A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation

Paper: arXiv 2302.02269

Authors: José-Manuel Peña, Fernando Suárez, Omar Larré, Domingo Ramírez, Arturo Cifuentes

Abstract

We propose a new approach to portfolio optimization that utilizes a unique combination of synthetic data generation and a CVaR-constraint. We formulate the portfolio optimization problem as an asset allocation problem in which each asset class is accessed through a passive (index) fund. The asset-class weights are determined by solving an optimization problem which includes a CVaR-constraint. The optimization is carried out by means of a Modified CTGAN algorithm which incorporates features (contextual information) and is used to generate synthetic return scenarios, which, in turn, are fed into the optimization engine. For contextual information we rely on several points along the U.S. Treasury yield curve. The merits of this approach are demonstrated with an example based on ten asset classes (covering stocks, bonds, and commodities) over a fourteen-and-half year period (January 2008-June 2022). We also show that the synthetic generation process is able to capture well the key characteristics of the original data, and the optimization scheme results in portfolios that exhibit satisfactory out-of-sample performance. We also show that this approach outperforms the conventional equal-weights (1/N) asset allocation strategy and other optimization formulations based on historical data only.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel approach by conditioning synthetic scenario generation on macroeconomic features (yield curve) via a modified CTGAN architecture for CVaR asset allocation. It provides solid empirical validation against 1/N and standard historical optimization baselines over a robust 14.5-year dataset.

Research Flowchart

  flowchart TD
    A[Research Goal: Optimal Asset Allocation via Generative AI & CVaR] --> B[Data Inputs: 10 Asset Class Returns + US Treasury Yield Curve Features]
    B --> C[Modified CTGAN Model: Captures Return Distributions & Context]
    C --> D[Synthetic Scenario Generation: Produce Feature-Conditioned Return Scenarios]
    D --> E[CVaR Portfolio Optimization Engine: Solves for Asset Weights]
    E --> F[Outcomes: Outperforms 1/N Strategy & Historical-Only Models Out-of-Sample]