Earnings Volatility and Earnings Predictability

By Ilia D. Dichev, Vicki Wei Tang. 2009

602 citations recorded by the source corpus.

Subfields: Accounting, Finance, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/3cc6cac178bdc5aa84298384562de35a52f54b02

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Citations in the corpus

The perceived credibility of forward-looking performance disclosures (2014, positive): “Earnings variability has been shown to work as an instrument for various earnings quality measures, such as earnings smoothness, earnings predictability and poor matching of revenue and expenses (Francis et al. 2004, Dichev and Tang 2009), that are closely related to management credibility.”
Capital versus Performance Covenants in Debt Contracts (2012, positive): “We measure predictability, PRED, as the R 2 from the following industry-level regression (e.g., Dichev and Tang 2009): 0 1 1 it it it E E        (6) 12 Ball et al. (2008) also use timeliness, the R 2 from regressing stock returns on accounting earnings and their changes.”
Does Tax Risk Affect Investor Valuation of Tax Avoidance? (2017, background): “In addition to the unfavorable outcomes associated with tax risk discussed above, the financial reporting literature documents that earnings with low volatility are more persistent in both the long- and short-term (e.g., Dichev & Tang, 2009).”
Why Have Measures of Earnings Quality Changed Over Time (2014, background): “But volatile earnings are less informative for predicting a firm's future fundamentals (Dichev and Tang, 2009; Barton et al., 2010).”
Nonfinancial Disclosure and Analyst Forecast Accuracy: International Evidence on Corporate Social Responsibility Disclosure (2013, background): “More volatile earnings are more difficult to forecast (Dichev and Tang 2009). Hence, we include the natural logarithm of the time-series standard deviation of earnings per share (VAREARN) and predict it to be positively associated with forecast error. Hope (2003) argues that losses make earnings mor”
Corporate Social Responsibility Report Narratives and Analyst Forecast Accuracy (2017, background): “Earnings volatility (ROAVOL) proxies for forecast difficulty (Dichev and Tang 2009).”
Non-GAAP earnings and board independence (2011, background): “11 Doyle et al. (2003), among others, find that, on average, non-GAAP exclusions are not transitory, while Black and Christensen (2009), among others, find that managers appear to use these exclusions opportunistically.”
A Lintner Model of Payout and Managerial Rents (2012, positive): “First, corporate earnings or cash flows are highly persistent for mature, stable companies with low earnings volatility (see Dichev and Tang (2009) and Frankel and Litov (2009)).36 As μ → 1 the term in Dt−1 in equation (54) vanishes and the omitted variable problem with respect to Dt−1 disappears.”
Dividend policy and share price volatility: UK evidence (2011, methodology): “Following Dichev and Tang (2009), earnings volatility is calculated by taking the standard deviation of earnings for the most recent preceding five years for each year.”
What drives the comparability effect of mandatory IFRS adoption? (2014, positive): “…of cash flow into accruals is a core feature of the financial reporting process (Dechow 1994) that substantially affects the volatility of earnings as well as their predictability and persistence (Dichev and Tang 2009), model (2) captures a central aspect of the accounting recognition process.”
Demand Uncertainty and Cost Behavior (2014, positive): “…as an empirical proxy for uncertainty is standard in the literature (e.g., Dechow and Dichev 2002; Kothari, Laguerre, and Leone 2002; Zhang 2006; Dichev and Tang 2009), and the standard deviation of log-changes in sales is commonly used as a measure of demand uncertainty in economics (e.g.,…”
The Quality of Analysts ’ Cash Flow Forecasts (2008, neutral): “Dichev and Tang (2007) elaborate further on this relation.”
Earnings quality: Evidence from the field (2013, background): “(iv) Other signals: The remaining red flags are an eclectic collection, including familiar themes like buildups in inventories and receivables (Thomas and Zhang 2002), large volatility in earnings (Dichev and Tang 2009), and lack of transparency in financial reporting (Li 2008).”
The effect of information quality on liquidity risk (2011, positive): “…volume, return volatility) and firm characteristics (e.g., sales growth, operating cycle, and capital intensity) that might be associated with either liquidity risk or information quality (e.g., Dechow and Dichev, 2002; Francis et al., 2005; LaFond, Lang, and Skaife, 2007; Dichev and Tang, 2009).”