Introduction

Exchange-Traded Funds (ETFs) have become a cornerstone in many investment portfolios, offering the benefits of diversification, liquidity, and cost-efficiency. However, with thousands of ETFs available, choosing the right one can feel overwhelming. How do you balance factors like fees, asset composition, historical performance, and risk? The answer lies in using data-driven tools to help simplify the decision-making process.

In our previous posts Navigating Grey Zones In Decision-Making: Introducing the Solver API and Navigating Grey Zones – Consistently Picking the Right Tasks, we covered some of the basics of how the Solver API works and different use cases as to how it can be leveraged.

In this post, we’ll show how the Solver, a powerful optimization tool, can assist in selecting an ETF by evaluating multiple factors and finding the best fit based on your specific requirements. Whether you’re looking for low fees, high returns, or sector-specific exposure, Solver can help narrow down the options.

What is the Solver and How Does It Work?

Solver is an optimization tool typically used for decision-making problems where you aim to maximize or minimize a specific outcome. It helps you explore the trade-offs between different factors by allowing you to set constraints and objectives. In financial contexts like ETF selection, Solver can be invaluable for balancing complex factors such as return on investment, risk, sector exposure, and costs.

By inputting data such as Management Expense Ratio (MER), historical returns, and holdings breakdowns, we can use Solver to find the ETF that best fits our investment criteria.

Step-by-Step: Using the Solver for ETF Selection

Step 1: Define Your Goal

The first step in using Solver is to define what you’re optimizing for. In this example, we want to select an ETF that balances multiple factors: minimizing fees while maintaining strong historical performance and broad exposure to key markets like the U.S. and Canada.

We’ll use the following requirements for our selection process:

• Management Expense Ratio (MER)
• Asset Value in Million
• Asset Count
• Last 5 Years Percentage Return
• Daily Average Trading Volume
• United States Based Holdings Percentage
• Canada Based Holdings Percentage
• Technology Holdings Percentage

Step 2: Set Your Constraints

In this case, you can set constraints for each of these factors. For instance:

• You may want the Management Expense Ratio (MER) to be under 1% to keep costs low.
• You may set a minimum Asset Value of $300 million and a daily average trading volume of 44000 to ensure you’re selecting a liquid, stable fund.
• For Last 5 Years Percentage Return, you might want an ETF that has returned at least 10% to target growth.
• For the Asset Count, having at least 100 holding or more would be desirable to avoid a fund that’s too highly concentrated on a few positions
• On the geography side, you introduce requirements for 40% of US and 10% of Canadian based holdings
• For the sector distribution, your only requirement is to have an allocation of at least 10% in technology because you believe it’s an important growth area to consider.

The next step would be to calibrate each of the requirements according to how important they are to you. In this example you can quickly see the importance of each requirement:

Step 3: Run the Solver

For the purpose of this example, let’s consider a few popular ETFs with varying characteristics, such as broad market funds, tech-focused funds, and geographically diversified funds.

The Solver will process the constraints and objective function and return an ETF (or several) that best meets your criteria.

By running the Solver with these parameters, we find that BlackRock’s iShares Core Equity ETF Portfolio is the best fit:

• It has a low MER (0.20%).
• It meets the 5-year return requirement (11.77%).
• It has solid exposure to the U.S. market (44.65%) and significant technology sector holdings (20.25%).

Vanguard All-Equity ETF Portfolio also looks promising, with similar returns and technology exposure, but its MER is higher, which may lead to higher costs over time.

Expanding the scoring of each option provides a quick overview of why certain options were rewarded and other options penalized depending how they compare with each requirement:

As you can see above, the Evolve Cryptocurrencies ETF is heavily penalized as it’s not meeting most of the requirements while the Vanguard S&P 500 Index ETF scores well but was penalized due to its lack of exposure to the Canadian market.

Conclusion

Using Solver to select an ETF streamlines the process by allowing you to set clear objectives and constraints. In our example, we balanced cost, returns, and sector exposure to select an ETF that best fit our criteria. While the results depend on your personal investment goals, Solver can help simplify complex decisions, ensuring you make data-backed investment choices.

For investors looking to make decisions based on multiple variables, Solver is a powerful tool to optimize ETF selection, helping you to find the perfect balance between risk, cost, and performance.