• In financial markets, portfolio performance is shaped by various market conditions that can evolve along different trajectories. Traditional models, such as Markowitz's Mean-Variance Optimization (MVO), often rely on historical data or single-path learning. While these approaches can be effective, they may struggle to generalize in the face of new, unforeseen market conditions. Multi-path scenario learning addresses this limitation by training models on multiple potential future market paths generated from a stationary distribution, an underlying probability distribution that remains constant over time.​
• This approach enhances the model's robustness and adaptability to unpredictable market environments. This case study will demonstrate that optimizing a portfolio based on multiple market paths from a stationary distribution leads to superior out-of-sample performance compared to relying on a single historical path.​

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