{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/245658"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/245658","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"ASSET PRICING OPTIMIZATION THROUGH GENERATIVE ADVERSARIAL NETWORKS","abstract":"In this thesis, we study the asset pricing optimisation through Generative Adversarial Networks (GAN). We have demonstrated that shallow learning can deliver similar performance for test data as compared to deep learning considered in the literature, with the added benefit of mitigating common challenges such as overfitting. This is an important finding, as it challenges the often-held belief that more complex, deeper models are invariably superior. 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