Analysis of Daredevil Issues 1 to 60
Introduction and Material and Methods
In order to
isolate potential key issues to focus my investing efforts on, I used a data
analysis approach based on established values from two collecting universes
within in the run of the silver age issues I gathered value data from a variety
of sources representing both serious investors (Insiders – I) and not serious
investors (Outsiders-O). This data is current as on 4/2015. I choose to keep my
methodology and data gathering proprietary at this time but I am giving my
readers a simplified representation of my data that I currently use for my
decisions.
Results – Daredevil Issues 1 to 60
Table 1. Bias Scores (B SCORE) of All Issues in the Run
Run
|
Issue
|
B
Score
|
Run
|
Issue
|
B
Score
|
|
DD
|
#1
|
2078
|
DD
|
#31
|
70
|
|
DD
|
#2
|
455
|
DD
|
#32
|
-41
|
|
DD
|
#3
|
569
|
DD
|
#33
|
-69
|
|
DD
|
#4
|
549
|
DD
|
#34
|
-33
|
|
DD
|
#5
|
340
|
DD
|
#35
|
-63
|
|
DD
|
#6
|
388.6
|
DD
|
#36
|
-24
|
|
DD
|
#7
|
5001
|
DD
|
#37
|
-1
|
|
DD
|
#8
|
400.2
|
DD
|
#38
|
27
|
|
DD
|
#9
|
48.6
|
DD
|
#39
|
-34
|
|
DD
|
#10
|
180.6
|
DD
|
#40
|
-78
|
|
DD
|
#11
|
3.6
|
DD
|
#41
|
-36.6
|
|
DD
|
#12
|
267.2
|
DD
|
#42
|
-53.6
|
|
DD
|
#13
|
76.6
|
DD
|
#43
|
13.6
|
|
DD
|
#14
|
245.6
|
DD
|
#44
|
-69.6
|
|
DD
|
#15
|
145.2
|
DD
|
#45
|
-49.6
|
|
DD
|
#16
|
760.4
|
DD
|
#46
|
-1.6
|
|
DD
|
#17
|
363.4
|
DD
|
#47
|
3.4
|
|
DD
|
#18
|
-21.2
|
DD
|
#48
|
-52.6
|
|
DD
|
#19
|
144.6
|
DD
|
#49
|
-67.6
|
|
DD
|
#20
|
95.6
|
DD
|
#50
|
-50
|
|
DD
|
#21
|
45.6
|
DD
|
#51
|
-56.6
|
|
DD
|
#22
|
-85.4
|
DD
|
#52
|
-87.6
|
|
DD
|
#23
|
230.6
|
DD
|
#53
|
1
|
|
DD
|
#24
|
-59.4
|
DD
|
#54
|
157.4
|
|
DD
|
#25
|
-44.4
|
DD
|
#55
|
-32.2
|
|
DD
|
#26
|
42.6
|
DD
|
#56
|
11.8
|
|
DD
|
#27
|
57.2
|
DD
|
#57
|
22.8
|
|
DD
|
#28
|
-32.4
|
DD
|
#58
|
-78.2
|
|
DD
|
#29
|
-5.4
|
DD
|
#59
|
-2.2
|
|
DD
|
#30
|
88.4
|
DD
|
#60
|
7.8
|
Using the data based on that described in the introduction, I
calculated the I/O bias between the two groups and adjusted the scores to
account for the extreme issues (DD 1 and 7). I note the very high insider bias
with dark purple and high bias using light purple. Grey issues (if present) are
on the watch list. Note the low plus/ low minus numbers reflect a sight bias by
the unsophisticated outsider group followed by the more extreme biases. I use
the Average followed by Std. Dev. as a guide into
these cutoffs. (Note the term guide). These numbers only refer to this data set
unless I combine multiple runs. So you cannot draw conclusions between
independent runs and numbers throughout this blog. The data suggests a bias
does exist within the run and invites a deeper focus (see Tables 2 and 3).
