Another Look at Calendar Anomalies

Abstract

Calendar anomalies interest researchers in the finance field nearly a century now. In a 27 years perspective reaching 2017, we employ daily S&P 500 data in a context of both aggregate and sector analysis to examine a possible focus of abnormalities on specific constituents of the market. Nonlinear models of GARCH and EGARCH are employed in this spirit. The findings reveal that day-of-the-week effects are present in all sectors, resulting to the conclusion that they are part of a wide phenomenon affecting the whole market structure. Moreover, a rolling regression approach is followed to test for sample selection bias. The presence of seasonality is indeed a small proportion of the total sample period. Four factors, namely recession, uncertainty, trading volume and bearish sentiment are lastly examined for bonding to the presence of daily structures through the intervention of a logit setup. A cross-factor comparison emerges the interactions between recession and uncertainty with the presence of significant anomalies as the most powerful ones. However, trading volume is doubted to experience an actual connection.