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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.
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Package details |
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| Author | Hardik Dixit [aut], Shijia Wang [aut], Bonsoo Koo [aut, cre], Cash Looi [aut], Hong Wang [aut] |
| Maintainer | Bonsoo Koo <bonsoo.koo@monash.edu> |
| License | AGPL (>= 3) |
| Version | 0.1.0 |
| Package repository | View on CRAN |
| Installation |
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