arush@miami.edu **
http://www.domainnamenews.com/domain-aftermarket/mo=
niker-silent-auction-ends-tomorrow/127
reports in “Moniker Si=
lent
Auction Ends Tomorrow” by
Adam Strong in Categories: Domain
Aftermarket the following: “Did
you miss the live auction at TRAFFIC in NYC ? Well, the second best thing i=
s Monikers
silent auction. To some, it might be even better than the li=
ve
auction as they diligently scour the lists looking for the hidden gems, hop=
ing
that others won’t see them as well. I’ve been through the list
tonight, and I’ll probably be making a few bids myself. If you haven&=
#8217;t
been through the list, you better get on it. You don’t have much time
left. The auctions will be ending tomorrow. So far the bids seem low in vol=
ume,
but I’d guess around 11am EST, when the auctions begin to close, there
will be a frenzy of snipe bids being made.” Adam Strong fails to make a clear
distinction between sellers and buyers, as we find out. For seller the live auction is a g=
reat
advantage, since bidders may excite each other. They keep bidding and whip the aud=
ience
into a “buyers’ frenzy” over-bidding and raising the pric=
e of
the domain. In contrast, for
buyers, the absence of such “buyers’ frenzy” is an advant=
age.
Monikers
silent auction,” will be incorrect, it will be false.<=
span
style=3D'mso-spacerun:yes'> Rather, it will be the 1st best
thing for buyers is Monikers silent auction, and the sec=
ond
best thing is Monikers live auction. In contrast, for sellers, it is co=
rrect
that “the second best thing is Monikers silent auction” while=
the 1st
best thing is Monikers live auction.
http:/=
/www.domaininformer.com/news/press/070216Moniker.html
in “About Moniker,”<=
/strong>
describes Moniker as the company that “pionee=
red
the industry's first domain appraisal formula. It is considered the industr=
y's
premier marketplace to buy and sell domain names.” To calculate the full value of the
domain name plus the lost advantage of missing the live auction, one should=
use
such a “domain appraisal formula.” For the present study such a formu=
la is
beyond the scope of the current issue, we may deal with it in a future stud=
y.
LIVE
VERSUS SILENT AUCTIONS
htt=
p://marketplacepro.moniker.com/auction/auction_faq.html#ql1
describes the relationship between live and silent auction on moniker.com
auctions.
“Live Domain Auc=
tion-
What is a Live Domain Auction? some of the most premier domains are available for sa=
le
to the highest bidder. Moniker partners … bring buyers and sellers
together in the top marketplace for premier domains. The Live Domain Auctio=
ns
are standard open cry auctions, managed by a professional auctioneer. Bidde=
rs
are required to register… in advance. This may be in addition to any
event registration required for admittance to the conference in which the
auction is being held. Please see registration requirements for each indivi=
dual
auction for complete details. Moniker also produces Silent Domain Auctions.
These are often run in conjunction with Live Domain Auctions, but may also
stand on their own…...
How will the domains g=
oing
into the live auction be determined?
In most
cases, Moniker will be operating a Silent Auction on conjunction with the L=
ive
Auction. Moniker selects the master list of domains to be auctioned in both
platforms based upon our evaluation of sales potential. This criteria may
include: Appraised value, Revenue generation and/or traffic history (Please
note, only DomainSponsor
monetization results will interface directly to the names in the Live Aucti=
on
and in Marketplace. Seller-submitted information may also be available in t=
he
comments section of the domain profile, but cannot be verified by Moniker. )
Marketability and appreciation potential, All brand-worthy names with no mi=
nimum
reserve price will also be considered.
Once the
master list for both Live and Silent Auctions has been selected, Moniker th=
en
applies more detailed analysis and statistics, and refines the list to iden=
tify
the best 200-300 domain values that will go into the live event. The total
number of domains offered in the live event is dependent on the time allott=
ed
and the type of audience attending.
In addi=
tion,
Moniker may include domains that receive greater interest as indicated by
potential bidders attending the event. Event attendees will be able to revi=
ew
the domains submitted in advance on the main Moniker Auction inventory syst=
em,
and may choose to indicate their interest using the designated links on a g=
iven
auction lot. Domains that receive the highest interest from bidders may be
included on the Live Auction. Moniker also reserves the right to add or rem=
ove
any domains from the final Live Auction list, based upon our evaluation of
sales potential. As the reader can see the auctioneer has a lot of discress=
ion
in deciding which domains will be included in which auction type, and that =
may
raise some disputes. Especial=
ly, if
this assumption affects the price on the domain and the ROI of the sellers =
and
buyer…
How do I know if my na=
mes
are good candidates for the auctions? What's the best way to attract potent=
ial
buyers in the auctions? best
position your names for selection and sale in Monikers Live and Silent Doma=
in
Auctions: Get your parked dom=
ains
running on the DomainSponsor
monetization service. Using DomainSponsor allows Moniker to verify any
revenue or traffic statistics. DomainSponsor statistics will be directly
interfaced to the auction names and will be published for potential buyers =
to
review. Have your domai=
ns
appraised using the Moniker
appraisal service. Moniker's service is currently the only system
recognized by the IRS and domain financing companies. Transfer registration of your doma=
ins to
Moniker. As the vendor of choice for the Live Auction, once a domain is sold
via auction, the closing and transfer of assets can be completed right away=
if
the domains are already at Moniker. “
DOMAIN
NAME LIVE AND SILENT AUCTION PRICE AND COST FORECASTING METHODOLOGY HYPOTHE=
SIS
TESTING
This
study deals with price and cost forecasting methodology. The sellers on live and silent dom=
ain
name auction want to maximize the price of the domain name that they are
selling. In contrast, the buy=
ers
want to minimize the same item, except that for them it constitutes the cost
rather than the price. We will
review the methodologies associated with such forecast and estimates. Managerial auditors frequently rev=
iew
cost forecasting methods. As
Rushinek and Rushinek suggest “Managerial auditors' mixed cost forecasting assumpt=
ion
departure error estimates for litigation and …” in “Managerial
Auditing Journal, 1997” the forecaster may make certain assumptions t=
hat
sometimes are not perfectly met.
