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 learning1. 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)

0 2 4 6 8 10 12

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

Period

% d

evia

tion

from

pro

pose

d va

lue

Deviation Chart Underwriter 1

Results from Signal 2 (UW2)

0 2 4 6 8 10 12 14

-30%

-20%

-10%

0%

10%

20%

30%

f(x) = − 0.0052163458707979 x − 0.0684780065027767R² = 0.0857269707847979

Deviation Chart Underwriter 2

Period

% d

evia

tion

from

pro

pose

d va

lue

Results from Signal 3 (UW3)

0 2 4 6 8 10 12 14

-35%

-25%

-15%

-5%

5%

15%

25%

35%

f(x) = − 0.0222264940900934 x + 0.012344908091813R² = 0.671052669845918

Deviation Chart Underwriter 3

Period

% d

evia

tion

from

pro

pose

d va

lue

0 2 4 6 8 10 12 14

-30.00%

-25.00%

-20.00%

-15.00%

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

20.00% Under-writer 1Linear (Un-derwriter 1)Under-writer 2

Performance of Underwriters

retu

rns

by u

nder

wri

t-er

s

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 testedo Uniformly distributed random variables may be usedo Bias of signals could be constant or dynamico Private signals or asymmetric information might be used

Your comments?