57 Pages Posted: 19 April 2021
Srikant Datar
Harvard University – Accounting & Control Unit
Apurv Jain
MacroXStudio; Harvard Business School; Microsoft Corporation – Microsoft Research – Redmond
Charles C.Y. Wang
Harvard Business School (HBS); European Corporate Governance Institute (ECGI)
Siyu Zhang
Harvard Business School
Date Written: December 1, 2020
Abstract
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the total time-series observations—and apply machine learning techniques to train, validate, and test the prediction models. For near-term (current and next-quarter) GDP growth, accounting does not improve the out-of-sample accuracy of predictions because the professional forecasters’ predictions are relatively efficient. Accounting’s predictive usefulness increases for more distant-term (three- and four-quarters-ahead) GDP growth forecasts: they contribute more to the model’s predictions; moreover, their inclusion increases the model’s out-of-sample predictive accuracy by 13 to 46%. Overall, four categories of accounting variables—relating to profits, accrual estimates (e.g., loan loss provisions or write-offs), capital raises or distributions, and capital allocation decisions (e.g., investments)—are most informative of the longer-term outlook of the economy.
Keywords: Accounting; Big Data; Elastic Net; GDP Growth; Machine Learning; Macro Forecasting; Short Fat Data
