You want to invest in your favorite trading platform and have the option to choose 5 funds out of a long list of 100 funds. Each fund has a descriptive sheet expliaining the level of risk of the product, their ESG impact, their sectors of activity…
You are quite busy and do not have a lot of time to make your investment decision. What is your strategy to choose the right funds?
Strategy 1: analytical thinking
You analyze one by one the elements which are important in your investments. You want of course a fund with a good past performance, but also with a strong ESG impact, if possible in climate change mitigation. You want if possible to avoid complex products like structured or derivative products since you are not familiar with their mechanisms… Finally, you end up with a list of about 7/8 criteria to find the right fund.
You can try to use your analytical system 2 by considering each criteria one by one. Unfortunately most of us cannot cope with more than five criteria at once, especially if they do not have the same importance. So the best thing you can do if you want to be rigorous is to write all our weighted criteria on a paper or excel file and make a few calculations.
If time allows, this is the best approach. However, the risk, if time is limited, is to do this job partially and to end up with the wrong choices.
Strategy 2: intuitive thinking
In practice, people rarely follow the first scenario. It is quite laborious and time-consuming.
Most investors will read quickly the fund descriptions and make their choice intuitively.
What is the most effective strategy?
Behavioral experiments have shown that for simple decisions (where, for instance, we would have only three criteria to choose between two wines), the analytical thinking approach (strategy 1) is the most effective.
Yet, for complex decisions, like in our example of multi-criteria fund selection, the intuitive approach is more effective, especially if our time is limited.
Why? Because our analytical brain is not able to consciously manage a huge quantity of data. If we try to use our analytical brain (System 2) in a too complex situation, we will not finish our reasoning and finally focus on one arbitrary criteria. For instance, we will focus only the funds’ risk or ESG impacts, ignoring the other criteria even if they are as important.
The reasoning will thus be incomplete and the result will not be satisfying, unless we have tools to help us manage rigorously this amount of data, like an excel file or a robo-advisory process.
If not, the intuitive approach will be more effective. The results may not be optimal, but more satisfactory than in the first approach.
Behavioral economic experiments
To test the hypothesis described above, Dijksterhuis and co ran the following experiment.
Two groups had to choose their favorite car in a list of four, each one described by 12 attributes.
The first group was asked to read the description of the cars before doing a 4-minute long task to finally spontaneously choose their favourite car.
On the contrary, participants of group 2 had 4 minutes to view and review the criteria to make up their mind. At the end, Group 1 made better decisions than Group 2. The spontaneous answer of Group 1 triggered by their intuitive mind, was better than the thoughtful answer of Group 2.
Further studies have shown that in both cases, groups use their prefrontal cortex which is correlated with high cognitive decision-making.
Conclusion: our intuition can sometimes provide better solutions than our analytical mind for complex problems
Unconscious errors enhance prefrontal-occipital oscillatory synchrony
Michael X Cohen1,2* †, Simon van Gaal1,3†, K. Richard Ridderinkhof1 and Victor A. F. Lamme3 Pessiglione, M., Schmidt, L.,
Draganski, B., Kalisch, R., Lau, H., Dolan, R. J., et al. (2007).
On Making the Right Choice: The Deliberation-Without-Attention Effect, Dijksterhuis,Bos,Nordgen,Baaren
Activation of the Cognitive Control System in the Human Prefrontal Cortex, HakwanC. Lau1,2 and Richard E. Passingham
How the brain translates money into force. A neuroimaging study of subliminal motivation. Science, 316, 904–906