20200616 LQG Evening Seminar – Giuliano De Rossi – 18:30 – On-Line – ETF flows & stock mispricing

ETF flows, mispricing and stock price dynamics

Giuliano De Rossi – Goldman Sachs International

The last decade has witnessed a surge in assets managed by ETFs globally. In this presentation we look at the implications of this phenomenon for the way in which single stocks trade. In particular, we use a large sample of daily data on ETF flows and ETF premia (or discounts) to build indicators of trading activity in individual securities. The key to obtaining accurate stock-level metrics is to observe daily holdings in each ETF. Thus, both indicators can be viewed as predictors of arbitrage activity in the short term.
The main conclusion is that ETF flows on day t tend to affect intraday volatility and the level of trading activity of the underlying stocks on t+1. In addition, short-lived deviations of the ETF price from the value of its underlying basket also tend to predict heightened intraday volatility and trading activity at the single stock level. Results highlight the importance of cross-impact, a concept that has been proposed in the literature on optimal trade execution and can help improve our models of risk and liquidity using exogenous information.

Giuliano De Rossi

Head of Innovation – Quantitative Execution Services – Goldman Sachs International

Giuliano is the Head of Innovation for the Quantitative Execution Services team at Goldman Sachs. Prior to this, he worked at Macquarie and PIMCO. He also spent six years in the Quantitative Equity Research team at UBS. Giuliano has a PhD in economics from Cambridge University, and worked for three years as a college lecturer in economics at Cambridge before joining the finance industry on a full-time basis. Giuliano’s Master’s degree is from the LSE and his first degree is from Bocconi University. He has worked on a wide range of topics, including pairs trading, low volatility, the tracking error of global ETFs, cross asset strategies, downside risk and applications of machine learning to finance. His academic research has been published in the Journal of Econometrics and the Journal of Empirical Finance and has been covered, among others, by the Economist and the Financial Times.