Analysis of Fantastic Four (Issues 1 to 53)
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 (Fantastic Four (Issues 1 to 53). 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 – Fantastic Four (Issues 1 to 53)
Table 1. Bias Scores (B SCORE) of All Issues in the Run
Issue
|
B Score
|
Issue
|
B Score
|
|
#1
|
216123
|
#28
|
401
|
|
#2
|
16246
|
#29
|
-403
|
|
#3
|
20014
|
#30
|
-766
|
|
#4
|
16799
|
#31
|
591
|
|
#5
|
28920
|
#32
|
-515
|
|
#6
|
9049
|
#33
|
-472
|
|
#7
|
15714
|
#34
|
-363
|
|
#8
|
5956
|
#35
|
-943
|
|
#9
|
1296
|
#36
|
1874
|
|
#10
|
321
|
#37
|
505
|
|
#11
|
6458
|
#38
|
-359
|
|
#12
|
14218
|
#39
|
-659
|
|
#13
|
9748
|
#40
|
-467
|
|
#14
|
-356
|
#41
|
-792
|
|
#15
|
-50
|
#42
|
-646
|
|
#16
|
916
|
#43
|
-638
|
|
#17
|
3421
|
#44
|
-683
|
|
#18
|
10005
|
#45
|
367
|
|
#19
|
557
|
#46
|
-616
|
|
#20
|
-17
|
#47
|
-798
|
|
#21
|
-329
|
#48
|
-287
|
|
#22
|
233
|
#49
|
-575
|
|
#23
|
2030
|
#50
|
2582
|
|
#24
|
-604
|
#51
|
1943
|
|
#25
|
1113
|
#52
|
1213
|
|
#26
|
433
|
#53
|
305
|
|
#27
|
-103
|
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. 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. 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. By now it is clear that in all
silver-age marvel comic-book runs I have looked at, biases exist and thus are
available for exploitation by the wise investor. The point is to use this data to make a
focused approach to buying investment grade comic-books.
Table 2. I/O SLN numbers
calculated for issues Fantastic Four (Issues 1 to 53)
Issue
|
I SLN
|
O SLN
|
Issue
|
I SLN
|
O SLN
|
|
#1
|
7512.7
|
5692.5
|
#28
|
61.7
|
99.6
|
|
#2
|
671.5
|
853.9
|
#29
|
22.4
|
29.9
|
|
#3
|
701.8
|
498.1
|
#30
|
16.0
|
42.7
|
|
#4
|
635.2
|
622.6
|
#31
|
50.3
|
27.0
|
|
#5
|
1003.8
|
640.4
|
#32
|
17.7
|
26.3
|
|
#6
|
360.2
|
426.9
|
#33
|
21.0
|
35.6
|
|
#7
|
489.0
|
185.0
|
#34
|
24.4
|
35.6
|
|
#8
|
244.6
|
284.6
|
#35
|
9.1
|
35.6
|
|
#9
|
129.5
|
284.6
|
#36
|
88.8
|
35.6
|
|
#10
|
101.7
|
284.6
|
#37
|
44.6
|
27.0
|
|
#11
|
255.0
|
256.2
|
#38
|
24.8
|
35.6
|
|
#12
|
545.6
|
426.9
|
#39
|
17.1
|
35.6
|
|
#13
|
315.8
|
99.6
|
#40
|
21.5
|
35.6
|
|
#14
|
40.6
|
99.6
|
#41
|
9.4
|
21.3
|
|
#15
|
52.0
|
99.6
|
#42
|
13.6
|
21.3
|
|
#16
|
78.6
|
106.7
|
#43
|
14.4
|
21.3
|
|
#17
|
140.3
|
99.6
|
#44
|
13.1
|
21.3
|
|
#18
|
322.7
|
128.1
|
#45
|
95.7
|
128.1
|
|
#19
|
62.2
|
99.6
|
#46
|
49.4
|
106.7
|
|
#20
|
49.6
|
99.6
|
#47
|
9.1
|
21.3
|
|
#21
|
35.4
|
85.4
|
#48
|
56.5
|
113.9
|
|
#22
|
44.3
|
56.9
|
#49
|
33.1
|
71.2
|
|
#23
|
92.9
|
56.9
|
#50
|
113.5
|
85.4
|
|
#24
|
20.8
|
56.9
|
#51
|
83.4
|
35.6
|
|
#25
|
89.1
|
128.1
|
#52
|
114.4
|
128.1
|
|
#26
|
70.1
|
113.9
|
#53
|
45.2
|
56.9
|
|
#27
|
40.4
|
71.2
|
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 between issues 39 to 99. Note issue FF# 1 is off-scale but shows a bias
to the Insiders. You are free to use these numbers to determine the percentage
difference and think what that may mean. For instance FF #13 has 315.8 Insiders
and 99.6 Outsider bias. So I just dip in to this water and show if you are look
between FF 13 and 14 it’s a big difference in the data. Insiders are loving the
FF13 twice as good as FF 14 for investment material. They are making a
statement that they are very bullish on the FF 13 vs. FF 14. So I would use
this as buy signal for 13 and sell signal for FF 14.