Such departure from the assumptions can be corrected (emeraldinsight=
.com
... Page 7. [ 291 =
] Avi
Rushinek and Sara Rushinek) by the Managerial auditors’
mixed cost forecasting, by assumption departure error estimates for litigat=
ion purposes.
This
article deals with “Managerial auditors’ mixed cost forecasting
assumption departure error estimates for litigation and professional liabil=
ity
risk reduction” in the “the electronic security industry.”=
; Our current study is not limited t=
o any
industry, although, the internet industry is very close to the electronic
security industry. As the art=
icle
states “Managerial auditors (MAs) are frequently relying on mistaken
mixed costs (MC) forecasting assumptions. Two such assumptions are that MC =
are
linear within the relevant range and variable costs vary proportionately wi=
th
activity levels. Departure from these assumptions can lead to understating =
expenses,
overstating profits, and even to fraud, litigation, and mounting profession=
al
liability risk. Develops a forecasting model of mixed cost error estimates,=
or
error difference (ED). MAs can reduce the risk of litigation and profession=
al
liability by including such an ED in the internal control structure of a
company. Uses Sensormatic Corporation and the electronics security industry=
as
sample case because of the recent prominence of litigation and professional
auditor liability verdicts. Focuses on the electronics security industry, b=
ut
some of the findings may apply to other industries.” The current study resembles this a=
rticle
in the following aspects: we deal with “mixed cost forecasting assump=
tion
departure error estimates for litigation and professional liability risk
reduction.” The auctioneer and the domain name seller and the
facilitator, in this case, Moniker.com, aim at minimizing their litigation
risk. We are relying much like
“Managerial auditors (MAs)” on “mistaken mixed costs (MC)
forecasting assumptions. Two such assumptions are that MC are linear within=
the
relevant range and variable costs vary proportionately with activity levels.
Departure from these assumptions can lead to understating expenses, oversta=
ting
profits, and even to fraud, litigation, and mounting professional liability
risk.” Likewise, the cu=
rrent
study “Develops a forecasting model of mixed cost error estimates, or
error difference (ED).”
We are looking at the problem from the perspective of “MAsR=
21;
that “can reduce the risk of litigation and professional liability by
including such an ED in the internal control structure of a company.”=
This article “Uses Sensormat=
ic
Corporation and the electronics security industry as sample case because of=
the
recent prominence of litigation and professional auditor liability verdicts.
Focuses on the electronics security industry, but some of the findings may
apply to other industries.”
In our case it applies to the internet industry, the reader can revi=
ew
this article further on the internet WWW (World Wide Web) http://www.emeraldinsight.c=
om/10.1108/02686909710180643
on the publisher’s site. Both
studies use linear regression forecasting models, this study examine 3 line=
ar
regression models, 1. a univariate model, 2. a multivariate model with an
intercept, and 3. A univariate
model without an intercept, which is the best and final model. The selection of the 3rd
model is based on maximizing the R Square and minimizing the error rate, and
the departure from the assumptions.
Like
another study we are “forecasting sales and expenses” like the
study about “Forecasting sales, expenses and stock market values=
by
quarterly financial statement ratio analysis” by A Rushinek, SF
Rushinek - Managerial Auditing Journal, 1995 - emeraldinsight.com.
As
this article describes “sales forecasting models…forecast stand=
ards
of sales and costs, and applies them to a budgeted financial statement vari=
ance
analysis for the EE (electronic and electrical) industry. Develops the conc=
ept
of industry base standards, integrating them into the more traditional
statistical and accounting concepts of quality control standards. Provides =
an
implementation example, and reviews possible improvements to the current
methodology and approach. Uses a similar methodology to forecast the stock
market value with some exceptions. Models sales and costs of an individual
company and an industry based largely on aggregate industry databases. For =
this
purpose, uses a multivariate linear trend regression analysis for the sales
forecasting model. Defines and tests related hypotheses and evaluates their
significance and confidence levels. For an illustration uses the EE industry
and the APM company. Also demonstrates a microcomputer-based FSRA software =
that
speeds, facilitates, and helps to accomplish the stated objectives. The FSRA
software uses industry financial statement databases, computes financial ra=
tios
and builds forecasting models.”
Likewise,
the current study deals with “sales forecasting models…forecast
standards of sales and costs, and applies them to a … variance analys=
is
for the” internet and domain name auction “… industry.
