The Dotcom Effect Revisited
Temporal Elements Of Market
Perceptions Of Electronic
Commerce Value
-Eric Walden
About the Thesis
History
ESSAY1-The dotcom
effect
ESSAY2-The dotcom
effect revisited
ESSAY3-Contracting
for IT Outsourcing
Motivation
DOTCOM Effect provided interesting results of
new phenomena.
Used only Q4 1998
Questions arose in presentation and review





Market Bubble
Other Categorizations
Other Explanations
Market Crash
Validity of Method
Staples
Goals of Study
Discover how generalizability results are
Compare short run and long run results



Temporal stability
Accuracy of method
Accuracy of Market
Understand how market perception
changed over time
Examine other categories/explanations
Dotcom Results
Market responds positively to EC
initiatives (CAR 4.3%-16.2%)
NET = Click-n-Mortar
Tangible > Digital
B2C > B2B
7.8%
2
3
4
9.4%
8.4%
-2
8.4%
-3
6.0%
5.3%
2.7%
3.0%
0.6%
8.0%
4.9%
13.0%
10.0%
12.5%
18.0%
-2.0%
-5
-4
-1
0
1
5
Categories to Use
B2B/B2C; Digital/Tangible; Net/Non-Net
Alliances/Unilateral Chen and Siems(2001)
Transformational/Executional Chatterjee,
Richardson and Zmud(2001); Dos Santos,
Peffers and Mauer(1992); Im, Dow and
Grover(2001)
Business Models Weill and Vitale(2001)
Data
Sample of 4744 announcements Jan 1,
1999 – Dec 31, 2000
3,000,000 words of text (~5000 pages)
Two independent coders—differences
resolved face to face
2097 coded as EC initiatives by public
firms
EC init is the development of a new
capability to deliver a product digitally
First Pass-Short Run Event Study
Regress Firm Return against market
return Rs ,t   s   s Rm,t   s ,t
Calculate cumulative abnormal return as
sum of errors ARs ,t  Rst   s   s Rm,t 
Correlation of errors causes regression
 

