Description
Monte Carlo analysis has become the dominant methodology for advisors to analyze retirement income planning strategies. But the way the results of Monte Carlo analysis are framed for clients can invoke different emotional responses and can affect portfolio withdrawal rate decisions. With this in mind, advisors can take advantage of a range of options to improve their use of Monte Carlo analysis, including framing the results as a “Probability of Adjustment” rather than “Probability of Success”, comparing results using historical scenarios, leveraging regime-based models, and using risk-based guardrails. Using these methods, advisors can potentially provide clients with greater peace of mind regarding their retirement income choices and better help them achieve their specific income and legacy goals.
Learning Objectives
LO #1: Understand what makes Monte Carlo more reliable than straight-line analysis when portfolio withdrawals are being taken
LO #2: Identify why framing Monte Carlo results as a “Probability of Adjustment” rather than “Probability of Success” can leave clients more confident during a market downturn
LO #3: Identify how Monte Carlo simulations can be improved by using a regime-based approach and by comparing the results to historical returns
LO #4: Understand how risk-based guardrails combine the advantages of standard guardrails and Monte Carlo analysis and can be used to account for client-specific cash flows and risk preferences
LO #5: Identify best practices for communicating results and information to clients for better informed decisions about risk and portfolio adjustments