Alternative data is proving invaluable in the bid to navigate volatile markets and better understand the scale and depth of the recession that will result from the global Covid-19 shutdown.
Banks have been slow to adopt alternative data and are far behind other users such as hedge funds. Those banks that have invested in alternative data, however, believe that it is invaluable and that we are likely see bank research change as a result of this crisis.
Abraham Thomas is founder and chief data officer at Quandl, a large alternative data provider. Thomas says that over the last few weeks his firm has seen a growing appetite for alternative data from bank in-house research teams that are trying to make sense of Covid-19 and its economic impact.
“Based on our own interactions, I would say this is more a question of when sell-side research will start to include alternative data rather than if,” says Thomas.
“Many banks have been using small but steadily increasing amounts of alternative data in their research for several years now. I expect the Covid-19 crisis to accelerate that adoption curve, simply because the crisis has certain characteristics – speed, uncertainty, unique macro impact – that make alternative data especially valuable right now compared to more traditional data.”
Thomas points out that it’s not just speed. “The granularity of alternative data – the way it can provide a more detailed picture than, say, financial statements – is also a benefit.
“We all know there is a huge economic fallout in sectors like hotels, events and travel, but alternative data can also help figure out the spillover into other less obvious industries. One might assume retail is doing badly, but grocery stores are doing well, as are logistics and shipping. One alternative data source I have seen is pricing for long haul truckers – that gives a key indication of what is happening in logistics.”
UBS is one bank that has embraced alternative data with enthusiasm. Investing heavily, it set up UBS Evidence Lab six years ago. This is now the largest alternative data lab in the world, with 45 labs and hundreds of employees working around the world gathering, testing and cleaning data, and building frameworks. Last year the Lab data fed into over 3,000 research reports.
“What is happening now within alternative data reminds me of what happened with 24-hour news after the Gulf War,” says Barry Hurewitz, global head of UBS Evidence Lab Innovations.
“Up to that point, people didn’t really understand the value of TV news 24 hours a day, but that war was a watershed moment. In the midst of this current crisis, people are having a similar moment with alternative data in that it’s helping people see what is going on in real time. People are now understanding its value.”
Hurewitz says alternative data is particularly helpful when markets cease functioning as normal. “People tend to use past events to extrapolate forwards – to provide some base rate – but we just don’t have that here. As a result, people are having to form new belief systems and recalibrate as more becomes known.
"The more those belief systems represent the reality, the greater the likelihood of making good decisions; and that’s where alternative data can help, by providing real-time facts instead of narratives that have yet to be validated.”
During the last few weeks, these narratives have not always matched reality. Markets have gone up in response to unemployment figures that would have ordinarily caused a selloff. At YipitData, an on-demand data provider, head of marketing, Travis Wittenburg, explains that while one might have thought that meal delivery services would be popular, their data is showing that deliveries have actually slowed.
And Hurewitz points to luxury goods as another anomaly. “Surprisingly, luxury price data in China started going up early in certain categories,” he says.
That’s where alternative data can help, by providing real-time facts instead of narratives that have yet to be validated- Barry Hurewitz, UBS Evidence Lab Innovations
Some data that UBS has been looking at with reference to the economic impact of the virus in China is pollution (a climatologist is on staff), mobility patterns of people in Shanghai, which are almost in line with last year’s levels, and also the number of private jets in and out of Macau to gauge the movements of high rollers.
Hurewitz gives another example of where alternative data has provided insights into risk management and investments. “When the price of oil came down a few years ago, we looked at the regions and towns where frackers were going bankrupt to plot which firms might be impacted using geospatial data. The greatest exposure in a number of towns with high exposure was a hospital company. We can use a similar technique during this period – to understand which companies are exposed to areas of biggest contagion.”
“It’s not for us to determine what that means in terms of economic recovery or whether that’s priced in. That’s up to analysts and our clients,” says Hurewitz. “But we have worked backwards trying to understand how analysts think and what they need to come to their decisions so we can work on getting data, insights and analysis that can help them.”
If the current crisis leads to a surge in demand for alternative data, Hurewitz says that some financial institutions will find themselves lagging. “Two thirds of the data we use we gather ourselves and we began six years ago. If you haven’t been collecting it already, it’s going to be hard. There are vendors of course, but it means the data you will be buying will be piecemeal.
“Having high-quality analysis at scale is hard. It’s a large commitment and the longer you’re at it, the better you get,” he continues. “I think that’s why some large financial institutions that ignored it or saw it as a ‘nice to have’ have now felt it too expensive to get into if it doesn’t support their core business.”
The investment is substantial, as exemplified in the division’s “tear-down lab”.
