We conduct exploratory data analysis on the economic determinants of quarterly earnings data. After surveying the industrial organization literature, we selected firm-size, product-type and barriers-to-entry as independent variables useful in explaining autocorrelation in quarterly earnings and sales. We perform cross-sectional regression analysis on a sample of 364 calendar year-end. New York Stock Exchange firms. As our dependent variable, we employed both seasonal and non-seasonal lags of the levels and first differences of the sample autocorrelation functions (SACF) of quarterly earnings and sales data. Our results support a pervasive impact of firm-size on the levels of the SACF. This outcome is consistent with the notion that larger firms exhibit mare stable, higher pronounced levels of serial correlation in their quarterly earnings numbers than smaller firms. Results for the other economic variables were more contextual, depending m whether we used the full sample or a subgroup and on whether the data were differenced. Stronger results on the product-type and barriers-to-entry variables were documented for the seasonal firm subgroup at seasonal lags. This result is suggestive of an economic rationale for the seasonal behavior of quarterly earnings and sales data.
ASJC Scopus subject areas
- Economics and Econometrics