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

48 Pages Posted: 29 May 2019

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: May 11, 2019

Abstract

We revisit the literature on using accounting earnings to estimate firm-level systematic risk. We use macroeconomic indicators to measure undiversifiable aggregate risk; conventional listed-firm indexes reflect an unrepresentative subset of aggregate assets and are expected to substantially mismeasure risk (Roll, 1977). Earnings and macroeconomic indicators are realized annual outcomes that are well aligned for capturing the contemporaneous co-movements that underlie systematic risk, whereas stock returns incorporate changes in expected future outcomes. The macroeconomic indicators we use reflect changes in aggregate supply and demand, providing a parsimonious model incorporating the two fundamental determinants of aggregate outcomes. We find that firms' earnings-based sensitivities (betas) to aggregate supply and demand shocks are negatively correlated, and explain twice the cross-section of returns as conventional "index" betas. They are correlated with firm characteristics employed in empirical asset pricing models, and explain one third of the explanatory power of those characteristics, suggesting that at least part of firm characteristics' predictive ability is due to their correlation with systematic risk. These results provide a theory-based equivalent to the empirically-based Ball, Sadka and Sadka (2009) results that principal components of earnings are correlated with principal components of returns, and explain a significant portion of the returns cross-section.

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 (May 11, 2019). 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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