Using Accounting Earnings and Aggregate Economic Indicators to Estimate Firm-level Systematic Risk

59 Pages Posted: 29 May 2019 Last revised: 15 Apr 2020

See all articles by Ray Ball

Ray Ball

University of Chicago - Booth School of Business

Gil Sadka

University of Texas at Dallas

Ayung Tseng

Indiana University

Date Written: April 10, 2020

Abstract

We revisit the literature on using accounting earnings to estimate firm-level systematic risk, using macroeconomic indicators rather than listed-firm indexes to measure aggregate risk. Conventional listed-firm indexes reflect an unrepresentative subset of aggregate assets and thus are expected to substantially mis-measure aggregate and systematic risk (Roll, 1977). That choice dictates using earnings rather than returns to measure firm-level outcomes. Earnings and macroeconomic indicators both are primarily realized annual outcomes and thus are better aligned in time than forward-looking returns for capturing the contemporaneous co-movements that underlie systematic risk. Our macroeconomic indicators are chosen to reflect shocks to aggregate supply and demand, providing a parsimonious model incorporating the two fundamental determinants of aggregate risk. We find that firms' earnings-based sensitivities (betas) to aggregate supply and demand shocks are negatively correlated, and explain the cross-section of returns better than conventional "index" betas. They are correlated with firm characteristics employed in empirical asset pricing models, and explain one quarter of the explanatory power of those characteristics.

Keywords: asset pricing, earnings beta, demand, supply, systematic risk

JEL Classification: G12, M41

Suggested Citation

Ball, Ray and Sadka, Gil and Tseng, Ayung, Using Accounting Earnings and Aggregate Economic Indicators to Estimate Firm-level Systematic Risk (April 10, 2020). Available at SSRN: https://ssrn.com/abstract=3387609 or http://dx.doi.org/10.2139/ssrn.3387609

Ray Ball (Contact Author)

University of Chicago - Booth School of Business ( email )

Gil Sadka

University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Ayung Tseng

Indiana University ( email )

1309 E. 10th Street
Bloomington, IN 47405
United States

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