Online Appendix 1. Conditional regression multiples – Fama-MacBeth (1973) two-step procedure
Industry Classification Benchmark (Datastream Level 3 supersector name: INDM3)
Parameter
E ̂ 0 
E̂1 
E̂ 2 
E ̂ 3 
E̂ 4 
Average
R-squared
10
11
12
13
14
15
16
17
18
19
20
21
22
23
-0.683
-0.543
-0.840
-0.576
-1.124
-0.847
-0.820
-0.722
-0.122
-0.876
-1.061
-0.680
0.634
-0.602
0.15
0.12
0.15
0.17
0.12
0.22
0.07
0.19
0.17
0.12
0.11
0.17
0.09
0.18
-4.62
-4.37
-5.79
-3.37
-9.05
-3.93
-12.27
-3.86
-0.72
-7.41
-9.92
-4.05
6.73
-3.38
0.750
0.746
0.744
0.687
0.741
0.640
0.653
0.644
0.734
0.674
0.668
0.618
0.506
0.650
0.03
0.03
0.03
0.02
0.04
0.04
0.02
0.05
0.02
0.02
0.03
0.03
0.07
0.03
21.50
25.40
23.73
28.96
20.32
17.02
39.55
14.25
29.66
29.28
25.79
18.40
6.91
22.89
0.225
0.227
0.267
0.269
0.300
0.356
0.332
0.350
0.229
0.318
0.355
0.359
0.335
0.309
0.03
0.02
0.03
0.03
0.03
0.04
0.01
0.04
0.02
0.02
0.02
0.03
0.05
0.03
6.89
9.91
9.58
10.04
10.12
9.37
26.20
8.66
10.82
14.86
17.68
12.89
6.18
10.70
-0.016
-0.042
-0.027
-0.043
0.000
0.001
-0.052
-0.061
-0.047
-0.053
-0.023
-0.060
-0.086
-0.059
0.01
0.01
0.01
0.01
0.01
0.02
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
-2.41
-7.62
-3.20
-5.53
0.03
0.04
-14.65
-6.09
-4.86
-10.47
-2.69
-7.36
-9.03
-7.82
0.413
-0.204
0.171
-0.205
0.204
0.439
-0.255
0.130
0.065
-0.147
-0.208
0.064
-0.173
-0.092
0.20
0.18
0.13
0.11
0.13
0.18
0.06
0.15
0.14
0.12
0.12
0.16
0.37
0.03
2.08
-1.10
1.28
-1.88
1.60
2.48
-4.33
0.87
0.45
-1.24
-1.69
0.41
-0.47
-2.66
0.93
0.90
0.92
0.87
0.89
0.89
0.85
0.87
0.95
0.82
0.89
0.83
0.91
0.86
Note. The estimated industry-year regression is given in Eq. (A.1) at the bottom of Online Appendix 2. The industries are as follows: (10): Automobiles & Parts; (11):
Basic Resources; (12): Chemicals; (13): Construct. & Material; (14): Food & Beverage; (15): Healthcare; (16): Industrial Goods & services; (17): Media; (18): Oil & gas; (19):
Personal and household goods; (20): Retail; (21): Technology; (22): Telecom; and (23): Travel & leisure. How to read the table: The first row returns the average value of each
coefficient from Eq. (O.A.1), the second row shows the Fama-MacBeth (1973) standard errors and the third row provides the corresponding t statistic.
