Momentum in individual stock returns relates to momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of six basis points following a year of losses and 51 basis points following a positive year. We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum‐neutral factors display more momentum. Momentum found in high‐eigenvalue principal component factors subsumes most forms of individual stock momentum. Our results suggest that momentum is not a distinct risk factor—it times other factors.

and the 2018 Midwest Finance Association. Linnainmaa is affiliated with Citadel and Research Affiliates. Neither Citadel nor Research Affiliates provided any funding for this research. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Given the exponential growth in exchange-traded fund (ETF) trading, ETFs have become a significant factor in the volatility generating process of their largest component stocks. A simple model of trading is developed for securities that are included in ETFs, and empirical support is provided for the model hypotheses. Volatility spillovers from ETFs to their largest component stocks are economically significant. These spillovers are increasing in liquidity, the proportion of each stock held by the fund, deviations from net asset value, ETF flow of funds and ETF market capitalization. The results are consistent with a positive volumevolatility relation and trading-based explanations of volatility, and are generally stronger for smaller stocks.

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We find that passive intensity (PI), measured by the passive‐linked share of total stock market trading volume, is strongly related to the overall pattern of stock price movements. A one‐standard‐deviation increase in PI is associated with an 8% higher price synchronicity. We further investigate the channels through which this relation is established by separately analyzing its impact on aggregate systematic and idiosyncratic volatility of stock returns. PI has a positive effect on systematic volatility and a negative impact on firm‐specific volatility. Consistent with the effect of passive trading on price dynamics, we find evidence that PI is negatively associated with mutual funds alpha dissimilarity. After controlling for market and idiosyncratic volatility, a one‐standard‐deviation increase in PI corresponds to a 0.20% decrease in fund dissimilarity. Our findings are robust after controlling for various macro and corporate factors known to affect systematic or firm‐specific volatility.