Outcome Bias and Portfolio Management

Executive Summary

Conventional economics assumes an individual will make the most logical choices during the decision-making process. Advancements in behavioral economics have proven that individuals, given various cognitive and emotional biases, do not consistently make choices that are logical and in fact, could be detrimental to the outcome of their decision. Sunstein & Thaler (2012) have shown in many cases where individual choices are not the most logical and have gone as far as offering ways to nudge the individual into making proper choices without sacrificing freewill, a process they call libertarian paternalism. They note, “research has raised serious questions about the rationality of many judgements and decisions that people make. (p 7)” Lowenstein & Thaler (1989) have gone to great lengths to prove the existence of anomalies within the decision-making process that cannot be explained through traditional economic theory. Ariely (2010) found that having a preconceived notion (stereotype) prior to a specific event will influence your opinion of the event upon conclusion. This essay will elaborate further on the research by Ariely and discuss Outcome Bias as it pertains to portfolio management and more specifically strategy or advisor selection.

Outcome Bias

Pompian (2012) describes Outcome Bias as the tendency of individuals to decide to do something based on the outcome of past events. Consider the following experiment from Baron & Hershey (1988). Subjects were given a questionnaire with a list of medical decisions. They were asked to use a 7-point grading system (exhibit 1) to evaluate the hypothetical medical decision.

Consider the following question:

A 55-year-old man had a heart condition. He had to stop working because of chest pain. He enjoyed his work and did not want to stop. His pain also interfered with other things, such as travel and recreation. A type of bypass operation would relieve his pain and increase his life expectancy from age '65 to age 70. However, 8% of the people who have this operation die from the operation itself. His physician decided to go ahead with the operation. The operation succeeded. Evaluate the physician's decision to go ahead with the operation.

The average score among those that answered was 1.0. They also asked the same question of those that practiced medicine and the average score among that group was 0.85.

Now consider the following question re-phrased:

A 55-year-old man had a heart condition. He had to stop working because of chest pain. He enjoyed his work and did not want to stop. His pain also interfered with other things, such as travel and recreation. A type of bypass operation would relieve his pain and increase his life expectancy from age '65 to age 70. However, 8% of the people who have this operation die from the operation itself. The man decided to go ahead with the operation. The operation failed and he died. Evaluate the man’s decision to go ahead with the operation.

The average score of those individuals that were asked this question was 0.75 and among physicians the average score was -0.05.

The two questions outlined the same scenario but with different outcomes. The evaluations among non-physicians was 33% higher for the scenario with the favorable outcome. In the case of the evaluations from physicians, the difference was even more striking, 0.85 vs. -.0.05 in favor of the positive outcome.

This shows that individuals can be very easily influenced in their decision-making process by previous outcomes. This has profound implications for investors when choosing an appropriate investment strategy/advisor.

Outcome Bias and Investment Strategy

When selecting an investment strategy/advisor, investors appear to be heavily influenced by past performance and participate in “performance chasing” strategies. These strategies have been shown to produce poor investment results (Bailey, Kumar, & Ng, 2011). Exhibit 2 below summarizes annual performance across several asset classes. One can see there is not a pattern of overperformance by one asset class over another.

Exhibit 2: Annual Performance by Asset Class by Year

One can see from the graphic that chasing returns is a futile effort. In 2017 Emerging Markets was a top performer returning 30.9% but in 2018 ranked last with a decline of -21.2%. Also, by avoiding the underperformers of the year, such as Energy in 2015 (returning -23.8%), an investor would have missed owning the top performing ETF in 2016 earning 24.9%.

Prior performance does not represent future returns. As the chart on the next page shows, there is a random relationship between the two, although one can also view a slight negative correlation between the two, possibly justifying choosing the prior year’s underperforming sector. This is the exact opposite of behavior influenced by Outcome Bias.

Exhibit 3: Comparison Between Previous Annual Returns and Future Annual Returns

Implications of Outcome Bias

Investors that chase returns are susceptible to poor decision-making that may lead to chronic portfolio underperformance. The two most important consequences of Outcome Bias are skill avoidance and increased portfolio risk.

Skill Avoidance

By selecting an investment strategy/advisor solely on prior performance, an investor will tend to overlook a strategy/advisor with superior asset management skills simply because the underlying strategy may have been out of favor. As an example, if a strategy/advisor with a superior specialty in emerging markets showed a loss of -5.0% in 2011. When compared on an absolute basis to a strategy/advisor that focused on economically defensive assets (Utilities, Staples and Healthcare) returning +5.0% in 2011, the choice influenced by Outcome Bias would be the strategy/advisor that focused on economically defensive assets (a +5.0% return vs. -5.0%).

