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The Winner’s Curse and Lottery-Allocated IPOs in China

Jerry Coakley, Norvald Instefjord and Zhe Shen*

University of Essex; *Xiamen University

CEF-QASS Empirical Finance ConferenceMay 2008

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Outline

Background Data and Summary Stats Hypothesis Testing Discussion

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1. Background 2 IPO puzzles --Short term underpricing

--Long term underperformance We focus on underpricing: opening

day trading p exceeds offer p Called leaving money on the table! How do we explain it?

Background: Why China? Underpricing is extreme relative to

other countries Average typically exceeds 100% but

can be 4000% or more! Emerging market – plausible to assume

that some investors are uninformed Hugely active market with unique data

availability

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Background: Chinese IPOs Recent large IPOs: Chinese banks: Industrial & Commercial

Bank of China $21.6bn in 2006Also Bank of China $11bn; China Construction Bank $9bn 2005

Credit cards: Visa $17.9bn March 2008Also Mastercard

Internet eg AT&T $10.6bn 2000Also Google, Amazon, Netscape, Yahoo

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Background: Winner’s curse WC one explanation for underpricing! Arises in auctions where bidders

have only estimates of the true value Winner is highest bidder who tends

to be over optimistic Tendency for winner to overpay

increases with number of bidders Applications: IPOs and oil fields!

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Rock (1986) WC model

Naïve (uninformed) and informed investors

Naïve investors receive small allocations in good IPOs as everyone bids for them

They get large allocations in bad ones (dogs) as informed investors don’t compete for these

Cf Groucho Marx: “I would never join a club that would have me for a member”

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Rock (1986) WC model

Without underpricing, naïve investors would systematically make losses

Underwriters deliberately underprice IPOs to attract them to dogs or bad issues

Prediction: Weighting abnormal returns by allocations will leave naïve investors with zero abnormal profits

Rock (1986) model tests

Direct tests of WC limited by LACK of detailed allocation data

Just a handful of extant studies All claim to support WC! Indirect studies contrast

institutional and individual investor allocations

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Contributions First study of Rock’s model where

oversubscribed IPOs are allocated by lottery as in Rock model

Lottery avoids biases against large orders by informed investors

Rock assumes latter exploit large orders

Lottery vs proration (Amihud et al JFE: an allocation is guaranteed in proration but not in a lottery

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Contributions cont’d Second, Rock’s model is consistent with

important aspects of underpricing in our Chinese IPO sample 1996-2001

Evidence of adverse selection: inverse relation between underpricing and allocation

Allocation weighting does indeed cause a very substantial drop in nominal abnormal returns - they a fall of more than 200-fold from 116% to just 0.51% (median).

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Contributions cont’d Finally our sample avoids some pitfalls of

extant studies of Chinese IPOs. We restrict our sample to IPOs that

employ the same issuing method and are subject to the same regulatory regime.

Both the stock issuing and pricing methodologies vary significantly during 1990s and early 2000s with extreme underpricing (5000%) in some cases.

Largest sample to date in studies of the winner’s curse hypothesis.

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2. Data & Summary Stats Sources: SinoFin CCER, DataStream,

and GTA CSMAR + IPO prospectus and listing announcements.

Sample selection A-share issue Remain listed until the end of 2001 Online fixed price offering to investors Data available on the number of applicants Data available on the rate of allocation

Data contd

Very recent market (1990) Huge underpricing – in excess of

100% even excluding 1000%+ outliers

Virtually no overpricing ie no dogs! Huge oversubscription – around 200

times Authorities (not underwriter) mostly

decide on pricing14

Underpricing  Initial run-up (Ritter & Welch)

Initial excess return (Amihud et al)

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%1001

0,

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jj

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%100

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Share issuance and allocation 1996-2001

Method of Share Issuance No. Initial Run-up MedianMethods of Share

Allocation

“Firm commitment” 70 1474.87% 1264.50% N/A

Certificates of deposits 8 162.38% 155.91% Normal / Pure Lottery

Prepayment in full, proportional allocation

111 148.81% 129.71% Proration

Online primary offering 592 129.72% 117.70% Pure Lottery

Online primary/secondary offering

35 158.08% 146.86% Pure Lottery/Pro ration

Online bookbuilding 6 160.24% 137.02% Pure Lottery

Online and offline bookbuilding

6 55.94% 57.19% Pure Lottery/Pro ration

Unknown 1 452.77% 452.77% -

Total 829 247.45% 128.95%

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Share pricing 1996-2001

Method of price determination

Total of 829 562 in sample%

includedFormula

No.Initial

Run-upNo.

