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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 |
| Reference manual: | REN.pdf |
| Vignettes: |
'REN': Regularization Ensemble for Portfolio Optimization (source, R code) |
| Package source: | REN_0.1.0.tar.gz |
| Windows binaries: | r-devel: REN_0.1.0.zip, r-release: REN_0.1.0.zip, r-oldrel: REN_0.1.0.zip |
| macOS binaries: | r-release (arm64): REN_0.1.0.tgz, r-oldrel (arm64): REN_0.1.0.tgz, r-release (x86_64): REN_0.1.0.tgz, r-oldrel (x86_64): REN_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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