An Introduction to
Behavioral Finance
SIP Course on “Stock Market Anomalies
and Asset Management”
Professors S.P. Kothari and Jon Lewellen
March 15, 2004
An Introduction to
Behavioral Finance

Efficient markets hypothesis




Large number of market participants
Incentives to gather and process information about
securities and trade on the basis of their analysis
until individual participant’s valuation is similar to the
observed market price
Prices in such markets reflect information available
to the participants, which means opportunities to
earn above-normal rates of return on a consistent
basis are limited
Prediction: Stock returns are (almost) impossible to
predict

Except that riskier securities on average earn higher rates
of returns compared to less risky firms
2
An Introduction to
Behavioral Finance

Behavioral finance

Widespread evidence of anomalies is inconsistent
with the efficient markets theory




Bad models, data mining, and results by chance
Alternatively, invalid theory
Anomalies as a pre-cursor to behavioral finance
Challenge in developing a behavioral finance theory
of markets

Evidence of both over- and under-reaction to events


Event-dependent over- and under-reaction, e.g., IPOs,
dividend initiations, seasoned equity issues, earnings
announcements, accounting accruals
Horizon dependent phenomenon: short-term overreaction,
medium-term momentum, and long-run overreaction
3
An Introduction to
Behavioral Finance

Behavioral finance theory rests on the following
three assumptions/characteristics



Investors exhibit information processing biases that
cause them to over- and under-react
Individual investors’ errors/biases in processing
information must be correlated across investors so
that they are not averaged out
Limited arbitrage: Existence of rational investors
should not be sufficient to make markets efficient
4
Behavioral finance theories

Human information processing biases


Information processing biases are generally
relative to the Bayes rule for updating our priors
on the basis of new information
Two biases are central to behavioral finance
theories



Representativeness bias (Kahneman and Tversky, 1982)
Conservatism bias (Edwards, 1968).
Other biases: Over confidence and biased self-attribution
5
Behavioral finance theories

Human information processing biases

Representativeness bias causes people to overweight recent information and deemphasize base
rates or priors


E.g., conclude too quickly that a yellow object found on
the street is gold (i.e., ignore the low base rate of finding
gold)
People over-infer the properties of the underlying
distribution on the basis of sample information


For example, investors might extrapolate a firm’s recent
high sales growth and thus overreact to news in sales
growth
Representativeness bias underlies many recent
behavioral finance models of market inefficiency
6
Behavioral finance theories

Human information processing biases



Conservatism bias: Investors are slow to update
their beliefs, i.e., they underweight sample
information which contributes to investor underreaction to news
Conservatism bias implies investor underreaction
to new information
Conservatism bias can generate


short-term momentum in stock returns
The post-earnings announcement drift, i.e., the tendency
of stock prices to drift in the direction of earnings news
for three-to-twelve months following an earnings
7
announcement also entails investor under-reaction
Behavioral finance theories

Human information processing biases

Investor overconfidence



Overconfident investors place too much faith in their
ability to process information
Investors overreact to their private information about the
company’s prospects
Biased self-attribution


Overreact to public information that confirms an
investor’s private information
Underreact to public signals that disconfirm an investor’s
private information


Contradictory evidence is viewed as due to chance
Genrate underreaction to public signals
8
Behavioral finance theories

Human information processing biases

Investor overconfidence and biased self-attribution



In the short run, overconfidence and biased selfattribution together result in a continuing overreaction
that induces momentum.
Subsequent earnings outcomes eventually reveal the
investor overconfidence, however, resulting in
predictable price reversals over long horizons.
Since biased self-attribution causes investors to down
play the importance of some publicly disseminated
information, information releases like earnings
announcements generate incomplete price adjustments.
9
Behavioral finance theories

In addition to exhibiting information-processing
biases, the biases must be correlated across
investors so that they are not averaged out




People share similar heuristics
Focus on those that worked well in our evolutionary past
Therefore, people are subject to similar biases
Experimental psychology literature confirms systematic
biases among people
10
Behavioral finance theories

Limited arbitrage


Efficient markets theory is predicated on the
assumption that market participants with incentives to
gather, process, and trade on information will arbitrage
away systematic mispricing of securities caused by
investors’ information processing biases
Arbitrageurs will earn only a normal rate of return on
their information-gathering activities


Market efficiency and arbitrage: EMH assumes arbitrage
forces are constantly at work
Economic incentive to arbitrageurs exists only if there is
mispricing, i.e., mispricing exists in equilibrium
11
Behavioral finance theories

Behavioral finance assumes arbitrage is limited.
What would cause limited arbitrage?




