Time-Series Properties of Quarterly Aggregate Earnings

This analysis examines the time-series properties of quarterly aggregate earnings. We find that when aggregated, quarterly earnings can be fairly well described as following a simple random walk (RW) process. That is, the best historical time-series predictor for quarterly aggregated earnings is aggregated earnings from the prior quarter and this specification, in particular, outperforms a seasonal random walk (SRW) specification. Hence, unlike firm level earnings, the seasonal earnings lag is not the best single value predictor for aggregate earnings. When we consider more complicated multi-variable RW changes specifications, we find that the use of first or second order autocorrelation provides some improvement in specification. Finally, drawing on prior work by Sadka and Sadka (2009), we demonstrate the empirical relevance of the choice between an RW and an SRW “surprise” specification by identifying substantive differences in how each is predicted by lagged market returns.