For the finance sector, big data keeps getting bigger
The value that can be extracted from a growing wealth of data across boundless sectors is only just beginning to be grasped. If you look at search engines, or digital commerce platforms, an almost direct relationship exists between the amount of data users willingly give up and the value this has. There is also the added fact that those with the most data at their disposal will probably have the best AI in the future, making them nigh-on invincible.
In the case of finance, data of one sort or another has always held intrinsic value. People who trade in the zero-sum game of capital markets all need to have a Bloomberg terminal, or Thomson Reuters data, and look at lots of traditional price information, earnings estimates and so on.
In addition to this, large quant funds have established in-house data science teams performing deep exploration of newer and larger data, and now this trend is spreading to traditional asset managers as well.
Alternative data being leveraged might include esoteric datasets like satellite data, GPS, IoT data used in industry or farming. It might also include so-called exhaust data, a by-product of industries like ecommerce, such as emailed transaction receipts. Another area is sentiment data which has become popular with the advent of social media. And in some cases these datasets can then be combined.
Generally these insights are used to make predictions about listed stocks, but they can also be of interest in areas such as private equity, relating to private, unlisted companies.
Rado Lipuš, CEO, Neudata, which allows money managers to outsource part of the data science function performed in-house by large quant funds, thinks look-throughs into unlisted companies for private equity firms is an interesting new area.
He said: “If you think about it, a lot of the data we see relates to non-listed stocks. It could be Uber or other fintechs or startups, or just privately held companies in various sectors.
“You have a lot of data on those; if they handle transactions, if there are receipts, if they have location data points. We see that you could use that data really effectively for things like private equity purposes, evaluations and so on.
