Biased Price Signals and Reaction of
Bidders in Vickrey Auction IPOs
Aytekin Ertan
December 2009
Background
• Auction:
– Uniform price sealed bid auction
– Common value, public and biased signals
• Winner’s Curse:
– Lower performance than equilibrium predictions
– Common value & bounded rationality, RNNE
• IPO:
– Three parties: Issuer  Underwriter  Investors
– Auction IPOs are not quite in practice now
*Motivation and Research Questions*
• Effects of misleading information and learning
1. How do biased noisy signals affect the bidders’
behaviors in nth (6th) price sealed bid auctions?
2. To what extent do winner’s curse and free riding exist
and -more importantly- persist in such settings?
3. Is detecting and overcoming unfavorable signals
possible by learning?
4. Do auction IPOs necessarily fail?
• In a common value Vickrey auction based IPO setting
with three different public and biased signals, how do
the investors modify their bids when they are
continuously informed by their and underwriters’
performance?
Experimental Setup
• True values of stocks are normally distributed, but proposed to
participants with biased signals (means are: U1: 0.95*TV, U2:
1.05*TV, U3: 1.15*TV) (variances are equal)
• Underwriters’ proposals depend on only true value, they are
independent from the previous or forthcoming rounds
• Among 22 bidders, first five bidders win the auction and their profit
(per share) is (TV - 6th price)
• 12 periods and 3 underwriters (36 rounds)
• Step 1:
– Bidder is informed about the performance of the corresponding
underwriter last round
– Bidder see the price proposed by underwriter 1, 2 or 3
– (S)he bids
• Step 2:
– Bids are ranked, transaction is realized and results are disclosed to all
participants
Results from Signal 1 (UW1)
Deviation Chart Underwriter 1
25%
% deviation from proposed value
20%
15%
10%
5%
0%
0
2
4
6
-5%
-10%
-15%
-20%
-25%
Period
8
10
12
Results from Signal 2 (UW2)
Deviation Chart Underwriter 2
% deviation from proposed value
30%
20%
10%
0%
0
2
4
6
8
10
12
14
-10%
y = -0.0052x - 0.0685
R² = 0.0857
-20%
Period
-30%
Results from Signal 3 (UW3)
Deviation Chart Underwriter 3
% deviation from proposed value
35%
25%
15%
5%
0
2
4
6
8
10
12
14
-5%
-15%
-25%
-35%
y = -0.0222x + 0.0123
R² = 0.6711
Period
Performance of Underwriters
20.00%
Underwriter 1
Underwriter 2
15.00%
Underwriter 3
returns by underwriters
10.00%
5.00%
0.00%
0
-5.00%
-10.00%
-15.00%
-20.00%
-25.00%
-30.00%
2
4
6
8
10
12
14
period
Summary of Findings
• Average deviations support general learning but profits
are not  some bidders became more conservative but
some kept bidding high
• Winner’s curse arose and persisted in these auctions
• We cannot see positive returns even after considerably
high losses  Greed is the reason
• A significant amount of participants stated in the exit
survey that they realized the differences among signals
• However some of them also emphasized they kept
bidding high in order to win the bid thinking the others
will bid low (after learning) and that IPO would be
profitable – free riding
Concluding Thoughts
• Findings support the existence and persistence of
winner’s curse in (6th price) Vickrey auctions in spite
of some mitigation
• The free rider problem and the winner's curse
impede price discovery and thus discourage
investors from participating in auctions
• Results are consistent with the argument stating
that auction IPOs are not favorable by investors
• Biased price signals have more to investigate
– Hurting hurt more than helping helped (UW1 vs. UW2)
– Bias and speed are positively associated (UW2 vs. UW3)
Potential Improvements & Comments
• Certainly, running the experiment for more periods is
crucial for observing the learning in the setting
• Distinction among underwriters can be emphasized more
(even more differences in signals and/or assigning colors)
• History information can be clearer and comprehensive??
o Number of winners might be random (kth price auction)
o A more basic setting may be used (basic trade instead of
IPO) or different IPO methods can be tested
o Uniformly distributed random variables may be used
o Bias of signals could be constant or dynamic
o Private signals or asymmetric information might be used
 Your comments?