Develops the concept of industry base standards, integrating them into the =
more
traditional statistical and accounting concepts of quality control standard=
s.
Provides an implementation example, and reviews possible improvements to the
current methodology and approach. Uses a similar methodology to forecast th=
e ..
value” of domain names “with some exceptions. Models sales and
costs of an individual company and an industry based largely on aggregate i=
ndustry
databases. For this purpose, uses a multivariate linear trend regression an=
alysis
for the sales forecasting model. Defines and tests related hypotheses and
evaluates their significance and confidence levels. For an illustration use=
s the”
domain name auction “… industry and the” …
Moniker.com…” company. Also demonstrates a
microcomputer-based” domain name opportunity cost “… soft=
ware
that speeds, facilitates, and helps to accomplish the stated objectives. Th=
e …
software uses industry financial statement data” … “compu=
tes
financial ratios and builds forecasting models.”
Rushinek et. al. in “The Construction of an International Investors̵=
7;
Perception Model for Corporate Published Forecasted …̶=
1;
by A
RUSHINEK, LS CHANG, KS MOST (MANAGERIAL AND DECISION ECONOMICS, 1983=
-
doi.wiley.com) calculates “the mean utility score of cost of
goods sold (COGS) is 64.7739 for buying decisions and slightly higher, 65.2=
024,
for ...” differentiates between the buyers and sellers. It discusses the following issues
“Although the utility of corporate published forecasted financial
reports to investors has been investigated, the findings are inconclusive.
These inconclusive findings may have resulted from the ambiguity of defining
the forecasted financial reports or its components. The clarification of the
definition of forecasted financial reports or its components is the main
objective of the present study. In addition, an investor's utility function=
is
used to measure the utility of such reports for investors regarding the ent=
ire
forecasted financial reports or their components.” Likewise, this study breaks down t=
he
auction into its components,
namely, the silent versus the live type of domain name auction, outlining t=
he
difference in their affect on the price.
Looking
at the domain name auction from the viewpoint of an “EDP auditors' role in evaluating computerized accou=
nting
information systems efficiency by queuing, …=
” as Rushinek & =
Rushinek explain
( Proceedings of the fourteenth SIGCSE technical symposium on …, 1983=
-
portal.acm.org)
“... scant at this point to forecast which routinesR=
21; optimizes
the objective of domain name auctions seller or buyer. An “EDP (Electronic Data Processing)=
auditors'
role in evaluating computerized accounting information systems efficiency by
queuing, simulation and statistical models” will include risk
assessment. This risk assessm=
ent is
the focus of the present study with an X Coefficient representing the risk =
of
suboptimal selection of the type of auction for domain name sales. The risk of sub-optimizing the auc=
tion
type equal to the X Coefficient for 1 domain name selling price (http://doi.acm.org/10.1145/952978.801048).
“This study is a summary of the various relevant aspects related to
evaluating system efficiency in computerized accounting information systems.
During recent years a vast body of knowledge central to the problem of comp=
uter
performance evaluation has accumulated. Unfortunately, however, the work on=
the
subject demonstrates enormous disparity. On the one hand, one finds numerous
reports and documents that present masses of empirical data obtained from
measurement or simulation. On the other hand, theoretical papers are, more
often than not, couched in advanced mathematics, not easily accessible to
engineers and/or Electronic Data Processing (EDP) Auditors. Among a number =
of
mathematical disciplines related to system modeling, “queuing
model,”“simulation method,” and “statistical
analysis” are the most important quantitative techniques. This paper
provides a cohesive introduction to the modeling and analysis techniques for
evaluating system efficiency. These techniques will certainly be applicable=
to
modeling activities of complex systems in general, and not merely computer
systems.” Likewise, the
current study develops a statistical model for cost forecasting of recovery=
for
the plaintiff and the cash outlay for the defendant, promoter, seller or bu=
yer
of domain names in live or silent auctions.
In “The=
control
of computerized information relating to branch manpower management follow-up
systems for banks Source ACM =
SIGCPR
Computer Personnel archive,” (Volume 12 , Issue 1 (April 1989) Pages: 1 - 12 Year of
Publication: 1989 ISSN:0160-2497) A. Rushinek S. Rushinek (Publisher ACM
Accounting
Information Systems (AIS) in general. Moreover, they are as applicable to a=
computerized
follow-up survey as to the initial manual system survey. In fact, this study
presents an approach for conducting a follow-up program using an on-line=
221;
system. Likewise, the domain =
name
auction is an online AIS using a “linear model to measure and
forecast.” “The
application of model-sampling and its effects in relation to accuracy”=
; is
also an issue and a limitation in the current study. Keep in mind that it is based on a
single sample of a single auction.
To be more accurate and more robust, it should be built into multiple
auctions, with a survey that lets users evaluate issues of concern includin=
g,
but not limited to sub-optimization of the auction type, whether it is live=
or
silent, and the monetary effect of the choice.
