po.SW.lasso

This function performs portfolio optimization using LASSO regularization and stochastic weight selection.

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.SW.lasso function

<dl><dt>y0</dt>
<dd>A numeric vector of response values.</dd>
<dt>x0</dt>
<dd>A numeric matrix of predictors.</dd>
<dt>b</dt>
<dd>Number of assets to select in each sample.</dd>
<dt>sample</dt>
<dd>Number of random samples to generate.</dd></dl>

Arguments

Perform LASSO Regularization with Stochastic Weight Portfolio Optimization — po.SW.lasso

<dl>

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


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


<dt>b</dt>
<dd>Number of assets to select in each sample.</dd>


<dt>sample</dt>
<dd>Number of random samples to generate.</dd>

</dl>

po.SW.lasso: Perform LASSO Regularization with Stochastic Weight Portfolio Optimization

Description

Usage

Value

Arguments