In this blog, we utilize some of EPFR’s oldest strategies to test the predictive powers of the retail flows captured in the universe of 150,000 mutual fund and ETF share classes encompassing some $47 trillion in AUM that EPFR tracks on a daily, weekly and monthly basis.
Azalea Micottis joined EPFR in 2020 as Quantitative analyst, navigating EPFR’s vast database of mutual fund and ETF flows and positioning data, and discovering new thematic insights. She provides consultative services to buyside and sell-side clients on systematic investment strategies, adding context to the direction and trends in EPFR data.
Prior to EPFR, Azalea worked at Bloomberg as a member of the Equity Analytics and BQuant Analytics teams, building her knowledge and expertise within the fund flows space. In her role, she worked closely with clients to integrate Bloomberg’s data and quantitative platforms into their everyday workflows and strategies.
Azalea holds an MSc Management from Imperial College Business School, London, and an MSc Chemistry from Imperial College, London and École Normale Supérieure, Paris.
Until now, the majority of the strategies developed and back tested by EPFR’s Quant Team have been based on the concept of selecting and rotating between different markets or asset classes.