How to Optimize & Rebalance a Stock Portfolio | R

Summary

This video demonstrates portfolio optimization and rebalancing using Vanguard ETFs. The presenter guides through setting up a portfolio, applying constraints like risk targets and position limits, and utilizing a custom function to rebalance quarterly. The optimization process includes adjusting asset weights and calculating returns, with the goal of maximizing returns per unit of risk. The performance of the optimized portfolio is compared to a benchmark (SPY), revealing that the portfolio underperformed, likely due to limited optimization iterations. The video offers a comprehensive look at portfolio management with a focus on rebalancing techniques and performance evaluation.

Takeaways

• 😀 You will need two key packages to optimize ETF portfolios: one for portfolio management and one for performance analytics.
• 😀 The script uses Vanguard ETFs for the portfolio optimization example, focusing on data from 2018 onwards.
• 😀 Merging ETF data into a single DataFrame is essential for efficient portfolio optimization, with a focus on adjusted closing prices.
• 😀 Calculating returns using discrete rates of change (ROC) and handling missing data by replacing `NA` with zeros is important for data integrity.
• 😀 Constraints such as limiting risk (using standard deviation) and setting weight boundaries for assets (between 5% and 50%) are key to optimizing portfolios.
• 😀 The portfolio optimization goal is to maximize return per unit of risk, with the optimizer focused on achieving this through iterations.
• 😀 A custom function is used to enable quarterly rebalancing of the portfolio, as the standard portfolio rebalancing options don't provide that functionality.
• 😀 Optimization uses a maximum of 10 iterations for finding the best portfolio, though a higher number of iterations is recommended for better accuracy.
• 😀 Quarterly rebalancing is enabled through the use of a rebalancing frequency parameter (denoted `K`), which is set to re-balance every 3 months.
• 😀 After optimizing the portfolio, weights are extracted for each period, and these are used to assess the portfolio's performance over the next 3 months.
• 😀 The final portfolio performance is compared to the SPY index to evaluate its effectiveness. In this case, the portfolio slightly underperforms, likely due to the limited number of iterations used.

Q & A

What is the goal of the portfolio optimization process discussed in the video?

The goal is to create an optimized portfolio by maximizing returns relative to risk while adhering to specific constraints such as position limits, asset weight restrictions, and risk targets. The optimization process involves rebalancing the portfolio at regular intervals to ensure optimal asset allocation.

What packages are required for this portfolio optimization process?

The required packages are 'theta' for data manipulation and 'distil' for environment management in the context of portfolio optimization.

Why is data cleaning and merging an important step in this process?

Data cleaning and merging are crucial to ensure the accuracy and consistency of the data used for analysis. By merging the closing prices of different ETFs into one data frame and handling missing values, the data becomes ready for calculating returns and building the portfolio optimization model.

How are the returns calculated for the assets in the portfolio?

The returns are calculated using the Rate of Change (ROC) method with a discrete type. Any missing or invalid returns are replaced with zero to ensure the integrity of the calculations.

What are the main constraints applied to the portfolio optimization process?

The main constraints include: a risk target as close to zero as possible, a maximum of 10 assets in the portfolio, a weight sum of 100% (with a range between 97% and 101%), and asset weights being between 5% and 50%.

What is the purpose of the custom function for quarterly rebalancing?

The custom function allows the portfolio to be rebalanced quarterly, as the standard portfolio optimization method doesn't support quarterly rebalancing. This function helps maintain optimal asset allocation over time.

How does the function for quarterly rebalancing work?

The function uses arguments to control the frequency of rebalancing, where 'decay' is introduced to manage the quarterly rebalance. It adjusts the rebalancing to occur every 3 months by modifying specific parts of the optimization function code.

Why is the iteration max set to 10 in the portfolio optimization process?

The iteration max is set to 10 to limit the number of iterations the optimizer performs when searching for the best portfolio. While 10 iterations may not lead to a fully optimized portfolio, it helps speed up the process for demonstration purposes. Increasing the number of iterations may yield better results.

What role does performance evaluation play in the portfolio optimization process?

Performance evaluation is used to assess the success of the optimized portfolio by comparing it to a benchmark, such as the SPY ETF. This step helps determine if the portfolio is performing as expected and if adjustments need to be made to the optimization strategy.

Why does the portfolio underperform the SPY ETF in the given example?

The portfolio underperforms the SPY ETF likely because the optimization process was limited to only 10 iterations, which may not have allowed the algorithm to fully converge on the optimal asset allocation. Increasing the iteration count could help improve performance.