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:

Reference manual:	REN.pdf
Vignettes:	'REN': Regularization Ensemble for Portfolio Optimization (source, R code)

Downloads:

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

Linking:

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