Psychological factors,
stock price paths, and
trading volume
Steven Huddart (Penn State),
Mark Lang (UNC Chapel Hill),
and Michelle Yetman (UIowa)
Discussed by Andrei Simonov
(Stockholm School of Economics)
Why should we care?
 Disposition
effect: reference point is purchase price of
the stock (Shefrin & Statman (85), Odean 98)

Note: disposition effect tends to disappear within ~ 240 trading
days (Jackson, 2002)
 Search
story: small investors are net purchasers on the
days when stock is in the news (Barber& Odean 2002)
 Option exercise literature: Heath, Huddard and Lang
(99), Core & Guay (01), Potesman & Serbin (01)
 Ferris, Haugen & Makhija: trading volume is higher
when firm pass previous ”high price”
This paper...



Builds on Ferris et. al.
Convincingly documents the association
between extreme points in past price path and
market volume
Suggests the association between behavioral
factors and trading volume
What is the idea?
9000000
Investors who bought in May-June 01, are more likely
to sell when the next high is hit (May-June 2002)
1.4
8000000
1.2
AMEX:ECO
7000000
1
6000000
5000000
0.8
4000000
0.6
3000000
0.4
Volume
Close
2000000
0.2
1000000
0
05/01/01
0
06/14/01
07/27/01
09/17/01
10/29/01
12/11/01
01/28/02
03/12/02
04/26/02
06/13/02
Volume Leaders: NASDAQ | AMEX | NYSE
Price % Gainers: NASDAQ | AMEX | NYSE
Price % Losers: NASDAQ | AMEX | NYSE
Symbol
Name
Last Trade
Change
Volume
CYPB
CYPRESS BIOSCI
10-Dec
2.55
1.11
77.08%
560,880
IAAC
INTL ASSETS HLD
10-Dec
2.2
0.6
37.50%
7,500
MRAE
MIRAE CORP
10-Dec
3.299
0.849
34.65%
700
CAMZ
CAMINUS CORP
10-Dec
3.52
0.77
28.00%
153,769
EUNI
EUNIVERSE INC
10-Dec
5.22
1.1
26.70%
942,125
INFT
INFORTE CORP
10-Dec
6.94
1.24
21.75%
47,243
ATML
ATMEL CORP
10-Dec
3.3
0.55
20.00%
21,521,940
EMMSP
EMMIS COMM PR A
10-Dec
37.5
6.14
19.58%
27,225
How is it done?


Weekly data in randomly chosen 1000
companies (why not all???? Possible biases as
firms tend to be small), NYAM &NASDAQ
Variables of interest:
 PRIORMAX
dummy
 LHIGH=PRIORMAX*MONTH SINCE PREV. HIGH


Volume: measured as turnover or ”abnormal”
turnover.
Control for ex-div and earning announcement
dates, price($ and relative), etc.
Model

Essentially, the model estimated is reminiscent of
VAR with contemporaneous exogenous
variables:
ABNVOLt    bPRIORMAX t  Controlst
  
5
L 0

tL
ret

tL


tL
ret

tL

b is STRONGLY significant, Min(t-stat)=14
Critique(1): Logic




Investors want to sell when the price reaches
new high
The more sophisticated investors are, the more
prone they are to behavioral biases (Huddart &
Lang 02, Poteshman & Servin, 01)
But... Barber & Odean 02: Unsophisticated
investors are net buyers on stock news date
It seems that constant supply of suckers is
necessary to support the story
Critique: Econometric



Why not full-fledged VAR
with past turnover?
Why weekly returns? Is it
possible that we are
missing real max?
Why raw returns? What
would happen if returns
are adjusted for market
and especially for
momentum?
Example:
CYPB (CYPRESS BIOSCI)
Date
Open
Close
Adj. Close*
DAILY MAX
$4.19
$4.26
$4.26
DAILY MIN
$0.96
$1.00
$1.00
WEEKLY MAX
$4.19
$4.10
$4.10
WEEKLY MIN
$1.05
$1.05
$1.05
What are alternative explanations?

Barber & Odean 2002: news story.
 Essentially
it says that effect of 52 weeks high is self-
reinforcing.
 Investors are buying stock which was in the news
 But being in max. is equivalent in being on the news
(both in High/Low section and on Lou Dobbs’
Moneyline, CNBC Power Lunch, etc.)
Can we test for alternative
explanations?

