Goal-based portfolio management in which behavioral finance and academic theory are combined can be beneficial. Because most retail investors invest to achieve a certain goal (e.g., pension income), the achievement of this goal at the end of the investment period is more important than a risk–return analysis. The author provides some recommendations about advising clients to achieve these goals and avoiding emotional decision making.
The author aims to provide investment advisers with practical tips that will allow them to deal with clients’ emotions during market turmoil. Classical investment advice applies risk tolerance from a questionnaire with quantitative tools, but the focus on attaining a certain goal is forgotten in that framework. Working from the stated goal, the author develops a model in which a required return is derived that can be adjusted over time depending on portfolio developments. By readjusting the portfolio regularly, the adviser ensures that the stated goal is the focus point of the potentially emotional ride.
How Is This Research Useful to Practitioners?
In previous research, the ideas behind goal-based mental accounting and Markowitz’s portfolio optimization were combined. The result was the definition of a portfolio’s required return that would be sufficient for obtaining a certain future value representing the client’s need. The required rate of return could also take into account a growing rate of contribution and the effects of inflation. But instead of obtaining the client’s growth requirement from a questionnaire, advisers would mainly obtain the client’s level of risk tolerance. Because the interpretation of the questionnaire is mainly subjective, the question arises about whether the outcomes should be restricting the investment choice as much as they do in current practice. Furthermore, as other academic research has shown, myopic loss aversion might occur: Clients are more focused on short-term losses than on longer-term gains, so they avoid higher-volatility assets even when those assets have a higher expected long-term return.
Instead of focusing on risk tolerance, the author focuses on goal attainment when determining an investment mix. He uses an asset mix consisting of bonds, stocks, and cash. The worsening effects of portfolio volatility are taken into account by using a volatility drag compensator variable. Furthermore, each year the portfolio’s future required return is assessed based on past returns over the previous year, which allows the investor to adjust the portfolio over time and reassess the development of portfolio loss tolerance over time.
The author also introduces the concept of theta risk. As the time between now and the moment when the goal has to be achieved shortens over the duration of the investment period, the client’s risk tolerance decreases over time. As the client’s loss tolerance changes over time, time diversification can be achieved in which lower tolerance in some periods can be offset with higher tolerance for losses in other periods. The author shows that clients who are willing to take on additional risks at the start of the investment period can be more flexible when the end of the investment period approaches. Interestingly, according to prospect theory, it appears that investors do not behave in this way.
This research will appeal to financial professionals advising retail clients on their asset portfolio construction. The traditional use of a questionnaire as a constraint tool could be adapted to find out more about the client’s needs and hopes instead.
How Did the Author Conduct This Research?
The author explains the framework in a practical application for three asset classes: bonds (10-year Treasuries), stocks (S&P 500 Index), and cash (3-month T-bill), for which data are taken from the time period of 1927 to 2013.
He then introduces a framework in which portfolio construction is less important but goal achievement is more important, resulting in a better understanding when losses become unrecoverable. This approach is accomplished by establishing a feasible range between the minimum required return and a portfolio exceeding the required return. As a result, some upward potential is built into the chosen portfolio but unrealistic returns are excluded. A maximum allowable loss is determined to allow for risk-mitigating strategies, such as options and hedges. Reducing potential downside risks, reviewing the plan annually, and possibly adjusting the portfolio are all part of the process.
The author uses the 60th–90th percentile of volatility-adjusted returns to determine the maximum downside tolerance. More extreme losses are reduced with the use of put options and stop losses. Overall, this approach reduces tail risks while increasing the chance that the portfolio achieves its goal at the end of the term.
Most current economic theory seems to indicate a gap between classical optimization theory and behavioral finance. Although the latter has been promoted over the past decade, the main use of it in practice has been for estimating a client’s constraints instead of estimating a client’s needs. The author shows how optimization theory and behavioral finance can work together to fulfill an investor’s requirements. The example the author uses helps in understanding the ideas behind his concept. Many financial professionals seem to be aware of both portfolio construction and behavioral finance, but they might be unaware of the potential leverage between the two concepts.