Hey there!

Hope you all have been well. Today, I bring you something we’ve been cooking up in the lab recently. A process, certainly not unique or groundbreaking outside this community.

During the last few community calls, we explored a portfolio management technique that focuses on attempting to maximize returns while managing risk and mitigating downside. The main concepts at play are volatility-weighted strategies, the rebalancing effect, and minimizing correlation between your strategies of choice. This process is designed to skew the balance between risk and reward as heavily in favor of reward as possible, while keeping risk as law. The effect is a maximizing of statistics like Serenity Index and Ulcer Index while attempting to flatten drawdown and volatility as far as can go. I’m going to walk you through the steps of constructing such a strategy (or arguably, a portfolio if you choose your strategies well.)

Let’s get to it.

1. What is Volatility Weighting?

2. Doing the Math

3. Setting up the Strategy

4. The Rebalancing Effect

5. Fine Tuning the Portfolio

6. Conclusion

What is Volatility Weighting?

In short, volatility weighting is the process of choosing a metric you want to minimize. In my case, I focused on the “worst day” statistic as provided through the Quantstats Report generation tool. If you want to be super-duper safe about this, pick the most volatile ticker in each strategy that has the worst drawdown, find out what your highest theoretical holding percentage of that ticker is, and assume you were holding that allocation on the worst day that ticker has ever had. A good example of this would be holding an equivalent of the Nasdaq (or even a leveraged version for added spice) on Black Monday 1987, where quick math says that it would have drawn down ~90%

This method involves creating a basket of diversified strategies based off their correlation (gathered through Maestro) and adjusting the weight of each strategy according to its drawdown through a calculation I’ll outline below. The goal is to ensure that the maximum daily loss of the portfolio remains within acceptable limits, which I have arbitrarily decided is 5%.

Why would I do this? A volatility-weighted portfolio allows you to reduce exposure to highly volatile assets, and increases exposure to cash equivalent assets. Thus, the maximum potential losses during downturns is minimized as well. It’s a method that offers a much more controlled risk profile than the standard “go all in on 3x leveraged ETF” strategies the community normally runs, but it also won’t ever get the super impressive 1000% backtests.

Doing the Math

Choosing the Strategies: We began by selecting a range of diversified strategies. In our example, we included various strategies covering asset classes from broad index and tech-focused, Commodities, Bonds, and Volatility. The overall idea is to have something that is always going up. Luckily, these baskets of strategies have already been made in the form of Investors Collaborative - Hawk and Serpent. Now, down to the steps.

Step-by-Step Process:

1. Identify Strategies: Start with a selection of strategies that you are familiar with and that have historical data. I’ll be using 4 strategies with decent correlation numbers.

2. Find the Maximum Daily Loss: Determine the maximum single-day loss for each strategy. Mine are as follows.

• Asset Focus - -11.29%

• Bond Focus - -9.76%

• Commodity Focus - -5.13%

• Volatility Focus - -11.25%

3. Determine Weights: Using the target maximum daily loss (in this case, 5%), calculate the allocation of each strategy in the portfolio. The formula used was:

Weight of Strategy = ((Target Max Loss%/Max Daily Loss %)x100)/total number of strategies included.

• Asset Focus - ((5/11.29%)x100)/4 = 11.07%

• Bond Focus - ((5/9.76%)x100)/4 = 12.80%

• Commodity Focus - ((5/5.13%)x100)/4 = 24.37%

• Volatility Focus - ((5/11.23%)x100)/4 = 11.13%

Set up the strategy

Set your weights: Once the weights are determined, the next step is weighting the blocks in the portfolio. The idea is to have a high allocation to cash (in this example, ~40% static in the cash equivalent of your choice.) BIL, SHV, BOXX and CSHI are all viable options, but CSHI and BOXX are lower volume than I like.

The outcome of this is that I’m only risking a small percentage in high-volatility strategies on any given day. It provides a cushion (as well as a continuous source of capital to harvest rebalancing alpha!) against significant losses while still participating in the upside of high-performing assets.

The Rebalancing Effect

Understanding Rebalancing: Rebalancing is the process of buying and selling a portfolios components to maintain the desired risk level or weight. For a drawdown-weighted portfolio like this one, rebalancing ensures that the proportion of each strategy stays aligned with its calculated weight, ensuing our statistical maximum daily loss stays within the target bounds.

The Free Lunch of Diversification: As explained in the Community Call (and the last newsletter) by Ray Dalio, and by professors at MIT, diversification can provide what’s often referred to as the closest thing to "free lunch" in finance. By combining assets that do not perfectly correlate to each other and rebalancing between them, you can reduce the overall portfolio risk (i.e., drawdown) which after compounding can lead to enhancing returns. I put that concept to work in every strategy I attempt to make, and the process Dereck pioneered ,at least within our community, in creating some of the first versions of these strategies takes it to the next level. It does not always result in higher risk adjusted returns, as we can see in the strategies linked below, but it does result in lower maximum drawdowns when that is what you’re weighting for.

Fine-Tuning the Portfolio

Adjusting Risk Tolerance: Not every investor will have the same risk tolerance. In the example during the community call, we tested different scenarios by adjusting the risk level (doubling and quadrupling the initial risk target). As expected, increasing the risk tolerance also increased the potential returns, but at the cost of higher drawdowns.

Finding the Sweet Spot: The key to using this strategy effectively is to find the balance that fits your risk profile. A super-conservative investor (hello!) might prefer a lower-risk setup (like the one I’ve created today), maintaining a significant cash position, while a more aggressive investor might opt for higher exposure to high-volatility strategies with a proportionally lower cash percentage, if any cash at all.

Metrics to Consider: When evaluating the performance of such portfolios, consider metrics like the Calmar and Sortino ratios. Calmar compares the average annual return with the maximum drawdown while Sortino Ratio compares the downside deviation against the portfolios return comparted to the Risk Free Rate. Higher ratios indicate better risk-adjusted returns. Similarly, serenity, ulcer index, and recovery factor are useful for assessing how well the portfolio manages drawdowns and recovers from losses.

Conclusion and Next Steps

The session highlighted the importance of a disciplined approach to portfolio management. By carefully weighting strategies based on volatility and consistently rebalancing, investors can create a robust portfolio that manages risk while still offering the potential for significant returns.

What’s Next?

• Experiment with Different Strategies: Use the principles discussed to explore different combinations of strategies that suit your risk tolerance, financial goals, and asset/logic diversification needs..

• Continuous Learning: The world of finance is ever-evolving. Stay informed and be willing to accept that maybe your strategy wasn’t that well built.

Final Thoughts: Remember, the key to successful investing isn’t just about chasing high returns; it’s about understanding and managing risk. With the right tools and strategies, you can build a portfolio that balances both effectively. Until next time, happy investing!

Links to the strategies