My small sample
4
month of daily data Feb-May 2000:
Returns (both raw and market-adjusted), volume,
dividend payouts, etc.
 Press releases dummy (=1 if release was issued
on that date)
 Number of times the company was mentioned in
the press/in TV news, etc. (daily)

 59
Swedish companies
Measures of press exposure

I am using abnormal exposure measured as
residual of regression of number of hits on
 Log10(MktCap),
press releases dummy (same day
and lagged), industry dummies, day of week dummies,
market index return, in different combinations (does
not affect results)
 Similar to the measure of abnormal analyst’ coverage.
Full fledged VAR model

Yt  TOt , rett , rett , PRIORMAX t , PRIORMIN t , Exposuret
T
XTt  Pr ess Re lt , IndustryDu mmy, FirmDummy 
L
M
l 1
m 0
Yt      lYt l    m X t  m
Number of lags (L, M) determined via AICC criteria

Results
RAW RETURN
c
2
Exposure
PRIORMIN, PRIORMAX
PRIORMIN
PRIORMAX
Exposure
Exposure
Exposure
Pos(Ret), Neg(Ret)
PRIORMIN, PRIORMAX
PRIORMIN
PRIORMAX
PRIORMIN, PRIORMAX
PRIORMAX
→
←
→
←
→
←
→
←
→
←
→
←
→
←
→
←
→
←
→
←
→
←
→
←
→
←
Turnover
Turnover
Turnover
Turnover
Pos(Ret), Neg(Ret)
Pos(Ret)
Neg(Ret)
Turnover
Exposure
Exposure
Exposure
Pos(Ret), Neg(Ret)
Pos(Ret)
DF
CAPM Adj RET
p-val
c
2
DF
p-val
CONTROL= PRESS RELEASES, firm fixed effects
14.16
3
0.0027
19.83
5
35.47
3
<.0001
34.59
5
2.33
6
0.8867
2.78
10
3.57
6
0.7343
3.39
10
1.39
3
0.708
1.05
5
1.38
3
0.7106
1.27
5
0.88
3
0.8304
1.67
5
1.86
3
0.6028
1.82
5
11.01
6
0.0882
22.33
10
63.63
6
<.0001
72.42
10
8.13
3
0.0435
10.44
5
48.61
3
<.0001
37.75
5
6.65
3
0.084
12.13
5
6.46
3
0.0914
13.87
5
16.53
6
0.0112
21.22
10
11.44
6
0.0757
13.15
10
13.59
6
0.0346
14.35
10
5.83
6
0.4424
8.17
10
0.75
3
0.8608
1.24
5
0.72
3
0.869
0.8
5
12.86
3
0.005
13.11
5
5.06
3
0.1675
7.25
5
11.77
12
0.464
14.83
20
11.92
12
0.4525
27.75
20
1.00
3
0.8012
1.36
5
2.33
3
0.507
2.44
5
0.0013
<.0001
0.9862
0.9708
0.9583
0.9382
0.893
0.8728
0.0135
<.0001
0.0638
<.0001
0.0331
0.0165
0.0196
0.2153
0.1577
0.6119
0.9409
0.9771
0.0224
0.2029
0.7863
0.1153
0.9281
0.7849
Results(2)


No causality between PRIORMIN, PRIORMAX
and either TO or returns
Causality from PRIORMAX to EXPOSURE
 Stronger
for raw returns (p=0.005), weaker for
market adjusted returns


Strong bi-directional causality between
exposure and turnover
Strong causality from returns to exposure,
weaker one from exposure to returns (but still
stat. signif. on 5% level, stronger for marketadjusted returns)
Results(3)




PRIORMAX is important, PRIORMIN is not. May
be, disposition effect?
How to understand Huddart-Lang-Yetman? As
chain
PRIORMAXExposure Turnover
Exposure effect is stronger and on short time
horizon can even drive returns.
So, it is still behavioral, but… Not on sellers’ but on
buyers’ side.
Net picking: AMEX:ECO
Average analyst’ ranking ~4/5 (run away!), worst CAN Au firm
”Au prices rising…Money is
flowing into gold stocks.”
Reuters, May 17
Credit Crunch,
fire sale,
S&P downgrade to B-
3-way merger announced,
Fidelity increases stake
9000000
1.4
8000000
1.2
7000000
1
6000000
0.8
5000000
4000000
0.6
3000000
First merger rumors
on Reuters
Volume
Close
2000000
0.4
0.2
1000000
0
05/01/01
0
06/14/01
07/27/01
09/17/01
10/29/01
12/11/01
01/28/02
03/12/02
04/26/02
06/13/02
Gold and Echo Mines
450
340
400
330
350
320
300
310
250
300
200
290
150
280
100
GOLD PM
Close *300
270
50
260
0
250
06/15/01 07/30/01 09/18/01 10/30/01 12/12/01 01/29/02 03/13/02 04/29/02 06/14/02 07/29/02 09/11/02 10/23/02 12/05/02