In a hypertex= t https://www.moniker.com/pub= /ShoppingcartOrderProductsServlet?cmd=3Dorderproducts&cmdaction=3Dappra= isal, Moniker.con lists the components of domain name appraisal: “Yo= ur domain name could be worth thousands of dollars and may even be tax deducti= ble! Join many others who discovered what their domains were worth using our Dom= ain Name Appraisal Service! Your domain will be appraised using our custom doma= in appraisal software based on 17 separate factors including marketability, br= and recognition, unique type in traffic, dotValue and comparison with other dom= ain name sales and appraisals, in addition to the following criteria:
|
|
NULL HYPOTHESIS STATES THAT THE R SQUARE AND THE X COEFFICIENT= S ARE BOTH EQUAL TO ZERO AND INSIGNIFICANT
It is virtually impossible to reduce all these variables to one
variable, but what is obvious is the omission of the type of auction as a
factor that determines the value of a domain name. Therefore, we want to focus on this
single factor to find out whether it is a significant factor. We start with the null hypothesis =
that
it is not a significant factor, due to the fact that nobody, so far, has
considered it a significant factor, including Moniker.com. Thus, our null hypothesis states t=
hat
the R Square and the X Coefficients are both equal to zero, and thus the ty=
pe
of auction, whether it is silent or live is insignificant in its affect on =
the
appraised value of domain name sold in an auction. In contrast, the alternative
hypothesis states that it is significant, and therefore should be part of t=
he
optimization of the seller and buyer trading strategy for domain name aucti=
ons,
and dispute resolutions.
&=
nbsp;
Data
Collection
We col=
lected
the data from the Master Auction Inventory of T.R.A.F.F.I.C. MIAMI 2007,
including in it is a Silent Auction: October 12th-18th, 2007, and a Live
Auction: October 12th, 2007 2pm-6pm EST, by Moniker.com. Moniker.com notes that the
"Opening Bid Range" does not indicate value of the domain name or
potential purchase price. Likewise, Inventory is subjec=
t to
change, and the “Statistic information has not been verified, please
consider this data as an ESTIMATE ONLY! Moniker cannot guarantee its accura=
cy.” Moniker adds that “This is t=
he
tentative list of all domains to be included in the 2007 Moniker/T.R.A.F.F.=
I.C.
Miami Live and Silent Auctions. Therefore, the current study is subject to =
the
same disclaimers. &n=
bsp; =
;
This d=
ata
contains the following information: Description and Domain names. For example, the data includes Ven=
ue/Sale
Price of $2,200,000 for computer.com the Opening Bid Range, of $1MM - $5MM,=
the
domain extension “.com,” the Length of 8 characters, the Catego=
ry
of the domain name, Computing, Uniques Per Mo. (estimate) 20,000, Rev Per Mo. (estimate) 15,000, COMPETE Page Count of 1,145, COMPET=
E Page
Rank of 805, Google pr (Page Rank) of 5 (1 the lowest up to 5 the highest),=
GOOG
Search Domain Results of 486,000, GOOG Search Name Results of 581,000,000, =
MSN
Search Domain Results of 1,112,795,762, MSN Search Name Result 1,086,237,89=
0,
Y! Search Domain Results N/A, Y! Search Name Results N/A, Alexa Rank of 7,4=
16,303,
URLTRENDS Description of “ Computer parts PC Components laptop comput=
ers
notebooks motherboards processors CPU?s and much more at TigerDirect.com, L=
inks
IN of 1,703, Links OUT of 149, Online Since 34,557, which is the Julian dat=
e of
Thursday, August 11, 1994.
Likewise, additional information on the type of the auction, such as
whether it was live or silent.
&=
nbsp;
Data
Analysis
=3D=3D=
=3D=3D=3DTable 1
Insert here
&=
nbsp;
|
Descriptive Statistics |
Table 1 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Mean |
$ &nbs=
p;
16,969 |
Mean |
Mean |
Standard Error |
$ &nbs=
p;
4,375 |
Standard Error |
Standard Error |
Median |
$ &nbs=
p;
1,770 |
Median |
Median |
Mode |
$ &nbs=
p;
360 |
Mode |
Mode |
Standard Deviation |
$ &nbs=
p;
105,003 |
Standard Deviation |
Standard Deviation |
Sample Variance |
$11,025,633,518 |
Sample Variance |
Sample Variance |
Kurtosis |
$ &nbs=
p;
335 |
Kurtosis |
Kurtosis |
Skewness |
$ &nbs=
p;
17 |
Skewness |
Skewness |
Range |
$ 2,199,700
|
Range |
Range |
Minimum |
$ &nbs=
p;
300 |
Minimum |
Minimum |
Maximum |
$
2,200,000 |
Maximum |
Maximum |
Sum |
$
9,774,266 |
Sum |
Sum |
Count |
