How comparison options affect stock buys
Should I invest my money with a small chance of big returns? Or is it better to pick investments that promise a series of modest returns? A psychologist from the University of Basel conducted a scientific experiment to study when people prefer certain types of investments.
21 March 2024 | Andreas Lorenz-Meyer
When a company first goes public on the stock exchange, the corresponding securities are referred to as IPO (initial public offering) shares. These shares are typically characterized by their below-average returns for the first few years after the initial offering – with the exception of a few outliers that boom right from the start. In other words, the likelihood of high returns is rather small.
So why do people still purchase IPO shares? Because they overestimate the probability of the stock becoming one of the rare super performers. This phenomenon is described by prospect theory, the leading theory used to explain decision-making in the face of uncertainty. It’s a similar story when people purchase lottery tickets: they are hoping to hit the jackpot.
There are also investments that result in a much different distribution of profits and losses, with a high likelihood of small returns. This is the standard case, so to speak. On the other hand, big losses are unlikely, such as with catastrophe bonds, or “cat bonds”. Insurance companies use these bonds to create a financial cushion that enables them to guarantee coverage in the event of a disaster. If nothing happens, investors receive a series of small payouts. In the statistically unlikely event of a natural disaster, however, all of the money they have invested is lost.
Then what stocks should I pick?
What circumstances affect how people select a given type of security in the first place? Dr. Sebastian Olschewski from the Faculty of Psychology has published a study on the topic in the journal PNAS.
In the experiment, test subjects were asked to choose between two or three different stocks, for example, one offering “a low probability of high returns” and another offering “a high probability of modest returns with rare but potentially high losses”.
To aid in the decision-making process, information was provided about the performance of the stocks, i.e. when and what returns were generated by the specific stocks on day 1, day 2, day 3, etc. This allowed test subjects to closely examine the volume and frequency of the returns from each individual stock.
Frequent returns preferred
The results showed that having the possibility to compare different stock types greatly affects a person’s decision – and in a way that favors investments on the cat bond end of the spectrum. “In our experiment, the test subjects selected stocks that generated the highest returns on the greatest number of days. The overall total of the returns had only an ancillary effect.” This is what experts refer to as the “frequent winner effect”.
To demonstrate the weight of this effect, the data on the stock returns was modified in a second experimental design in such a way that the “lottery-like” investments more frequently showed the higher yields, which quickly shifted the test subjects’ preference towards this type of stock.
Gaining a better understanding of the stock market
What are the possible conclusions of the study? “If we want to predict how the stock market will perform, we also need to consider how people go about finding information,” says Olschewski. “Whether they simply research a single stock or compare two or three options.”
Being able to predict things like this is important for economists or analysts who want to predict price trends on the stock market. But it is also important for social resources planning – for instance, in the case of governments investing for the benefit of their citizens. After all, the Swiss pension system is partially invested in the capital market, as the researcher points out.
Original publication
Sebastian Olschewski, Mikhail S. Spektor, Gaël Le Mens
Frequent winners explain apparent skewness preferences in experience-based decisions
PNAS (2024), doi: 10.1073/pnas.2317751121