CONTRIBUTORS
Deep Ratna Srivastav
Chief AI Officer
Franklin Templeton Technology
Wylie Tollette, CFA
Chief Investment Officer,
Franklin Templeton Investment Solutions
Preview
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.
Preview
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.
WHAT ARE THE RISKS?
The views expressed are those of the investment manager and the comments, opinions and analyses are rendered as of the publication date and may change without notice. The underlying assumptions and these views are subject to change based on market and other conditions and may differ from other portfolio managers or of the firm as a whole. The information provided in this material is not intended as a complete analysis of every material fact regarding any country, region or market. There is no assurance that any prediction, projection or forecast on the economy, stock market, bond market or the economic trends of the markets will be realized. Probabilities predict the chance of an event and are only current as of the date indicated. This is no assurance that probabilities of targets or expectations will be achieved. The value of investments and the income from them can go down as well as up and you may not get back the full amount that you invested. Past performance is not necessarily indicative nor a guarantee of future performance. All investments involve risks, including possible loss of principal.
Hypothetical performance results may have many inherent risks. One of the limitations of hypothetical performance is that they are constructed with the benefit of hindsight. Alternative simulations, techniques, modeling or assumptions might produce significantly different results . Actual results will vary, perhaps materially, from the hypothetical results presented in this document. No representation is being made that any account will, or is likely to, achieve profits or losses similar to those described here.