Economic incentive to arbitrageurs exists only if there
is mispricing
Therefore, mispricing must exist in equilibrium
Existence of rational investors must not be sufficient
Notwithstanding arbitrageurs, inefficiency can persist
for long periods because arbitrage is costly



Trading costs: Brokerage, B-A spreads, price impact/slippage
Holding costs: Duration of the arbitrage and cost of short
selling
Information costs: Information acquisition, analysis and
monitoring
12
Behavioral finance theories

Why can’t large firms end limited arbitrage?



Arbitrage requires gathering of information about a firm’s
prospects, spotting of mispriced securities, and trading in the
securities until the mispricing is eliminated
Analysts with the information typically do not have the capital
needed for trading
Firms (principals) supply the capital, but they must also delegate
decision making (i.e., trading) authority to those who possess the
information (agents)



Agents cannot transfer their information to the principal, so decisions
must be made by those who possess information
Agents are compensated on the basis of outcomes, but the
principal sets limits on the amount of capital at the agent’s
disposal (the book)
Limited capital means arbitrage can be limited
13
Behavioral finance theories

Like the efficient markets theory, behavioral
finance makes predictions about pricing behavior
that must be tested



Need for additional careful work in this respect
Only then can we embrace behavioral finance as
an adequate descriptor of the stock market
behavior
Recent research in finance is in this spirit just as
the anomalies literature documents
inconsistencies with the efficient markets
hypothesis
14
Stock Returns, Aggregate Earnings
Surprises, and Behavioral Finance
S.P. Kothari, Jonathan Lewellen,
Jerold B. Warner
SIP Course on “Stock Market
Anomalies and Asset Management”
March 15, 2004
15
Objective of the study

We study the relation between market
index returns and aggregate earnings
surprises



We focus on concurrent and lagged
surprises
Do prices react slowly?
Is there discount rate information in
aggregate earnings changes?
16
Motivation



At the firm level, post-earnings announcement drift is
well-known
The slow adjustment to public information is
inconsistent with market efficiency
Slow adjustment is consistent with behavioral finance






Barberis/Shleifer/Vishny (BSV, 1998)
Daniel/Hirshleifer/Subrahmanyam (DHS, 1998)
Hong/Stein (HS, 1999)
Aggregate return-earnings relation serves as an out-ofsample test of the behavioral hypothesis of investor
underreaction
Literature concentrates on cross-sectional return
predictability
We provide time-series evidence
17
Main findings

Aggregate relation does not mimic the firm-level
relation



Market returns are negatively (not positively) related
to concurrent earnings news




Market returns do not depend on past earnings surprises
Inconsistent with underreaction (or overreaction)
#s seem economically significant
Earnings and interest/ discount rate shocks are positively
correlated
Good aggregate earnings news can be bad news
Decomposing earnings changes does not fully
eliminate the negative correlation between earnings
news and returns, a troubling result
18
Firm level drift and behavioral
models




Drift could occur if investors systematically ignore the
time-series properties of earnings.
Bernard/Thomas (1990) show that quarterly earnings
changes have positive serial dependence (.34,.19,.06
at the first 3 lags)
If investors underestimate the dependence, prices
will respond slowly and they will be surprised by
predictable changes in earnings.
Consistent with this, the pattern of trading profits at
subsequent earnings announcements matches the
autocorrelation pattern.
19
Evidence



Time-series properties of earnings
Stock returns and aggregate earnings
surprises
Returns, earnings, and discount rates
20
Earnings series


Compustat Quarterly database, 1970 – 2000
NYSE, Amex, and NASDAQ stocks with …




Earnings before ext. items, quarter t and t – 4
Price, quarter t – 4
Book value, quarter t – 4
Plus …



