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Executive summary:

Franklin Templeton’s Goals Optimization Engine (GOE®) utilizes dynamic programming, a mathematical technique that optimizes investment asset allocation by working backward from the end financial goal.

Unlike Monte Carlo simulations,1 which assess the probability of success for a given portfolio, GOE uses dynamic programming to determine the optimal asset allocation that achieves a specific goal and how it should evolve over time.

Key benefits:

  • Delivers personalized, actionable goals-based investment allocations.
  • Selects optimal portfolios for success rather than just measuring the probability of success.
  • Adapts asset allocations over time based on market changes and life events.

Introduction

Franklin Templeton’s Goals Optimization Engine (GOE®) is built on a powerful idea: Investment advice should be dynamic, personalized and anchored in client goals. At the core of GOE is technology rarely seen in financial planning software—dynamic programming.

While many advisors are familiar with Monte Carlo simulations as a way to assess the probability of success, GOE takes a fundamentally different approach. This paper explores how dynamic programming works inside GOE, why it’s better suited to optimizing client outcomes, and how it compares to Monte Carlo analysis.

For a broader view of how GOE connects financial planning with portfolio construction, we encourage readers to explore our companion paper, “The missing link: Connecting goals-based wealth management to investing.”



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