"Man vs machine" on Wall Street is a big topic for arguments. Artificial intelligence is far from taking over the industry of active asset management, but there are certain strategies where autonomous learning algorithms add significant value. Mean reversion statistical arbitrage is one of those strategies. Many quant teams have been actively exploiting machine learning to process big data, extract deep factors and model market impact.
ML powered stat arb has limitations though, it is capacity constrained and requires sizable investments in building infrastructure, buying data, hiring and retaining quant talent.
Quants are not the only users of ML and big data in asset management. Fundamental discretionary hedge funds, banks and even private equity managers have started using products of machine learning in the form of increasingly popular alternative data.
In the future every asset manager regardless of the strategy will have at least some exposure to information products created by applying machine learning techniques to processing big data.