Analysis of Amazing Spider-man (AF 15, ASM 1 to 80)
Amazingly Secret Approach Using an Incredibly Novel Data Analysis Procedure for Comic Book Investing and Speculation.
Introduction and Material and Methods
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 ASM run of the silver age issues (AF 15, and ASM 1 to
80)), 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 3/2015. 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 – Amazing Spider-man Part 1 (AF 15, ASM 1
to 80)
Table 1. Bias Scores (B SCORE) of All
Issues in the Run
Issue
|
B Score
|
Issue
|
B Score
|
|
15
|
157242
|
#41
|
363
|
|
#1
|
58288
|
#42
|
33
|
|
#2
|
8493
|
#43
|
88
|
|
#3
|
3158
|
#44
|
296
|
|
#4
|
5327
|
#45
|
-230
|
|
#5
|
3716
|
#46
|
31
|
|
#6
|
-2446
|
#47
|
-270
|
|
#7
|
257
|
#48
|
-219
|
|
#8
|
-4156
|
#49
|
-136
|
|
#9
|
-649
|
#50
|
1858
|
|
#10
|
-3947
|
#51
|
1
|
|
#11
|
2258
|
#52
|
100
|
|
#12
|
-3844
|
#53
|
-215
|
|
#13
|
-439
|
#54
|
-127
|
|
#14
|
-2088
|
#55
|
-137
|
|
#15
|
-215
|
#56
|
-198
|
|
#16
|
-71
|
#57
|
-155
|
|
#17
|
-798
|
#58
|
-119
|
|
#18
|
-438
|
#59
|
-239
|
|
#19
|
-768
|
#60
|
-123
|
|
#20
|
-248
|
#61
|
-296
|
|
#21
|
-767
|
#62
|
-269
|
|
#22
|
-757
|
#63
|
290
|
|
#23
|
-74
|
#64
|
-328
|
|
#24
|
-280
|
#65
|
-214
|
|
#25
|
-1107
|
#66
|
-107
|
|
#26
|
-416
|
#67
|
-272
|
|
#27
|
-81
|
#68
|
-339
|
|
#28
|
4266
|
#69
|
-287
|
|
#29
|
280
|
#70
|
-189
|
|
#30
|
-604
|
#71
|
-265
|
|
#31
|
703
|
#72
|
-135
|
|
#32
|
137
|
#73
|
-233
|
|
#33
|
-243
|
#74
|
-291
|
|
#34
|
-86
|
#75
|
-24.2
|
|
#35
|
311
|
#76
|
-40.2
|
|
#36
|
-594
|
#77
|
-152.2
|
|
#37
|
41
|
#78
|
-195.2
|
|
#38
|
206
|
#79
|
-228.2
|
|
#39
|
1349
|
#80
|
-132.8
|
|
#40
|
579
|
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 of AF 15 ASM
1,2 and 3. I note the very high insider bias with dark purple and high bias
using light purple. Note the low plus/ low minus numbers reflect a sight bias
by the unsophisticated outsider group followed by the more extreme biases (-328
and down). 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 can not 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). I think these data show
promise in this run.
Table 2.
I/O SLN numbers calculated for issues AF 15, ASM 1 to 80.
