I’ve worked on earnings surprise prediction for about 17 years now. In the late ‘90s and early 2000’s as the head of research at StarMine I built innovative models on top of detailed sell side forecasts, using data that was commercially available but had not yet been analyzed sufficiently. And over the last three years I’ve been an advisor to Estimize, whose groundbreaking dataset of crowdsourced forecasts turns out to be consistently more accurate than the pros and represents a logical next step in earnings prediction.
So I’m very excited to announce ExtractAlpha’s recent collaboration with alpha-DNA on the Digital Revenue Signal (DRS). DRS gives us a completely new and unique way to get at the likelihood of a company beating expectations. Rather than collecting explicit forecasts of company financials, DRS builds an implicit estimate of the likelihood of a company to beat or miss revenue expectations,based on trends in a company’s digital demand footprint. That is, it measures changes in the demand for a company’s products and brands, collected from datasets covering Site, Search, and Social trends. Integrity Research, a well regarded equity research-focused blog, recently wrote about this new product.
This isn’t just another Twitter sentiment feed. alpha-DNA collects from a wide swath of different publically and commercially available data sets to create a comprehensive view of consumer demand trends, all with a view towards measuring errors in the market’s revenue expectations. The underlying technology also includes the Digital Bureau, a proprietary mapping of companies, brands, products, and digital properties which allows us to associate these entities with a security. Doing this is a lot harder than it might seem at first, as the Digital Bureau must be both comprehensive and regularly updated.
The hard work that goes into building DRS has payed off. Historically, the top 5% of companies most likely to beat sell side revenue expectations according to DRS’s underlying algorithm did so 70.8% of the time, per the chart below, and the bottom ranked 5% did so only 36.6% of the time. The most recent quarter was no exception, with 70% of the top 5% beating and only 35% of the bottom 5% beating expectations. DRS recently correctly called big revenue beats for FOSL and KORS (both up about 30% on earnings day) and a big miss for Verint (VRNT), which took a 15% hit when they reported last week.
Not surprisingly, DRS’s ability to predict errors in the Street’s earnings expectations also translates into return performance. Below is a chart of a market-neutral portfolio based on DRS deciles going back to 2012, for a liquid US universe. DRS exhibits a Sharpe of nearly 2 and performs comparably across large, mid, and small cap names, with modest turnover compared to other earnings and revenue forecast signals. As far as we know it’s also fairly uncorrelated to other commonly used factors, so it should be additive to most quant processes.
In earnings season a tool like DRS can be invaluable in capturing big consumer data in an innovative yet intuitive way. Several of the top US quant managers are currently evaluating DRS as part of their systematic process. Are you also looking for a way to figure out which companies are likely to beat or miss this quarter? Contact us!