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Autoencoding Conditional GAN for Portfolio Allocation Diversification

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  • Jun Lu
  • Shao Yi

Abstract

Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN) and conditional GAN (CGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN framework stands in putting too much emphasis on generating series rather than keeping features that can help this generator. In this paper, we introduce an autoencoding CGAN (ACGAN) based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed ACGAN model leads to better portfolio allocation and generates series that are closer to true data compared to the existing Markowitz and CGAN approaches.

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  • Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Papers 2207.05701, arXiv.org.
    • Handle: RePEc:arx:papers:2207.05701
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    References listed on IDEAS

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    Cited by:

    1. Timothé Gronier & William Maréchal & Christophe Geissler & Stéphane Gibout, 2022. "Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control," Energies, MDPI, vol. 16(1), pages 1-17, December.
    2. Jos'e-Manuel Pe~na & Fernando Su'arez & Omar Larr'e & Domingo Ram'irez & Arturo Cifuentes, 2023. "A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation," Papers 2302.02269, arXiv.org, revised Feb 2023.
    3. Jun Lu & Danny Ding, 2022. "A Hybrid Approach on Conditional GAN for Portfolio Analysis," Papers 2208.07159, arXiv.org.

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