Name : Rishabh Singh

Email : rishusingh11116432@gmail.com

College : BIET Jhansi

1.) Introduction

An online bidding is an auction which is held over the internet.Online biddings come in various formats,but most popular of them are the Ascending English auctions,Descending Dutch auctions,First-price Sealed-bid,Vickrey auctions,or sometimes even a combination of multiple auctions,taking elements of one and fusing them with those of another.The scope and reach of these auctions have been propelled by the Internet to a level beyond what the initial purveyors had anticipated.This is mainly because online auctions break down and eliminate all the physical limitations of traditional auctions such as geography,presence,time,space,and a small target audience.This influx in reachability has also made it easier to commit unlawful actions within an auction.In 2002,online auctions were reported to account for 30% of all online e-commerce transactions due to the rapid expansion and the popularity of various forms of electronic commerce.

2.) Literature Review

The analysis mainly focusses on the study of online bidding which takes place on eBay.eBay is a publicly visible market which has attracted a lot of attention from economists,who have been using it to analyze various aspects of buying and selling behavior,auction formats,etc. and comparing them with previous theoretical and empirical findings.Computer Information Systems Researchers have also shown a lot of interest in eBay.Michael Goul,Chairman of the Computer Information Systems Department of the W.P.Carey School of Business at Arizona State University,has published an academic case study based on eBay’s big data management and use in which he discusses how eBay is a data-driven company that processes 50 petabytes of data per day.eBay uses a system that allows different departments in the company to check out data from their data-mart into sandboxes for analysis.According to Goul,eBay has already experienced a huge business success through its data analytics stream.eBay employs 5,000 data analysts to enable data-driven decision making.

Hypothesis : During any online auction,the Final Selling Price of the Product is always greater than the Proxy Bid

3.) Data Description

For this study,I had collected the data from http://www.modelingonlineauctions.com/datasets. What is the ‘Winner’s Curse’?The Winner’s Curse is a tendency in an auction for the winning bid to exceed the intrinsic value of the item purchased.Because of incomplete information,emotions or any other number of factors regarding the item being auctioned,bidders can have a difficult time in determining the item’s intrinsic value.As a result,the largest over-estimation of an item’s value ends up winning the auction.

4.) Model Analysis

In order to test the above Hypothesis,we can propose the following model : price=k0+k1(bid)+k2(openbid)+k3(bidderrate)

5.) Discussion

Let’s start analyzing the given dataset to get the insights.

Reading & Viewing the Dataset and it’s Different Fields

setwd("C:/Users/rishu/Downloads")
bid.df <- read.csv(paste("CartierForWinnersCurse.csv", sep="new"))
View(bid.df)
library(psych)
attach(bid.df)
describe(bid.df) 
##            vars    n         mean         sd       median      trimmed
## auctionid     1 1348 1.644515e+09 3597127.86 1.644198e+09 1.644464e+09
## bid           2 1348 5.985700e+02     659.81 3.530000e+02 4.822600e+02
## bidtime       3 1348 4.010000e+00       2.50 4.170000e+00 4.090000e+00
## bidder*       4 1348 2.576500e+02     148.11 2.675000e+02 2.584400e+02
## bidderrate    5 1348 3.386000e+01      87.51 6.000000e+00 1.550000e+01
## openbid       6 1348 1.486800e+02     373.16 5.000000e+00 7.391000e+01
## price         7 1348 9.613100e+02     812.41 6.232600e+02 8.400900e+02
##                   mad           min        max       range  skew kurtosis
## auctionid  4642816.76 1638843936.00 1650986455 12142519.00  0.18    -1.12
## bid            375.10          1.00       5400     5399.00  2.32     8.69
## bidtime          3.82          0.01          7        6.99 -0.14    -1.57
## bidder*        190.51          1.00        509      508.00 -0.07    -1.22
## bidderrate       8.90         -4.00       1303     1307.00  7.59    82.65
## openbid          7.40          0.01       5000     4999.99  7.56    85.25
## price          545.98        103.50       5400     5296.50  1.53     3.01
##                  se
## auctionid  97974.02
## bid           17.97
## bidtime        0.07
## bidder*        4.03
## bidderrate     2.38
## openbid       10.16
## price         22.13
summary(bid.df)
##    auctionid              bid            bidtime        
##  Min.   :1.639e+09   Min.   :   1.0   Min.   :0.007535  
##  1st Qu.:1.642e+09   1st Qu.: 151.7   1st Qu.:1.505715  
##  Median :1.644e+09   Median : 353.0   Median :4.170885  
##  Mean   :1.645e+09   Mean   : 598.6   Mean   :4.005524  
##  3rd Qu.:1.648e+09   3rd Qu.: 821.5   3rd Qu.:6.725284  
##  Max.   :1.651e+09   Max.   :5400.0   Max.   :6.999965  
##                                                         
##           bidder       bidderrate         openbid            price       
##  lass1004    :  22   Min.   :  -4.00   Min.   :   0.01   Min.   : 103.5  
##  pascal1666  :  19   1st Qu.:   1.00   1st Qu.:   1.00   1st Qu.: 355.0  
##  freembd     :  17   Median :   6.00   Median :   5.00   Median : 623.3  
##  happyrova   :  17   Mean   :  33.86   Mean   : 148.68   Mean   : 961.3  
##  restdynamics:  17   3rd Qu.:  31.00   3rd Qu.: 155.00   3rd Qu.:1525.0  
##  adammurry   :  16   Max.   :1303.00   Max.   :5000.00   Max.   :5400.0  
##  (Other)     :1240
describe(auctionid)     ## Unique Identifier of an Auction
##    vars    n       mean      sd     median    trimmed     mad        min
## X1    1 1348 1644515087 3597128 1644197869 1644463742 4642817 1638843936
##           max    range skew kurtosis       se
## X1 1650986455 12142519 0.18    -1.12 97974.02
describe(bid)           ## The Proxy Bid placed by a Bidder
##    vars    n   mean     sd median trimmed   mad min  max range skew
## X1    1 1348 598.57 659.81    353  482.26 375.1   1 5400  5399 2.32
##    kurtosis    se
## X1     8.69 17.97
describe(bidtime)       ## The Time from the start of the auction when the bid was placed
##    vars    n mean  sd median trimmed  mad  min max range  skew kurtosis
## X1    1 1348 4.01 2.5   4.17    4.09 3.82 0.01   7  6.99 -0.14    -1.57
##      se
## X1 0.07
describe(bidderrate)    ## eBay Feedback Rating of the Bidder
##    vars    n  mean    sd median trimmed mad min  max range skew kurtosis
## X1    1 1348 33.86 87.51      6    15.5 8.9  -4 1303  1307 7.59    82.65
##      se
## X1 2.38
describe(openbid)       ## The Opening Bid set by the Seller
##    vars    n   mean     sd median trimmed mad  min  max   range skew
## X1    1 1348 148.68 373.16      5   73.91 7.4 0.01 5000 4999.99 7.56
##    kurtosis    se
## X1    85.25 10.16
describe(price)         ## The Closing Price that the item was sold for(highest bid+an increment)
##    vars    n   mean     sd median trimmed    mad   min  max  range skew
## X1    1 1348 961.31 812.41 623.26  840.09 545.98 103.5 5400 5296.5 1.53
##    kurtosis    se
## X1     3.01 22.13

