Putting The Focus Back On Client Goals

Key Takeaways

· Investors’ goals are unique and should always come first

· Market regime changes happen

· Factor modeling may uncover hidden risks

· Alternative strategies may enable more effective diversification

What do we mean by outcome-based investing?

Demand for alternative investments (alts) has risen further over recent months among advisors as both components of the 60/40 portfolio have fallen.^[1] Fixed income has provided little shelter from the equity market decline, and may be entering a new paradigm of positive correlation with equities. However, the trend towards alts has been playing out over many years, as advisors and investors increasingly recognize the need to broaden their toolkit as they look for solutions to specific client goals.^[2] This demand has contributed to the rise of outcome-based investing.

Outcome-based investing is often used interchangeably with goals-based or defined-outcome investing. Whatever you call it, it starts with clearly defining a desired outcome, then finding a solution that helps deliver this objective from a more holistic viewpoint. The emphasis for the specific investment is on meeting the unique goals of each client or class of clients, providing a customized, goal-centric approach rather than a generically optimized long-term investment (for example, a passive, quarterly-rebalanced, 60/40 portfolio).

An outcome-based approach does not reject existing Modern Portfolio Theory, it simply introduces practical constraints or variables that take into account an investor’s income and estate needs, cash flow goals, needs around life-changing events, ESG considerations, tax optimization, and many other factors. It also introduces investment strategies with different time horizons into the allocation process, including different alts strategies, which expand the toolkit available to execute outcome-based strategies.

Why consider an outcomes-based approach?

There are several fundamental reasons for using an outcome-based framework.

Investors’ risk preferences are neither uniform nor constant through time. Furthermore, there is no single universally accepted asset allocation model, and the total value of investible assets is important when setting long-term objectives. Certain investors can simply target the highest return possible with limited regard for risk, while other—often institutional—investors think more commonly in the context of risk-adjusted returns (return per unit of risk assumed). Open-ended traditional asset classes like stocks and bonds allow investors to benefit from global prosperity, but only over very long investment horizons. Moreover, portfolio diversification assumptions may fail in periods where correlations between traditional asset classes invert (like in the first quarter of 2022).[3] Extended periods of increased correlation between traditional asset classes can bring into question long-term expectations.

Many institutional investors, for example, publish capital market assumptions (CMAs) on an annual basis to generate long-term asset class risk, return, and correlation expectations that guide allocation decisions. CMA methodologies generally aim to include as much data as possible and incorporate multiple market cycles. However, running a CMA-based portfolio diversification process using only traditional markets implicitly assumes a very long-term investment horizon, which is unlikely to be applicable to all investors. In addition, stationary correlation assumptions may prove unrealistic as highlighted by the market regime shifts during the recent transition from a low to high inflation environment.

Advisors need better control over portfolio diversification, volatility, and time horizon given that each client’s financial condition and expectations are unique. This is where an outcome-based investment approach can help, as an alternative method of looking at risk, return, and portfolio diversification. In this case, an outcome is what satisfies a client’s specific investment objectives via an expanded set of asset classes and strategies, both traditional and alternative.

While one client may consider inflation protection to be the highest priority, another client, perhaps with a shorter time horizon, may be most concerned about preparing their portfolio for a severe recession over the coming years. Yet another client may be more aggressively seeking excess returns while simultaneously reducing volatility.

None of these clients are having their needs fully satisfied by a standard 60/40 portfolio built to outperform or track its corresponding benchmarks. For example, a generically optimized liquid portfolio may ignore the complexities associated with an income-oriented strategy and their implications for concerns such as liquidity and tail risks.

Limitations of a traditional approach to portfolio construction

To be clear, a long-term and consistent approach remains a key investing principal. However, market regime changes are real, and they demand dynamic portfolio adjustments. A traditional view of portfolio construction and management struggles to account for these changes in market conditions and often falls prey to “market timing” concerns even if the changes are actually structural or secular.

Simple asset class-based allocations in a portfolio can suffer from unintended overlapping risk exposure between stocks, bonds, commodities, and other instruments. Therefore, it is very important to fully understand the drivers of risk and returns in a multi-asset portfolio to help mitigate exposure to comingled risks and/or unexpected increases in correlations under certain market conditions.

How factor modeling supports outcome-based investing

Decomposing portfolio risk exposure by “factors” helps map specific portfolio allocations to long-term outcomes. Factor modeling can augment outcome-based analysis for a more granular and accurate view of risks.

While factor analysis has deep academic research behind it, it is much more than just an academic subject, with numerous real-world applications. For example, factor analysis could reveal that returns from an aggressive growth equity fund may actually move in lockstep with bonds due to its “factor exposure” to interest rates. What looks like pure 100% stock exposure becomes highly correlated with bonds leading to simultaneous portfolio losses as interest rates rise. Conversely, a low-quality bond portfolio has the potential to exhibit equity-like downside while reporting very attractive bond-like results under normal market conditions.

Factor modeling can uncover such hidden exposures in a portfolio where observed asset class allocations and cross-asset correlations may not fully reveal portfolio risks.

Conclusion

Increased uncertainty in public markets due to the shift into a high inflation and high volatility environment may put an end to the relatively straightforward investment landscape that prevailed following the Global Financial Crisis, as unusually low and declining interest rates drove outsized gains among a narrow range of stocks in tech and growth.

Advisors may need to shift their focus back to more specific client needs and actively manage excessive volatility. This requires the use of an expanded toolkit with more advanced strategies, potentially incorporating an outcome-based framework. Private alternative funds, hedge funds, and structured notes offer unique diversification, risk management, and return enhancement opportunities for the more challenging market environment ahead.