problems
2




 1
Rm,  R m
2

var( ARs , )  S s 1   T
  T
Rm,t  R m
 

t 1
 



2 
 


THIS PAPER
(-1,+1) Window (-5,+5) Window
n = 1273
n = 1269
All Data
Mortar
Net
Diff
Mortar-Net
B2B
B2C
Diff
B2B-B2C
Tangible
Digital
Diff
Tan-Digital
Unilateral
Alliance
Diff
Uni-Allience
Transform
Incremental
Diff
Trans-Increm
Content
Direct to Cust
Full Service
Intermediary
Shared Infra
Value Net
Virtual Comm
Whole of Ent
Relation
Data
Transaction
SW
Mean T value
0.0% -0.07
Mean t value
-1.4% -2.37*
(-10,+10)
(-5,+5) Window
Window
n = 251
n = 1264
Mean T value
Mean t value
-4.0% -4.91**
7.5% 5.45**
0.4% 1.24
-0.9% -1.51
1.3% 2.06*
-0.4% -0.63
-3.3% -2.88**
2.9% 2.33*
-1.2% -1.31
-9.4% -5.94**
8.2% 4.81**
4.9% 2.41*
9.6% 5.19**
-4.7% -1.72+
14.0% 5.03**
18.1% 6.96**
-4.1% -1.07
0.4% 0.76
-0.3% -0.72
0.7% 1.05
-1.0% -1.00
-1.6% -2.24*
0.6% 0.53
-2.5% -1.83+
-4.8% -4.84**
2.4% 1.41
5.2% 2.93**
9.3% 4.29**
-4.1% -1.53
12.8% 6.88**
21.0% 5.38**
-8.2% -2.21*
1.0% 2.18*
-0.6% -1.58
1.6% 2.59**
0.1% 0.16
-2.3% -2.98**
2.5% 2.06*
-1.5% -1.25
-5.5% -5.10**
4.0% 2.44*
9.4% 4.42**
5.8% 3.29**
3.7% 1.33
23.4% 8.02**
10.2% 4.10**
13.2% 3.44**
0.7% 1.46
-0.7% -1.66+
1.3% 2.20*
-1.0% -1.15
-1.7% -2.24*
0.6% 0.55
-3.2% -2.52*
-4.7% -4.49**
1.5% 0.92
1.0% 1.58
-0.3% -0.90
1.4% 1.81+
0.3% 0.22
-1.8% -2.78**
2.1% 1.46
-2.0% -1.15
-4.5% -4.92**
2.4% 1.22
-3.2%
0.4%
1.8%
-0.5%
0.3%
7.3%
-1.2%
0.4%
0.0%
0.2%
0.8%
-5.9%
-0.5%
2.0%
-1.3%
-4.2%
12.9%
-4.7%
3.4%
-1.4%
-1.0%
0.0%
-6.0%
-3.1%
1.3%
-3.9%
-9.2%
17.2%
-9.4%
6.6%
-4.0%
-3.4%
-1.9%
-2.21*
1.13
1.29
-0.94
0.33
3.05*
-0.93
0.14
0.00
0.47
2.14*
-2.15*
-0.73
0.75
-1.16
-2.12*
2.82*
-1.85+
0.61
-2.40*
-1.66+
0.04
-1.57
-3.27**
0.37
-2.60**
-3.35**
2.66*
-2.69**
0.85
-4.83**
-4.00**
-1.82+
(-10,+10)
Window
n = 251
Mean t value
16.2% 8.53**
Old Results
Overall +
Mortar=Net
B2B>B2C
Tan>Digital
Short Run
New Results
Overall –
Mortar>Net
B2B=B2C
Tan>Digital
Uni>=Ally
Tran>=Incre
Biz models ?
Long Run Event Study
More info should be more accurate
Makes use of Buy and Hold Abnormal
Return (BHAR) = Δ stock - Δ portfolio
Δ = 1 day before to 1 year after
Issues with long run



Skewness and Kurtosis (Winsorize)
Survival Bias (Note)
Rebalancing Bias
T
CAR   Rit  ERit 
t 1
BHAR  1  Rit   1  ERit 
T
T
t 1
t 1
All Data
Mortar
Net
Diff
Mortar-Net
B2B
B2C
Diff
B2B-B2C
Tangible
Digital
Diff
Tan-Digital
Unilateral
Alliance
Diff
Uni-Alliance
Transform
Incremental
Diff
Trans-Incre
Content
Direct to Cust
Full Service
Intermediary
Shared Infra
Value Net
Virtual Comm
Whole of Ent
Relation
Data
Transaction
Short Run (CAR)
(-1,+1) Window
Mean t value
1.2% 2.18*
1.5% 2.87**
-1.8% -0.75
3.3% 1.90+
Long Run (BHAR)
1 Year Window
Mean t value
11.0% 2.39*
11.5% 2.35*
7.3% 0.36
4.2% 0.26
n
322
288
34
1.4% 1.71+
1.0% 1.40
0.4% 0.39
22.3% 2.68**
2.6% 0.51
19.8% 2.12*
138
184
2.6% 3.67**
-0.3% -0.36
2.9% 2.65**
14.5% 2.26*
7.4% 1.11
7.1% 0.77
165
157
1.8% 2.47*
0.2% 0.30
1.6% 1.44
9.5% 1.52
13.3% 1.94+
-3.8% -0.40
191
131
1.6% 1.39
1.0% 1.71+
0.6% 0.46
7.8% 0.96
12.0% 2.18*
-4.3% -0.39
74
248
-1.6%
1.8%
6.7%
0.6%
-1.8%
8.0%
-2.6%
n/a
1.2%
1.2%
2.1%
-0.55
2.92**
2.72*
0.46
-0.99
2.27
-0.99
n/a
2.12*
2.13*
3.21**
9.3%
13.3%
-14.2%
-9.9%
7.7%
59.9%
-23.1%
n/a
10.2%
13.2%
16.3%
0.40
2.45*
-1.33
-1.42
0.40
2.41
-2.45*
n/a
2.17*
2.72**
2.74**
11
248
15
70
20
2
19
0
300
299
216
Comparison of Short
and Long Run
Note Sample Size and Bias
Note Discrimination
CAR
Overall +
Mortar>Net
B2B=B2C
Tan>Digital
Uni=Ally
Tran=Incre
Biz models ?
BHAR
Overall +
Mortar=Net
B2B>B2C
Tan=Digital
Uni=Ally
Tran=Incre
Biz models ?
Intermission
Results not stable across time periods
Results not stable for same firms over
time
Business model approach does not
show much promise
Perceptions changing over time
Need way to look at change over time
To address changes over time
CAR = f(time)
If CAR changes over time mean is bias
Connect the dots => too much variance
Need to trade off
variance and bias
to get a
meaningful idea
of changes over
time
Linear Regression for CAR
all data 1999
5%
4%
3%
2%
1%
0%
-1%
-2%
-3%
-4%
-5%
Jan Feb Mar Apr May Jun
CAR
Jul
Aug Sep Oct Nov Dec
Regression
Kernel Estimation
If f is smooth then values
1
ˆ
of CAR near t are good
f time0  
N
estimates of CAR at t
N
 w time CAR
i 1
i
0
1  x0  xi 
K