Histogram Representation of Price Field

library(lattice)
histogram(~price, data = bid.df,main = "Distribution of Price Difference", xlab="Difference in Price",col='green' )

ScatterPlot of Price versus OpenBid

library(car)
## 
## Attaching package: 'car'
## The following object is masked from 'package:psych':
## 
##     logit
library(psych)
scatterplot(price ~ openbid ,data=bid.df, main="ScatterPlot of Price versus OpenBid ")

ScatterPlot of ProxyBid versus OpenBid

scatterplot(bid ~ openbid ,data=bid.df, main="ScatterPlot of ProxyBid versus OpenBid ")

ScatterPlot of all the required Fields

scatterplotMatrix(~openbid+price+bid+bidderrate+bidtime, data=bid.df,main="Interdependence Variations among Various Fields")

Corrgram of Biddings

library(Hmisc)
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
## 
## Attaching package: 'Hmisc'
## The following object is masked from 'package:psych':
## 
##     describe
## The following objects are masked from 'package:base':
## 
##     format.pval, units
colbid <- c("price","openbid","bid","bidderrate")
corMatrix <- rcorr(as.matrix(bid.df[,colbid]))
corMatrix
##            price openbid   bid bidderrate
## price       1.00    0.42  0.82      -0.07
## openbid     0.42    1.00  0.56       0.02
## bid         0.82    0.56  1.00      -0.04
## bidderrate -0.07    0.02 -0.04       1.00
## 
## n= 1348 
## 
## 
## P
##            price  openbid bid    bidderrate
## price             0.0000  0.0000 0.0087    
## openbid    0.0000         0.0000 0.4164    
## bid        0.0000 0.0000         0.1759    
## bidderrate 0.0087 0.4164  0.1759
library(corrgram)
corrgram(bid.df[,colbid],order=TRUE,main="Difference in Biddings",lower.panel=panel.pts,upper.panel=panel.pie,diag.panel=panel.minmax, text.panel=panel.txt)

Test to check whether the Final Selling Price of the Product is always greater than the Proxy Bid

t.test(price, bid)
## 
##  Welch Two Sample t-test
## 
## data:  price and bid
## t = 12.725, df = 2585.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  306.8423 418.6353
## sample estimates:
## mean of x mean of y 
##  961.3078  598.5690

As we can see that the p-value is less than 0.01,which means that we can reject the null hypothesis.

6.) Conclusion

The sole purpose of this project was to analyze the Winners curse Effect among the bidders.So from the above analysis of the given dataset,we can deduce that the Final Selling Price of the Product is always greater than the Proxy Bid.

7.) References

About the Winner’s Curse Effect -> https://www.investopedia.com/terms/w/winnerscurse.asp Details about eBay -> https://en.wikipedia.org/wiki/EBay Source of the Dataset -> http://www.modelingonlineauctions.com/datasets