Bernd Wuebben
6 Years at AB|27 Years of Experience
Bernd Wuebben oversees the Quantitative Research Group for Fixed Income, where he is responsible for developing the quantitative models and methods that are an important aspect of the firm's investment process. Prior to joining AB, Wuebben worked from 2008 to 2018 as a quantitative investment portfolio manager, managing portfolios that invested in liquid traded assets across all major geographic locations and asset classes. His quantitative investment process made heavy use of big data and machine learning. Prior to this, Wuebben worked for 10 years as a macro and relative-value strategist as well as a proprietary trader for major investment banks including Morgan Stanley, Bear Stearns and Deutsche Bank, where he started his career in 1998 as a fixed-income strategist. He holds a BS in mathematics and a BS in physics from Heidelberg University, Germany, and an MS in mathematics and an MS in computer science from Cornell University. Wuebben also passed the doctoral exam in mathematics at Cornell University, where his research focused on the mathematical aspects of superstring theory. Location: New York
Published Work
Systematic fixed-income investing is attracting increased attention but needs specialist skills and resources. Does your manager have what it takes?
Systematic fixed-income strategies aim to outperform their benchmarks but with similar volatility.
Systematic investing offers untapped opportunities for fixed-income investors.
At AllianceBernstein, our systematic team works seamlessly alongside experienced fixed income professionals.
Bond investors have been looking for an approach that delivers attractive, repeatable, uncorrelated active returns. Is their wait over?
There are different ways to apply AI to portfolio management processes. We discuss three distinct approaches in fixed income, hedge funds and equities.
Bond investors have been looking for an approach that delivers attractive, repeatable, uncorrelated active returns. Is their wait over?
Leading active fixed-income managers have long sought to make their investment performance outcomes more consistent and repeatable through process improvements. These enhancements have typically systematized aspects of the investment process without substantially altering the main sources of outperformance or reducing the levels of beta risk.
For most investors, there aren't enough hours in the day to comb through the onslaught of data coming at them. Artificial intelligence and machine learning are changing this—and bond managers are starting to take notice.