This would in fact be a questionable choice. The average return for economically defensive assets (Utilities, Staples and Health Care) in 2011was +11.9%. In this case, the economically defensive strategy/advisor that was selected returned +5.0% in 2011, or 6.9% below the benchmark average. This represents alpha of -6.9%. Emerging Markets in 2011 was down -33.8%. The strategy/advisor that lost only -5% had beaten the appropriate benchmark by +28.8%. This represents alpha of +28.8%. This may indicate that the strategy/advisor in the emerging markets category has superior asset management skills than the economically defensive strategy/advisor (alpha of +28.8% vs. -6.9%). Outcome Bias would have led to questionable choices in this example.

Increased Portfolio Risk

Outcome Bias may also lead to unwanted portfolio risk. Suppose an investor was choosing between two strategies/advisors within the Financial sector. Strategy/advisor #1 showed an annual return of +20% and strategy/advisor #2 showed an annual return of +12%. Outcome Bias would steer the choice to strategy/advisor #1. This decision does not consider risk variables. We will measure risk (portfolio volatility and diversification) using the standard deviation of returns and number of stocks held over the course of the holding period. Let’s assume strategy/advisor #1 held 5 stocks over the course of the year and their standard deviation of returns was +/-10%. Strategy/advisor #2 held 20 stocks over the course of the year and their standard deviation of returns was +/-3%.

If we were to analyze the risk adjusted returns or the return captured per unit of risk one would get a different outcome. Return per unit of risk in this example will be represented by total return divided by the standard deviation of returns. Return per unit of risk for strategy/advisor #1 is 2% (20%/10%) while the return per unit of risk for strategy/advisor #2 is 4% (12%/3%). Strategy/advisor #2 has a superior risk adjusted return profile in this instance but would not be the first choice. Outcome Bias would have led to a poor decision. These poor decisions by investors lead to portfolio underperformance.

Overcoming Outcome Bias

There are four steps an investor can take to overcome Outcome Bias. The steps include (1) determining an appropriate sector weighting to the overall portfolio, (2) choosing a strategy/advisor with the best alpha generation to the appropriate benchmark (skill), (3) choosing a strategy/advisor with the best risk adjusted return profile, (4) rebalancing.

• Determine an appropriate sector weighting. When constructing a portfolio, one needs to consider asset exposure. This portfolio construction should be determined using CFA suitability measures (Stokes, n.d.). An example would be 50% in US equities, 30% in fixed income, 10% in international equities, and 10% in gold.

• Choose a strategy/advisor that has a superior skill within their respective asset class. This can be measured as relative performance to an appropriate benchmark.

• Choose a strategy/advisor with the most favorable risk/return profile and diversification criteria.

• Rebalance at the point when the asset weights described in the first step reach a predetermined misallocation.

Summary

Investors have exhibited behavior that amounts to “chasing returns”. Such a strategy is heavily influenced by Outcome Bias and may lead to portfolio underperformance. Understanding the bias exists, understanding the consequences of the bias, and most importantly, following steps to mitigate the bias may led to better portfolio performance.

Joseph S. Kalinowski, CFA

Ariely, D. (2010). Predictably irrational: the hidden forces that shape our decisions. New York: Harper Perennial.

Bailey, W., Kumar, A., & Ng, D. (2011). Behavioral biases of mutual fund investors. Journal of Financial Economics, 102(1), 1–27. doi: 10.1016/j.jfineco.2011.05.002

Baron, J., & Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of Personality and Social Psychology, 54(4), 569–579. doi: 10.1037/0022-3514.54.4.569

Lowenstein, G. & Thaler, R. (1989). Anamolies: Intertemporal choice. Journal of Economic Perspectives, 3(4), 181-193.

Pompian, M. M., & Pompian, M. (2012). Behavioral finance and wealth management : How to build investment strategies that account for investor biases. Retrieved from http://ebookcentral.proquest.com Created from tcsesl on 2020-04-02 09:54:34.

Stokes, J. (n.d.). Standard of Professional Conduct III(C) Suitability. Retrieved from https://www.cfainstitute.org/en/research/multimedia/2018/professional-conduct-standard-IIIC-suitability

Sunstein, C. R., & Thaler, R. H. (2012). Nudge: Improving Decisions About Health, Wealth and Happiness. London: Penguin.