What is big data and how
is it reshaping the landscape
of investing?
From personalised recommendations on Netflix to predicting how diseases will evolve over the next century and which drugs will be best placed to tackle them, we are currently in the midst of a data-driven revolution.
The term “big data” has become ubiquitous for describing the increasingly vast amount of information generated and stored through everything from social media feeds to banking transactions, and it has become increasingly ingrained in every aspect of our lives.
Of the 2.5 quintillion bytes of data generated every day, much is available for organisations to mine for useful insights using sophisticated computing algorithms [1]. In the world of investing, the ability to acquire this data, plus advances in computing power which make it possible to analyse it in large quantities, has drastically reshaped the fund management industry in recent years.
While fund managers once travelled far and wide, garnering key industry insights through hundreds of meetings with company CEOs, now they harvest data. “We continuously fetch new data,” says Simon Weinberger, managing director and portfolio manager at BlackRock. “Every day we’re consuming roughly three terabytes. We look at about 10,000 companies globally, and pretty much all the time, you can pick up new information that’s relevant for a subset of those companies, which helps you come up with good forecasts for each and every stock.”
A new look for data
Of course, the idea of data-informed predictions influencing stock picks is far from new. Fund managers have relied on analysts combing through company accounts spreadsheets for some time, albeit on a much smaller scale. But one of the things which has really revolutionised the industry is what fund managers call alternative datasets: reams of unstructured data from sources which would have been inconceivable a few years ago.
Alternative datasets can range from imaging data collected by drones flying over oilfields to footfall patterns detected by smart sensors on busy high streets. This can reveal useful investing information such as retail hotspots in major cities at different times of the year, or the quantity of oil in the hands of a particular company.
These alternative data sources are becoming increasingly powerful because of continuing advances in artificial intelligence, and in particular, ever more sophisticated machine learning approaches for extracting patterns and meaning from them. “The promise of computer vision is to be able to transcribe everything which happens in the world, visually,” says Logan Graham, a PhD student at the Machine Learning Research Group at the University of Oxford. “One of the brand-new trends in investing is estimating important economic indicators using things that we can see, for example through satellite images.”
In addition, progress in other fields of machine learning is turning existing but previously relatively uninformative information into valuable data streams. “We view language as a data source, whether that’s social media or forums or how public companies report,” says Ben Hookway, CEO of Relative Insight. “This is data which is largely overlooked at the moment, so we’re extracting business value out of that.”
Transcripts of conference calls with company CEOs have been around for decades, but now, due to improvements in natural language processing, fund managers can use these to make predictions regarding a company’s future performance. “We can go back through 10 years of transcripts and look at the companies that have succeeded in improving their cash flows,” Weinberger says. “What did those CEOs talk about? What kind of language did they use? And then you can use that to get a view of who will be the winners next quarter.”
DATA COMES WITH A PRICE
But with more data comes more challenges. Fund managers feel compelled to continually invest in purchasing more alternative data sources to keep improving alpha, but such resources don’t come cheaply. Weinberger estimates that his team pay fintechs approximately $10 million each year for data which helps them forecast stock returns. This expenditure can make it difficult to keep fees down in line with client expectations.
“Clients want good investment performance at a low fee,” says Brad Betts, managing director and data scientist at BlackRock. “That’s hugely important, so we’re constantly trying to balance our investment across people, data and technology.”
The greater availability of data surrounding different companies also means that prospective clients are demanding more information from fund managers on the societal impact of their investing. “The new generation are not only thinking about short-term financial returns, but what these companies are doing from a societal perspective, and they think about that to a larger degree than maybe other generations,” Weinberger says. “So over the last few years, we’ve thought a lot about how to measure those dimensions. You need to be able to articulate and demonstrate what you’re doing in that space.”
“Clients want good investment performance at a low fee. That’s hugely important, so we’re constantly trying to balance our investment across people, data and technology.”
Brad BettsFear of the new competition
Such is the value of big data to the new landscape of investing that some experts have suggested that fund managers may face new competitors in the coming years from unexpected quarters, such as Amazon and Google, simply due to the amount of data they hold. But while managers do see this as a risk, they also see it as a potential opportunity. “I think the opportunity for partnership is there,” Betts says. “With the success they’ve had, you underestimate the likes of Amazon at your peril. The understanding of regulation, the language, the way to speak to people, the history that relationship managers and advisers have developed with clients, there’s real and deep expertise there which they have and can’t easily be replaced.”
While there are certainly hurdles presented by the new big data-centric investing world, fund managers feel that overall, the scope for new ways of increasing alpha is far greater than before. “For example, getting a complete picture of the European consumer requires piecing together lots of different alternative data sources,” Weinberger says. “But how many others will take the time and effort to onboard 12 data vendors to do that? So you can either see this as a challenge or as an opportunity.”
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