Loss Aversion, Gambler’s fallacy
There are two particular areas of investors’ preference that have been highlighted by behavioural finance. The first is loss aversion, which in behavioural finance fills the roles of risk aversion in traditional finance, and the second is mental accounting.
Loss Aversion is a pervasive phenomenon in human decision making under risk and uncertainty, according to which people are more sensitive to losses than gains. It plays a crucial role in Prospect Theory (Tversky and Kahneman, 1974)53, and (Tversky and Kahneman, 1992). A typical financial example is in investor’s difficulty to realize losses. Shefrin (2000)55 calls this phenomenon ‘Get-evenities’ that is, people hope that markets will work in their advantage and they will be able to terminate their investment without incurring losses.
The human tendency to take extreme measures to avoid loss leads to some behaviour that can inhibit investment success. So the human attitude to risk and reward can be very complex and subtle, which changes over time and in different circumstances.
Loss aversion is an important concept associated with prospect theory and is encapsulated in the expression “losses loom larger than gains” (Kahneman & Tversky, 1979). It is thought that the pain of losing is psychologically about twice as powerful as the pleasure of gaining. As people are more willing to take risks to avoid a loss, loss aversion can explain differences in risk-seeking versus aversion. Loss aversion has been used to explain the endowment effect and sunk cost fallacy, and it may also play a role in the status quo bias.
The basic principle of loss aversion can explain why penalty frames are sometimes more effective than reward frames in motivating people (Gächter, Orzen, Renner, & Starmer, 2009) and is sometimes applied in behavior change strategies. The website Stickk, for example, allows people to commit to a positive behavior change (e.g. give up junk food), which may be coupled the fear of loss—a cash penalty in the case of non-compliance.
Description: Reasoning that, in a situation that is pure random chance, the outcome can be affected by previous outcomes.
I have flipped heads five times in a row. As a result, the next flip will probably be tails.
Explanation: The odds for each and every flip are calculated independently from other flips. The chance for each flip is 50/50, no matter how many times heads came up before.
Eric: For my lottery numbers, I chose 6, 14, 22, 35, 38, 40. What did you choose?
Steve: I chose 1, 2, 3, 4, 5, 6.
Eric: You idiot! Those numbers will never come up!
Explanation: “Common sense” is contrary to logic and probability, when people think that any possible lottery number is more probable than any other. This is because we see meaning in patterns — but probability doesn’t. Because of what is called the clustering illusion, we give the numbers 1, 2, 3, 4, 5, and 6 special meaning when arranged in that order, random chance is just as likely to produce a 1 as the first number as it is a 6. Now the second number produced is only affected by the first selection in that the first number is no longer a possible choice, but still, the number 2 has the same odds of being selected as 14, and so on.
Maury: Please put all my chips on red 21.
Dealer: Are you sure you want to do that? Red 21 just came up in the last spin.
Maury: I didn’t know that! Thank you! Put it on black 15 instead. I can’t believe I almost made that mistake!
Explanation: The dealer (or whatever you call the person spinning the roulette wheel) really should know better — the fact that red 21 just came up is completely irrelevant to the chances that it will come up again for the next spin. If it did, to us, that would seem “weird,” but it is simply the inevitable result of probability.
Exception: If you think something is random, but it really isn’t — like a loaded die, then previous outcomes can be used as an indicator of future outcomes.