基于PolyModel理论和iTransformer的对冲基金投资组合构建
Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer
AI总结：
针对金融时间序列数据稀疏导致机器学习方法应用困难的问题，本文结合PolyModel理论和iTransformer模型构建对冲基金投资组合，通过多风险因素选择、定量指标设计及深度学习技术提升，实现了更优的夏普比率和年化收益率。
摘要：
在构建投资组合时，一个关键问题是大量金融时间序列数据稀疏，这给机器学习方法的应用带来挑战。PolyModel理论能够解决该问题，并在投资组合构建的多个方面展现出优越性。为将PolyModel理论应用于对冲基金投资组合构建，我们首先确定资产池，使用超过10,000只对冲基金过去29年的数据。PolyModel理论还涉及选择广泛的风险因素集，包括各类金融指数、货币和商品价格，这种全面选择反映了现实环境的复杂性。利用PolyModel理论，我们创建了长期Alpha、长期比率和SVaR等定量指标，同时使用夏普比率或晨星MRAR等更经典的指标。为提升构建的投资组合表现，我们还采用最新的深度学习技术（iTransformer），利用所有特征捕捉上升趋势，同时有效控制下行风险。iTransformer模型专为应对高维时间序列预测的挑战而设计，能大幅改进我们的策略。更具体地说，我们的策略实现了更优的夏普比率和年化收益率。上述过程使我们能够创建多种投资组合策略，与各类基准相比，这些策略旨在实现高回报和低风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