$ &nbs=
p;
576 |
Count |
Count |
Largest(1) |
$
2,200,000 |
Largest(1) |
Largest(1) |
Smallest(1) |
$ &nbs=
p;
300 |
Smallest(1) |
Smallest(1) |
Confidence Level(95.0%) |
$ &nbs=
p;
8,593 |
Confidence Level(95.0%) |
Confidence Level(95.0%) |
Differences=3DLive-Silent |
Mean |
$ &nbs=
p;
55,613 |
$ &nbs=
p;
51,053 |
Mean |
$ &nbs=
p;
4,561 |
||
|
Standard Error |
$ &nbs=
p;
17,306 |
$ &nbs=
p;
16,192 |
Standard Error |
$ &nbs=
p;
1,114 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Median |
$ &nbs=
p;
13,000 |
$ &nbs=
p;
12,058 |
Median |
$ &nbs=
p;
943 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Mode |
$ &nbs=
p;
8,000 |
$ &nbs=
p; &=
nbsp;
7,640 |
Mode |
$ &nbs=
p;
360 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Standard Deviation |
$ &nbs=
p;
204,768 |
$ &nbs=
p;
181,511 |
Standard Deviation |
$ &nbs=
p;
23,257 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Sample Variance |
$41,929,833,151 |
$ &nbs=
p;
41,388,937,598 |
Sample Variance |
$
540,895,554 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Kurtosis |
$ &nbs=
p;
90 |
$ &nbs=
p; &=
nbsp;
(106) |
Kurtosis |
$ &nbs=
p;
195 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Skewness |
$ &nbs=
p;
&nbs=
p; 9
|
$ &nbs=
p; &=
nbsp;
(5) |
Skewness |
$ &nbs=
p;
13 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Range |
$
2,199,250 |
$ &nbs=
p;
1,829,550 |
Range |
$ &nbs=
p;
369,700 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Minimum |
$ &nbs=
p;
750 |
$ &nbs=
p; &=
nbsp;
450 |
Minimum |
$ |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Maximum |
$
2,200,000 |
$ &nbs=
p;
1,830,000 |
Maximum |
$ &nbs=
p;
370,000 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Sum |
$
7,785,850 |
$ &nbs=
p;
5,797,434 |
Sum |
$ &nbs=
p;
1,988,416 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Count |
$ &nbs=
p;
140 |
$ &nbs=
p; &=
nbsp;
(296) |
Count |
$ &nbs=
p;
436 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Largest(1) |
$
2,200,000 |
$ &nbs=
p;
1,830,000 |
Largest(1) |
$ &nbs=
p;
370,000 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Smallest(1) |
$ &nbs=
p;
750 |
$ &nbs=
p; &=
nbsp;
450 |
Smallest(1) |
$ &nbs=
p;
300 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Confidence Level(95.0%) |
$ &nbs=
p;
34,217 |
$ &nbs=
p;
32,028 |
Confidence Level(95.0%) |
$ &nbs=
p;
2,189 |
&=
nbsp;
Table 1
includes the Descriptive Statistics, including the sale price and the aucti=
on
type, Live or Silent auction or both.
It shows the mean, Median, Mode, the Largest(1) of the prices of $ 2,200,000, for the computer.com d=
omain
name, etc. Some of the important issues that it reveals is the great differ=
ence
in the means of the prices. T=
he
mean of the live auction is about $55,613, deduct $51,053 the difference, and
you get the mean of the Silent auction at $4,561. This raises one of the main issues=
in
this study, the impact of the type of auction on the value of the auction
price. To further test this
difference look at the next table.
=3D=
=3D=3D=3DTable 2
insert here
&=
nbsp;
|
Table 2 |
||||||
|
t-Test: Two-Sample Assuming
Unequal Variances |
Table 2 |
|||||
|
Live |
Silent |
|||||
|
Mean |
$ &nbs=
p;
55,613 |
$ &nbs=
p;
4,561 |
||||
|
Variance |
$
41,929,833,151 |
$
540,895,554 |
||||
|
Observations |
&nbs=
p; &=
nbsp; 140
|
&nbs=
p; =
436
|
||||
|
Hypothesized Mean Difference |
$ &nbs=
p; &=
nbsp;
- |
|||||
|
df |
t Stat |
P(T<=3Dt) one-tail |
t Critical one-tail |
P(T<=3Dt) two-tail |
t Critical two-tail |
|
=
=
REJEC=
TING
THE NULL HYPOTHESIS THAT THERE IS NO DIFFERENCE IN THE PRICE OF SILENT AND =
LIVE
AUCTIONS
Table=
2 shows
the t-Test for Two-Samples the live and silent option, Assuming Unequal
Variances, as we can see in the descriptive statistics. This table includes=
a
confirmation of the Mean of $55,613, $4,561, respectively for the Live and
Silent auctions. The variance=
is
different as we can see with the variance of $41,929,833,151, and $ 540,895,554, and the Observat=
ions
of 140 domains sold in the live auction versus 436 domains sold in the sile=
nt
auction. It states the null
hypothesis stating that the Hypothesized Mean Difference=3D $0. Finally, it concludes with the t S=
tat &=
nbsp; of
2.943899, P(T<=3Dt) one-tail of &nb=
sp; 0.001898,
which is less than .05, and thus reject the 1 tail null hypothesis, with a t
Critical one-tail of 1.655811.
