Browsing by Author "Ramirez, Domingo"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A modified CTGAN-plus-features-based method for optimal asset allocation
    (2024) Na, Jose-Manuel Pe; Suarez, Fernando; Larre, Omar; Ramirez, Domingo; Cifuentes, Arturo
    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 10 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.
  • No Thumbnail Available
    Item
    A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization
    (2023) Pagnoncelli, Bernardo K.; Ramirez, Domingo; Rahimian, Hamed; Cifuentes, Arturo
    Features, or contextual information, are additional data than can help predicting asset returns in financial problems. We propose a mean-risk portfolio selection problem that uses contextual information to maximize expected returns at each time period, weighing past observations via kernels based on the current state of the world. We consider yearly intervals for investment opportunities, and a set of indices that cover the most relevant investment classes. For those intervals, data scarcity is a problem that is often dealt with by making distribution assumptions. We take a different path and use distribution-free simulation techniques to populate our database. In our experiments we use the Conditional Value-at-Risk as our risk measure, and we work with data from 2007 until 2021 to evaluate our methodology. Our results show that, by incorporating features, the out-of-sample performance of our strategy outperforms the equally-weighted portfolio. We also generate diversified positions, and efficient frontiers that exhibit coherent risk-return patterns.