The context of earnings management and its ability to predict future stock returns

Nguyet T. M. Nguyen (), Abdullah Iqbal () and Radha K. Shiwakoti ()
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Nguyet T. M. Nguyen: University of Roehampton
Abdullah Iqbal: University of Kent
Radha K. Shiwakoti: Brunel University London

Review of Quantitative Finance and Accounting, 2022, vol. 59, issue 1, No 5, 123-169

Abstract: Abstract This paper constructs a signal-based composite index, namely ESCORE, which captures the context of earnings management. Specifically, ESCORE aggregates 15 individual signals related to both accrual and real earnings management based on prior relevant literature. After establishing that ESCORE is capable of capturing the context in which earnings management is more likely to occur, the study finds that low ESCORE firms outperform those with high ESCORE by an average of 1.37% per month after controlling for risk loadings on the market, size, book-to-market and momentum factors up to one year after portfolio formation in the UK. This finding implies that investors tend to ignore the observable context of earnings management. In addition, with ESCORE model, investors do not need to estimate the magnitude of earnings management, rather it is sufficient to look at the surrounding context to differentiate between low and high earnings management firms. Finally, when tested using the US data, most of the main results of the study appear to hold.

Keywords: Earnings management; Market anomaly; Stock returns predictability; Earnings management detection models; Real earnings management; Accruals (search for similar items in EconPapers)
JEL-codes: G14 M41 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11156-022-01041-3

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