1
Online Appendix 2. Decomposing market-to-book at the firm-level
Valuation component
mit  bit
(1)
mit 
v 
ˆ
it ;  jt 
v  it ; ˆ jt  

v  it ; ˆ j
(2)

(2) + (3)
v it ; ˆ j  
bit
Sample size
(3)
(4)
(5)
0.04
0.04
0.58
30,446
-0.41
0.17
-0.11
0.25
-0.24
-0.25
0.22
-0.25
-0.01
0.15
0.16
-0.08
0.12
0.13
0.70
0.10
0.58
0.17
0.75
1.17
0.39
1.04
0.70
0.36
0.58
0.86
0.78
0.39
871
1,258
1,132
1,947
2,155
1,790
7,944
1,870
728
2,752
1,810
4,387
488
1,314
-0.14
-0.08
0.06
-0.01
-0.01
0.12
-0.10
0.04
-0.31
0.09
0.06
0.09
-0.04
0.22
0.39
0.58
0.56
0.62
0.59
0.47
0.68
0.42
0.60
0.65
0.25
0.48
0.59
0.65
652
1,287
1,379
5,928
7,350
577
415
1,993
121
2,118
582
1,129
2,253
4,662
Panel A: Full sample
0.62
0.00
Panel B: Distribution across industries
Automobiles & Parts
Basic Resources
Chemicals
Construct. & Material
Food & Beverage
Healthcare
Ind. Goods & services
Media
Oil & gas
Pers. & househ. goods
Retail
Technology
Telecom
Travel & leisure
0.29
0.26
0.47
0.42
0.51
0.92
0.61
0.80
0.69
0.51
0.73
0.78
0.90
0.52
-0.26
0.19
-0.11
0.10
-0.15
-0.24
0.14
-0.25
-0.02
0.05
0.14
-0.11
-0.24
0.08
-0.15
-0.02
0.00
0.15
-0.09
-0.02
0.08
0.00
0.01
0.09
0.01
0.04
0.35
0.04
Panel C: Distribution across countries
Austria
Belgium
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Switzerland
UK
0.25
0.50
0.62
0.62
0.58
0.59
0.58
0.46
0.29
0.74
0.31
0.56
0.55
0.87
-0.23
-0.09
0.05
-0.06
-0.04
0.01
-0.13
0.00
-0.47
0.02
-0.19
0.04
-0.04
0.20
0.08
0.01
0.02
0.05
0.03
0.12
0.03
0.04
0.16
0.08
0.24
0.04
0.00
0.01
Note. The decomposition of the market-to-book ratio in its fundamental and non-fundamental component follows RhodesKropf et al. (2005) and is similar to Li et al. (2011). The description here is therefore restricted. We specifically estimate the
following industry-year regression and subsequently capture the point estimates:
 
 


lnMVit    0 jt  1jt lnBVit    2 jt ln NIit   3 jt I(NI0) ln NIit   4 jt ln LEVit   it
fQ 
ˆ it
ˆ it
mit  m
Debt it  m
and mQ 
TAit
TAit
(O.A.1)
(O.A.2)
Lower case letter denote the natural logarithm of a focal variable. Thus, m stands for the log of market value. MV =
market value; BV = book value; NI+ = positive net income while I is an indicator variable suggesting negativity of net income and
LEV is leverage as defined in Appendix A.1 (ratio of total debt-to-assets). The non-fundamental component (mQ) is the
difference between the market value and the fitted value, divided by firm’s total assets. In contrast, the fundamental component
(fQ) which tracks long-run growth options is the sum of book value of debt and fitted market value, divided by firm’s total assets.
2
Online Appendix 3: Managerial Information (Earnings Surprises)
Online Appendix 3 reports summary statistics and results from univariate tests, contrasting Europe and the U.S. I use the U.S. as
benchmark since much of research about firm learning from stock prices is predominantly U.S.-related. The sample covers 2,312
European firms and 2736 U.S. (that trade on the NYSE and NASDAQ) and spans the period from 1991 to 2011. To examine the
impact of country-level infrastructure, this period is further broken into two sub-periods: before 2000 (pre-Reg FD) and after 2000
post-Reg FD). The variable of interest is earnings surprise as a proxy for managerial information about fundamentals. I use the
earnings surprise percent (ESPCT) item as calculated by Thomson One. I show not only the average earnings surprise, the average
of its absolute value, but also the country-industry-year adjusted value of earnings surprise percent and the related differences of
that latter value relative to the U.S. Finally, the division of Europe in continental Europe and the U.K. helps verify the conjecture
that the U.K. and continental Europe have different country-level infrastructure that affects the information environments of firms.
It also helps examine whether the U.K. shares common characteristics with the U.S.
Average Adjusted ESPCT
Average
ESPCT
EUROPE
before year 2000
after year 2000
Continental Europe
before year 2000
after year 2000
U.K.
before year 2000
after year 2000
0.12
0.25
0.05
0.14
U.S.
-0.06
-0.26
0.03
before year 2000
after year 2000
0.01
Average
Absolute
ESPCT
Value
Diff.
relative to
the U.S.
t-statistic
# obs.
0.80
0.74
0.82
0.89
0.84
0.92
0.29
0.30
0.29
0.38
0.28
0.43
0.43
0.31
0.49
0.15
0.16
0.15
0.18
0.10
0.22
0.23
0.13
0.28
-0.05
-0.04
-0.05
17.13
6.45
16.13
19.05
7.30
17.78
-3.23
-1.48
-3.48
23,343
7,698
15,645
19,606
6,328
13,278
3,737
1,370
2,367
0.44
0.52
0.40
0.20
0.18
0.21
25,380
7,959
17,421
Online Appendix 4: Percentage Shareholdings by Employees and Family Members
Online Appendix 4 reports summary statistics and results from univariate tests, contrasting Europe and the U.S. I use the U.S.
because benchmark as much of research about firm learning from stock prices is predominantly U.S.-related. The sample covers
2,312 European firms and 2736 U.S. (that trade on the NYSE and NASDAQ) and spans the period from 1991 to 2011. The
variable of interest is NOSHEM (source: Datastream), which is the percentage of strategic holdings of 5% or more held by
employees or by family members. This variable can affect information flow between insiders and outsiders and thus the
quality of investor private information contained in stock prices. I show not only the average NOSHEM but also the differences
relative to the U.S. Finally, the division of Europe in continental Europe and the U.K. helps verify the conjecture that the U.K. and
continental Europe have different country-level infrastructure that affects the information environments of firms. It also helps
examine whether the U.K. shares common characteristics with the U.S.
