The missing link

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What is goals-based investing?

Our novel take on goals-based investing, called the goals optimization approach, simply means working backwards from one’s investment goals or objectives to choose and vary one’s investments over time so as seek to maximize the likelihood of achieving each goal, subject to the constraints imposed by reality. As such, goals-based investing flips an established process on its head. It is not a magic formula for success, but rather a set of disciplines and processes that help a financial professional to guide his or her clients in ways that are likely, but not guaranteed, to beat more traditional approaches.

Our approach to goals-based investing thus upends the tradition of maximizing savings, choosing an “optimal” portfolio (based on one’s risk tolerance), rebalancing periodically, and hoping that the end result is satisfactory. This tradition, while based in real science, is in dire need of updating. We believe the approach we propose should lead to better outcomes because, as Exhibit 1 on the next page suggests, it allows for—requires—change in the investment mix and risk level as circumstances change, something we all do in every other aspect of our lives.

The goals are typically multiple, and expressible as “needs, wants, wishes, dreams.”

To that end, Franklin Templeton has created a Goals Optimization Engine, or GOETM, which converts needs, wants, wishes and dreams to portfolio allocations that respond to changing market conditions and individual circumstances. Needs should be fulfilled with as high a probability as is practical.

Achieving any of the goals involves risk, which the goals-based process seeks to manage, so as to increase risk when it is most likely to pay off (increase the probability of achieving the goal) and reduce risk as the investor gets closer to their goal.

In this paper, we look at:

• What’s wrong with the traditional method?
• Moving toward a better process.
• The probability of success: a driver, not a check on performance.
• Adaptive asset allocation: probability of success guides the risk level for each goal.
• The flexibility of the goals-based method.

Readers interested in the use of dynamic programming within the GOE should also read our other paper entitled How—and why—GOE® uses dynamic programming to drive asset allocation decisions.