Turning Over Turnover

Abstract

The methodology of Bai and Ng (2002, 2003) for decomposing large panel data into systematic and idiosyncratic components is applied to both returns and turnover. Combining this with a GLS-based principal components approach, we demonstrate that their procedure works well for both returns and turnover despite the presence of severe heteroscedasticity and non-stationarity in turnover of individual stocks. We then test Lo and Wang's (2000) theoretical model's restriction that returns and turnover should have the same number of systematic factors. This is songly rejected by the data, suggesting stock price and trading volume may not be compatible under the existing multi-factor asset pricing-trading framework. We also demonsate that several commonly used turnover measures may understate the price impact of stock trading.