Explanations for Cross‐Sectional Return Anomalies: Evidence on the IVOL Puzzle

ABSTRACT Considering the time‐series dimension alongside the cross‐sectional dimension helps to almost entirely account for the idiosyncratic volatility (IVOL) puzzle. Based on a purely cross‐sectional approach, the previous literature shows that numerous candidate theories collectively fail to explain the puzzling finding that high‐IVOL stocks underperform low‐IVOL stocks. We propose a novel panel decomposition approach that also takes into account variation in the aggregate level of the explanatory variables. For most variables, the majority of the explanatory power arises from this time‐series dimension. The most important explanatory variables are delay, the bid–ask spread, and aggregate financial uncertainty, consistent with mispricing and limits‐to‐arbitrage mechanisms.