You know that feeling when your dishwasher breaks but you chose to not pay 50 euros more for a 5 year guarantee because you did not expect it would break in the first place? Not the best I will admit, but the protagonist of this story is not the dishwasher nor how stingy we are, it is the expectations.
Expectations were also the central topic of the event our association hosted on the 16 February titled “Behavioral finance: the latest frontier of agent expectations”. We invited professor of financial economics of the University of Zurich, Thorsten Hens and the experimental and behavioral economist for the EU policy lab, Ginevra Marandola, to discuss with us new behavioral insights into the complex world of expectations and human decision making and to deepen our understanding of human behavior. The event has been moderated by Bocconi University professor Massimo Marinacci.
The concept of expectation is central for the explanation of real-life behavioral phenomena and economic expectations are crucial in determining economic and financial activity as they affect agent’s decisions. The classical models of economic theory that have been developed and applied in the last 50 years rely on the assumption that agents hold rational expectations. The idea of rational expectations was first developed by American economist John F. Muth in 1961 and was popularized in the 1970’s by economists Robert Lucas and T. Sargent and was widely used since. In few words, the rational agent is assumed to take account of all available information, to use probability for the evaluation of events, to use discounting when decisions affect choices (such as consumption) at different points in time and to act consistently in choosing the self-determined best choice of action. In simpler terms, this theory dictates that every person, even when carrying out the most mundane of tasks, perform their own personal cost and benefit analysis in order to determine the optimal action for the best possible outcome.
The rational agent theory has been put into discussion and has been at the center of behavioral economics and finance research as the real-life human behavior is systematically different from the rational model assumptions, just think about your dishwasher. In fact, since the development of “prospect theory” by Daniel Kahneman and Amos Tversky in 1979, the research about human behavior has boomed and so did the uncovering of biases and heuristics that affect humans’ decision-making processes.
In this context professor Hens, who broke the ice with his presentation, together with Phd student Mei Ding-Hirschfeld has researched the link between personality traits and investment styles. A rational investor would base his investment choices purely on rational expectations about the future returns given his own risk preferences in order to maximize his expected utility. Therefore, psychological factors, personality and emotions would not influence the decision making. To investigate whether the rationale behind this assumption holds or that personality traits do play a significant role in investment choices, they firstly defined categories of investment styles. From the simplest to the most complex style, the categories used for their research are: safety, buy and hold, rebalance, value, carry, momentum and growth. At this point, by conducting an online questionnaire proposed to 20000 randomly selected individuals of the German population they assess the personality traits and the preferred investment style among those proposed and described by the researchers. The personality traits used for the survey refer to the OCEAN 5 personality traits, that is, openness (inventive/curious vs. consistent/cautious), conscientiousness (efficient/organized vs. easy-going/careless), extraversion (outgoing/energetic vs. solitary/reserved), agreeableness (friendly/compassionate vs. challenging/detached) and neuroticism (sensitive/nervous vs. secure/confident). From the results of the survey they found out a strong link between personality traits and preferred investment styles. For example, more extrovert individuals seem to prefer a growth strategy, while openness would lead to momentum strategies. These results refer to the in-sample analysis, and to further support the findings they conduct an out-of-sample analysis by developing a style profiling tool based on personality traits. This consists in creating a treatment and a control group, where the first ones are given an investment recommendation based on their personality traits following the in-sample analysis results, while those in the control group are assigned random investment style recommendations. Afterwards the individuals from both groups are asked their level of satisfaction about the recommended style, and as expected by the researchers, those who were given personality based recommendations where more satisfied than those who did not. They also conducted various robustness checks like accounting for the influence of the distance between the correct and the false recommendation, finding that the results hold. The professor also took questions at the end of the presentations explaining that they are extending their research to assess the link between returns, personality and investment styles.
