The Biases of Individual Investors
Brad M. Barber
Graduate School of Management
UC Davis
Habits of Individual Investors
•
•
•
•
Trade too much.
Diversify Naively
Hold on to losers
Chase Performance
Trade too Much
Your Driving Ability
Compared to other students, how good of a driver do you consider
yourself? (Of course you don’t know the driving habits of many of
your fellow students, use your best judgment in ranking yourself.)
Check one choice.






probably in the top 10%.
I am probably in the top 25% but not the top 10%.
I am probably in the top half but not the top 25%.
I am probably in the bottom half but not the bottom 25%.
I am probably in the bottom 25% but not the bottom 10%.
I am probably I am in the bottom 10%.
Typical Results
Are Investors Expectations Biased?
1999-2000: I’ll surely win. By a big margin
2001-2002: I’ll win. But maybe by a small margin
18
Return investors expect from their own portfolio
16
14
12
10
Return
8
Return investors expect from the stock market
6
4
2
Feb-02
Dec-01
Oct-01
Aug-01
Jun-01
Apr-01
Feb-01
Dec-00
Oct-00
Aug-00
Jun-00
Apr-00
Feb-00
Dec-99
Oct-99
Aug-99
Jun-99
Apr-99
0
End of 6-month moving average
Source: UBS Index of Investor Optimism
Why might we expect those who trade
in financial markets to be overconfident?
•
•
•
•
•
Most people are overconfident
Slow, noisy feedback
High difficulty
Selection bias
Survivorship (and self-attribution) bias
When All Traders Are Above Average
Odean (1998) Journal of Finance
Assumption:
Investors are overconfident
Predictions:
How do investors behave?
• Underdiversify
• Trade more
• Earn less
What happens in Markets?
• Volatility increases
• Liquidity increases
Trading is Hazardous to Your Wealth
Percent Annual Return Monthly Turnover
Barber and Odean (2000) Journal of Finance
Monthly Turnover and Annual Performance of
Individual Investors
25
20
15
10
5
0
1 (Low
Turnover)
2
3
4
Net Return
Turnover
5 (High
Turnover)
Boys will be Boys
Barber and Odean (2001) Quarterly Journal of Economics
Percent Annual Turnover
100
80
60
40
20
0
All
All Men
Women
Single Single
Women Men
“Own Benchmark” Annual Net Returns
0
All
All Men
Women
Single
Women
Single
Men
-1
-2
-3
Don’t gamble ...
Who Wins, Who Loses?
The Taiwan Evidence
Just How Much is Lost to Trade?
Barber, Lee, Liu, and Odean (2006)
1.8%
Market-Adjusted Return
1.5%
1.2%
Institutions
0.9%
0.6%
0.3%
Individuals
0.0%
-0.3%
1
21
41
61
81
101 121 141 161 181 201 221 241 261 281
Days since Trade
Diversify Naively
Underdiversification
Own-Company Stock in Defined Contribution Plans
Poterba (2003), American Economic Review
Plan Assets
($ Billion)
% in
OwnComp.
Stock
$25.7
68
Exxon
14.7
64
SBC
14.6
64
Procter and Gamble
10.5
93
Pfizer
6.2
82
Company
General Electric
The diversification heuristic
• When making simultaneous choices, individuals tend
to pick one of each of the available choices.
• Halloween night illustration: Read and Lowenstein
(1995) offered trick-or-treaters two different kinds of
candies.
– Sequential Choice:
Choose one candy from each of two houses.
– Simultaneous Choice:
Choose two candies from one house.
The diversification heuristic
Kids are asked
to choose two
candies at…
Condition
Sequential choice
condition
Two different
houses
Simultaneous
choice condition
One house
Source: Read and Lowenstein (1995)
Percentage selecting
two different candies
48%
100%
Implications for savings plans
• The “1/n” heuristic: Participants tend to
spread contributions equally among the
available funds.
For example, if two funds are available,
50% invested in each.
• This is a very appealing heuristic, even
Harry Markowitz used it.
Harry Markowitz
“I should have
computed the
historical co-variances
of the asset classes and
drawn an efficient
frontier.”
Source: Money, January 1998
Instead: “I split my
contributions fifty-fifty
between bonds and
equities…My intention
was to minimize my
future regret.”
