论文标题
用于投资组合分配多元化的有条件gan的自动编码
Autoencoding Conditional GAN for Portfolio Allocation Diversification
论文作者
论文摘要
在过去的几十年中，Markowitz框架已在投资组合分析中广泛使用，尽管它过多地强调了对市场不确定性的分析，而不是趋势预测。虽然已经探索了生成对抗网络（GAN）和有条件的GAN（CGAN）来生成财务时间序列并提取可以帮助投资组合分析的功能。 CGAN框架的局限性在于过多地强调生成系列，而不是保留可以帮助该发电机的功能。在本文中，我们基于深层生成模型引入了自动编码CGAN（ACGAN），该模型了解了历史数据的内部趋势，同时对市场不确定性和未来趋势进行了建模。我们在美国和欧洲市场的几个现实世界数据集上评估了该模型，并表明拟议的Acgan模型可提供更好的投资组合分配，并生成与现有的Markowitz和CGAN方法相比，它们更接近真实数据。
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.
