po.bhu

This function performs portfolio optimization using clustering and LASSO regularization.

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.

Bonsoo Koo

Regularization Ensemble for Robust Portfolio Optimization

Hardik Dixit

Shijia Wang

Cash Looi

Hong Wang

po.bhu function

<dl><dt>y0</dt>
<dd>A numeric vector of response values.</dd>
<dt>x0</dt>
<dd>A numeric matrix of predictors.</dd>
<dt>group</dt>
<dd>A list of asset clusters.</dd>
<dt>rep</dt>
<dd>The number of repetitions for optimization.</dd></dl>

Arguments

Perform Portfolio Optimization Using Clusters and LASSO — po.bhu

<dl>

<dt>y0</dt>
<dd>A numeric vector of response values.</dd>


<dt>x0</dt>
<dd>A numeric matrix of predictors.</dd>


<dt>group</dt>
<dd>A list of asset clusters.</dd>


<dt>rep</dt>
<dd>The number of repetitions for optimization.</dd>

</dl>

po.bhu: Perform Portfolio Optimization Using Clusters and LASSO

Description

Usage

Value

Arguments