Issue
|
I SLN
|
O SLN
|
Issue
|
I SLN
|
O SLN
|
|
15
|
161.7
|
131.2
|
#41
|
0.9
|
0.7
|
|
#1
|
59.5
|
56.2
|
#42
|
0.4
|
0.5
|
|
#2
|
12.6
|
11.2
|
#43
|
0.4
|
0.4
|
|
#3
|
8.8
|
9.4
|
#44
|
0.5
|
0.4
|
|
#4
|
8.7
|
7.5
|
#45
|
0.2
|
0.4
|
|
#5
|
6.9
|
5.6
|
#46
|
0.4
|
0.4
|
|
#6
|
3.5
|
4.7
|
#47
|
0.2
|
0.4
|
|
#7
|
2.6
|
3.2
|
#48
|
0.2
|
0.4
|
|
#8
|
1.3
|
2.4
|
#49
|
0.3
|
0.4
|
|
#9
|
3.9
|
3.2
|
#50
|
2.1
|
1.9
|
|
#10
|
1.7
|
2.6
|
#51
|
0.4
|
0.5
|
|
#11
|
5.1
|
2.2
|
#52
|
0.4
|
0.3
|
|
#12
|
1.6
|
2.2
|
#53
|
0.2
|
0.3
|
|
#13
|
4.2
|
2.4
|
#54
|
0.2
|
0.3
|
|
#14
|
3.9
|
4.7
|
#55
|
0.2
|
0.3
|
|
#15
|
1.5
|
2.2
|
#56
|
0.2
|
0.3
|
|
#16
|
1.5
|
1.9
|
#57
|
0.2
|
0.3
|
|
#17
|
1.0
|
2.1
|
#58
|
0.2
|
0.3
|
|
#18
|
1.0
|
1.3
|
#59
|
0.2
|
0.3
|
|
#19
|
0.7
|
0.9
|
#60
|
0.2
|
0.3
|
|
#20
|
1.3
|
1.7
|
#61
|
0.1
|
0.2
|
|
#21
|
0.6
|
0.9
|
#62
|
0.1
|
0.2
|
|
#22
|
0.6
|
0.9
|
#63
|
0.4
|
0.2
|
|
#23
|
1.1
|
1.1
|
#64
|
0.1
|
0.2
|
|
#24
|
0.9
|
0.7
|
#65
|
0.1
|
0.2
|
|
#25
|
0.6
|
1.3
|
#66
|
0.2
|
0.2
|
|
#26
|
0.9
|
0.9
|
#67
|
0.1
|
0.2
|
|
#27
|
1.0
|
0.9
|
#68
|
0.1
|
0.2
|
|
#28
|
4.0
|
2.2
|
#69
|
0.1
|
0.2
|
|
#29
|
1.1
|
0.6
|
#70
|
0.2
|
0.2
|
|
#30
|
0.5
|
0.6
|
#71
|
0.1
|
0.2
|
|
#31
|
1.0
|
0.7
|
#72
|
0.2
|
0.2
|
|
#32
|
0.4
|
0.5
|
#73
|
0.1
|
0.2
|
|
#33
|
0.3
|
0.5
|
#74
|
0.1
|
0.2
|
|
#34
|
0.4
|
0.5
|
#75
|
0.2
|
0.1
|
|
#35
|
0.5
|
0.5
|
#76
|
0.2
|
0.1
|
|
#36
|
0.4
|
0.5
|
#77
|
0.2
|
0.1
|
|
#37
|
0.4
|
0.7
|
#78
|
0.1
|
0.1
|
|
#38
|
0.5
|
0.5
|
#79
|
0.1
|
0.1
|
|
#39
|
1.3
|
0.7
|
#80
|
0.2
|
0.1
|
|
#40
|
0.9
|
0.9
|
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 ASM between issues AF 15 and 1 to 80. Note issue AF 15 is off-scale but shows a bias
to the Insiders
As
I look across the data landscape, I see the main action plus or minus is from
issue 50 down. 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 would not focus on these
except as noted by the Table 3 data. (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.
Table 3. Adjusted Average Differences of
I/O Data at Selected Grades
Issue
|
C9 ADF
|
C8 ADF
|
C6 ADF
|
Issue
|
C9 ADF
|
C8 ADF
|
C6 ADF
|
|
15
|
$$
|
$$
|
$$
|
#41
|
$$
|
$$
|
$
|
|
#1
|
$$
|
XX
|
XX
|
#42
|
?
|
X
|
?
|
|
#2
|
$$
|
XX
|
X
|
#43
|
$
|
$
|
X
|
|
#3
|
$$
|
X
|
X
|
#44
|
$$
|
$
|
X
|
|
#4
|
$$
|
XX
|
X
|
#45
|
X
|
X
|
?
