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Scenario-based systematic risk in earnings
|Title:||Scenario-based systematic risk in earnings|
|Date Issued:||15 Aug 2020|
|Abstract:||We develop a scenario-based measure of risk in earnings. We start by forming a range of scenarios, or plausible states of the economy, represented by earnings persistence parameters from cross-sectional earnings prediction regressions. We then combine out-of-sample earnings components with these scenarios to predict earnings. The standard deviation of predicted earnings of all scenarios is our firm-year risk measure, sigma. We demonstrate that $\sigma$ captures systematic risk in earnings and that our measure complements firm characteristics that have been proposed as measures of risk in predicting cross-sectional returns. The differential one-year returns of the interquartile range for $\sigma$ is 1.9\%. We contribute to the literature by developing a priced scenario-based risk measure using only fundamental information.|
|Appears in Collections:||
08 Financial Accounting 1: Stock Analysts/Equity Valuation|
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