These includechess,arcade games,Go,self-driving cars,protein folding, and much more.
This rapid technological progress has also had a huge impact on the financial services industry.
Among so many successful cases, one seems conspicuously absent: AI making money in financial markets.
However, they do not release official performance information.
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On the other hand, academic research has repeatedly reported highly accurate financial forecasts based on machine learning algorithms.
These could in theory translate into highly successful mainstream investment strategies for the financial industry.
And yet, that doesnt seem to be happening.
What is the reason for this discrepancy?
Is it entrenched manager culture, or is it something related to practicalities of real-world investing?
AIs financial forecasts
We analyzed 27 peer-reviewed studies by academic researchers published between 2000 and 2018.
These describedifferent kinds of stock market forecasting experimentsusing machine learning algorithms.
We wanted to determine whether these forecasting techniques could be replicated in the real world.
In the real world, this isnt likely to inspire investors confidence.
It is also likely to be an issue from a regulatory perspective.
Whats more, most experiments did not account for trading costs.
This would stop the AI from investing in companies that may harm society, for example.
Are humans better?
We also wanted to compare the AIs achievements with those of human investment professionals.
As such, we concluded that there is currentlya very strong casein favor of human analysts and managers.
Despite all their imperfections, empirical evidence strongly suggests humans are currently ahead of AI.
This may be partly because ofthe efficient mental shortcuts humans takewhen we have to make rapid decisions under uncertainty.
In the future, this may change, but we still need evidence before switching to AI.
