Portfolio Construction using Black-Litterman Model and Factors

ArXiv ID: 2311.04475 “View on arXiv”

Authors: Unknown

Abstract

This paper presents a portfolio construction process, including mainly two parts, Factors Selection and Weight Allocations. For the factors selection part, We have chosen 20 factors by considering three aspects, the global market, different assets class, and stock idiosyncratic characteristics. Each factor is proxied by a corresponding ETF. Then, we would apply several weight allocation methods to those factors, including two fixed weight allocation methods, three optimisation methods, and a Black-Litterman model. In addition, we would also fit a Deep Learning model for generating views periodically and incorporating views with the prior to achieve dynamically updated weights by using the Black-Litterman model. In the end, the robustness checking shows how weights change with respect to time evolving and variance increasing. Results using shrinkage variance are provided to alleviate the impacts of representativeness of historical data, but there sadly has little impact. Overall, the model by using the Deep Learning plus Black-Litterman model results outperform the portfolio by other weight allocation schemes, even though further improvement and robustness checking should be performed.

Keywords: Deep Learning, Black-Litterman model, Portfolio construction, Factor investing, Weight allocation

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced mathematical frameworks including the Black-Litterman model, mean-variance optimization, and deep learning for view generation, with substantial notation and matrix algebra. However, it relies heavily on theoretical models and backtesting with limited discussion of transaction costs, data cleaning, or robust out-of-sample validation, focusing more on methodology than implementation details.

  flowchart TD
    A["Research Goal:<br>Portfolio Construction via<br>Black-Litterman & Factors"] --> B["Factor Selection<br>20 Factors via 3 Aspects<br>(Market, Asset Class, Idio.)"]
    B --> C["Data Inputs:<br>ETF Proxies &<br>Historical Market Data"]
    C --> D{"Weight Allocation<br>Methods"}
    D --> E["Fixed Allocation<br>(Equal/Risk Parity)"]
    D --> F["Optimization Methods<br>(Mean-Variance, etc.)"]
    D --> G["Black-Litterman Model<br>Prior + Views"]
    G --> H["Deep Learning Model<br>Generates Dynamic Views"]
    H --> G
    E & F & G --> I["Robustness Checks<br>Time Evolution &<br>Variance Sensitivity"]
    I --> J["Key Findings:<br>DL + BL Model Outperforms<br>Others; Shrinkage<br>Variance Minimal Impact"]