Following professor Hens presentation, Ginevra Marandola presented, after an initial introduction on the benefits of behaviorally informed policy making, the findings of a research conducted by the economists of the EU Competence Center of Behavioral Insights of the Joint Research Center (JRC) on request by the Directorate-General for Financial Stability, Financial Services and Capital Markets Union (DG FISMA). The research question was understanding what behavioral insights play a role in consumer’s decision-making with regard to switching of payment accounts and mortgages and what solutions at policy level could be implemented. The context research showed that consumers rarely change their financial services providers due to administrative hassle, trust or lack of awareness of better offers. This phenomena lead to a reduction in competition and in non-switchers subsidizing the switchers. As a result, finding solutions only on the supply side would not be sufficient as the inertia and reluctance on the buy side would remain. For their research they conduct 2 laboratory experiments for payment accounts and 2 online experiments for mortgages. The behavioral factors that they consider as playing a role in switching reluctance are inattention (failure to notice the opportunity to act), cognitive overload (too much complexity involved in decision.-making), present bias (disproportionate preference for rewards now rather than in the future), risk aversion (the choice is made under some degrees of uncertainty which prevents the risk averse consumers to change) and loss aversion (the fear of incurring losses weighs more heavily than the prospect of gains).
For the payment account switching reluctance they create 2 experiments of the same design, one for the general population (age 25-60) and one for university students, with 753 subjects in the first category and 734 for the second one. The experiments are conducted in 3 countries: Spain, Poland and Germany. The experiment design consisted in a multi-step process. Firstly, the subjects had to perform a distractor task which consisted in counting numbers on a matrix and if they got the correct number they would earn money. The task was repeated 36 times that in the experiment represented 36 months. The earned money would be directly placed in a current account that requires a fee payment. The subjects had also an associated savings account that produced interests. As the subjects were generating money by solving the matrices, they could choose they preferred current and savings account contracts. To choose they contracts they had to go through a “hassle action”, that is, they had to go through a list of contract proposals and choose. The optimal contracts for the two accounts would change at each period. The experiment had a baseline treatment ( a control group) where the individuals did not receive any further information through the experiment, and 4 treatment groups. The first treatment consisted in a reminder after the individuals solved 12 matrices in which they were told what the most important features were to consider when making a switching decision, but together with this message they also gave a negative one (that reminded them that they would lose time in the process and therefore be able to solve less matrices). In the second treatment they removed the switching costs. In the third treatment they provide personalized advice and a summary of the contracts which allowed subjects to avoid the whole procedure of reading through contracts. Lastly, in the fourth treatment they provide the feedback by peers (whose names, age and date of participation to experiment would appear next to their statements) which had the same message as the first reminder treatment.
The result of the experiment showed that for the general population 46% of the baseline group never switched (inertia), the percentage for the reminder group (first treatment) was 27,52% and this group switched 1,3 times while it was 35% for the third treatment and they switched 1,1 times. The optimal choice would have been switching twice. For students the percentages were 13% (baseline), 10% (reminder), 5% (costless) and 12% (feedback). The costless group of students also switched more than optimal. From this we gauge that inertia was not really a problem for students and that when making complex choices peer feedback is irrelevant, which is important as in other situations peer feedback has a crucial role in decision making. Another result is that for the general population the reminder worked not only in improving the switching frequency but also its quality, while for the students only the frequency was increased. This difference between the general population and students is currently being further investigated by the researchers. Overall, the policy implications are that reducing switching cost (time and effort) would increase the frequency in the short term, summary tables are effective only for the active consumers, reminders in bank annual statements should be specific and designed to keep in mind behavioral factors such as present bias and loss aversion, advice allows to reduce the cognitive efforts and facilitates better choices and finally, policies relying on peer effect for changing financial behavior are not recommended based on the research evidence.
The research results presented during the event are a step forward in understanding the complex puzzle picture of agent expectations and consequent decision making. The behavioral insights can sometimes seem unreal or complex as every economist likes some nice jargon to spice up his tables and graphs, but this only lasts till we do not find ourselves falling into biases and heuristic traps.