Likewise, the P(T<=3Dt) two-tail&=
nbsp; 0.003796,
reject the 2 tails null hypothesis, with a t Critical two-tail of 1.977054=
, in
favor of the alternative hypothesis stating that there is a significant
difference in the prices, between the silent and live auctions. &=
nbsp;
Table =
3
|
|
Table 3
Multivariate
With
Regression Statistics Univariate
LiveSilent
Intercept
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
Table 3
shows the SUMMARY OUTPUT including the Regression Statistics of Live
and Silent auction models with and without Intercept, respectively. The Multiple R, the R Square of 0.=
066557441,
indicating that the type of the auction explains about 7% of the sample auc=
tion
price, this can be a large amount when dealing with domains that sell for
millions of dollars. This sam=
ple
includes all the observations of a total of 576. The three model states the followi=
ng: ANOVA Table 4 df SS MS F Significance F Regression Residual Total ANOVA B C D E F G 15 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 16 Live $ 55,613.21 18 Silent $
(51,052.63) Omitted from the model due to =
excessive
redundancy correlation 19 Difference $ 4,560.59 Errors due to excessively
correlated independent variables 20 =3DB18+B20 21 Therefore, if a seller was forced into a silent aucti=
on,
this seller may have lost as much as $50,000 due to the type of auction,
especially in the more valuable domain names. In the event of a dispute, and a
litigation, the settlement and the recovery may amount to such sums of mone=
y. In
a dispute resolution that a seller lost opportunity due=
to
missing the live auction and selling instead in a silent auction, settling
domain type disputes should lead to some compensations at about the value of
the X coefficient. That X
Coefficient describes the silent auction seller’s opportunity loss du=
e to
missing of the live auction. =
In
contrast, for the buyer the litigation will focus on the opposite. It will focus on the litigation and
damages calculation of “buyers’ frenzy” suboptimal live
auction bids, leading to a live auction price that is too high compared to =
the
silent option.Model =
1-Y
the auction price=3D f(Live=3D1, or Silent=3D0 Auction type)*X Coefficient =
value [ univariate
model]
Model =
2-Y
the auction price=3D f(Live=3D1 Auction type)*X1 Coefficient value+(Silent=
=3D0
Auction type)*X2 Coefficient value [ multivariate model]
Model =
3-Y
the auction price=3D f(Live=3D1 Auction type)*X1 Coefficient value+(Silent=
=3D0
Auction type)*X2 Coefficient value+ Intercept [ multivariate with intercept=
model]
The on=
ly
valid model is the univariate model due to the correlation between the dummy
variables that represent the mutually exclusive type of auction, whether the
domain was sold in the live or the silent auction. The intercept was statistically in=
significant
and therefore we dropped it. =
The
gain in the R Square from .o4 to .o6 shows the advantage of dropping
insignificant components of the model.
ANOVA =
REJECTS
THE NULL HYPOTHESIS THAT THE AUCTION TYPE HAS NO EFFECT ON THE DOMAIN NAME
PRICE.
Table =
4
Table 4
shows the ANOVA, the Analysis of Variance with a significant F and Signific=
ance
F, with the Regression values of 40.99933959, 3.16993E-10, which is much le=
ss
than .o5, and therefore reject the null hypothesis that the auction type ha=
s no
effect on the domain name price. We
reject this null hypothesis in favor of the alternate hypothesis stating the
auction type affects the auction price to the tunes of about 7%.
SILENT=
AUCTION
MAY EXACT A SIGNIFICANT PENALTY ON THE DOMAIN NAME PRICE
The 2<=
sup>nd
part of the table shows the X coefficient and its statistics. It shows the columns of a spreadsh=
eet : C
D E F G, and the rows 15, 16 etc….&n=
bsp;
The table includes the Coefficients, Standard Error, t Stat, and P-v=
alue
of 3.16607E-10, which is way less than the threshold of .05 to reject the n=
ull
hypothesis stating that the x coefficient is equal to zero, in favor of the
alternative hypothesis that it is not equal to zero. In our case the X coefficient of S=
ilent
is equal to $(51,052.63), whi=
ch
means that a silent auction may exact a significant penalty on the domain n=
ame
price, in the vicinity of a negative penalty of some $51,000. We have omitted from the model due=
to excessive
redundancy correlation the 2nd x variable leading to a Differenc=
e of
$4,560.59, and avoiding Errors due to excessively correlated independent
variables calculated as =3DB18+B20.
SUMMAR=
Y,
CONCLUSIONS, AND IMPLICASTION
In sum=
mary,
we have demonstrated the differences in the domain mane prices of a live
compared to a silent auction of domain names. We have used a sample of only one
auction and therefore, we need to repeat it over a longer period of time to=
confirm
our finding. However, this is=
consistent
with the press and the opinion expressed by Monkier.com that the live aucti=
on
tends to result in higher prices, or that they auctioneer usually will steer
high value domain names to a live auction, rather than a silent auction.
We con=
clude
that the seller will be more profitable in a live auction environment, since
the seller may be able to get a higher price for the sale of his domain
name. In contrast, the buyer =
will
be saving cost if he can acquire the domain name in a silent auction. In the event of a dispute, we have
developed a regression model that can easily calculate the damages and
compensation due to the opportunity cost of sub optimization.
The
implications of this study are that seller and buyer of auctions will tend =
to
pull in different directions with regard to the optimal type of auction. The sellers will prefer to sell in=
a
live auction, while the buyer should prefer to bid in a silent auction. Otherwise, if the some participant=
s are
not fully aware of such issues, they may choose a suboptimal type of auctio=
n,
and lose money.
