Abstract: 

Dynamic multi-asset allocation heuristics like “risk parity” are used widely in institutional asset management. A major driver for this is the favorable performance during the Global Financial Crisis and during the European Sovereign Debt Crisis. However, changing correlations can be a challenge for this type of portfolio allocation technique. For example, rising interest rates together with falling equities in 2022 led to drawdowns and event to failing financial institutions in 2023-Q1. In the last years, several machine learning innovations have been introduced to improve the robustness of asset allocation with hierarchical clustering and seriation-based approaches, to improve the transparency of these heuristics with explainable AI and to generate synthetic correlations and correlated market returns to improve the coverage of backtests and scenario analysis beyond the historical paths. Together, these innovations offer a consistent pipeline for better understanding rule-based dynamic portfolio allocation strategies. This talk reviews recent developments and puts them into the context of the current market challenges.

Bio: 

Peter Schwendner leads the Institute of Wealth & Asset Management at Zurich University of Applied Sciences, School of Management and Law, Switzerland. His interests are financial markets, asset management and machine learning applications. With the European Stability Mechanism (ESM), he has been developing analytics for primary and secondary bond markets and tools for optimizing the issuance process. Currently, he is working on the BRIDGE Discovery project "Spatial sustainable finance: Satellite-based ratings of company footprints in biodiversity and water". Within the European COST Action «Fintech and AI in Finance», he leads the working group «Transparency into Investment Product Performance for Clients».