Faculty & Research

The paper investigates stock return dynamics in an environment where executives have an incentive to maximize their compensation by artificially in ating earnings. A principal-agent model with financial reporting and managerial e ort is embedded in a Lucas asset-pricing model with periodic revelations of the firm's underlying profitability. The return process generated from the model is consistent with a range of empirical regularities observed in the return data: volatility clustering, asymmetric volatility, and high idiosyncratic volatility. The calibration results further indicate that earnings management can be quantitatively important in accounting for the dynamic patterns of stock returns.