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 form https://CRAN.R-project.org/package=REN to link to this page.
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