“We bought a Tesla, a Chevy Bolt and a BMW and took them apart to price every part to understand the profitability. Similarly, we bought ball bearings that are used in wheels and machinery from several different manufacturers and spun them billions of times over months to see which would last so analysts might better understand which manufacturer ultimately had the highest quality ball bearings.”
Hurewitz says the lab also has an incubator team that is tasked fulltime with building new frameworks and scouting for brand new data. “That way in two to three years, half of the data we produce and use will be new.”
Skills and access
Citi began its foray into alternative data in 2017 – initially to analyze the bank’s own data but since expanding to include third party data, such as browsing, travel and website data.
“Back then we assumed we could hire data scientists and be off and running, but that was not the case,” says Rich Webley, head of Citi Global Data Insights. “Not only do you need the new skills – we have seven data scientists on board now – but then you have to access the information. If you are a large firm, you need the architecture, the engineering, systems, legal approvals and discussions with regulators, as well the assurance that all internal rules are being met.
"It’s not an easy lift. But we reached critical mass last year, so now we are at a point where in just a few hours we can spin out interesting analyses of data we are using.”
Webley says alternative data is slowly changing the sell-side research model. “Research departments are fairly antiquated in how they think about information – working on a quarterly cycle and focusing on company fundamentals. This new range of data techniques has been slowly creeping into bank research and the coronavirus crisis has ignited and accelerated that adoption.”
All of a sudden, mutual funds and asset managers want information to help them judge the long-term implications or when the markets will bounce- Abraham Thomas, Quandl
At Quandl, Thomas says the adoption is likely to see banks collecting their own primary data, mashing up multiple datasets, building models or dashboards, making economic forecasts using alternative data, as well as other means of implementation. “Research teams are strongly motivated to add value, not just redistribute raw data or third-party material,” he says.
“I know of teams doing all of the above. At the same time, it’s simply more efficient for these teams to leverage external resources like Quandl where they can, instead of reinventing the wheel for every single dataset or insight. So I expect a combination of in-house work and external collaboration and that’s the pattern we’re already seeing in our sell-side engagements.”
Ultimately the question of whether sell-side research teams will adopt alternative data is going to be driven by the customers for that sell-side research. “Our own experience working with the buy-side firms who make up that same customer base suggests that they do indeed find alternative data to be valuable,” says Thomas.
Citi provides not just its bankers with data but also its clients. At UBS, Evidence Lab has been taken out of the bank’s research division and now provides insights and analytics not just to the firm’s analysts but also its bankers and its clients through the institutional and wealth management divisions. Hurewitz says UBS will even be looking to sell its data to corporates in the future.
“All of a sudden, mutual funds and asset managers want information to help them judge the long-term implications or when the markets will bounce,” says Thomas.
Other useful data during this period has been that around sales of car insurance. “Car sales data is slow to get, but we can see into car sales by looking at sales of new car insurance policies – that dataset updates daily,” says Thomas. “Similarly, we can understand what the financial stress of the economy is by using datasets that measure the timing of small business bill payments. That will give us earlier insights than SBA [US Small Business Administration] or BEA [Bureau of Economic Analysis] data.”
Citi’s Webley points to job posting data as offering insights into which sectors and regions are being most affected and which will recover fastest. “That data is down to the zip code so we can really see into where geographies are being hit.”
Webley also points to data from third party vendors like SimilarWeb that are analyzing cookies from browsers accessing websites and apps around the world. “That browsing activity translates into analysis on company and sector performance. Unsurprisingly, browsing of travel sites has fallen.”
Finally, Webley mentions air pollution data. “The last six weeks of data shows New York City air pollution to be the best it has been in 20 years. Sadly, when that air quality starts to deteriorate we will know that the economy is getting back to work.” That data, he adds, might also support environmental, social and governance investing. “It might encourage more innovation for us to do things differently.”
Where alternative data is lacking, however, is in comparisons with the last financial crisis. “We’ve only had alternative datasets during a 12-year bull market,” says Thomas. “Twitter was just getting started in 2008. Satellite data is only about five years old. We just don’t have granular alternative data around prior crises. It’s always been top down or anecdotal. So I think whatever data we are gathering now will be really crucial in helping us prepare for future crises.”
However, the current crisis is providing a “rubber hits the road moment” for alternative data according to Webley, who is expecting more content to come out of the sell side. But he also says there are challenges ahead. “The discussion of what data is appropriate to use will be ongoing. We have decided not to use geolocation data at present, for example, and data rights will be a key topic particularly if there is a change in the administration in the next US election.
“But I think we have recognized in this crisis that good data is helping us to understand the world. It’s right that we have increasing scrutiny, but I think the momentum is here now that appropriate and high-quality data will become part of mainstream research.”