h  h 
Take weighted average
wi x0  
N
1
1  x0  xi 
with weights summing to
K


N i 1 h  h 
unity
Weights decrease as
distance from t increases


K (u )  3 1  u 2  I | u | 1
4
i
Two Notes
Bandwidth (h) is the only variable under
researcher’s control, controls tradeoff
between bias and variance


h->0 => Var->max Bias->min
h->∞ => Var->min Bias->max
Standard error is complex and does not
capture bias so use bootstrap for
confidence intervals
Kernel Results (1999-2000)
6%
5%
4%
3%
2%
1%
0%
-1%
-2%
-3%
-4%
-5%
4%
3%
2%
1%
0%
-1%
-2%
-3%
N D J F M A M J J A S O N D J F M A M J J A S O N D
Month
Month
Note convergence over
time. Has EC stabilized?
4%
2%
B2C
J F M A M J J A S O N D J F M A M J J A S O N D
5%
3%
B2B
Tan
Dig
Spike at end of 1999 could
be second wave of EC
using earlier know how
1%
0%
-1%
-2%
-3%
-4%
N D J F M A M J J A S O N D J F M A M J J A S O N D
Month
Kernel Results (1999-2000)
5%
5%
4%
4%
3%
2%
3%
CnM
1%
0%
-1%
Net
2%
Uni
1%
Ally
0%
-2%
-1%
-3%
-2%
-4%
-3%
-5%
6%
5%
4%
3%
2%
1%
0%
-1%
-2%
-3%
-4%
-4%
N D J F M A M J J A S O N D J F M A M J J A S O N D
N D J F M A M J J A S O N D J F M A M J J A S O N D
Month
Month
Inc
Tra
N D J F M A M J J A S O ND J F M AM J J A S O ND
Month
Does convergence in
governance indicate value
from standardization of
contracts software and
relationships?
Conclusions
Market perceptions not stable over time
Convergence over time to zero
Second Wave at end of 1999
Tangible > digital in general
Kernel estimation provides powerful tool
Future Directions
What were the causes of change in
market perceptions
Irrational valuations of technology
promise?
Information cascade
How does competition play out
Infrastructure and the superiority of
tangible production
Correlated Error Problem
Estimate VAR = Sum (mean-ob)^2
= VAR(x) + VAR(y) if i.i.d. but
= VAR(x)+VAR(y)-2COV(x,y) if corr
So VARHAT = TRUEVAR – COV
VAR underestimated