‘REN’: Regularization Ensemble for Portfolio Optimization

Overview

The ‘REN’ package provides a set of tools for performing portfolio optimization using various regularization and ensemble learning methods. The package is designed to produce stable out-of-sample return predictions, particularly in the presence of strong correlations between assets. The core functions enable users to prepare data, set up parallel processing, and perform in-depth portfolio analysis.

Installation

To install the ‘REN’ package, you can use the following command:

# Install the development version from GitHub
devtools::install_github("bonsook/REN")

Main Functions

1. setup_parallel()

This function sets up parallel processing to speed up the computation of portfolio optimization tasks.

• Parameters:
  • num_cores: The number of cores to use for parallel processing. Default is 7.
• Example:

# Set up parallel processing with the default number of cores
cl <- setup_parallel()

# Set up parallel processing with 4 cores
cl <- setup_parallel(num_cores = 4)

# Stop the cluster after completing the analysis
stopCluster(cl) 

2. prepare_data()

This function prepares the input data for portfolio optimization by structuring it into the required format and calculating necessary metrics.

• Parameters:
  • data: A data frame containing the asset returns and other relevant metrics.
• Example:

# Prepare the data for analysis
ff25 <- read.csv("data/FF25.csv")

# Define the date column index, start date, and end date
date_column_index <- 1  # Update this based on your data
start_date <- "19990101"  # Adjust as needed
end_date <- "20231231"  # Adjust as needed

# Prepare the data for analysis
data_prep <- prepare_data(ff25, date_column_index, start_date, end_date)
x <- data_prep$x
mon <- data_prep$mon
count <- data_prep$count
Date <- data_prep$Date

3. perform_analysis()

This function performs portfolio analysis using various methods such as Mean-Variance (MV), James-Stein (JM), LASSO, Ridge Regression, and Equal Weighting (EW). It calculates portfolio weights, turnover, returns, Sharpe ratios, volatility, and maximum drawdown for each method.

• Parameters:
  • x: A numeric matrix where each column represents asset returns and rows represent time periods.
  • mon: A numeric vector representing the number of months since the start date for each time period.
  • count: A numeric vector indicating the number of entries per month.
  • Date: A vector of Date objects representing the dates of the time periods.
  • num_cores: The number of cores to use for parallel processing. Default is 7.
• Returns:
  • A list containing components such as cumulative return plots, cumulative turnover plots, and performance metrics (Sharpe ratio, volatility, max drawdown).
• Example:

# Perform the portfolio analysis
result <- perform_analysis(x, mon, count, Date, num_cores)

# Accessing the results
cumulative_return_plot <- result$cumulative_return_plot
turnover_mean <- result$turnover_mean
sharpe_ratio <- result$sharpe_ratio
volatility <- result$volatility
max_drawdown <- result$max_drawdown

# Display the cumulative return plot
print(cumulative_return_plot)

Example Workflow

Here’s an example workflow using the ‘REN’ package:

# Step 1: Set up parallel processing
cl <- setup_parallel(num_cores = 4)

# Step 2: Prepare the data
data_prep <- prepare_data(ff25, date_column_index, start_date, end_date)
x <- data_prep$x
mon <- data_prep$mon
count <- data_prep$count
Date <- data_prep$Date

# Step 3: Perform portfolio analysis
result <- perform_analysis(x, mon, count, Date, num_cores)

# Step 4: Plot and interpret the results
print(results$cumulative_return_plot)
print(results$turnover_mean)
print(results$sharpe_ratio)

# Remember to stop the cluster after completing the analysis
stopCluster(cl)

Contributing

Contributions to the ‘REN’ package are welcome. Please feel free to submit a pull request or report any issues you encounter.

License

This package is licensed under the MIT License.