Goal-based investing
By contrast, GBI optimizes an investment mix to minimize the probability of failing to achieve a minimum wealth level within a set period of time.
It is a similar approach to asset-liability management for insurance companies and the liability-driven investment strategy for pension funds, but GBI further integrates financial planning with investment management which insures that household goals are funded in an efficient manner.
GBI takes into account the progress against goals which are categorized as either essential needs, lifestyle wants or legacy aspirations depending on level of importance to an individual or family.
Source:[3] Goals-based investors are typically assumed to have a collection goals which compete for a limited pool of wealth.
To overcome this recursivity the optimal mix of investments can first be found for discrete levels of wealth allocation, then a Monte Carlo engine can be used to find maximal utility.
The fundamental difference between goals-based investing and modern portfolio theory (MPT) turns on the definition of "risk."
[5] In the case where investors are not limited in their ability to borrow or sell short, there is no cost to dividing wealth across various accounts,[6] nor is there a mathematical difference between mean-variance optimization and probability maximization.
Under those real-world constraints, the efficient frontier has an endpoint and probability maximization produces different results than mean-variance optimization when a portfolio's required return (
Goals-based investing grew out of observations made by behavioral finance and ongoing critiques of modern portfolio theory (MPT).
[7][8] Behavioral portfolio theory (BPT) combined mental accounting with the redefinition of risk as the probability of failing to achieve a goal,[4] and investors balance returns over-and-above their requirement with the risk of failing to achieve the goal.
It was eventually shown, however, that this physical manifestation of the mental accounting framework was not necessarily inefficient, so long as short-sales and leverage were allowed.
[6] Other researchers further questioned the use of MPT when applied to individuals because the risk-aversion parameter was shown to vary through time and in response to different objectives.
"[9] MPT was synthesized with behavioral portfolio theory, and in that synthesis work the risk-aversion parameter was eliminated.
Rather than assess her risk aversion parameter, the investor is asked to specify the maximum probability of failure she is willing to accept for a given goal.
This probability figure is then mathematically converted into MPT's risk aversion parameter and portfolio optimization proceeds along mean-variance lines.
In an effort to promote goals-based investing research, The Journal of Wealth Management was formed in 1998.
The key challenge for goal-based investing (GBI) is to implement dedicated investment solutions aiming to generate the highest possible probability of achieving investors’ goals, and a reasonably low expected shortfall in case adverse market conditions make it unfeasible to achieve those goals.
Deguest, Martellini, Milhau, Suri and Wang (2015),[10] introduce a general operational framework, which formalises the goals-based risk allocation approach to wealth management proposed in Chhabra (2005),[11] and which allows individual investors to optimally allocate to categories of risks they face across all life stages and wealth segments so as to achieve personally meaningful financial goals.
One key feature in developing the risk allocation framework for goals-based wealth management includes the introduction of systematic rule-based multi-period portfolio construction methodologies, which is a required element given that risks and goals typically persist across multiple time frames.
Academic research has shown that an efficient use of the three forms of risk management (diversification, hedging and insurance) is required to develop an investment solution framework dedicated to allowing investors to maximise the probabilities of reaching their meaningful goals given their dollar and risk budgets.
Holding a leverage-constrained exposure to a well-diversified performance-seeking portfolio (PSP) often leads to modest probabilities of achieving such ambitious goals, and individual investors may increase their chances of meeting these goals by holding aspirational assets which generally contain illiquid concentrated risk exposures, for example under the form of equity ownership in a private business.
On the one hand, it involves the disaggregation of investor preferences into groups of goals that have similar key characteristics, with priority ranking and term structure of associated liabilities, and on the other hand it involves the mapping of these groups to optimised performance or hedging portfolios possessing corresponding risk and return characteristics, as well as an efficient allocation to such performance and hedging portfolios.
Reporting Outputs - Updated Probabilities of Reaching Goals In most developed countries, pension systems are being threatened by rising demographic imbalances as well as lower growth in productivity.
The principles of goal-based investing can be applied to the retirement problem (Giron, Martellini, Milhau, Mulvey, and Suri, 2018).
The first step is the identification of a safe “goal-hedging portfolio” (GHP), which effectively and reliably secures an investor’s essential goal, regardless of assumptions on parameter values such as risk premia on risky assets.
A target level of replacement income that the investor would like to reach but is unable to secure given current resources is said to be an aspirational goal.
It can be shown that the optimal payoff can be approximated with a simple dynamic GBI strategy in which the dollar allocation to the PSP is given by a multiple of the risk budget, defined as the distance between current savings and a floor equal to the present value of the essential goal.
This form of strategy is reminiscent of the dynamic core-satellite investment approach of Amenc, Malaise, and Martellini (2004),[13] with the GHP as the core and the PSP as the satellite.
It allows the tracking error with respect to the replacement income portfolio to be managed in a non-symmetric way, by capturing part of the upside of the PSP while limiting funding ratio downside risk to a fixed level.
In order to achieve the highest success probability, the GBI strategy embeds a stop-gain mechanism, by which all assets are transferred to the GHP on the first date the aspirational goal is hit, that is if and when current wealth becomes sufficiently high to purchase the target level of replacement income cash flows.
