https://www.sciencedaily.com/releases/2019/12/191219132913.htm

In finance, there's growing interest in using imprecise but frequently generated consumer data -- called "alternative data" -- to help predict a company's earnings for trading and investment purposes. Alternative data can comprise credit card purchases, location data from smartphones, or even satellite images showing how many cars are parked in a retailer's lot. Combining alternative data with more traditional but infrequent ground-truth financial data -- such as quarterly earnings, press releases, and stock prices -- can paint a clearer picture of a company's financial health on even a daily or weekly basis.

But, so far, it's been very difficult to get accurate, frequent estimates using alternative data. In a paper published this week in the Proceedings of ACM Sigmetrics Conference, the researchers describe a model for forecasting financials that uses only anonymized weekly credit card transactions and three-month earning reports.