论文标题
用于投资组合分析的条件GAN的混合方法
A Hybrid Approach on Conditional GAN for Portfolio Analysis
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。尽管已经探索了生成的对抗网络（GAN），有条件的GAN（CGAN）和自动编码CGAN（ACGAN），以生成财务时间序列和提取功能，从而有助于投资组合分析。 CGAN或ACGAN框架的局限性在于过多地强调生成系列并找到该系列的内部趋势，而不是预测未来的趋势。在本文中，我们基于深层生成模型介绍了有条件gan的混合方法，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。我们在来自美国和欧洲市场的几个现实世界数据集上评估了该模型，并表明与现有的Markowitz，Cgan和Acgan方法相比，拟议的Hybridcgan和Hybridacgan模型可以更好地分配投资组合。
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), conditional GAN (CGAN), and autoencoding CGAN (ACGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends. In this paper, we introduce a hybrid approach on conditional GAN 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 HybridCGAN and HybridACGAN models lead to better portfolio allocation compared to the existing Markowitz, CGAN, and ACGAN approaches.
