A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation
A Modified CTGAN method for portfolio optimization using synthetic data and CVaR-constraint, outperforming traditional strategies.
What it examines
This paper proposes a new approach to portfolio optimization using a Modified CTGAN algorithm with features, focusing on synthetic data generation and a CVaR-constraint. The study aims to demonstrate the effectiveness of this method in generating realistic return scenarios and achieving satisfactory out-of-sample performance.
What it concludes
The study concludes that the Modified CTGAN-plus-features method is promising for portfolio optimization, offering better performance than traditional methods. Potential applications include improving investment strategies and risk management. Future research could explore additional features and apply the method to other financial variables.
Evidence objects
The study concludes that the Modified CTGAN-plus-features method is promising for portfolio optimization, offering better performance than traditional methods. Potential applications include improving investment strategies and risk management. Future research could explore additional features and apply the method to other financial variables.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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… ▽ More 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. △ Less
Source row: 52 · abstract type: unknown