&=
nbsp;
&=
nbsp;
&=
nbsp;
&=
nbsp;
&=
nbsp;
|
Appendix 1 Sample Data |
|||
|
**IMPORTANT NOTE: "Opening
Bid Range" does not indicate value of the domain name or potential
purchase price. |
|||
|
*Note: Statistic information h=
as
not been verified, please consider this data as an ESTIMATE ONLY! Moniker
cannot guarantee its accuracy. |
|||
|
This is the tentative list of =
all
domains to be included in the 2007 Moniker/T.R.A.F.F.I.C. Miami Live and
Silent Auctions. |
|||
|
Silent Auction: October 12th-1=
8th,
2007 SUBJECT TO CHANGE |
|||
|
Live Auction: October 12th, 20=
07
2pm-6pm EST. |
T.R.A.F.F.I.C. MIAMI 2007 |
Inventory is subject to change=
. |
Master Auction Inventory |
|
Description |
Domain1 |
Domain2 |
Domain3 |
|
Venue/Sale Price |
$ &nbs=
p;
2,200,000 |
$ &nbs=
p; &=
nbsp;
900,000 |
$ &nbs=
p;
450,000 |
|
Domain Name |
computer.com |
investment.com |
sportinggoods.com |
|
Opening Bid Range |
$1MM - $5MM |
$750,001 - $1MM |
$250,001 - $500,000 |
|
Extension |
com |
com |
com |
|
Length |
8 |
10 |
13 |
|
Category |
Computing |
Finance |
Shopping |
|
Uniques Per Mo. (estimate) |
&nbs=
p;
&nbs=
p;20,000
|
N/A |
N/A |
|
Rev Per Mo. (estimate) |
&nbs=
p; &=
nbsp;
15,000 |
N/A |
N/A |
|
Additional Information |
|||
|
COMPETE Page Count |
&nbs=
p; &=
nbsp;
1,145 |
N/A |
&nbs=
p; &=
nbsp;
3,781 |
|
COMPETE Page Rank |
&nbs=
p;
&nbs=
p; 805,431
|
N/A |
&nbs=
p; &=
nbsp;
327,475 |
|
Google pr |
&nbs=
p; &=
nbsp; &nbs=
p;
5 |
&nbs=
p; &=
nbsp; &nbs=
p;
4 |
&nbs=
p; &=
nbsp; &nbs=
p;
2 |
|
GOOG Search Domain Results |
&nbs=
p; &=
nbsp;
486,000 |
&nbs=
p; &=
nbsp;
219,000 |
&nbs=
p; &=
nbsp;
12,200 |
|
GOOG Search Name Results |
&nbs=
p;
581,000,000 |
&nbs=
p; &=
nbsp;
92,100,000 |
&nbs=
p; &=
nbsp;
793,000 |
|
MSN Search Domain Results |
&nbs=
p;
1,112,795,762 |
&nbs=
p; &=
nbsp; &nbs=
p;
441 |
&nbs=
p; &=
nbsp;
24,836 |
|
MSN Search Name Result |
&nbs=
p;
1,086,237,890 |
&nbs=
p; &=
nbsp;
230,190,168 |
&nbs=
p;
55,577,396 |
|
Y! Search Domain Results |
N/A |
N/A |
N/A |
|
Y! Search Name Results |
N/A |
N/A |
N/A |
|
Alexa Rank |
&nbs=
p; &=
nbsp;
7,416,303 |
&nbs=
p; &=
nbsp;
1,336,316 |
&nbs=
p;
5,583,424 |
|
URLTRENDS Description |
Computer parts PC |
egX Group |
Pool Tables Billiard Supply |
|
Links IN |
|
&nbs=
p; &=
nbsp; &nbs=
p;
2,600 |
&nbs=
p; &=
nbsp;
25 |
|
Links OUT |
&nbs=
p; &=
nbsp;
149 |
&nbs=
p; &=
nbsp; &nbs=
p;
17 |
&nbs=
p; &=
nbsp;
138 |
|
Online Since |
34557.00 |
35488.00 |
35143.00 |
|
Online Since |
Thursday, August 11, 1994 |
Thursday, February 27, 1997 |
Tuesday, March 19, 1996 |
|
DOMAIN |
Computer.com |
Investment.com |
SportingGoods.com |
|
SALE PRICE |
$ &nbs=
p;
2,200,000 |
$ &nbs=
p; &=
nbsp;
900,000 |
$ &nbs=
p;
450,000 |
|
VENUE SOLD |
LIVE |
LIVE |
LIVE |
|
DOMAIN |
Cowboys.com |
Xmas.com |
fishingpole.com |
|
SALE PRICE |
$ &nbs=
p; &=
nbsp;
370,000 |
$ &nbs=
p; &=
nbsp;
294,200 |
$ &nbs=
p; &=
nbsp;
58,900 |
|
VENUE SOLD |
Silent |
Silent |
Silent |
|
Top 3 Domain Differences |
$ &nbs=
p;
1,830,000 |
$ &nbs=
p; &=
nbsp;
605,800 |
$ &nbs=
p;
391,100 |
&=
nbsp; &nbs=
p; &=
nbsp; &nbs=
p;
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
References
Managerial auditors' mixed cost forecasting assumpt=
ion
departure error estimates for litigation and … - all 4 versions »A Rushinek
- Managerial Auditing Journal, 1997 - emeraldinsight.com <=
span
style=3D'font-size:10.0pt;font-family:"Arial","sans-serif"'>...<=
span
style=3D'font-size:10.0pt;font-family:"Arial","sans-serif"'> Page 7. [ 291 =
] Avi
Rushinek and Sara Rushinek Managerial auditors’ mixed c=
ost forecasting
assumption departure error estimates for litigation ... Related Articles=
- Web Search - BL Direct=
Rushinek A., =
"Managerial
auditors’ mixed cost forecasting assumption departure error estimates=
for
litigation and professional liability risk reduction: the electronic securi=
ty
industry" Journal: Managerial Auditing Journal, 1997, Volume: 12, Issu=
e:
6, Page: 285 - 297, DOI: 10.1108/02686909710180643, Publisher: MCB UP Ltd http://www.emeraldinsight.c=
om/10.1108/02686909710180643
Forecasting sales, expenses and stock market values=
by
quarterly financial statement ratio analysis - all 4 versions »A Rushinek,
SF Rushinek - Managerial Auditing Journal, 1995 - emeraldinsight.com=
...<=
span
style=3D'font-size:10.0pt;font-family:"Arial","sans-serif"'> Avi =
Rushinek
and Sara F. Rushinek ... For similar details about the stock =
market
value/price forecast, write or fax the authors. ... Related Articles=
- Web Search - BL Direct=
Avi Rushinek,=
Sara F.