InitialRun-up

Earnings Forecasts 88 84.41% 47 70.46% 53.41 YES

EPS in the past 3 years 224 147.34% 146 141.51% 65.18 YES

50/50 77 148.09% 53 138.53% 68.83 YES

Weighted Average 169 122.64% 165 120.82% 97.63 YES

Negotiated 189 148.33% 151 146.93% 79.90 NO

Authorities 701474.87

%0 - - NO

Bookbuilding 12 108.09% 0 - - NO

Total 829 247.45% 562 130.67% 75.34

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Empirical summary: Underpricing

Year No. Initial returns

Min Median Max Std. Dev.

1996 99 100.95% -18.35% 96.20% 336.88% 69.87%

1997 116 142.80% 34.58% 124.75% 463.65% 71.75%

1998 87 131.59% 1.27% 117.29% 430.65% 80.35%

1999 96 111.19% 6.01% 92.96% 820.50% 99.23%

2000 99 147.24% 0.70% 137.42% 477.98% 86.68%

2001 65 143.42% 3.41% 136.63% 413.56% 87.47%

Total 562 129.15% -18.35% 115.90% 820.50% 84.06%

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20

40

60

80

100

0 100 200 300 400 500

5

30

46 50

92

87

78

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23 19

14

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3 3 3 2 1 1 1

Freq

uenc

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Initial return (%)

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20

40

60

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100

0 100 200 300 400 500

5

30

46 50

92

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78

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Freq

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Initial return (%)

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40

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120

0.0 0.5 1.0 1.5 2.0 2.5 3.0

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101

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64

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21

27

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20 15

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3 5 5 5 5 3 2 2 1

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Freq

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Allocations (%)

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Summary results: AllocationMean Min. Median Max. N

1.4008 0.0558 0.4761 90.5777 562

ALLOC (%)

Good IPOs: IRj>median

2.2408 0.1326 0.6789 90.5777 281

Bad IPOs: IRj<median

0.5604 0.0558 0.3770 5.8139 281

Good IPOs: IRj>mean

2.0427 0.1247 0.6697 90.5777 326

Bad IPOs: IRj<mean

0.5141 0.0558 0.3485 5.8139 236

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Determinants of underpricing (allocation)

IRj=α0+ α1PROCEEDSj + α2SDIRj +uj

Larger the issue size (Proceeds), the smaller the valuation uncertainty

Greater the information asymmetry (SDIR) , the greater underpricing

IR is inversely related to size but positively related to standard deviation

In Rock’s model, these should be unrelated to allocation

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Determinants of UnderpricingNo. α1 α2 R2 (%)

1996 99 -90.94 (-3.90)*** 40.79 (5.89)*** 41.00

1997 116 -96.13 (-4.25)*** 41.74 (2.88)*** 36.55

1998 87 -168.34(-7.75)*** 70.26 (4.83)*** 64.41

1999 96 79.44 (-2.05)** 114.72 (2.87)*** 57.35

2000 99 -162.28 (-5.34)*** 44.04 (2.79)*** 40.35

2001 65 -208.23 (-5.35)*** 20.21 (2.10)** 60.17

Total 562 -56.61 (-6.06)*** 43.18 (6.20)*** 23.83

IRj=α0+ α1PROCEEDSj + α2SDIRj +uj

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Determinants of Allocation

Allocation is a proxy for excess demand Sig related to SDIR in only 3/6 years and

not in overall szample But size (negatively related to IR) is

positively related allocation! May suggest that underpricing is

greater than necessary to ensure a given level of excess demand

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Determinants of AllocationNo. β 1 β 2 R2 (%)

