Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer

ArXiv ID: 2408.03320 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: PolyModel Theory, iTransformer, Hedge Funds, Risk Factors, Time Series Forecasting, Hedge Funds

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper presents advanced mathematical frameworks like PolyModel theory and polynomial regression (Eq 2, 3), coupled with deep learning (iTransformer), but its empirical rigor is grounded in substantial data (10k+ hedge funds over 29 years) and reported performance metrics like Sharpe ratio, though lacking visible implementation details.

  flowchart TD
    A["Research Goal: Construct Hedge Fund Portfolio<br>Using PolyModel Theory & iTransformer"] --> B["Data Collection & Input"]
    
    subgraph B ["Data Sources"]
        B1["10,000+ Hedge Funds<br>29 Years Data"]
        B2["Risk Factors:<br>Indices, Currencies, Commodities"]
    end

    B --> C{"PolyModel Theory<br>Implementation"}
    
    subgraph C ["PolyModel Processes"]
        C1["Address Sparse Data<br>& High-Dimensionality"]
        C2["Calculate Quantitative Measures<br>Long-term Alpha, SVaR"]
        C3["Calculate Classical Measures<br>Sharpe Ratio, MRAR"]
    end

    C --> D["iTransformer<br>Deep Learning Enhancement"]
    
    subgraph D ["Computational Analysis"]
        D1["High-dimensional<br>Time Series Forecasting"]
        D2["Capture Upward Trends<br>Control Downside Risk"]
    end

    D --> E["Portfolio Strategy<br>Construction"]
    
    subgraph E ["Strategy Outcomes"]
        E1["High-Return Portfolio"]
        E2["Low-Risk Portfolio"]
    end

    E --> F["Key Findings & Outcomes"]
    
    subgraph F ["Performance Results"]
        F1["Better Sharpe Ratio"]
        F2["Higher Annualized Return"]
        F3["Outperforms Benchmarks"]
    end