The Bank of England’s Chief Economist, Andy Haldane, recently launched the Data Analytics for Finance and Macro (DAFM) Research Centre at King’s College Business School.
As just part of his launch speech he said, “To get my punchline in early, I believe the application of data analytic techniques to the many pressing questions in finance and macro holds great promise. That is the reason the Bank of England, around four years ago, set up its own data analytics division. And that is why I very much welcome the setting up of this new centre, as a means of realising that promise. But will Big Data keep its promise? I want to try and illustrate some of that promise of Big Data, as well as the potential pitfalls, by drawing on examples from recent Bank of England research on the economic and financial system. I will conclude with some, more speculative, thoughts on future Big Data research.”
He went on, “If Big Data holds promise, it is probably useful to start by defining just what it is. This is not entirely straightforward. Like beauty, what counts as Big Data lies in the eye of the beholder. It is also a fluid concept. For example, it is clear that data no longer means just numbers; it means words too. Indeed, growth in research on semantics has taken off over recent years, including in economics and finance. What is less contentious is that there has been the most extraordinary revolution in the creation, extraction and capture of data, broadly defined, over the course of the past decade or so.”
“Unlike oil, whose resources are finite, new data is being created at an unparalleled rate and has almost limitless supply. It is estimated that 90% of all data ever created occurred in the past two years. A good chunk has come courtesy of social media. Around 1.5 billion people use Facebook daily and 2.2 billion monthly. In 2017, there were 4.4 billion smartphone subscriptions, more than one for every second person on the planet. By 2023, there are projected to be 7.3 billion smartphone subscriptions, almost one for every person.”
“An estimated 1.2 trillion photos were taken in 2017, as much as 25% of all photos taken ever.”
“A different window on this information revolution is provided by looking at numbers of data scientists. Using vacancies data from the job search website Reed, there were recently over 300 UK job adverts for data scientists.”
“As recently as 2012, there were hardly any. Estimates based on self-identification on social networking site Linked-In suggest there may be upwards of 20,000 data scientists globally.”
“There has, at the same time, been rapid growth in new techniques for handling, filtering and extracting information from these data. Machine learning techniques are developing rapidly. So-called “deep learning” techniques are complementing existing approaches such as tree-based models, support vector machines and clustering techniques.”
“Within text mining, dictionary techniques, vector space models and semantic analysis are rapidly gaining traction.”
“All of these methods offer different means of teasing out information, and making robust inferences, in situations where empirical relationships may be complex, non-linear and evolving and where data may be arriving at different frequencies and in different formats. These approaches differ significantly from classical econometric techniques for inference and testing often used in economics and finance. This revolution in data provision, and in techniques to understand it, offers analytical riches. Mining those riches requires, however, considerable care. For example, issues of data privacy loom much larger with granular, in some cases personalised, data. These issues have, rightly, risen in prominence recently. At the same time as putting it to use, safeguarding Big Data is a key preoccupation of the Bank in its research.”
“So, will Big Data keep its promise? I am optimistic it will. Economics and finance needs to make an on-going investment in Big Data and data analytics if it is to rebalance the methodological scales. And early research, including at the Bank, suggests the returns to such activity could be high, deepening our understanding of the economy and financial system.”
“These returns will best be harvested if there is strong collaboration between statistical authorities, policymakers, the commercial sector, research centres and academia. The Bank of England can play a catalytic role in bringing this expertise together. So too can DAFM. I wish DAFM every success and look forward to working in partnership with you.”
Mr. Haldane also urged bankers to look at the musical mood of the nation when gauging how an interest rate rise could impact on the economy. He cited a recent study by Claremont University tracking popular music against the sentiment of the markets, saying it was just as good as a noted traditional study of consumer confidence.
In his speech, he also said it was “devilishly difficult” to work out the mood of consumers through traditional means.
“These realities may call for exploring non-traditional means of revealing people’s preferences and
sentiments,” he said.
“To give one recent example, data on music downloads from Spotify has been used, in tandem with semantic search techniques applied to the words of songs, to provide an indicator of people’s sentiment.”
“Intriguingly, the resulting index of sentiment does at least as well in tracking consumer spending as the Michigan survey of consumer confidence.”
Mr Haldane also plans to use virtual worlds, like World of Warcraft, to test reactions to economic changes. Bankers are to monitor how players in the “primitive economies” react to sudden policy changes or tighter regulations.
Mr Haldane said it was a “test bed for policy action – a large-scale dynamic, digital focus group”.
In his speech, he added: “And why stop at music? People’s tastes in books, TV and radio may also offer a window on their soul.”