INVESTOR BEHAVIOUR
AND BENCHMARKS
Presentation to Finansmarkedsfondet
Executive Board
Sari Carp
Norwegian School of Management (BI)
8 December 2005
PART I:
Background on Behavioural Finance
Isaac Newton (losing investor in the
South Sea Bubble):
“I can calculate the motions of the
heavenly bodies, but not the madness
of people.”
Three Viewpoints on Market Efficiency
(borrowed from Richard Thaler)
1) Efficient Market Zealot (vanishing breed)
 Security prices are always equal to intrinsic value.
 Price movements are random, and hence unpredictable.
2) Behavioural Finance Zealot (figment of EMZs’
imaginations)
 Prices depend only on market psychology.
 It is easy to predict price movements.
3) Sensible Middle Ground (nearly everyone)
 Prices are highly correlated with intrinsic value, but
sometimes diverge significantly.
 It is sometimes possible to predict prices, but
generally not with great precision.
The Development of Behavioural
Finance Research
 Documentation of anomalies
 Theory building: Economics + Psychology
 Testing of theories
 Experimental
 Empirical
“Bounded” Rationality

Decision Making Rules =
Heuristics

Biases

Market Imperfections =
Anomalies
PART II:
Investor Behaviour and Benchmarks
Psychology of Benchmarking
 Fundamental human desire to evaluate one’s own
abilities
 Cannot assess abilities directly, so evaluate the result
of abilities; i.e., performance
 Even if performance can be measured unambiguously
(e.g. time to run a race), how do we know if it’s good?
 compare with others’ times
 If an objective, non-social criterion exists, then
evaluation relative to others is not used
 Focus on a reference point, or goal, decreases as the
difference between it and one’s own ability increases
Antecedents
 Prospect Theory (Kahneman and Tversky, 1979)
 ascribes value to gains and losses, not total wealth
 decision makers are risk averse in gains, risk seeking in
losses
 pain of loss is sharper than pleasure of gain
 March and Shapira, 1992
 two reference points: aspiration and survival
 decision makers tolerate high risk when resources are
below reference point, lower risk when resources are above
reference point
 decision makers focus only on one of two reference points
at a time
Model of Investor Risk Behaviour
 Performance (return) evaluated relative to two
benchmarks:
 Success (S)
 Exit (X)
 In each period, investors choose portfolio risk
according to:
 benchmark(s) focused on
 distance from focal benchmark(s)
Timeline
time 0
portfolios homog.
in value, variance;
heterog. in
assets
time 2
portfolio values change
again; Success and Exit
benchmark returns also
change; investors
restrategize based on
new values
time 1
portfolio values
change according to
random walk;
investors choose risk
strategies based on
own returns relative
to Success and Exit
levels
time t
evaluation period ends;
investors performing
below Exit are eliminated
from the market; all
others may invest again
Risk Taken Relative to Performance Benchmarks
Risk
Success Focus
Exit Focus
Mixed Focus
X
S
Performance (Cumulative Return)
Risk
Risk Taken Relative to Performance Benchmarks
X
S
Performance (Cumulative Return)
Mutual Fund Manager Data
Offshore
U.S.
 7,606 funds (CRSP)
 equity and bond
 2001-2002
 787 funds (Datastream)
 25 countries
 all equity
 1993-2002
 actively managed (no index funds)
 single country focus
Results from Fund Manager Data
 Results support
significant
model;
highly
statistically
 Model holds across economic, political and legal
contexts
 But…how can we know if benchmarking is driven
by behavioural or compensation based factors??
 By comparing individual and institutional
investors, we can “factor out” the compensation
based element
VPS Data
 Unique in the world
 “Gold mine” for behavioural research!!!
 Some behavioural research has been done on
similar databases from other Scandinavian
countries…but, these databases are incomplete
 Very famous behavioural research has been done
on an individual investor database from a U.S.
brokerage firm…but, this database is both
incomplete and biased
VPS Data
 Complete database of ownership in Norwegian stock
market
 10 years of monthly portfolios for each investor
 Investors categorized as individual, financial
institution, non-financial corporation, government or
foreign investor
 Tracks each investor by ID over time, allowing
comparisons of investment decisions under same
conditions
 Eliminates sample bias
Predicted Results on VPS Data
 Both professionals and individuals will take
increased risk as their performance improves
above the Success or Exit points; this result will
be more pronounced for professionals
 Both groups will take increased risk as
performance deteriorated below Success or Exit
points; this result will also be more pronounced for
professionals
 So, the pattern will be the same, but more
extreme for professionals