REN: Regularization Ensemble for Robust Portfolio Optimization
Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
lubridate, glmnet, quadprog, doParallel, Matrix, tictoc, corpcor, ggplot2, reshape2, foreach, stats, parallel |
| Suggests: |
knitr, rmarkdown, KernSmooth, cluster, testthat (≥ 3.0.0) |
| Published: |
2024-10-10 |
| DOI: |
10.32614/CRAN.package.REN |
| Author: |
Hardik Dixit [aut],
Shijia Wang [aut],
Bonsoo Koo [aut, cre],
Cash Looi [aut],
Hong Wang [aut] |
| Maintainer: |
Bonsoo Koo <bonsoo.koo at monash.edu> |
| License: |
AGPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
REN results |
Documentation:
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