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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. Notably, the analysts had access to any available private or public data and other machine-learning models, while the researchers’ model used a very small dataset of the two data types. “We asked, ‘Can you combine these noisy signals with quarterly numbers to estimate the true financials of a company at high frequencies?’ Turns out the answer is yes.” The model could give an edge to investors, traders, or companies looking to frequently compare their sales with competitors. Credit card data for, say, every week over the same period is only roughly another 100 “noisy” data points, meaning they contain potentially uninterpretable information. Also, including alternative data to help understand how sales vary over a quarter complicates matters: Apart from being noisy, purchased credit card data always consist of some indeterminate fraction of the total sales. Then, it matches the observed, noisy credit card data to unknown daily sales. Using the quarterly numbers and some extrapolation, it estimates the fraction of total sales the credit card data likely represents.