Issue
|
I Bias
|
O Bias
|
Total Bias
|
Diff
|
% I Bias
|
#13
|
315.8
|
99.6
|
415.4
|
216.2
|
76.0
|
#14
|
40.6
|
99.6
|
140.3
|
-59.0
|
29.0
|
As I look
across the data landscape, I see the this run has a lot of extremes as it’s
either a strong buy or a do not buy message. 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. Note no
light green issues exist. 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
|
C9.4 ADF
|
C8 ADF
|
C6 ADF
|
Issue
|
C9.4 ADF
|
C8 ADF
|
C6 ADF
|
|
#1
|
$$
|
$$
|
XX
|
#28
|
$
|
X
|
X
|
|
#2
|
$$
|
XX
|
XX
|
#29
|
X
|
?
|
?
|
|
#3
|
$$
|
$
|
X
|
#30
|
XX
|
?
|
?
|
|
#4
|
$$
|
X
|
X
|
#31
|
$
|
$
|
?
|
|
#5
|
$$
|
$
|
$
|
#32
|
X
|
?
|
?
|
|
#6
|
$$
|
XX
|
X
|
#33
|
X
|
?
|
?
|
|
#7
|
$$
|
?
|
X
|
#34
|
X
|
?
|
?
|
|
#8
|
$$
|
XX
|
X
|
#35
|
XX
|
?
|
?
|
|
#9
|
$
|
X
|
X
|
#36
|
$
|
$
|
?
|
|
#10
|
X
|
X
|
X
|
#37
|
$
|
?
|
?
|
|
#11
|
$$
|
X
|
X
|
#38
|
X
|
?
|
?
|
|
#12
|
$$
|
$
|
$
|
#39
|
X
|
?
|
?
|
|
#13
|
$$
|
$
|
$
|
#40
|
X
|
?
|
?
|
|
#14
|
X
|
X
|
?
|
#41
|
XX
|
?
|
?
|
|
#15
|
X
|
?
|
?
|
#42
|
X
|
?
|
?
|
|
#16
|
$
|
?
|
$
|
#43
|
X
|
?
|
?
|
|
#17
|
$
|
X
|
?
|
#44
|
X
|
?
|
?
|
|
#18
|
$$
|
?
|
?
|
#45
|
$
|
$
|
$
|
|
#19
|
$
|
X
|
?
|
#46
|
X
|
$
|
$
|
|
#20
|
X
|
X
|
?
|
#47
|
XX
|
?
|
?
|
|
#21
|
X
|
X
|
?
|
#48
|
$
|
$
|
$
|
|
#22
|
$
|
X
|
?
|
#49
|
X
|
$
|
?
|
|
#23
|
$
|
?
|
?
|
#50
|
$
|
X
|
?
|
|
#24
|
XX
|
X
|
?
|
#51
|
$
|
?
|
?
|
|
#25
|
$
|
X
|
?
|
#52
|
$
|
$
|
$
|
|
#26
|
$
|
?
|
?
|
#53
|
$
|
X
|
?
|
|
#27
|
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 10 issues are highly favored across the
three grade levels by the I crowd (Issues 1, 2, 3, 5, 12, 13, 31, 36, 45, 48,
and 52). In contrast, 20 issues (Dark Green) 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. (none in this run) 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 10 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 if any 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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