Three investment decision making mistakes to avoid in uncertain times
Over the last few weeks, in the face of an unknown outcome, every man and his dog has offered an opinion on markets.
Behavioural economists and psychologists have shown that people, even experts in their field, have poor track records of making proper assessments in times of uncertainty.
There are three ways we quickly assess the probability of an outcome. All of these are susceptible to flaws. An understanding of these can help investors better assess periods of uncertainty and thus lead to better decisions in uncertain environments.
Fans of the book (and film) Moneyball, know the story of the Oakland A’s manager Billy Beane and his use of sabermetrics. After reading the book, behavioural economist Richard Thaler argued Moneyball author Michael Lewis had missed the larger story. According to Thaler, the success of the Oakland A’s could be traced, not only to the world of baseball’s sabermetricians, but also to the work of psychologists Daniel Kahneman and Amos Tversky. Moneyball was a story about behavioural economics.
Kahneman and Tversky have shown that people, even experienced researchers, make poor decisions when faced with uncertainty. In a research paper, Judgement Under Uncertainty, Kahneman and Tversky highlighted three decision making processes that people use to assess probabilities or make estimations:
- representativeness;
- availability; and
- adjustment and anchoring.
According to Kahneman and Tversky (1974), “A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty.”
Representativeness
One example Kahneman and Tversky use to explain a judgement that uses representativeness asks us to consider Steve. Steve is described as a shy, withdrawn person with little interest in people. Steve needs order and structure. He is described as a meek and tidy soul. Now we have to assign Steve an occupation from a list of probabilities (for example, farmer, salesman, pilot, librarian, GP). Representativeness is the process that we use to assess Steve. We assess what we are told about Steve against the degree to which he is representative of the stereotype of the occupations in the list and many people would conclude that Steve is a librarian. This approach can lead to serious errors because representativeness does not consider several factors that should affect judgements of probability. These errors include insensitivity to probability and sample size.
Insensitivity to probability - There are more farmers in the world than librarians, yet when we consider what occupation Steve has, we do not consider this information. It is actually much more likely that Steve is a farmer because there are so many of them. Even when researchers’ experiments include relevant population statistics, the relevant probabilities were ignored when descriptions, even irrelevant ones, were given.
Insensitivity to sample size – People assign their knowledge or ‘averages’ to represent samples without regard to the size of the sample. This can lead to errors. For example, after being told the average height of a man is 5’9”, when people are then asked to assign the probability that a random sample of men would be higher than 6’ for different sample sizes, time and time again people suggest the probability is the same for a sample of 10,000, 100 or 10. This is flawed because only as a sample gets larger it is less likely to stray from the mean, therefore is more likely with smaller samples. In other words, representativeness causes us to underestimate probabilities in smaller samples. People don’t recognise this.
Availability
Availability is the decision making process of assigning the likelihood of an outcome by the ease with which instances can be remembered.
According to Kahneman and Tversky, “For example, one may assess the risk of heart attack among middle-aged people by recalling such occurrences among one’s acquaintances.” This leads to biases, often overestimating the likelihood of occurrence.
Recent occurrences too are likely to be more readily remembered and therefore will increase the estimation of occurrence. Furthermore the impact of an experience will impact decisions. For example, the impact of seeing (or even hearing about) a shark attack will have an impact on your assessment of the probability of a shark attack.
Adjustment and anchoring
The experiment that highlights anchoring and adjustment involves two groups of students. One group is asked to estimate the answer for:
1 x 2 x 3 x 4 x 5 x 6 x 7 x 8
The other estimates the answer for:
8 x 7 x 6 x 5 x 4 x 3 x 2 x 1
Kahneman and Tversky hypothesised that adjustments we make quickly when estimating are typically too low, so the resulting guess will be lower than the real answer. Furthermore because the result of calculating an estimate is based on a starting point, which is established in the first few steps of making an estimate, estimates will be higher in the descending sequence than the ascending sequence. This is because the descending sequence, anchored by a higher starting point, should give a higher ‘estimate.’ The results of Kahneman and Tversky’s experiment confirmed both predictions. “The median estimate for the ascending sequence was 512, while the median estimate for the descending sequence was 2,250. The correct answer is 40,320.”
Psychologists have found, that because of anchoring we are also bad at estimating probabilities of (i) conjunctive events (for example, drawing a red marble seven times in a row, with replacement, from a bag containing 90% red marbles and 10% white marbles); and (ii) disjunctive events (for example, drawing a red marble seven times in a row, with replacement, from a bag containing 10% red marbles and 9% white marbles).
We tend to overestimate the probability of conjunctive events and underestimate the probability of disjunctive events. This is because our estimation is ‘anchored’ in the simple probability of success at any one stage and our adjustments are insufficient.
Applying this to the current crisis
Markets have moved with such speed and velocity since the beginning of the coronavirus outbreak it has been difficult to accurately ‘anchor’ risks. It’s almost impossible to adjust with so much information and misinformation being circulated. We are making decisions based on small samples. Many adjectives and considerable emotive language has been used to describe the outbreak and its impact.
It’s important to look past the positive and negative commentary and concentrate on your long term goals. Assess risks, understand the data, without emotion or haste. Often we are ignoring probabilities relevant to our decision. The chance of rate cuts, which impacts prices, is changing hourly. In markets, unpredictability is the only constant. Successful long-term investors survive short-term falls by sticking to investment principles that have withstood the tests of time. For portfolios, this may include better diversification. For equities, investing in profitable companies with strong balance sheets and stable earnings has historically given resilience to portfolios.
Kahneman and Tversky’s lifelong passion was to understand why people do not detect the biases and flaws of their own judgements. They proved we are poor decision-makers when faced with uncertainty. Their work transcended psychology, into military, sports, medicine, politics and economics. In 2002, Kahneman was awarded the Nobel Prize in Economics for his work on decision-making and uncertainty and his 2011 book Thinking, Fast and Slow provides the collected wisdom of his lifetime’s work. The mistakes discussed above are highlighted, so too our aversion to losses. The subject for another Vector Insights.
Published: 06 March 2020
