When constructing portfolios, a key problem is that a lot of financial time series data are sparse, making it challenging to apply machine learning methods. Polymodel theory can solve this issue and demonstrate superiority in portfolio construction from various aspects. To implement the PolyModel theory for constructing a hedge fund portfolio, we begin by identifying an asset pool, utilizing over 10,000 hedge funds for the past 29 years' data. PolyModel theory also involves choosing a wide-ranging set of risk factors, which includes various financial indices, currencies, and commodity prices. This comprehensive selection mirrors the complexities of the real-world environment. Leveraging on the PolyModel theory, we create quantitative measures such as Long-term Alpha, Long-term Ratio, and SVaR. We also use more classical measures like the Sharpe ratio or Morningstar's MRAR. To enhance the performance of the constructed portfolio, we also employ the latest deep learning techniques (iTransformer) to capture the upward trend, while efficiently controlling the downside, using all the features. The iTransformer model is specifically designed to address the challenges in high-dimensional time series forecasting and could largely improve our strategies. More precisely, our strategies achieve better Sharpe ratio and annualized return. The above process enables us to create multiple portfolio strategies aiming for high returns and low risks when compared to various benchmarks.

翻译：在构建投资组合时，一个关键问题在于大量金融时间序列数据具有稀疏性，这使得应用机器学习方法面临挑战。PolyModel理论能够解决这一问题，并从多个维度展现其在组合构建中的优越性。为应用PolyModel理论构建对冲基金投资组合，我们首先确定资产池，利用过去29年间超过10,000支对冲基金的数据。PolyModel理论还涉及选取广泛的风险因子集合，涵盖各类金融指数、货币及商品价格。这种全面的选择反映了现实环境的复杂性。基于PolyModel理论，我们构建了长期阿尔法、长期比率、SVaR等量化指标，同时采用夏普比率或晨星MRAR等经典指标。为提升所构建组合的表现，我们还采用最新的深度学习技术（iTransformer），利用全部特征捕捉上行趋势并有效控制下行风险。iTransformer模型专为应对高维时间序列预测中的挑战而设计，可显著提升策略效果。具体而言，我们的策略实现了更优的夏普比率与年化收益。上述流程使我们能够创建多种投资组合策略，相较于各类基准，这些策略在追求高收益的同时实现了低风险目标。