Title: Advancing Markowitz: Asset allocation forest Authors:  Alla Petukhina - ASE Bucharest (Romania) [presenting]
Anastasija Tetereva - Erasmus University Rotterdam (Netherlands)
Abstract: A novel Asset Allocation Forest (AAF) model is proposed that combines the well-established machine learning (ML) tool with the conventional portfolio optimization method. The determination of locally optimal portfolio weights, which dynamically respond to market conditions, effectively captures market regimes, structural breaks, and smooth transitions in a data-driven manner. We illustrate the proposed model using a multi-asset portfolio consisting of equities, bonds, credit, high yield, and commodities. The AAF consistently outperforms established benchmarks, including the Hidden Markov Model (HMM), even when trading costs are taken into account. It also opens the door to valuable economic insights. By constructing accumulated local effects (ALE) plots, we find evidence of flight-to-safety, suggesting a strategic shift from riskier assets to less volatile bonds during periods of increased market turbulence. Furthermore, our model shows a pronounced preference for bonds in inflationary periods, demonstrating its adaptability to different economic conditions.