Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective

57 Pages Posted: 19 Apr 2021 Last revised: 26 Apr 2021

See all articles by Srikant Datar

Srikant Datar

Harvard University - Accounting & Control Unit

Apurv Jain

Harvard Business School; Microsoft Corporation - Microsoft Research - Redmond; Massachusetts Institute of Technology (MIT) - Finance Department

Charles C. Y. Wang

Harvard Business School (HBS)

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

JEL Classification: E01, E32, E37, E60, M41

Suggested Citation

Datar, Srikant and Jain, Apurv and Wang, Charles C. Y. and Zhang, Siyu, Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective (December 1, 2020). Available at SSRN: https://ssrn.com/abstract=3827510 or http://dx.doi.org/10.2139/ssrn.3827510

Srikant Datar

Harvard University - Accounting & Control Unit ( email )

Soldiers Field
Boston, MA 02163
United States
617-495-6543 (Phone)
617-496-7363 (Fax)

Apurv Jain

Harvard Business School ( email )

Soldiers Field Road
Morgan 270C
Boston, MA 02163
United States

Microsoft Corporation - Microsoft Research - Redmond ( email )

Building 99
Redmond, WA
United States

Massachusetts Institute of Technology (MIT) - Finance Department ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Charles C. Y. Wang (Contact Author)

Harvard Business School (HBS) ( email )

Soldiers Field
Boston, MA 02163
United States

Siyu Zhang

Harvard Business School ( email )

Boston, MA 02163
United States

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