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Rachev-Ratio Portfolio Optimization with TimeGAN Scenarios: Replacement versus Supplementation
This study evaluates the reliability and investment consequences of scenarios from a specified time-series generative adversarial network (TimeGAN) in Rachev-ratio portfolio allocation. Using 12 exchange-traded funds, we compare historical, replacement and hybrid policies across five training seeds
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