Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning

Paper: arXiv 2301.07318

Authors: Chuting Sun, Qi Wu, Xing Yan

Abstract

The dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. To achieve this, we propose a dynamic generative factor model which uses random variable transformation as an implicit way of distribution modeling and relies on the Attention-GRU network for dynamic learning and forecasting. The proposed model captures the dynamic dependence among multivariate stock returns, especially focusing on the tail-side properties. We also propose a two-step iterative algorithm to train the model and then predict the time-varying model parameters, including the time-invariant tail parameters. At each investment date, we can easily simulate new samples from the learned generative model, and we further perform CVaR portfolio optimization with the simulated samples to form a dynamic portfolio strategy. The numerical experiment on stock data shows that our model leads to wiser investments that promise higher reward-risk ratios and present lower tail risks.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative integration of dynamic generative factor models using Attention-GRU architectures with CVaR portfolio optimization. It demonstrates strong mathematical grounding via implicit distribution modeling and tail-risk capture, alongside empirical validation on stock market data showing improved risk-adjusted returns.

Research Flowchart

  flowchart TD
    A[Goal: Dynamic Portfolio Construction & Tail-Risk Reduction] --> B[Data Input: Multivariate Stock Returns]
    B --> C[Attention-GRU Generative Factor Model]
    C --> D[Two-Step Iterative Training Algorithm]
    D --> E[Parameter Forecasting & Tail Sample Simulation]
    E --> F[CVaR Portfolio Optimization]
    F --> G[Outcome: Higher Reward-Risk Ratio & Lower Tail Risks]