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

TL;DR

This paper introduces a novel portfolio optimization method combining a modified CTGAN for synthetic data generation with CVaR constraints, improving out-of-sample performance over traditional strategies.

Contribution

It presents a new asset allocation approach using a modified CTGAN that incorporates contextual features for better synthetic data generation in portfolio optimization.

Findings

Synthetic data captures key data characteristics.

Optimized portfolios outperform equal-weight strategies.

Method demonstrates satisfactory out-of-sample performance.

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.