一种基于改进CTGAN-Plus-Features的最优资产配置方法
A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation
AI总结：
本研究提出一种结合改进CTGAN合成数据生成与CVaR约束的投资组合优化方法，以美债收益率曲线为上下文信息生成收益情景，经14.5年多资产实例验证，其样本外表现优于等权重策略及纯历史数据优化方法。
摘要：
我们提出了一种投资组合优化的新方法，该方法独特地结合了合成数据生成与CVaR（条件风险价值）约束。我们将投资组合优化问题表述为资产配置问题，其中每类资产都通过被动（指数）基金进行投资。资产类别权重通过求解包含CVaR约束的优化问题来确定。优化过程通过一种改进的CTGAN算法实现，该算法融入了特征（上下文信息），用于生成合成收益情景，再将这些情景输入优化引擎。对于上下文信息，我们采用美国国债收益率曲线上的多个点位数据。我们通过一个基于10个资产类别（涵盖股票、债券和大宗商品）、时长14.5年（2008年1月至2022年6月）的实例验证了该方法的优势。我们还表明，合成生成过程能够很好地捕捉原始数据的关键特征，且该优化方案得到的投资组合表现出令人满意的样本外性能。我们同时证明，该方法优于传统的等权重（1/N）资产配置策略，以及其他仅基于历史数据的优化公式。
英文摘要：
We propose a new approach to portfolio optimization that utilizes a unique combination of synthetic data generation and a CVaR-constraint. We formulate the portfolio optimization problem as an asset allocation problem in which each asset class is accessed through a passive (index) fund. The asset-class weights are determined by solving an optimization problem which includes a CVaR-constraint. The optimization is carried out by means of a Modified CTGAN algorithm which incorporates features (contextual information) and is used to generate synthetic return scenarios, which, in turn, are fed into the optimization engine. For contextual information we rely on several points along the U.S. Treasury yield curve. The merits of this approach are demonstrated with an example based on ten asset classes (covering stocks, bonds, and commodities) over a fourteen-and-half year period (January 2008-June 2022). We also show that the synthetic generation process is able to capture well the key characteristics of the original data, and the optimization scheme results in portfolios that exhibit satisfactory out-of-sample performance. We also show that this approach outperforms the conventional equal-weights (1/N) asset allocation strategy and other optimization formulations based on historical data only.
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