- Global markets are in an unprecedented period of change. This kind of market shift requires an evolved solution that looks beyond the tools and models that were used prior to the pandemic.
- Alternative data and big data models can support sell-side investors’ understanding of the major blind spots that market and economy is experiencing in real-time.
- DIS takes a “nowcasting” lens to provide an additional layer of intelligence into modeling traditional financial markets and collaborates with equity sector analysts to generate ideas and insights.
The growth of RBC’s Digital Intelligence Strategy program
Given how tumultuous global markets have been, there has been no shortage of major market themes that will need a big data lens to try and solve.
In response, RBC recently launched a Digital Intelligence Strategy (DIS) research platform, a macro research product powered by data science and alternative data. Working alongside RBC’s in-house data science team, RBC Elements, DIS has been able to harness the power of big data to unearth actionable insights and ideas by quantifying market inflection points across industries and geographies.
Nowcasting with alternative data
While sell-side research has typically focused around near-term and longer-term market forecasting, since the pandemic, investor needs have evolved. Now more than ever investors need to understand not just what markets are going to look like two or five years down the line, but what they’ll look like in the immediate-term.
“When the pandemic hit, I can tell you that investors didn't care what energy demand looked like two years from now. They cared about what oil demand would look like two hours or two days from now. We started rebuilding our models and discovered the power of nowcasting, and that has since evolved into what we call our Digital Intelligence Strategy here at RBC.”
Mike Tran, Commodity and Digital Intelligence Strategist, RBC Capital Markets
“Nowcasting” with DIS enables more immediate insight for investors to understand how demand is changing in real-time. This is done using a combination of natural language processing and geospatial intelligence, neural networks and other forms of big data that help guide our understanding of what's happening both on and to the earth as it happens.
Data is gathered from multiple sources. These include connecting cell phone pings to car data to understand how people are moving around in real-time and trends around how people are grouping in certain places, such as shopping centers. It also leverages natural language processing – with algorithms that funnel through hundreds and thousands of documents to highlight keywords and phrases to inform trends.
Assessing market inflection points
DIS is tackling some of the biggest problems in the market that have historically been difficult to quantify. Big data allows RBC’s research teams gain a clearer view into the pace of change across themes. With DIS, RBC has been able to build out regularly updated indices that support investors’ understanding of some of the most pressing market inflection points.
A major theme of the past year has been on the reopening post-pandemic, and how societal behavior is changing in real-time. DIS has built our GOAT and our GOAL indices, which stand for “get out and travel” and “get out and live”, and measure various components of consumer or societal mobility.
“As we come out of a pandemic, the reopening of trade has certainly been very topical. You add in inflationary pressures at multi-decade highs, a gummed-up supply chain at historic levels, record gasoline prices and surging food prices, and on and on and on, and this market has really been ripe for injecting a new, a deeper degree of innovation into traditional Wall Street research.”
Mike Tran, Commodity and Digital Intelligence Strategist, RBC Capital Markets
Supply chain disruptions have been another major global storyline of the past year. DIS is using geospatial intelligence to understand the rate of change across all major ports and to build out a metric that anticipates turnaround time flows, known as TOT (time of turnaround). Additionally, across the energy sector, DIS has been able to use several layers of alternative data to better understand demand elasticity. Using layered analysis, RBC was able to augment a view of how consumer behavior was changing and various price points for retail gasoline at a time when prices were hitting record highs in North America.
The future of DIS
Alternative data enables RBC to use expanded layers of analysis to shape how investors think about major problems in the global market. While all sell-side investors require differentiated deep dive analytics, they also care about the level of conviction around that data. DIS’s conviction to noise ratio is extremely high because of the layers and components that are baked into the big data workflows, enabling a clear view into how demand across markets is unfolding in real time.
DIS, in collaboration with RBC Elements, has developed a Next-Generation Elements web portal that updates regularly, allowing RBC clients to see the pace of change across markets using critical indices. DIS can be positioned to tackle many different corners of the market. Here at RBC, we encourage audiences to connect with the DIS team on any market problems or puzzles that could be solved by data science.
