An introduction to behavioural finance!!!!
Michael Pompian (2006) provides an in depth coverage of behavioural biases that affect
investors and their portfolios and even more importantly, how to manage these biases to the
benefit of the investor. Heuristics or biases are rules of thumb that allow us to deal with
informational deluge (Montier 2007).
Defined by Pompian as ‘the application of psychology to finance’, behavioural finance has
become an increasingly dominant part of investing and investment strategy, especially since
the run up in stock prices in the 1990’s, the dot com bubble and the recent financial crisis.
The human aspect of markets is the reason that bubbles form and burst. Markets would all
clear immediately and prices would reflect all information available if they worked in line with
traditional finance and economics.
Two of the most common biases that investors display are overconfidence and overoptimism
that stem from the illusion of control and the illusion of knowledge (Montier 2007). People
are poorly calibrated in establishing probabilities- events that they are certain to happen are
often less than 100 per cent certain to occur (Pompian 2006). An investor who has a large
amount of historical data on a company and its stock price and performance does not
necessarily make the best decision.
The illusion of knowledge is the tendency of investors to believe the accuracy of their
predictions or forecasts increases with more information (Montier 2007). For investors to
understand the importance of information, a Montier (2007) quote best explains; “The
simple truth is that more information is not necessarily better information; it is what you do
with it, rather than how much you have, that matters” . In analysing the FTSE 100 as a whole
and finding the link between it and the money supply and the index price, it should give
investors a better picture of where their success comes from skill or spurious reasons (their
knowledge and skills or upward or downward trends in the market).
Tests done by Slovic (1973) to assess confidence versus accuracy in relation to bets made
by bookmakers as a function of information sets, shows a relatively constant rate of
accuracy with a smaller information base of five data points. Accuracy also remains at a
similar level with forty data points 15 per cent. On the other hand, confidence at five data
points is around 17.5 per cent, but when the amount of data points increased to forty, it
increased to over 30 per cent. Instead of trying to acquire more information than competitors
do, investors should make better use of the information they have.
The illusion of control refers to people’s belief that they have influence over the outcome of
uncontrollable events (Montier 2007). An example of this is an individual’s willingness to pay
four and a half times more for a lottery ticket that contains numbers they choose rather than
a random draw of numbers (Montier 2007). Here information also plays a major role as the
more of it the investor has the more in control they feel.
Another important bias that investors should be weary of is self-attribution. The significance
of this on portfolio performance is that investors use the bias as one of the key mechanisms
for protecting self-image. Humans have a tendency to attribute positive outcomes to skilful
decision making and negative outcomes with bad luck or chance. Applying this to investor
behaviour, it could be represented by individuals holding on to losers and selling winners
(Shefrin & Statman 1985) meaning their portfolio performs below its optimum.
The opportunity to learn from past mistakes is very important to investor’s future success;
the self-attribution bias is one of the key limitations to investor learning as they do not
recognise mistakes as mistakes.
The anchoring bias mentioned earlier, researched extensively by Tversky and Kahneman
(1974) and highlighted by research asking participants to solve eight factorial (8!), presented
in two different ways gave vastly differing results. The first was 1*2*3*4*5*6*7*8 or second
8*7*6*5*4*3*2*1; the median answer on the first scenario was 512; the median answer on
the second scenario was 2250. So people appear to anchor on the early numbers in forming
expectations; the actual answer is 40, 320 (Montier 2007). This sort of anchoring applied to
investors in the stock market could have drastic consequences with regard to their earnings
potential. A stop loss strategy is an example of a tool that investors could use to limit losses
and combat the anchoring bias.
Northcraft and Neale (1987) highlight anchoring by real estate agents in the housing market;
this goes to show that anchoring bias can affect any asset market. Areily et al. (2003)
highlights the influence that even irrelevant anchors, such as the last two digits of an
individual’s social security number can have on valuing an asset, and the dangers this plays
on their perception of the maximum purchase price they are willing to offer.
If an investor forms his anchor by latching on to the current market price, this can be
hazardous as anchoring has obvious implications for valuations. The degree of anchoring is
heavily influenced by the salience of the anchor. The more seemingly relevant the anchor,
the more people will tend to cling to it (Montier 2007). An example of an anchor that would
lead investors to make losses by holding their position too long is the purchase price or a
previous price high in the share they own
I have not provided a reference page as the authors name and date of the publication should be sufficient for you to find the necessary literature.
Questions
1. Are you aware of any biases that may affect your investment portfolio?
2. Have you been anchored to a position without reasonable merit for doing so?
3. Have you been affected by the self attribution bias in your daily life outside of investing?
