Autoencoding Conditional GAN for Portfolio Allocation Diversification
نویسندگان
چکیده
Over the decades, Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on of market uncertainty rather than trend prediction. While generative adversarial network (GAN) and conditional GAN (CGAN) have explored to generate financial time series extract features that can help analysis. The limitation CGAN stands putting generating keeping this generator. In paper, we introduce an autoencoding (ACGAN) based deep models learns internal historical data while modeling future trends. We evaluate model several real-world datasets from both US Europe markets, show proposed ACGAN leads better allocation generates are closer true compared existing approaches.
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ژورنال
عنوان ژورنال: Applied Economics and Finance
سال: 2022
ISSN: ['2332-7294', '2332-7308']
DOI: https://doi.org/10.11114/aef.v9i3.5610