CFEM and UBS AI & Data Research Seminars: Daniel Wu (Vanguard)

Description

Welcome to the CFEM and UBS AI & Data Research Seminars! We're kicking off the Fall 2023 semester with our first speaker, Daniel Wu of Vanguard. He will be presenting on "How Does the Fed Make Decisions: A Machine Learning Augmented Taylor Rule." Join us on Tuesday, Sep. 26th!

This webinar is free and open to all. Registration is required (please RSVP here). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from no-reply@zoom.us). 

Abstract (link to article): The Federal funds rate is a cornerstone of asset pricing that has a significant impact on asset valuation and portfolio performance. However, estimating it reliably can be a challenging issue given that the FOMC makes monetary policy decisions based on complex economic conditions. The authors leveraged existing literatures’ findings on factors and combined those major factor categories into the new model, which includes inflation, labor markets, financial condition, and proxy of global market, and the authors selected the optimal data series to optimize the effectiveness of detecting Fed decisions through a classification factor selection process. Also, the authors improved the regression from fixed coefficients to gradient boosting nonlinear regression approach to reflect the dynamic interconnections among all the factors and their lags through different periods. Upon conducting out-of-sample forecasting, with these selected factors and machine learning gradient boosting regression, the out-of-sample RMSE improved by 77% from traditional Taylor rule model, which offered an alternative robust solution for forecasting the Federal fund rates that can be further applied to asset pricing and investing.

Speaker Bio: Boyu (Daniel) Wu, Ph.D., is a senior investment strategist in Vanguard’s Investment Strategy Group, where he specializes in asset return forecasting, quantitative modeling, portfolio construction, machine learning, and multi-asset investments.

His research on topics ranging from stock and bond correlation to the use of machine learning for understanding and forecasting Federal Reserve policy has been published in leading journals including The Journal of Financial Data Science, The Journal of Fixed Income, and The Journal of Investing.

Daniel joined Vanguard in 2018 as a strategist on the Investment Management Fintech Strategy Team. In that capacity, he employed quantitative methods and machine learning techniques to design fixed income trading strategies, including those involving credit default swaps, municipal bonds, and Treasuries.

Daniel earned a Bachelor’s degree in mathematics from the University of Washington, an master’s degree in applied mathematics and statistics from Johns Hopkins University, an master’s degree in financial economics from Harvard University, an master’s degree in computer science from the University of Pennsylvania, and a Ph.D. in systems engineering and engineering management from George Washington University.