Table 2. I/O SLN numbers calculated for issues
Run
|
Issue
|
I
SLN
|
O
SLN
|
Run
|
Issue
|
I
SLN
|
O
SLN
|
|
DD
|
#1
|
459.6
|
720.7
|
DD
|
#31
|
9.1
|
7.2
|
|
DD
|
#2
|
76.5
|
132.5
|
DD
|
#32
|
4.2
|
7.2
|
|
DD
|
#3
|
60.8
|
86.5
|
DD
|
#33
|
3.6
|
7.2
|
|
DD
|
#4
|
51.2
|
72.1
|
DD
|
#34
|
4.3
|
7.2
|
|
DD
|
#5
|
35.2
|
44.6
|
DD
|
#35
|
3.3
|
7.2
|
|
DD
|
#6
|
31.4
|
30.3
|
DD
|
#36
|
5.5
|
7.2
|
|
DD
|
#7
|
273.1
|
132.5
|
DD
|
#37
|
6.0
|
7.2
|
|
DD
|
#8
|
29.6
|
24.5
|
DD
|
#38
|
8.1
|
7.2
|
|
DD
|
#9
|
12.6
|
23.1
|
DD
|
#39
|
4.5
|
7.2
|
|
DD
|
#10
|
18.6
|
23.1
|
DD
|
#40
|
2.5
|
7.2
|
|
DD
|
#11
|
9.6
|
15.9
|
DD
|
#41
|
4.2
|
5.8
|
|
DD
|
#12
|
20.7
|
17.3
|
DD
|
#42
|
3.1
|
5.8
|
|
DD
|
#13
|
12.6
|
15.9
|
DD
|
#43
|
11.7
|
8.6
|
|
DD
|
#14
|
18.6
|
15.9
|
DD
|
#44
|
2.6
|
5.8
|
|
DD
|
#15
|
14.7
|
17.3
|
DD
|
#45
|
3.5
|
5.8
|
|
DD
|
#16
|
47.2
|
21.9
|
DD
|
#46
|
5.4
|
5.8
|
|
DD
|
#17
|
26.9
|
21.9
|
DD
|
#47
|
5.0
|
5.8
|
|
DD
|
#18
|
10.7
|
15.0
|
DD
|
#48
|
4.8
|
5.8
|
|
DD
|
#19
|
12.8
|
10.4
|
DD
|
#49
|
2.6
|
5.8
|
|
DD
|
#20
|
12.2
|
10.4
|
DD
|
#50
|
3.4
|
7.2
|
|
DD
|
#21
|
10.2
|
8.6
|
DD
|
#51
|
2.6
|
5.8
|
|
DD
|
#22
|
2.9
|
8.6
|
DD
|
#52
|
2.6
|
5.8
|
|
DD
|
#23
|
16.6
|
8.6
|
DD
|
#53
|
4.9
|
3.6
|
|
DD
|
#24
|
4.5
|
8.6
|
DD
|
#54
|
11.5
|
5.8
|
|
DD
|
#25
|
4.8
|
8.6
|
DD
|
#55
|
4.1
|
4.3
|
|
DD
|
#26
|
8.1
|
8.6
|
DD
|
#56
|
5.4
|
4.3
|
|
DD
|
#27
|
9.2
|
10.1
|
DD
|
#57
|
5.7
|
4.3
|
|
DD
|
#28
|
5.3
|
8.6
|
DD
|
#58
|
3.4
|
4.3
|
|
DD
|
#29
|
6.1
|
8.6
|
DD
|
#59
|
4.7
|
4.3
|
|
DD
|
#30
|
10.3
|
9.4
|
DD
|
#60
|
4.9
|
4.2
|
In order to begin
this deeper analysis I developed a measure called SLN. The SLN looks at the
slope of the values in both the I/O databases from 9.4 to 6 conditions. Table 1
shows the SLN values of DD 1 to 60.
As I look across the
data landscape, I see a lot of Weakly Positive biased issues to focus your
investments. DD #7 is very unusual in this and other runs. It was really
ignored by the Outsiders as compared to the insiders! To bring that observation
home see Table 2A. DD#7 is really odd and make me wonder should I invest in any
other DD issue in this issue group! Compare to FF!
Big difference.
Table 2A. Counts and % of SLN from All Runs
Counts
|
SLN
|
SLN
|
SLN
|
Issues
1 to 40
|
High
I
|
Weak
I
|
O
Biased
|
DD
|
1
|
7
|
32
|
Avengers
|
5
|
5
|
30
|
Xmen
|
8
|
20
|
12
|
Thor
|
8
|
9
|
23
|
ASM
|
8
|
6
|
26
|
FF
|
13
|
0
|
27
|
%
of 40
|
High
I
|
Weak
I
|
O
Biased
|
DD
|
2.5
|
17.5
|
80
|
Avengers
|
12.5
|
12.5
|
75
|
Xmen
|
20
|
50
|
30
|
Thor
|
20
|
22.5
|
57.5
|
ASM
|
20
|
15
|
65
|
FF
|
32.5
|
0
|
67.5
|
I
have added some more color indicators. Deep Green and Light Green are denoting
the issues with definite Insider Bias and are worthy of further digging for
investment choices. On the other hand, I denoted the issues with Outsider Bias
with Deep Red and Light Rose. I also see an interesting pattern in that most of
the first issues of the DD are in the Red.
(I note that extreme high grades equal or above
9.4 are always going to be valued greatly by both groups just due to the
rarity).
