No open-access location is known for this record yet.
No open-access location is known for this record yet.
ABSTRACT Researchers typically infer real earnings management when a firm's operating and investing activities differ from industry norms. A significant problem with classifying deviations from industry averages as myopic earnings management is that companies can change their operating and investing decisions for strategic business reasons rather than to mislead stakeholders. Using principal components analysis, we systematically evaluate existing measures and develop a comprehensive real activities measure to better capture earnings manipulation. Our measure reflects (i) deviations from industry averages across multiple activities and (ii) other signals of manipulation. This approach is promising because, although there are many sources of abnormal activities, manipulation is more likely the cause when managers engage in multiple income‐increasing abnormal activities that coincide with other signals that indicate an elevated risk of manipulation. This simple approach results in a metric that associates negatively with future operating performance and earnings persistence, yields high‐power tests, and captures manipulation reasonably well across most life‐cycle stages. Importantly, this approach performs better than the standard real earnings management metrics across all dimensions. Specifically, it generates the expected reduction in future earnings and reduced earnings persistence in 82% of the tests compared to 36% and 46% in common alternatives. Also, because this innovation does not require a long time‐series or rely on future period realizations for classification, it can be useful in more research settings than other recent innovations in the literature.
Loading…
Stored only when both papers are in the corpus — 21 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.