December fiscal year end
Price > $1
Exclude top and bottom 0.5% based on dE/P
21
Sample
Quarterly returns (%), 1970 – 2000
N
Returns
VW
EW
CRSP
avg.
std. deviation
6,062
--
3.34
8.79
3.82
12.60
Sample
avg.
std. deviation
2,423
--
3.26
8.38
3.42
11.40
22
E/P, 1970 – 2000
0.06
0.04
0.02
0.00
1970.1
-0.02
1974.1
1978.1
1982.1
1986.1
1990.1
1994.1
1998.1
E/P-agg
E/P-ew
-0.04
23
Firms w/ positive
earnings, 1970 – 2000
5000
1.0
4000
0.8
3000
0.6
2000
0.4
Number of firms (left scale)
1000
0
1970.1
Fraction E > 0 (right scale)
0.2
0.0
1974.1
1978.1
1982.1
1986.1
1990.1
1994.1
1998.1
24
Quarterly earnings changes (%),
1970 – 2000
Aggregate
dE/P
Full sample
avg
stdev
Small stocks
avg
stdev
Large stocks
avg
stdev
Low B/M
avg
stdev
High B/M
avg
stdev
VW
dE/B
dE/E
dE/P
EW
dE/P
0.15
0.39
0.25
0.59
8.26
18.58
0.10
0.36
0.30
0.55
0.42
1.18
0.39
1.14
---
0.56
0.90
0.86
1.13
0.14
0.37
0.25
0.58
7.90
17.60
0.10
0.35
0.08
0.38
0.17
0.23
0.54
0.73
12.11
16.69
0.16
0.22
0.60
0.69
0.19
1.13
0.11
0.81
---
0.09
1.02
0.22
1.21
25
Aggregate earnings growth,
1970 – 2000
0.60
0.40
0.20
0.00
1970.1
1974.1
1978.1
1982.1
1986.1
1990.1
1994.1
1998.1
-0.20
-0.40
-0.60
dE/E-AGG
26
dE scaled by lagged price,
1970 – 2000
.015
.010
.005
.000
1970.1
1974.1
1978.1
1982.1
1986.1
1990.1
1994.1
1998.1
-.005
-.010
-.015
dE/P-VW
dE/P-EW
27
Autocorrelations






Seasonally-differenced earnings (dE = Et – Et-4)
Estimation
dE/St = 0 + k dE/St-k + t
dE/St = 0 + 1 dE/St-1 + 2 dE/St-2 + ….. +
5 dE/St-5 + t
Market: Time-series regressions
Firms: Fama-MacBeth cross-sectional
regressions
28
Autocorrelations, dE/P, 1970 – 2000
Simple regressions
Multiple regressions
Lag
Slope
T-stat
Adj. R2
Slope
T-stat
Adj. R2
Firms
1
2
3
4
5
0.38
0.22
0.08
-0.28
-0.11
18.48
14.58
5.67
-16.82
-7.03
------
0.40
0.14
0.06
-0.42
0.16
18.39
11.20
6.47
-22.83
12.93
--
EW
1
2
3
4
5
0.64
0.40
0.14
-0.15
-0.21
8.81
4.62
1.49
-1.62
-2.26
0.39
0.14
0.01
0.01
0.03
0.61
0.11
0.00
-0.30
0.04
6.33
1.05
0.01
-2.76
0.40
0.43
VW
1
2
3
4
5
0.73
0.52
0.23
-0.00
-0.12
11.54
6.65
2.55
-0.03
-1.30
0.52
0.26
0.04
-0.01
0.01
0.73
0.22
-0.22
-0.18
0.07
7.75
1.93
-1.92
-1.62
0.80
0.57
29
Implications

Basic message




Specifics
Transitory, idiosyncratic component in firm
earnings



Pattern similar for firms and market
Persistence stronger for market – good for tests
Aggregate earnings changes are permanent
Earnings changes predictable but volatile ( = 18.6%)
AR1 similar to AR5
30
Returns and earnings surprises





Rt+k =  +  dE/Pt + et+k
k = 0, …, 4
Changes and surprises
Market: Time-series regressions
Firms: Fama-MacBeth cross-sectional
regressions
31
Returns and earnings, 1970 – 2000
Earnings change
Earnings surprise
k
Slope
T-stat
Adj. R2
Slope
T-stat
Adj. R2
Firms
0
1
2
3
4
0.53
0.58
0.20
0.09
0.00
26.94
28.70
10.66
5.24
0.03
------
EW
0
1
2
3
4
-1.30
-3.75
-2.81
-1.36
-3.14
-0.90
-2.60
-1.97
-0.95
-2.23
0.00
0.05
0.02
0.00
0.03
1.54
-3.70
-3.03
1.15
-4.48
0.85
-2.04
-1.65
0.63
-2.43
0.04
0.05
0.01
0.03
0.03
VW
0
1
2
3
4
-4.98
-5.23
-0.80
-1.34
-0.90
-2.31
-2.41
-0.37
-0.63
-0.42
0.03
0.04
-0.01
-0.01
-0.01
-2.59
-10.10
0.51
-1.41
-3.05
-0.83
-3.34
0.16
-0.45
-0.97
0.04
0.07
-0.01
-0.01
-0.01
32
Contemporaneous relation


