But inter-personal relationships and human experience cover far more than that.
Human expertise allows us to make links that machines aren’t able to, and face-to-face interaction can get us to the source quickly and efficiently. This covers far more than obscure market research, too: it means we can take decisive action when shocks happen in well-researched industries, making sense of them while financial algorithms freak out. While stock-picking algorithms are good at handling day-to-day financial developments like earnings reports and commercial orders, they’re often useless when the stakes are high and the unexpected happens.
Take the Wells Fargo scandal, for example. Following an investigation last September, the bank was found to have been fraudulently opening fake accounts for customers, and as a result saw fines of $185m and high-profile resignations including its CEO.
Investors had a keen eye on the political reaction to the scandal. Earlier this year, Wells Fargo were back in the headlines when news broke that district attorneys in two US states would be investigating the company, and this time it was for improper mortgage lending allegations. Wells Fargo faced another huge hit – $1.2bn in settlement claims, as it turns out.
Naturally, clients wanted to understand the nature of these investigations, and what the possible outcomes might be: was it something that investors who held stock in the bank should be worried about?
Experts, Experts, Experts
We found that experts in banking regulation and compliance were able to shine a light on what was at stake. Specifically, a former compliance and risk manager at Wells Fargo Bank was able to discuss the implications of the scandal – both on the bank itself and on the industry as a whole – and a former senior vice president at PNC Bank had insights on how client retention reacts to reputation-damaging scandals like these, having plenty of experience on how sticky retail banking clients are to their banks.
Think about what a computer programme would need to even get close to that kind of insight. Even if we assume that we might one day be able to quantify reputation and client ‘stickiness’, the programme would need reams of similar cases to build up a picture that can be applied to new cases. How often do scandals like this erupt? It would take decades to craft a system that even remotely matches up to human experience. And that’s without even starting on those assumptions.
Ultimately, there’s a reason why most investors still trust humans above all else, and it’s not just that they’re slow to change. They already know and trust the value they get from other people. A CFA Institute survey last year found that in the UK, 69% of investors believe they will still prefer the help of an investment professional over the latest technologies and tools for the three years after they were polled. This figure stands at 73% in the US, and as high as 81% in Canada.
Still Our World
There’s little reason to think that this will change much beyond those three years. McKinsey recently tried to weigh up the potential for automation in a wide range of human activities, covering some 800 different occupations in the US. The results for data collection and processing were predictable, but they found no evidence that the use of expertise and managing others could feasibly be automated.
This reflects more or less perfectly what is happening in the investing world. Quant approaches to investment and research stand to be dominated by machines as technology advances, while people will still rule when it comes to using expertise and, of course, other people.
Ideas like Big Data are immensely powerful, and certainly have lots of potential. But if we are just riding the crest of another boom in unrealistic tech expectations, then it’s important to think critically about what one is committing to when trusting investments with data and algorithms, and not get carried away. And even if we’re not in a bubble ready to burst, there is no need to risk putting all our chips on the digital world. There will always be a place for human insight, hand-in-hand with whatever the future brings.
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