  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.