Another illustration of the diversification heuristic
Number of
fixed income
funds
Number of
equity funds
Allocation
to equities
University of
California
4
1
34%
TWA
1
5
75%
Plan
Source: Benartzi and Thaler (2001)
Hold on to Losers
Reference Points and Risk Seeking
In addition to whatever you own, you have been given
1,000. You are now asked to choose between
A. Winning $1,000 with a probability of 50%
B. Winning $500.
In addition to whatever you own, you have been given
2,000. You are now asked to choose between
A. Lossing $1,000 with a probability of 50%
B. Losing $500.
Typical Responses
(Kahnemann and Tversky, 1979)
Gamble
Certain Outcome
$1,000
Endowment
A = 16%
B = 84%
$2,000
Endowment
C = 69%
D = 31%
Same Payoffs: Different Choices
$1,000
Endowment
$2,000
Endowment
Gamble
Certain Outcome
A=
2,000, 50%;
1,000, 50%
C=
2,000, 50%;
1,000, 50%
B = 1,500
D = 1,500
Rate at which Gains are Realized Relative to
Losses
Discount Brokerage
PGR/PLR
2.0
1.0
Taxable
TDAs
0.0
jan
feb
mar april may june
july
aug sep
oct
nov
dec
Chase Performance
The Representativeness Heuristic
Linda is 31, single, outspoken, and very bright. She
majored in philosophy in college. As a student, she
was deeply concerned with discrimination and
other social issues, and participated in anti-nuclear
demonstrations. Which statement is more likely?
a. Linda is a bank teller.
b. Linda is a bank teller and active in the feminist
movement.
45
40
35
39
15
(B
e
st)
2
3
4
5
6
7
9
3
8
3
2
6
4
10
9
7
1
(W
or
st)
30
25
20
15
10
5
0
10
Percentage of Buys
Mutual Funds: Money Pours into
Last Year’s Winners
Performance Decile
US Equity Mutual Fund Flows
60%
$150.000
40%
$100.000
20%
$50.000
0%
6-Mth Moving Average Domestic Equity Fund Flows
jun-05
jun-04
jun-03
jun-02
jun-01
jun-00
jun-99
jun-98
jun-97
jun-96
jun-95
jun-94
-80%
jun-93
-$150.000
jun-92
-60%
jun-91
-$100.000
jun-90
-40%
jun-89
-$50.000
jun-88
-20%
jun-87
$0
12-month Moving Average SP 500 Return
SP 500 Return (12-mth Moving Average)
$200.000
jun-86
Fund Flows ($Mil)
Source: ICI Data
How do investors think about fund
selection?
• A skilled manager will outperform the market
therefore,
• A manager who outperforms the market is
skilled.
– Good performance is “representative” of a good
manager
What’s wrong with this logic?
Consider Mutual Funds
• Assume the returns of actively managed
mutual funds are random.
• How many consistent winners would you
expect over ten years among 1,000 mutual
funds?
Are Mutual Fund Returns Random Outcomes?
Source: Growth, Growth/Income in CRSP Database
300
Number of Funds
250
200
150
100
50
0
0
1
2
3
4
5
6
7
8
9
Years beating the SP 500, 1999-2008
Realized Number of Funds
Expected Number of Funds under Null of Randomness
10
New Investors Learn What is Expected of them and
What to Expect from…
•
•
•
•
Friends and family.
Financial advisors.
Books, magazines articles, and news.
… and advertising.
Where do Investors get Financial Advice?
Source: 1983 and 2007 Survey of Consumer Finances
Any advice
Internet
Media/ad advice
Personal advice
Professional advice
0%
20%
40%
2007
1983
60%
80%
100%
Sound Investment Advice
•
•
•
•
•
•
Save
Invest for the long run.
Buy and hold.
Diversify.
Control trading costs.
Pay attention to taxes.
What Investors Learn from Ads
• You are in control.
• Data = expertise.
• Trading is easy; anybody can do it.
• Trading is exciting.
• Opportunities may arise at any moment. Be
vigilant; trade quickly and actively.
Bio-Finance
• Do behaviors have genetic origins?
• Three types of studies
– Twin Studies
– fMRI studies (brain imaging)
– Genome (candidate gene or genome-wide
associate studies)
Twin Studies
Risk Tolerance has Genetic Origins
Barnea, Cronqvist, and Seigel (Journal of Financial Economics, 2010)
Share in Equities
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Age < 30
Additive Genetic Effect (A)
30 ≤ Age < 55
55 ≤ Age < 80
Shared Environmental Effect (C)
Age ≥ 80
Individual-specific Error (E)
The overall picture
Figure 2. Individual differences in gain and loss learning
account for assets and debt.
“Different affective learning systems contribute to accumulated assets and debt”
Knutson, Samanez-Larkin, and Kuhnen (2010)
Realization Utility
Frydman, Barberis, Camerer, Bossaerts, Rangel (2010)
Conclusions
Investors are not always rational
1. Overconfidence  trade too much
2. Rules of thumb (e.g., 1/n)  diversify naively
3. Loss aversion  hold on to losers
4. Representativeness Heuristic  chase performance
These behaviors may have biological origins
– Can financial education help? If so, what type of education
is most useful?
– If behaviors have biological origins
• Can/should we develop biological treatments?
• How does this affect our approach to public policy?