Theoretical approaches to decision making assume rationality. However, early assumptions about how humans would make decisions, particularly with regard to finances, failed to account for how we as humans make choices in the face of uncertainty. The reality is that human beings actually make decisions by considering the probabilities of gains and losses associated with competing alternatives, rather than solely considering the end-state.
Behavioral economists were among the first to consider human irrationality in decision making. Research showed that in scenarios where people could choose certain loss of a small amount vs. possible loss of a great amount, people consistently chose the latter, because it had the benefit of uncertainty. The same model framed as gains led to the opposite result. As such, researchers concluded that utility in decision outcomes is a perception held by individuals, rather than an objective state.
But the reality is that human beings are far more complex than even this simple understanding allows. Three different investors with the same portfolios, information and risk often do not make the same choice — meaning that probability, odds, and systematic irrationality are not the sole factors that explain financial decision making. And so behavioral finance evolved to consider a wide array of cognitive and psychological mechanisms. There are three main areas to consider: cognition (biases, heuristics), biodata (experiences, demographics), and individual factors (personality, motivation). Let’s have a look at each, to better understand our clients.
In a perfect world, we would evaluate all information in order to make decisions. But the reality we must all come to grips with is that human beings do not have the cognitive and memory capacities to retain the vast amount of information available in most scenarios, let alone the resources to make decisions in that manner.
The problem is not the quantity of information, but rather that people are inherently poor at evaluating the relevance of various information presented to them, particularly in areas where they have less expertise (for example, investing/financial planning). In fact, there is research that shows that investors make consistently better decisions and are subject to less bias when advisors present them with information that has been aggregated.
Biases themselves are fairly systematic, and can account for a lot of the variance in financial decisions and investment success. One of our goals is to help advisors identify the various types individuals who are prone to a particular bias so that we might help you, the advisor, understand what you are up against when providing counsel.
The first such bias type is informational bias. These are the most common when examining what may have led us to decision failures. Confirmation bias, a subset, is where an individual lends more credence to information that confirms an already existing belief, and views information counter to that belief as more likely to be unstable or irrelevant. In financial situations, this may be why investors hold onto an investment longer — they may be overweighting the information that has a favorable view. People also tend to rely more on information that is broadly known and easily available, in some cases more so than perhaps information their own advisors may possess.
Referential biases are another challenge that advisors must identify and work to overcome with clients. One such bias is known as the Disposition Effect — whereby a client tends to want to sell assets that have appreciated in value and sell those that are underperforming. The trouble here is that people assume the average performance of an asset is the price they bought the asset at, and they use that as their primary point of reference, also known as anchoring. People assume that in a state of loss, the asset will inevitably return to its original cost. Similarly, they assume a gain indicates an asset will eventually plummet, because they believe the price they paid is the neutral price for that asset.
Another referential bias is very similar to the psychological process of stereotyping. An investor might consider that their holding in a company of good historical stature and prominence to be an inherently “good” investment, however the characteristics that have created the perception of a company being “good” are not the same as the variables that one would evaluate to deem something a “good” investment.
Predictive biases are the final category to examine as pertains to cognitive influences over investors. Overconfidence is a good example of this, and is very common in behavioral finance. It is effectively the tendency of people to overestimate the likely occurrence of an event because these people have faulty perceptions about their own knowledge. This bias rears its head most often as people review information. When people are overconfident, they rely far too heavily on information they perceive as representative of all information. This leads them to overestimate their ability to predict an outcome. Research shows that overconfidence bias tends to lead to a statistically higher number of trades with an absence of equivalent returns.
It would be remiss not to mention demographic considerations when discussing the variables that influence financial decision-making. However, the caveat to the utility of demographic information in predicting investor behavior is that though these tangible characteristics have very little measurement error in what they are intended to measure (eg: biological sex is an extremely accurate metric), they have a HIGH degree of measurement error when we apply them to other intangible characteristics (eg: risk propensity).
Another way of explaining this is to consider what is meant when we talk about gender differences in investment performance. For example, one study might show that when we compare all the males from a group of investors to a group of all females, the men on average tend to “trade more frequently.” In reality, the results skew that way because of a small sample set of males within the group. This is not to imply that demographic information such as sex, age, race, culture, environmental and social standing are to be dismissed as irrelevant — but taken in isolation they are far less useful in understanding clients.
Perhaps more interesting is the psychological effect demographics has on the clients themselves. Research shows that people, aware of their surface-level identity and perhaps to a degree affected by stereotypes assigned, are prone to what is called “stereotype threat” (ie: they match their behavior to the surface level stereotype assigned to them). For example, in meeting with an advisor, men may posture as being more open to risk and risky investments because they feel this is expected of them, that their advisor is looking for them to behave in a certain way. Women may take a more cautious role in such meetings, perhaps feeling that this is expected. Advisors who are aware of these possibilities are in an excellent position to empower their clients to make decisions armed with the best possible aggregated data, and expert counsel, resisting the urge to conform to what they believe may be expected, or other dangerous assumptions.
In the fourth and final installment of this series, we will explore the least researched aspect of investors, characteristics we may refer to as personality traits, emotional tendencies, or individual motivators.