Jonathan Berkow
7 Years at AB|22 Years of Experience
Jonathan Berkow is a Senior Vice President and the Director of Quantitative Research and Data Science in the Equities division at AB. He leads the research and adoption of alternative data in equity research and systematic strategies. Prior to joining the firm in 2018, Berkow was a systematic portfolio manager and researcher at hedge funds Element Capital Management and Kepos Capital. He started his career at Goldman Sachs Asset Management, where he managed quantitative research and was a portfolio manager for global equity portfolios. Berkow's research has spanned equities and macro asset classes. He holds a BS in economics from the Massachusetts Institute of Technology. Location: New York
Published Work
From stock picking to benchmark selection, the construction of an active thematic equity portfolio will play a crucial role in its ability to deliver on a theme’s return potential.
Thematic portfolios that tap into big global trends offer exciting opportunities for equity investors. But the devil is in the detail.
How can equity investors build portfolios that capture the potential of exciting thematic stories?
From potentially brand-damaging ethical risks to regulatory uncertainty, AI poses challenges for investors. But there is a path forward.
There are different ways to apply AI to portfolio management processes. We discuss three distinct approaches in fixed income, hedge funds and equities.
Many companies are talking up the potential of artificial intelligence. But equity investors shouldn’t jump blindly on the AI bandwagon.
AI isn't going to replace human analysts anytime soon. But it can help execute many menial functions to free up analysts for deeper research dives, armed with more information than they could ever process alone.
US vacationers are out in force while the holiday scene remains subdued in Europe and Asia. How can equity investors capture the unfolding global mobility recovery?
Many investors are looking for emerging signs of a return to normalcy from the coronavirus crisis. While there are many indicators to choose from, we've assembled a group of signals, with the help of big data, that may point the way.
Data science is most effective when it starts with a deep, fundamental question. What's the key question that the research is trying to answer? From that starting point, data scientists work very closely with research analysts to deploy the right data and tools to find an answer.
The COVID-19 crisis has made data science even more important. Today, data scientists are collaborating with research analysts to fill in the gaps with a few mouse clicks when social distancing restrictions limit the mobility of on-the-ground efforts.