In
conclusion based on this data, I suggest the green (dark and light) issues are
the investment targets to focus on the highest grades possible while the
red/rose/white denoted issues are targets for much less if any focus except if
Table 3 data suggests otherwise. I would consider DD#7 for its
unusualness!
Table 3. Adjusted Average Differences of I/O Data at Selected
Grades
Run
|
Issue
|
C
9.4
|
C
8
|
C
6
|
Run
|
Issue
|
C
9.4
|
C
8
|
C
6
|
|
DD
|
#1
|
$$
|
$$
|
$$
|
DD
|
#31
|
$
|
?
|
||
DD
|
#2
|
$$
|
XX
|
DD
|
#32
|
XX
|
||||
DD
|
#3
|
$$
|
XX
|
DD
|
#33
|
XX
|
?
|
|||
DD
|
#4
|
$$
|
XX
|
X
|
DD
|
#34
|
XX
|
|||
DD
|
#5
|
$$
|
X
|
DD
|
#35
|
XX
|
||||
DD
|
#6
|
$$
|
X
|
DD
|
#36
|
X
|
?
|
|||
DD
|
#7
|
$$
|
$$
|
$
|
DD
|
#37
|
X
|
|||
DD
|
#8
|
$$
|
X
|
?
|
DD
|
#38
|
?
|
$
|
||
DD
|
#9
|
XX
|
XX
|
X
|
DD
|
#39
|
XX
|
|||
DD
|
#10
|
$
|
X
|
X
|
DD
|
#40
|
XX
|
|||
DD
|
#11
|
X
|
X
|
?
|
DD
|
#41
|
XX
|
?
|
||
DD
|
#12
|
$$
|
X
|
X
|
DD
|
#42
|
XX
|
|||
DD
|
#13
|
?
|
X
|
DD
|
#43
|
$
|
$
|
?
|
||
DD
|
#14
|
$$
|
X
|
DD
|
#44
|
XX
|
||||
DD
|
#15
|
$
|
X
|
X
|
DD
|
#45
|
XX
|
?
|
||
DD
|
#16
|
$$
|
$
|
X
|
DD
|
#46
|
X
|
|||
DD
|
#17
|
$$
|
X
|
X
|
DD
|
#47
|
X
|
|||
DD
|
#18
|
X
|
$
|
DD
|
#48
|
X
|
$
|
|||
DD
|
#19
|
$
|
X
|
DD
|
#49
|
XX
|
||||
DD
|
#20
|
$
|
?
|
DD
|
#50
|
XX
|
X
|
|||
DD
|
#21
|
$
|
$
|
DD
|
#51
|
XX
|
||||
DD
|
#22
|
XX
|
DD
|
#52
|
XX
|
?
|
||||
DD
|
#23
|
$$
|
?
|
DD
|
#53
|
X
|
?
|
|||
DD
|
#24
|
XX
|
DD
|
#54
|
$
|
|||||
DD
|
#25
|
XX
|
DD
|
#55
|
X
|
?
|
||||
DD
|
#26
|
X
|
DD
|
#56
|
X
|
?
|
||||
DD
|
#27
|
DD
|
#57
|
X
|
||||||
DD
|
#28
|
XX
|
DD
|
#58
|
XX
|
$
|
||||
DD
|
#29
|
X
|
DD
|
#59
|
X
|
|||||
DD
|
#30
|
$
|
DD
|
#60
|
X
|
Given the I/O differences in the data across grades, I focused on the average
of those differences between the I and O data. I adjusted these averages to
normalize it and allow a sharper clarity in the results. That data is presented
in Table 3.
Analysis of the Table 3 data revealed that I/O data in Tables 1 and 2 may not
reflect the whole truth, the “Buy” signal disappears as you see the data lands
on the grade of 9.4 for the Insiders choice on these issues. Note only 3 issues
are favored across the three grade levels by the I crowd (DD #1, #7 and #16).
In contrast, 19 issues show only the I/O bias by the insider investors mainly
in the Grade of 9.4. I also have denoted via gray color on the issues
that certainly need to be watched. Finally the other issues are all pretty much
biased by the outsiders over the insiders’ opinions and buying habits. These
issues would not be recommended.
Based on
this data, I would conclude one might focus on the 3 bright green denoted
issues in the lesser cheaper grades while the dark green issues would those you
should focus on only in highest grades or not at all. The only concern is the
SLN number that shows DD#7 could be the show for this run. I do not know how to
dig deeper into this issue now but I urge you to consider this data.
The grey
ones if any are on the watch list. Other issues are not recommended for a focused
effort at this time. Note this table may be of greater usage and certainly
suggests a different approach as did the data in Table 2.
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