The growth in big data

Every year our use and creation of data grows exponentially. It impacts every part of our lives and is only likely to continue growing, and the investment world is no different. Today I’m going to talk about what has been happening with so called ‘Big Data’, and also take a look at the ever growing environmental, social and governance (ESG) data space.
So what exactly am I talking about when I refer to big data? Firstly, it generally refers to the ability to process and use ever growing quantities of data within investment models by use of new technology. The data itself is also changing. We can split it into two parts, traditional and non-traditional. In the traditional data category, you might include economic indicators, company specific data, normally publicly available, and market data. For this set, what has changed is the volume and velocity of the data and the ability to use it in that quantity. In other words, this type of data has been available for many decades, but now the volume is much higher, and that is something managers are able to process to their advantage with the right systems. In recent years, non-traditional data has been increasingly used. That might include sources such as internet data from social media or search engines, satellite imagery such as number of cars in shopping mall car parks or consumer data including credit card purchases. There is an incredible array of data available to be bought for a price.
Of course, the key with any of this data is to be able to link it to a way of generating alpha – excess returns from investing. Something we’ve also been hearing more of recently is that the non-traditional data has an increasingly swift decay rate. In other words, once something is found to have efficacy and generate alpha, it may not be too long before enough investors catch on to this and the market inefficiency is eroded away. Another consideration is cost. You can buy credit card data, but each set of data is going to be an incremental cost, so it needs pay its way through performance.
This sort of big data usage has been associated with hedge funds primarily, but there are an increasing number of more traditional long only managers who are utilising it. I met with a large asset manager recently who now has a dedicated unit utilising big data across their investment desks. That unit was available to all desks to use if they felt it would add value. They certainly aren’t the only ones in that position. On the flip side, as more and more investors use big data to draw conclusions, certain niche areas of the market might find themselves with less attention. One US small-cap manager we know highlighted spin-offs had become ever more ignored as they weren’t something that data was very good at dealing with, and so for the discerning fundamental investor, it represented an opportunity. I suspect that might be the case with the flow of new special purpose acquisition companies (SPACs) as well. In both cases they are fairly niche areas for alpha generation, more suited for boutique investors.
The other area I wanted to highlight today was the world of ESG data. I wouldn’t include it within the big data sector, but it is certainly something that seems to get more and more discussion at our manager meetings. The first point is that there is a well-known disparity between the views and ratings of the biggest ratings providers in the space, notably MSCI and Sustainalytics. In part, this is due to differences in methodology, but the bottom line is that company ratings can differ wildly. Given how much these are being relied upon, it doesn’t seem very sustainable, pardon the pun.
The other point on data is that, like our earlier discussion, it all has a cost to use, and the more in-depth the data, the higher the cost. This is where both the asst manager and the fund research team have some common ground. Often the best of breed asset managers will buy in data to help them create the best ESG process, albeit some are building teams to create their own in-house ratings. Likewise, we as fund researchers equally are best equipped when we have access to data from these providers to help us ask the right questions of managers in regard to ESG. On both sides of the fence, scale is often an advantage, whether that be bigger teams or deeper pockets.
In a world where data is growing so rapidly everywhere, it is no surprise that the highly quantitative investment industry is expanding its use on a number of fronts. I don’t think I’m going out on too much of a limb to say that the use of big data by asset managers is only set to increase. It seems to be something of an arms race for those that make most use of it, although perhaps as more do, the length of time any of those pieces of data are able to add alpha may continue to decrease. Of course, for some, this may create opportunity, as with the US small cap manager example. Equally, investors willing to look through short- and medium-term noise may find these big data signals create volatility that provides them with opportunities when investing from a longer-term perspective, seeking to own stocks for multiple years rather than timing quarters.
Cost is clearly the other hot topic here. I’ve talked in previous podcasts about my view that we are moving more towards a position where larger asset managers continue to consolidate and use scale, whilst niche boutique managers concentrate on specific areas of expertise. I think the data discussion really just adds further to that argument. The mid-sized asset manager is potentially the loser here, neither able to move with the times and use big data, nor necessarily having that niche expertise. As ever only time will tell whether that is borne out.

Written by

Nick Wood
Head of Investment Fund Research

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