The AI Debate

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There’s a debate raging today about artificial intelligence -- between proponents and sceptics, mainly centering on how it will change the nature of the work that humans do in the economy.  There has been a lot of hyperbole and no shortage of “end-of-the-world-as-we-know-it” scenarios.  This debate has also made its way into investment management – but with a slightly different focus, with a key question being:

Can artificial intelligence power profitable trading/investing strategies?  

I don’t even need to tell you how I would answer that question, but believe it or not, many have come out strongly against trading/investing through the use of AI – and unfortunately, much of the media coverage has been sensationalist, without the technical substance to demonstrate what this technology can and cannot do.  The main thrust of the criticism is that AI is a commodity, deployable by any organization and will provide the same answers to anyone who uses it.  Ergo, if everyone uses it, then it cannot possibly provide a trading edge. 

The problem with this logic is that AI is not uniform – there are countless techniques, model configurations, design choices, and vastly different data.  What data researchers use and how they process it can make all the difference in the world.  How one trains a model, and what inputs are going to be used for training is going to be completely different across asset classes, applications and the interests of different managers.  Different market participants have different interests – e.g. compare market-makers to long-only mutual funds – they look at opportunities differently and are going to deploy technology in different ways.  Could it be profitable in both contexts?  Absolutely.  Like computers in general, AI is a tool, which depends critically on who is wielding it and how.  Not all machine-learning algorithms are created equal.

I think what most invigorates the opposition to AI is a belief that AI is threatening human analysis and contribution – knowledge work in general.  This is not the case yet, and it is not likely any time soon.  Building AI models and processing data, takes a lot of human toil – just ask the team at Greyfeather.  Most importantly, though, machines are not self-aware even if it might be possible to fool humans into thinking that they are.  But let’s be honest about human limitations.  Any given trader, PM, investment manager - how many opportunities can they intelligently look at? Can we reasonably expect them to respond to important signals systematically and effectively?  And do so optimally?

The debate on AI and investment management is really the “Efficient Markets” debate masquerading as something else.  The line of reasoning used by AI sceptics is quite similar to those who refuse to acknowledge the drawbacks of the efficient market hypothesis, i.e. that market prices reflect all relevant information about a security.  Empirical evidence, both published and unpublished, along with recorded market history – the irrational bubbles and severe crashes, makes this argument untenable from our perspective.  We certainly don’t mind though that much of academia along with other practitioners continue to believe in the EMH.  I say believe, because it is an act of faith at this point.