NOSHEM
(%)
EUROPE
Continental Europe
U.K.
18.02
19.74
5.61
U.S.
5.75
Diff. relative
to the U.S.
(%)
12.26
13.99
-0.14
t-statistic
# obs.
60.62
64.12
0.52
19,896
17,462
2,434
18,729
3
Online Appendix 5: The moderating role of the depth of (peer) analyst coverage using an alternative
definition of investment
The sample includes 2,312 non-financial, non-utility individual firms that are listed in 14 European countries. It spans the
1991–2011 period. This table 6 reports the regression coefficients and p-values obtained from IV estimations of Eq. (2). The
dependent variable is as calibrated in Richardson (2006) scaled by lagged book assets. Richardson (2006) defines investment as
follows: capex + R&D + acquisitions – (sales of PPE + depreciation). Firm Q and RelPeer Q are as defined in Table 1. All other
variables included in the regression but not reported herein (age, return, etc.) are defined as in Appendix A.1. Peer firm average
variables are constructed as the average across all other firms in a given firm's peer industry. A firm's industry peer group is
defined based on its ICB level 3 classification. In all specifications, I estimate Eq. (2) using an unbalanced panel with year fixed
effects and time-invariant firm fixed effects. I correct for arbitrary heteroskedasticity and for correlation within firms. I provide
overidentifying restrictions (Sargan test statistic and the related p-value). First-stage estimates are available upon request (Cragg–
Donald Wald F-statistics suggest that weak instruments are of no concern in this study). Symbols ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively.
Variables
RelPeer Qt-1
Firm Qt-1
Ψ +-jt-1
Ψ +jt-1
Number of observations
R-squared
Sargan statistic
A: Peer analyst coverage
low
high
-0.0015
0.0311
(0.6983)
(0.0113)
0.0113
***
0.0074
(0.0000)
(0.0008)
0.0119
-0.0110
(0.1185)
(0.0790)
-0.0009
-0.0016
(0.2542)
(0.0624)
B: Firm analyst coverage
low
high
**
***
0.0050
0.0053
(0.2226)
(0.3550)
0.0049
**
(0.0159)
*
0.0094
(0.0784)
*
0.0111
***
(0.0000)
*
-0.0063
(0.2509)
-0.0010
0.0001
(0.1369)
(0.9073)
8856
11013
9801
10003
0.1012
0.0554
0.0709
0.1015
0.685
0.026
0.700
4.310
(0.4079)
(0.8710)
(0.4027)
(0.0379)
In this online appendix, I further explore the robustness of firm learning from (peer) stock prices
using an alternative definition of investment following Richardson (2006). As in Table 5 of the
manuscript, I structure the discussion using the depth of peer firms’ and individual firms’ own analyst
following. Sample splits take place by industry and by year, allowing firms to migrate across the states
over time.
First, I use peer analyst coverage to partition the sample. Similar results generally obtain as above
when I use now the composite investment ratio akin to Richardson (2006) as the dependent variable. In
this specification, firms generally do not learn from peer stock prices when their peers have lower analyst
following (p = 0.6983). Instead, the average firm relies uniquely on its own stock price (1.13%, p < 0.001)
when deciding on investment spending (column (1) of online appendix 5). This effect reverses as peer
4
firms’ analyst coverage becomes deeper. A one standard deviation increase in peer firms’ stock price
innovations results in a 3.11% increase in investment spending. Furthermore, the reliance on a firm’s own
stock prices does not disappear completely; it merely decreases from 1.13% to 0.74%, and remains highly
significant. However, the impact on investment of peer stock price shocks turns out to be moderately
higher than that of a firm’s own stock price (p = 0.077). Surprisingly, both measures of price
informativeness load negatively and significantly on investment. It is unclear whether this signals
perceived reduces growth opportunities.
In a second step (Panel B of online appendix 5), I split the sample again by year and by industry
based on the depth of individual firms’ own analyst coverage. The evidence proves also consistent with
firm learning from (peer) stock prices. In fact, firms respond more positively to innovations their own
stock prices when their own stock is followed by more analysts (1.11% vs. 0.49%) than their stock is
followed by fewer analysts. Interstingly, peer residual stock price informativeness has a positive impact on
investment of the average firm.
5