1996 99 0.79 (2.34)** -0.15 (-2.76)*** 13.33

1997 116 0.24(2.92)** 0.01 (0.24) 6.51

1998 87 0.56(5.47)*** -0.12 (-2.84)*** 35.41

1999 96 0.77 (8.17)*** -0.05 (-1.22) 61.06

2000 99 0.60 (6.93)*** -0.09 (-3.70)*** 36.06

2001 65 0.60 (5.01)*** -0.03 (-0.55) 39.53

Total 562 0.12(2.32)** -0.03 (-1.41) 1.68

ALLOCTj=β0+ β1PROCEEDSj+ β2SDIRj +vjALLOCTj = log((ALLOCj+a)/(1-ALLOCj+a))

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3. WC Hypothesis Tests

Hypothesis 1: there is no relationship between IR and allocation (adverse selection)

Coeff is sig negative at 1% level in all cases

Bigger IR associated with stronger XD or smaller allocations

Adverse selection is also supported if we compare good vs bad IPO allocations

Median allocations are 0.38% vs 0.68% Top vs bottom quintile means: 0.5% vs

4.34%

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Rock’s Model: Adverse selection

IRj=α0+α1ALLOCTj+εj

Year N α1 t-value R2 (%)

1996 99 -54.94 -7.26* 24.13

1997 116 -55.34 -2.08** 5.51

1998 87 -137.92 -5.45* 26.88

1999 96 -130.50 -3.95* 15.33

2000 99 -154.72 -7.56* 29.33

2001 65 -183.49 -6.81* 41.54

Total 562 -87.84 -9.46* 17.99

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Adverse selection

Underpricing could lead to an increase in order size or in the no. of applicants

Hypothesis 2: there is no relationship between number of applicants (Orders) and the degree of underpricing

ORDERSj = a+0.18IRj+ 0.23PROCEEDSj - 0.13SDIRj +εj

(9.41) (5.37) (-7.61)

Positive relationship between underpricing and orders is consistent with prediction that underpricing attracts more investors to the IPO

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Break even prediction

Underpricing does not imply gains for all investors in Rock’s model

Define allocation-weighted initial returnAWIRj = ALLOCj * IRj - interestj

Hypothesis 3: adjusting for allocation and risk, uninformed investors earn zero abnormal returns

Lottery allocation involves risk so implies AWIR >0

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0

10

20

30

40

50

60

70

80

0.0 0.5 1.0 1.5 2.0 2.5 3.0

23

57

65

60 60

52

46

29 29

13 13 12

8 12

10

3

8

3 4 3 1

6 4 4 3 4

2 2 2 2

Freq

uenc

y

Allocation weighted initial return (%)

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Break-even cont’d

The mean value of AWIR is 0.78% while the median is even lower at 0.51%.

Abnormal profits are positive but small in economic terms.

Consistent with the break-even prediction after allowing for lottery risk

Cf Yu and Tse (2006) AWIR = 0

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Break-even cont’d

Studies in other countries: Our sample is considerably larger, twice

at a minimum than those in extant studies

Our sample more consistent. Eg allocation bias against large orders in

Singapore, Finland and UK Results are mixed: Neg AWIR

(Finland/Israel), AWIR>0 (UK/Singapore)

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Break-even cont’d

129.15%

0.78%

27%

1%8.60%

5.14%8.70%

0%

11.99%

-1.18%

-20.00%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

140.00%

China Singapore UK Finland Israel

unweightedweighted

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4. Discussion Apparent evidence supporting WC:

Negative relationship between IR and ALLOC ie underpricing used to offset bias in allocation

Allocation-wgted abnormal profits are positive but economically close to zero

However, need to reexamine participation since pricing may be seen as exogenous in China (multiple of earnings)

It’s endogenous in WC model

Discussion Proration vs Lottery IPOs Lottery = proration with (lottery) risk Sample of 74 out 111 proration IPOs

with relevant data over same period No adverse selection and mean AWIR

of 5.1% > 6 times lottery AWIR! Contrary to rational participation as

proration issues are less risky!

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Discussion Authorities use lottery IPOs to

promote mass participation or popular capitalism

Naïve investors focus more on upside potential in lottery IPOs

ie focus on nominal IR rather than AWIR

This encourages herding into lottery IPOs and may explain their lower AWIR!

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