Date: Thursday, 23 January 2025, at 12.30pm (CET)

Dual format: ESSEC Paris La Défense (CNIT), Room TBA
and via Zoom, please click here

Economic tracking portfolios (ETPs) play a crucial role in economic forecasting, risk management, and in uncovering the links between financial markets and macroeconomic dynamics. This study enhances ETP construction by innovatively adapting random forests (RFs) and local linear forests (LLFs) to better model complex, non-linear dependencies between asset returns and macroeconomic variables. Specifically, RF and LLF-based ETPs are utilized to track inflation, consumption growth, and industrial production growth across various horizons. Our analysis reveals that machine learning-based ETPs consistently outperform traditional linear approaches. For instance, at the 1-year horizon, LLF ETPs significantly enhance the ability to track inflation and consumption growth, as evidenced by increases in R^2 values from 4.4% to 6.9% for inflation and from 3.1% to 7.4% for consumption growth. Furthermore, Shapley value analysis uncovers that the relationships between asset returns and macroeconomic factors are highly sensitive to prevailing economic conditions. Additionally, kernel principal component analysis applied to LLF kernels identifies distinct economic regimes, providing a novel lens for analyzing dynamic economic relationships.

Kind regards,
Jeremy Heng, Olga Klopp, Roberto Reno, Marie Kratz and Riada Djebbar (Singapore Actuarial Society - ERM)