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Title: The context of earnings management and its ability to predict future stock returns
Authors: Nguyen, NTM
Iqbal, A
Shiwakoti, RK
Keywords: Earnings management;Market anomaly;Stock returns predictability;Earnings management detection models;Real earnings management;Accruals
Issue Date: 16-Mar-2022
Publisher: Springer
Citation: Nguyen, N.T.M., Iqbal, A. & Shiwakoti, R.K (2022). The context of earnings management and its ability to predict future stock returns. Rev Quant Finan Acc 59, p.123–169.
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.
ISSN: 0924-865X
Appears in Collections:Brunel Business School Research Papers

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