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In chaotic systems (like weather or stock market) there is a fair share of randomness, which cannot be predicted no matter what approach we use.

Not to mention huge externalities. In the case of stock markets, things like financial crises, central bank decisions, political events (brexit etc).

Some of those externalities could be predicted by deep learning (the crash of the housing market for example)

I think these could have easily be predicted using common sense ;)