Behavioural finance teaches a great deal about the types of errors that investment practitioners make in their analysis and pricing of market securities.
But it also shows how investors view their portfolios and how these portfolios should be constructed to meet investor objectives. Goals-based investing aims to do just that: understand how individuals frame investment objectives, express risk in understandable terms and construct portfolios best positioned to meet these objectives. This is a shortened version of a longer article published by Nevins in the Journal of Wealth Management (1).
Explaining the imperfections
ECONOMICS is a social science. And, like many social sciences, economics attempts to model aggregate human behaviour by setting out a number of assumptions regarding market players, constructing relationships between these players and following these relationships to their logical conclusions. Most practitioners agree that the assumptions are imperfect but few agree on the sensitivity of results to violation of the foundational axioms.
Behavioural finance has for some time been used to explore and explain imperfections in the capital markets, for few suggest that the markets behave perfectly in line with Markowitz's classic model. For more than two decades researchers have studied how behavioural biases - such as overconfidence, belief perseverance, overreaction, regret avoidance and hindsight bias - have affected players in the market, calling into question the most important of foundational assumptions:
Rational decision-making.
This article isn't about irrational trading decisions and their effects on capital markets.
It's about recognising that all investors, sophisticated or not, tend to think in boxes, aiming to attain a variety of objectives rather than attempting to manage all assets (and the corresponding liabilities) in a single block. Successful investing depends on attaining these goals more than on achieving that utopian, theoretical and practically out of reach ideal: the optimised portfolio.
Goals-based investing combines traditional and behavioural finance, recognising that investors:
* Are often as concerned about losses during their period of investment as the potential for a poor outcome at the end of the period.
* Are seldom interested in volatility as a measure of risk, as it doesn't reflect the real value to the investor of positive and negative outcomes.
* Prefer to consider each goal in isolation and invest in a way that gives the highest opportunity to meet that particular goal with the lowest risk of failure, as defined by the investor.
And it's the definition of failure that most strongly differentiates goals-based investing from traditional optimised portfolio investing. Behavioural finance lessons illustrate the difficulties in determining the attitude of investors to risk.
The familiar questionnaire promising to provide an understanding of the socalled risk profile frequently fails as a result of the very tilts it's trying to avoid.
Individual biases such as loss aversion, decision framing and mental accounting blemish the promise of accurate results - how difficult it is to avoid the middle option of the three no matter how the options are framed.
The designers of these questionnaires miss the whole point: it's not possible to shoehorn investor decision-making into the computationally convenient meanand- standard-deviation framework of the capital asset pricing model.
We consider just two examples of investor needs to illustrate the benefit of goals-based investing techniques.
Need No 1: Current lifestyle requirements
An investor has a capital sum and needs to use it over a number of years. Pensioners without fixed annuity protection are frequently in that position; charities and foundations as well, though they usually wish to end the period in good financial shape, their capital intact.
There are a number of ways to meet this need. For example, absolute return mandates may be useful, or explicit cash flow matching techniques. But whatever approach is chosen, expressing the expected returns and potential downside in a way that the investor understands is crucial to selecting an investment mix that's most likely to lead to success as defined by the investor.
Graph 1 demonstrates a revised framework for expressing risk and return. We use US data to determine the return distribution for each portfolio and Monte Carlo simulation methods to project forward the potential outcomes. The investor is assumed to be drawing on a pool of US$1m, exhausting the pool at the end of a period of 10 years.
Reward is defined as the expected rate of spending, not far off the conventional expression of reward as an annual investment return but far more relevant to the investor. The objective is to maximise the expected spending rate for a given level of risk.
But defining risk is more important, for the very definition of risk influences the outcome of the exercise. In this case, we define it as the worst sustainable spending rate at any time during the period(2).
This approach recognises: (1) that investors are just as concerned about investment performance during the period, as they're the eventual outcome at the end of the period; and (2) that investors see risk in terms of an outcome, not some theoretical construct, like standard deviation.
The key is that the decision is framed in terms of the goal.
As it happens, the ideal portfolio may be close to the result that would be obtained from a typical optimisation exercise; but the results are obtained with far more confidence and understood more clearly.
Furthermore, the methodology allows alternative risk measures to be used. For example, potential loss is the amount that the investor could lose between any two points of time within the investment period - the peak-to-trough gap that more often hits the news and causes investors to respond inappropriately.
Need No 2: Investing for a fixed planning horizon
An individual has an amount to invest and no need to access this capital (or income arising from it) for a number of years. This could be a retirement saving, or preparation for tuition fees.
Reward can be expressed as the expected capital amount at the horizon date. After all, we invest for a period aiming to maximise the value of that investment at the end of the period.
There are a number of ways of framing the risk of this investment that make sense to the individual. One way is to define it as the probability of losing money over the period: ending with less than we started.
That's a little simplistic and unlikely to lead to a sensible allocation of assets(3).
An alternative is to describe risk as the worst case value of the portfolio at the horizon date. Graph 2 shows an illustration of the trade off between the expected portfolio value and the worst case portfolio value 10 years in the future for a current lump sum of $500 000.
Again, the outcome may be familiar to those more used to traditional capital asset pricing models. The benefit is that the investor has been given the opportunity to consider risk in terms that are easily understandable. A strategy understood is likely to be a strategy maintained.
Goals-based investing is an approach to wealth management that draws from both traditional and behavioural theories.
Multiple strategies are linked to multiple goals and the approach improves on traditional approaches in the areas of measuring risk, risk profiling and managing behavioural biases.
Dan Nevins
NEVINS joined SEI in 1999 and currently leads SEI's portfolio strategy team, overseeing research on investment strategies for individual and institutional investors.
Nivens holds two degrees from the University of Pennsylvania: a Bachelor of applied science from the Moore School of Electrical Engineering and a Bachelor of science in economics from the Wharton School of Business.
He's a member of the Financial Analysts Society of Philadelphia and the Association for Investment Management & Research. He also holds AIMR's chartered financial analyst designation
Rob Rusconi
RUSCONI is a member of the global asset allocation team at SEI Investments and responsible for providing strategic advice to South African clients on asset allocation and related issues.
He has experience in a wide variety of financial services roles in SA and Britain. He's worked for Old Mutual and Sanlam, the actuarial consultancy Hewitt Bacon & Woodrow and the Internet business of London's Financial Times, FT.com.
He has a statistics degree from the University of Cape Town and was admitted as a Fellow of the Institute of Actuaries in 1998.