|
|
#5
|
$$
|
XX
|
X
|
#46
|
$
|
$
|
?
|
|
#6
|
XX
|
X
|
X
|
#47
|
X
|
X
|
X
|
|
#7
|
?
|
X
|
X
|
#48
|
X
|
X
|
X
|
|
#8
|
XX
|
X
|
X
|
#49
|
X
|
?
|
X
|
|
#9
|
?
|
$$
|
$
|
#50
|
$$
|
X
|
X
|
|
#10
|
XX
|
X
|
$
|
#51
|
X
|
X
|
?
|
|
#11
|
$$
|
$
|
$
|
#52
|
$
|
?
|
$
|
|
#12
|
XX
|
$
|
X
|
#53
|
X
|
X
|
?
|
|
#13
|
?
|
$$
|
$
|
#54
|
X
|
X
|
X
|
|
#14
|
XX
|
$
|
?
|
#55
|
X
|
X
|
X
|
|
#15
|
X
|
$
|
?
|
#56
|
X
|
$
|
X
|
|
#16
|
$
|
$
|
?
|
#57
|
X
|
X
|
X
|
|
#17
|
XX
|
X
|
?
|
#58
|
X
|
X
|
X
|
|
#18
|
X
|
$
|
$
|
#59
|
X
|
X
|
?
|
|
#19
|
XX
|
$
|
$
|
#60
|
X
|
?
|
X
|
|
#20
|
X
|
$
|
X
|
#61
|
X
|
$
|
$
|
|
#21
|
XX
|
$
|
X
|
#62
|
X
|
$
|
$
|
|
#22
|
XX
|
$
|
X
|
#63
|
$$
|
?
|
?
|
|
#23
|
$
|
$
|
X
|
#64
|
X
|
?
|
$
|
|
#24
|
$
|
$$
|
$
|
#65
|
X
|
?
|
X
|
|
#25
|
XX
|
$
|
?
|
#66
|
X
|
$
|
?
|
|
#26
|
X
|
$
|
$
|
#67
|
X
|
$
|
$
|
|
#27
|
$
|
$
|
$
|
#68
|
X
|
$
|
?
|
|
#28
|
$$
|
?
|
X
|
#69
|
X
|
?
|
?
|
|
#29
|
$$
|
$$
|
$
|
#70
|
X
|
$
|
?
|
|
#30
|
XX
|
X
|
$
|
#71
|
X
|
$
|
?
|
|
#31
|
$$
|
$
|
$
|
#72
|
X
|
$
|
?
|
|
#32
|
$
|
X
|
X
|
#73
|
X
|
$
|
X
|
|
#33
|
X
|
?
|
?
|
#74
|
X
|
$
|
$
|
|
#34
|
X
|
$
|
X
|
#75
|
?
|
$
|
?
|
|
#35
|
$
|
X
|
X
|
#76
|
$
|
$
|
$
|
|
#36
|
XX
|
$
|
$
|
#77
|
X
|
$
|
?
|
|
#37
|
X
|
X
|
X
|
#78
|
X
|
$
|
?
|
|
#38
|
$
|
X
|
$
|
#79
|
X
|
$
|
$
|
|
#39
|
$$
|
$
|
$
|
#80
|
X
|
$
|
?
|
|
#40
|
$$
|
X
|
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. It can be seen
that 9 issues are favored across the three grade levels by the I crowd (AF 15,
ASM 11, 24, 27, 29, 31, 39, 41, and 76). In contrast, 17 issues show only the
I/O bias by the insider investors mainly in the Grade of 9.4 (1, 2, 3, 4, 5,
16, 23, 28, 32, 35, 40, 43, 44, 46 50, 52, and 63). 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.
Based on this data, I would conclude
one might focus on the 9 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 grey ones are on the watch list. Other issues are not
recommended for a focus 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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