“Greater rewards, lower costs”


Investment Philosophies, Theories and Practices (continued…)


Aspects of Behavioural Finance: Common Biases


1.              Over-optimism


Probably the most common bias found among investors (and in many other walks of life). Typically, one is exaggerating one’s own abilities which are underpinned by an illusion of control or an illusion of knowledge. This bias leads to a tendency to rate oneself above-average, maybe average, but seldom below-average.


For example:


How good a driver are you?

How good at your job are you?

How good a lover are you?


While some individuals may still be honest or modest enough by giving oneself an average or below-average rating in answering the first two questions, I bet no one will do so with the last question!


2.              Confirmatory bias


The tendency to look for information that agrees with one’s view and at the same time to ignore those that may be contradictory to one’s own. Karl Popper, the famous 20th century philosopher, once said: formulate a view and spend the rest of the day looking for information that disagrees or proves you wrong. But that is not what we do!


It would make probably more sense to have discussions with people that have differing views. If you cannot identify the logical flaws in their arguments, one should not be so certain of one’s own view at all.


For example:


You are playing a card game where letters are printed on the one side and numbers on the other side. You are given the following statement: the letter E is accompanied by the number 4. You are dealt with four cards to prove this statement true or false. Which two cards should be turned around?












Most people would select the first two cards. While the first one is the correct choice, it is not the second card since the statement reads: the letter E is accompanied by the number 4 and not that a 4 should be accompanied by the letter E.  Therefore, the second card cannot prove anything, but quite often people grasp at information that may seem to confirm their view, but after careful analysis it may prove to be worthless.


3.              Representativeness


People are often misled by the way how things appear rather than how statistically likely they are. This tendency to be led by the narrative of the description rather than by the logic of the analyses is known as the conjunction fallacy.


For example:


Give your best estimates of:


                 The percentage of men living in a suburb who had one or more heart attacks,

                 The percentage of men living in a suburb and over the age of 55 who had one or more heart                  attacks


Most people would opt to allocate a higher percentage to the second proposition, but after careful consideration one should realise that the second proposition is a subset of the first; therefore the first proposition should always have the higher value.


Other examples include the hot hand and gamblers fallacies – the tendency to predict the outcome of a random event based on previous outcome patterns, although the outcomes are totally uncorrelated with each other (no memory).


4.              Anchoring


The tendency to make use of irrelevant inputs in making predictions about uncertain outcomes.


For example:


You ask an audience to write down the last four digits of their telephone numbers. Then ask them to guess the number of physicians in a big city. Most often one will find the audience’s estimates are linked to their telephone numbers – those with telephone numbers ending in high numbers usually have high predictions, while those with low digit numbers will have lower predictions!


  5.            Framing


Sometimes we are misled quite easily by the way how information is presented to us. For example, we will react differently to a proposal if we are told that our decision has a certain negative connotation versus the same proposal, but where the positive consequences are emphasized.


Typically, we tend to favour a guaranteed positive outcome versus options which may offer even better positive results, but where losses are a possibility. When facing negative consequences we tend to favour the riskier option where our potential losses can be severe, but there is a slight probability there is no loss at all versus a guaranteed loss, albeit relatively small.


6.              Loss Aversion


The tendency to dislike (avoid) losses far more than we like gains. Studies have shown that people dislike losses about two times more than they enjoy gains.


For example:


You are offered the following bet. On the toss of a fair coin, if you lose you must pay R100, what is the minimum amount that you need to win in order to make this bet attractive to you?


The typical reply from respondents would be somewhere between R180-R200, which confirms people’s asymmetrical attitude towards gains and losses.


The consequence hereof is that people take naively huge risks avoiding losses, but may end up with disastrous consequences.


7.              Anticipate the anticipation of others


“The actual, private object of the most skilled investment to-day is “to beat the gun”, to outwit the crowd, and to pass the bad, or depreciating, half-crown to the other fellow. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be.”

- John Maynard Keynes


Many investors view the market as some sort of game where the participants try to outsmart each other, knowing that there are going to be losers at the end, rather than concentrating on the fundamentals why an investment should be a good long-term prospect.


For example:


Each member of an audience is asked to pick any number between 0 and 100. The winner of the game is the one who guesses the number closest to two-thirds of the average number picked by the audience.


This problem can be solved rationally as follows: The maximum 2/3 value cannot be more than 67, since 100 would have been the highest possible average. The next best estimate would have been 2/3 of 67, namely 44, thereafter 2/3 of 44 is equal to 30 and so forth until the optimal solution is reached, namely zero (mathematically it can be shown that x=2/3x is not solvable unless x = zero). But this optimal solution would only apply if all participants are rational, and in a game like this it is very unlikely zero will be the correct answer.


 Typically, many people would assume the starting point to be 50. Therefore, 2/3 thereof is 33. Some would apply further reasoning and guess answers like 22 (2/3 of 33) and less. While those that selected the number zero correctly applied rational reasoning, the most likely answer in a game like this will be somewhere between 20 and 30. 


The lesson learnt from exercises like these is that all participants are not rational; hence it is very difficult to make accurate predictions since the rational outcome and actual outcome will differ.




Montier, James,  2006.  Behaving Badly, a Global Equity Strategy publication by Dresdner Kleinwort Wasserstein, February 2.