Despite significant advances in computing power and a relatively extensive body of research on the nature of retirement, assumptions in retirement research and income planning tools have evolved only modestly over the last 30 years. Improving our retirement income models can have a notable impact on advice and guidance for clients in multiple domains (e.g., withdrawal rates, portfolio risk levels, annuity allocations, etc). Even if some models can’t be completely implemented because of software limitations, advisors can at least tweak their modeling assumptions/approach to better calibrate their advice/guidance with a more robust approach.
Learning Objectives:
Key assumptions in retirement income projections (e.g., Monte Carlo simulations) have changed relatively little in three decades (e.g., most models rely on static models and success rates and the primary outcomes metrics).
A cohesive series of models that both improve retirement income projections and could actually be implemented in financial planning tools.
How to understand the limitations of your current tools (and how to work around them, as best as possible).
