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

Nguyet Nguyen; Abdullah Iqbal; Radha Shiwakoti

doi:10.1007/s11156-022-01041-3

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

Nguyet Nguyen, Abdullah Iqbal, Radha Shiwakoti

Centre for Sustainability and Responsible Management

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Journal	Review of Quantitative Finance and Accounting
DOIs	https://doi.org/10.1007/s11156-022-01041-3
Publication status	Published - 16 Mar 2022

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10.1007/s11156-022-01041-3

https://link.springer.com/article/10.1007/s11156-022-01041-3

Cite this

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title = "The context of earnings management and its ability to predict future stock returns",

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. ",

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AB - 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.

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