Identifying the Relationship between Earnings and Prices

ABSTRACT The relationships between accounting earnings and stock prices, as well as between unexpected earnings and returns, have received substantial attention in the empirical literature. Several theoretical models predict the shapes of these relationships. However, a comprehensive empirical description that could be used to evaluate these predictions is lacking. By integrating recent advances in statistics and machine learning with findings in the accounting literature, we develop an empirical method to identify the relationships, which is consistent with the firm-specific and nonlinear features of the theoretical models. Our approach provides a clear description of stylized and robust patterns in the relationships that are relevant to distinguish between existing models and to aid future theory development. The findings are consistent with recently proposed dynamic option models for both the earnings-price and unexpected earnings-returns relationships. Data availability: Data are available from the public sources cited in the text. A summary of the R code used in the article is available in Starica and Marton (2024). JEL Classifications: G10; G30; M41.

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Publication Details

Journal
The Accounting Review
Published
2024-12-04
DOI
https://doi.org/10.2308/tar-2023-0205
Citations
3
Primary Topic
Auditing, Earnings Management, Governance
Type
article
Field-Weighted Citation Impact
2.84
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article

Identifying the Relationship between Earnings and Prices

Jan Marton, Cătălin Stărică
3 citations
The Accounting Review
Auditing, Earnings Management, Governance
2.84
article

Identifying the Relationship between Earnings and Prices

Jan Marton, Cătălin Stărică
article en
3 citations

Abstract

ABSTRACT The relationships between accounting earnings and stock prices, as well as between unexpected earnings and returns, have received substantial attention in the empirical literature. Several theoretical models predict the shapes of these relationships. However, a comprehensive empirical description that could be used to evaluate these predictions is lacking. By integrating recent advances in statistics and machine learning with findings in the accounting literature, we develop an empirical method to identify the relationships, which is consistent with the firm-specific and nonlinear features of the theoretical models. Our approach provides a clear description of stylized and robust patterns in the relationships that are relevant to distinguish between existing models and to aid future theory development. The findings are consistent with recently proposed dynamic option models for both the earnings-price and unexpected earnings-returns relationships. Data availability: Data are available from the public sources cited in the text. A summary of the R code used in the article is available in Starica and Marton (2024). JEL Classifications: G10; G30; M41.

The Accounting ReviewVol. 100(2)
University of Neuchâtel (CH), University of Gothenburg (SE)
Decent work and economic growth
Openalex Percentile: Top 8%
Auditing, Earnings Management, Governance
2.84
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