Explanatory power: 4 – 8%
Fitted values: dE/P-vw




Std. dev. of earnings surprises = 0.25%
Slope = –10.10
Two std. deviation shock  –5% drop in prices
Historical


Earnings change in top 25%: return  1% (s.e. =
1.7%)
Earnings change in bottom 25%: return  7% (s.e. =
1.6%)
33
Contemporaneous relation

Early overreaction




No theory
Not in firm returns
Movements in discount rates
Rt = d,t – r,t
Cash flow news vs. expected-return news
34
Returns and past earnings




Zero to negative
No evidence of under-reaction
Inconsistent with behavioral theories
Results are robust




Alternative definitions of earnings
Subperiods
Annual returns and earnings
Subsets of stocks (size, B/M terciles)
35
Summary observations

Large portfolio




Earnings more persistent
Initial market reaction more negative
Puzzling from a cashflow-news perspective
Small portfolio


Reversal at lag 4
Negatively related to CRSP, but not own
returns
36
Earnings and discount rates





Rt = d,t – r,t
d,t = cashflow news
r,t = expected-return news = discount-rate news
Returns and earnings
cov(dEt, Rt) = cov(dEt, d,t) – cov(dEt, r,t)
cov(dEt, r,t)?
inflation and interest rates (+)
consumption smoothing (–)
changes in aggregate risk aversion (–)
37
Earnings and the macroeconomy,
1970 – 2000: Correlations
Nominal dE
EW
VW
Real dE
EW
VW
TBILL
TERM
DEF
0.35
-0.35
-0.59
0.60
-0.52
-0.37
0.27
-0.33
-0.66
0.50
-0.52
-0.49
SENT
GDP
IPROD
CONS
0.37
0.13
0.39
0.20
0.40
0.67
0.29
0.54
0.65
0.42
0.61
0.72
0.53
0.67
0.74
0.52
dE = seasonally-differenced earnings
Macro = annual changes or growth rates, ending in qtr t
38
Earnings and the macroeconomy,
1970 – 2000
dEt =  +  TBILLt +  TERMt +  DEFt +  dEt-1 + 
Nominal dE
EW
VW
Real dE
EW
VW
TBILL
0.04
1.39
0.04
2.72
0.02
0.73
0.03
1.78
TERM
0.00
0.09
-0.01
-0.29
-0.01
-0.23
-0.02
-0.69
-0.55
-4.95
-0.22
-3.96
-0.64
-5.70
-0.26
-4.79
dEt-1
0.39
4.62
0.53
7.53
0.35
4.29
0.53
7.71
Adj. R2
0.49
0.62
0.53
0.62
Adj. R2 w/o AR1
0.41
0.44
0.46
0.43
DEF
39
Controlling for discount rates
Two-stage approach
dEt =  +  TBILLt +  TERMt +
 DEFt +  dEt-1 + 
 Rt+k =  +  Fitted(dEt) +  Residual(dEt) + et+k
 Timing?
Rt
Rt+1
Rt+2
Rt+3
Rt+4
dEt

40
Returns and earnings, 1970 – 2000
Rt+k =  +  Fitted(dEt) +  Residual(dEt) + et+k,
k
Fitted dE
Slope
T-stat
Residual dE
Slope
T-stat
Adj. R2
EW
0
1
2
3
4
-6.86
-5.01
-2.93
-4.20
-1.55
-3.44
-2.51
-1.45
-2.09
-0.76
3.57
-3.02
-2.44
1.47
-4.53
1.89
-1.55
-1.23
0.75
-2.28
0.10
0.05
0.01
0.02
0.03
VW
0
1
2
3
4
-9.08
-2.58
-2.84
-1.09
0.29
-3.27
-0.95
-1.02
-0.39
0.10
0.76
-9.27
2.30
-1.65
-2.53
0.23
-2.84
0.69
-0.49
-0.75
0.07
0.05
0.00
-0.01
-0.01
41
Annual dE/P, 1970 – 2000
Rt+k =  +  Fitted(dEt) +  Residual(dEt) + et+k,
Fitted dE
Residual dE
k
Slope
T-stat
Slope
T-stat
Adj. R2
EW
0
1
2
3
-4.49
-0.64
2.19
1.11
-2.03
-0.26
0.88
0.45
-2.30
1.29
0.71
-0.27
-1.15
0.58
0.32
-0.13
0.11
-0.06
-0.04
-0.07
VW
0
1
2
3
-5.86
-1.19
2.95
1.41
-2.04
-0.40
0.91
0.44
-3.97
7.74
-1.75
0.71
-1.23
2.29
-0.48
0.20
0.11
0.11
-0.04
-0.07
42
How big are the effects?

