Tariffs, Turbulence, and Factor Fallout: Navigating Global Market Risk with Bloomberg’s MAC3 Models
In early April 2025, sweeping new U.S. tariff policies triggered a dramatic shift in global trade dynamics, sparking heightened volatility and sharp declines across global equity markets. As geopolitical tensions rise and retaliatory measures loom, understanding the ripple effects across style, industry, and country exposures has become mission-critical for institutional investors.
Join Bloomberg’s Tizian Otto as he analyzes the market impact of the new U.S. tariff regime using Bloomberg’s MAC3 Global and US Equity Risk Models. This webinar will explore the systemic shocks unleashed by “Liberation Day,” identify which equity factors suffered or outperformed, and demonstrate how advanced risk models provide critical transparency and actionable insights during periods of macro-driven uncertainty.
Key Takeaways:
Join Bloomberg’s Tizian Otto as he analyzes the market impact of the new U.S. tariff regime using Bloomberg’s MAC3 Global and US Equity Risk Models. This webinar will explore the systemic shocks unleashed by “Liberation Day,” identify which equity factors suffered or outperformed, and demonstrate how advanced risk models provide critical transparency and actionable insights during periods of macro-driven uncertainty.
Key Takeaways:
- Understand how multi-factor risk models decompose market reactions to geopolitical shocks.
- Learn which style and industry exposures proved most sensitive to tariff risk—and why.
- See how a sharp policy pivot (the 90-day tariff suspension) produced a “whipsaw” effect in factor performance.
- Discover how Bloomberg’s MAC3 models can enhance portfolio risk diagnostics and inform strategic positioning.
Speakers
Tizian Otto
Portfolio and Analytics Research
Bloomberg
Tizian Otto, Ph.D., is a Researcher in the Portfolio & Risk Analytics Team at Bloomberg in London where he is leading the acceleration of machine learning adoption. Prior to that, he was a Research Fellow at Yale University as well as a Visiting Research Scholar at Stanford University and Harvard University. During his doctoral and postdoctoral studies, Tizian worked on the development of machine learning-based models to forecasting return, risk, and liquidity in different financial markets. Holding a Ph.D. in Finance from the University of Hamburg, Germany, he reviewed and published several articles on this topic in leading academic and practitioner journals.