Estimating profitability decomposition frameworks via machine learning: Implications for earnings forecasting and financial statement analysis

Oliver Binz, Katherine Schipper and Kevin R. Standridge

Journal of Accounting and Economics, 2025, vol. 80, issue 2

Abstract: We find that nonlinear estimation of profitability decomposition frameworks yields more accurate out-of-sample profitability forecasts than forecasts from both a random walk and linear estimation. The improvements derive from nonlinear estimation and synergies between nonlinear estimation and profitability decomposition frameworks. We analyze three essential financial statement analysis design choices to provide insights for the practice of fundamental analysis and find robust evidence that higher levels of profitability decomposition, focusing on core items, and using up to three years of historical information improve forecast accuracy. We find that our forecasts predict returns and profitability changes before and after controlling for analyst forecasts and common asset pricing factors.

Keywords: Financial statement analysis; Machine learning; Earnings forecasting (search for similar items in EconPapers)
JEL-codes: C53 G10 M41 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165410125000412
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jaecon:v:80:y:2025:i:2:s0165410125000412

DOI: 10.1016/j.jacceco.2025.101805

Access Statistics for this article

Journal of Accounting and Economics is currently edited by J. L. Zimmerman, S. P. Kothari, T. Z. Lys and R. L. Watts

More articles in Journal of Accounting and Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().