Rushinek, “Forecasting sales, expenses and stock market values by
quarterly financial statement ratio analysis: a microcomputer software
development model,” Managerial Auditing Journal, Year: 1995, Volume: =
10,
Issue: 2, Page: 7 - 33, DOI: 10.1108/02686909510079620, Publisher: MCB UP L=
td http://www.emeraldinsight.c=
om/10.1108/02686909510079620
The
Construction of an International Investors’ Perception Model for
Corporate Published Forecasted … - all 2 versions » A RUSHINEK,
LS CHANG, KS MOST - MANAGERIAL AND DECISION ECONOMICS, 1983 - doi.wiley.com=
...
AVI RUSHINEK SARA F. Rushinek ... Similarly, the
mean utility score of cost of goods sold (COGS) is 64.7739 for buying decis=
ions
and slightly higher, 65.2024, for ... Related Articles=
- Web Search
The construct=
ion of
an international investors' perception model for corporate published foreca=
sted
financial reports for the USA, the UK and New Zealand Avi Rushinek, Sara F.
Rushinek, Lucia S. Chang, Kenneth S. Most Managerial and Decision Economics
Volume 4, Issue 4 , Pages258 - 265 Copyright © 1983 John Wiley &
Sons,Ltd. http://www3.interscience.wi=
ley.com/journal/113456935/abstract?CRETRY=3D1&SRETRY=3D0
EDP auditors' role in evaluating computerized accou=
nting
information systems efficiency by queuing, …=
- all 3 versions » SF Rushinek,
A Rushinek - Proceedings of the fourteenth SIGCSE technical symposiu=
m on
…, 1983 - portal.acm.org... Sara F. Rushinek =
Avi
Rushinek University of Miami Coral Gables ... scant at this p=
oint
to forecast which routines ... 3. Generally low cost is invol=
ved,
although this ... Related Articles=
- Web Search
Sara F. Rushi=
nek Avi
Rushinek "EDP auditors' role in evaluating computerized accounting inf=
ormation
systems efficiency by queuing, simulation and statistical models," Vol=
ume
15 , Issue 1 (February 1983) Proceedings of the=
14th
SIGCSE technical symposium on Computer science education, Pages: 205 - 210,
Year of Publication: 1983, ISSN:0097-8418 Publisher ACM New York, NY, USA Bibliometrics
Downloads (6 Weeks): 1,
Downloads (12 Months): 14, &nb=
sp;
Citation Count: 0 http://doi.acm.org/10.1145/952978.801048
The control of computerized information relating to
branch manpower management follow-up systems for …<=
/span> - all 2 versions » A Rushinek,
S Rushinek - ACM SIGCPR Computer Personnel, 1989 - portal.acm.org...
Avi Rushinek Sara Rushinek School of ... For
example, historical cost accounting reports are accurate since they depict
actual events which are recorded ... Related Articles=
- Web Search
A. Rushinek S.
Rushinek, “The control of computerized information relating to branch
manpower management follow-up systems for banks,” Source ACM SIGCPR Computer Personnel arch=
ive Volume
12 , Issue 1 (April 1989) Pages: 1 - 12 Year of
Publication: 1989 ISSN:0160-2497 Publisher ACM New York, NY, USA Bibliometrics Downloads (6 Weeks):
5, Downloads (12 Months=
):
48, Citation Count: 0 http://doi.acm.org/10.1145/66058.66059
------=_NextPart_01C8CD81.8C29E410
Content-Location: file:///C:/F83B2E2E/6-13-8-MHTDISPUTERESOLUTIONSETTLINGFORSILENTORLIVEDOMAINNAMEAUCTIONSELLERSSUB-OPTIMIZATION_files/props0009.xml
Content-Transfer-Encoding: quoted-printable
Content-Type: text/xml