Monte Carlo Analysis for Bucket Strategies

Monte Carlo analysis for a fixed asset allocation is relatively straightforward. Regardless of how markets behave, the portfolio is periodically rebalanced back to its chosen asset allocation.

With a bucket strategy, however, there is another important question: when should the Growth Bucket refill the Safety Bucket, and when should it skip the refill?

CherishBuckets.com has developed a rule-based approach to address this question.

The model looks at the Growth Bucket's previous three-year return and compares it with a threshold selected by the user. For example, suppose the threshold is -5%.

When a refill is skipped, the Safety Bucket continues paying expenses, so its balance gradually declines. If weak Growth Bucket returns persist for several years and the Safety Bucket is eventually exhausted, the Growth Bucket begins paying expenses directly.

The important feature is the clarity and verifiability of the rule. Rather than relying on an unspecified judgment about whether markets have "fallen enough" to justify skipping a refill, the model applies a clearly defined threshold. Users can also examine sample simulations generated by the Monte Carlo analysis to see exactly how the rule behaves under different market sequences.

This gives users a practical way to evaluate different refill thresholds and determine which threshold they are comfortable applying to their own portfolio.

The Monte Carlo tool on CherishBuckets.com also goes beyond simply reporting a probability of success. Users can specify the probability of success they want to target.

For example, a user might ask:

What level of discretionary spending would give me an 87% probability of ending with at least $3 million after 30 years?

The model can then solve for the level of discretionary spending associated with that probability and legacy target.

In this way, Monte Carlo analysis becomes a useful complement to the deterministic bucket calculators on CherishBuckets.com. The deterministic calculators help users understand the mechanics and outcomes of specific bucket strategies, while Monte Carlo analysis allows users who prefer probabilistic planning to examine how those strategies behave across many different market sequences.

To explore the models and run the analysis yourself, visit CherishBuckets.com.