A General Framework for Portfolio Construction Based on Generative Models of Asset Returns

ArXiv ID: 2312.03294 “View on arXiv”

Authors: Unknown

Abstract

In this paper, we present an integrated approach to portfolio construction and optimization, leveraging high-performance computing capabilities. We first explore diverse pairings of generative model forecasts and objective functions used for portfolio optimization, which are evaluated using performance-attribution models based on LASSO. We illustrate our approach using extensive simulations of crypto-currency portfolios, and we show that the portfolios constructed using the vine-copula generative model and the Sharpe-ratio objective function consistently outperform. To accommodate a wide array of investment strategies, we further investigate portfolio blending and propose a general framework for evaluating and combining investment strategies. We employ an extension of the multi-armed bandit framework and use value models and policy models to construct eclectic blended portfolios based on past performance. We consider similarity and optimality measures for value models and employ probability-matching (“blending”) and a greedy algorithm (“switching”) for policy models. The eclectic portfolios are also evaluated using LASSO models. We show that the value model utilizing cosine similarity and logit optimality consistently delivers robust superior performances. The extent of outperformance by eclectic portfolios over their benchmarks significantly surpasses that achieved by individual generative model-based portfolios over their respective benchmarks.

Keywords: Vine-copula, Multi-armed bandit, Portfolio blending, High-performance computing, LASSO performance attribution, Crypto

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper utilizes advanced statistical and machine learning methods like vine copulas, multi-armed bandits, and LASSO, reflecting high mathematical complexity. It is also data-heavy, employing extensive simulations on cryptocurrency data and real-world performance evaluation, indicating substantial empirical rigor.

  flowchart TD
    A["Research Goal:<br>Develop General Framework for<br>Portfolio Construction & Optimization"] --> B["Methodology: Simulation & Blending"]
    B --> C["Input: Cryptocurrency Price Data"]
    C --> D["Computational Process 1:<br>Generative Model Forecasts<br>Vine-Copula vs. Others"]
    D --> E["Computational Process 2:<br>Portfolio Optimization<br>Sharpe-Ratio Objective"]
    E --> F["Computational Process 3:<br>Portfolio Blending<br>Multi-Armed Bandit Framework"]
    F --> G["Performance Evaluation:<br>LASSO Performance Attribution"]
    G --> H["Key Finding:<br>Vine-Copula + Sharpe Ratio<br>outperforms benchmarks"]
    G --> I["Key Finding:<br>Eclectic Blended Portfolios<br>deliver robust superior performance"]