In finance, there is 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.
It has been difficult to get accurate, frequent estimates using alternative data. MIT researchers describe a model for forecasting financials that uses only anonymized weekly credit card transactions and three-month earning reports. Tasked with predicting quarterly earnings of more than 30 companies, the model outperformed the combined estimates of expert Wall Street analysts on 57 percent of predictions. 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.
“Alternative data are these weird, proxy signals to help track the underlying financials of a company,” says Dr. Michael Fleder, a postdoc in the Laboratory for Information and Decision Systems (LIDS). “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. Beyond finance, the model could help social and political scientists, for example, to study aggregated, anonymous data on public behavior. “It’ll be useful for anyone who wants to figure out what people are doing,” Fleder says.
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