Over the last 30 years, CRSP VWT
portfolio


Increased 6.5% in value in the quarters with
negative earnings growth
Increased 1.9% in value in quarters with
positive earnings growth
43
Conclusions

Market’s reaction to earnings surprises much
different at the aggregate level




Negative reaction to good earnings news
Past earnings contain little (inconsistent) information
about future returns
Investment strategy: Long in quarters when aggregate
earnings changes are negative
Open questions


Do earnings proxy for discount rates?
Is there a coherent behavioral story for the patterns?
44
Richardson and Sloan (2003): External
Financing and Future Stock Returns

Prior evidence: Market is sluggish in rationally
incorporating information in managers’ market timing
motivation for external financing



Sluggish reaction means opportunities for abnormal
returns



Market timing: Raise funds when the firm is overvalued and
repurchase shares when the firm is undervalued.
Slow assimilation of the information can be because of investors’
information processing biases
How large are the returns to a trading strategy?
What is the source of the abnormal returns? Is it related to the
use of proceeds from external financing?
Richardson and Sloan: Examine returns to a trading rule
based on net external financing (not individual decisions
like share repurchasing)
45
Returns following external financing

Prior evidence





Low returns following equity offerings, debt offerings,
and bank borrowings
High returns following share repurchases
Managers seem to time external financing
transactions to exploit mispricing
Market’s immediate reaction to the financing decisions
is incomplete (underreaction to public announcements
of voluntary decisions)
Market gradually reacts over the following one-tothree years – inconsistent with market efficiency and
consistent with some of the information-processing
46
biases
Returns following external financing

Richardson and Sloan show that

Net external financing generates a 12-month
abnormal return of about 16% (Table 5)



The return is on long-minus-short position that has
a zero initial investment
Long position is in firms that raise the least external
financing (i.e., repurchase shares or retire debt)
Short position is in firms that raise the most
external financing – issue equity or debt or borrow
from a bank
47
Returns following external financing

Richardson and Sloan show that

Use of the proceeds from external financing
matters (Table 6)

Investment in operating assets generates highest
return on the zero-investment portfolio



Suggests managers over-invest in assets
Market fails to fully assimilate information in accruals
What are accruals?



Earnings (X) = CF + Accruals (A)
When you sell on credit, earnings increase, cash flow
does not, but accruals in the form of accounts receivables
increase
48
Investment in operating assets is a form of accrual
Returns following external financing
49
Returns following external financing

External financing decisions as well as
exceptional corporate performance (high sales
growth or extreme decline) are all associated
with large accruals


A large increase in sales translates into a large
increase in receivables, so an accrual increase is
associated with increased sales
Accruals also present opportunities to the
management to manipulate them and/or create
them fictitiously

A fictitious dollar of sales and receivables accruals
contributes dollar for dollar to earnings before taxes
and also enhances profit margin (because the cost of
50
goods sold is not increased with fictitious sales)
Returns following external financing


Since extreme performance or financing activities or
fictitious sales are typically not sustainable, accruals
revert
If investors suffer from information processing biases, do
they recognize the time-series properties of accruals and
its implications for future earnings?




In particular, does the market recognize that “The persistence of
current earnings is decreasing in the magnitude of accruals and
increasing in cash flows?”
Market overvalues accruals (i.e., fails to recognize that accrualsbased earnings are not permanent)
Trading strategy implication: Long in low accrual stocks and short
in high accrual stocks to generate above-normal performance.
Trading strategy based on external financing is based on
accruals – raise capital means high accruals means go short 51
Conclusions



Investors exhibit many behavioral biases
If the biases are similar across individuals and arbitrage
forces are limited, then the behavioral biases can cause
prices to deviate systematically from economic
fundamentals
Recent attempts to test the effects of behavioral biases in
stock price data


Aggregate earnings data and stock returns
Individual firms’ financial data and stock returns



Stock returns associated with external financing decisions
Stock returns due to investors’ alleged inability to process
information in accounting accruals
Next set of issues

How large is the mispricing? Can it be exploited? What are the
barriers to implementation and what are the implications for asset
52
management?