Funds have long used AI and big data analysis alongside more traditional research analysis. There is certainly nothing new in this. It was probably ten years ago that large US banks turned their attention to data scientists over finance majors.
But as Robin Wigglesworth wrote in yesterday’s FT (Stockpickers turn to big data to arrest decline) it has not all been plain sailing, with some funds having more success than others.
Clearly with so much data available, having the ability to break such information down to bite size chunks can be hugely valuable. There is a proprietary edge to having your own big datasets. But it also comes with its own set of complications, one of which is integrating alongside more traditional research and deciding on how much weight you give to one over the other.
Speaking to a well-known manager last week, he pointed out that all too often we can over-complicate the simple things in life and while he agreed that there is value from big data, and certainly used such data, it is the small datasets that can be every bit as valuable as the large.