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:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=RENto link to this page.