going public in bear markets motivation...high financial distress cost (20% pre-distress firm value)...

1

Going public in bear markets

Motivation: • IPO clustered in Bull markets (Hot market)

•  Ritter (1984); Loughran et al. (1994); Helwege et al. (2004); Lowry and Schwert (2002); Alti (2005); Pastor and Veronesi (2005); Yung et al. 2008; Chemmanur and He (2011)

• Bull market: Advantageous for equity issuance

1

0

500

1000

1500

2000

2500

S&P 500

Data source: Yahoo finance

2


Motivation: It is not always easy to time the market in matured economy.

2 Data source: Yahoo finance

3


Motivation: Trade off problem : Wait for bull market ⇨ Equity issuance ⇨ Waiting cost e.g., Cost of default risk; Opportunity cost of investment projects;

Sales growth ratio (Market share improvement) When the cost of waiting is high, firms might need to consider

IPO in bear market

How about the reality?

3

4


Motivation: •  Reality: One-fourth (458) of our sample companies (1793

Japanese IPOs) decided to go public in bear markets. ‒  Hereafter denoted by Bear IPOs

‒  Definition of Bear IPO : ‒  -10% or lower buy-and-hold return of TOPIX during

Month -7 to Month -1 (Month 0 is listing month).

4

0 200 400 600 800 1000 1200 1400 1600 1800

0

50

100

150

200

250

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Frequency of IPO and TOPIX

Bear IPO Non-BEAR IPO TOPIX

5


Motivation: Research question: What firms do go public in bear markets?

•  Previous studies have paid much attention to hot market IPOs and firm’s market timing incentive

•  Cost of waiting have been ignored Significance of the research:

•  An important aspect of IPO in matured economy •  Highlight the cost of market timing of equity issuance

5

6


Our idea: Not focus on the decision to IPO or not It takes 2-3 years for private firms to prepare their IPOs Focus the Timing of IPO: wait for bull markets Kim and Weisbach (2008)) The cost of waiting > benefits of equity issuance ⇒Which firms are less likely to waiting (or high waiting cost)?

6

7


Our idea: We pay attention to the avoidance of default risk as the one of the

most important waiting cost

7

-The cost of financial distress is arguably high Almeida and Philippon 2007; Elkamhi, Ericsson and Parsons 2012

8


Our idea: IPO provide a vital opportunity to work down debt ratio. -Pagano, Panetta and Zingales (1998) Go public for Capial sturture adjustment -Baker and Wurgler (2002) The effect of IPO on capital structure is persistent

8

9


Our idea: -Hypothesis: Private firms with high short term debt ratio are more likely to go

public in bear markets than do firms with low short term debt ratio to avoid potential bankruptcy cost.

Why Japanese data? -Since 1990s, the stock market shows a long-term downturn

trend. Firms may need to consider IPOs in bear markets.

9

10


What do we do: 1.  Examine the relation between the extent to which firms intend

to adjust capital structure and the probability of going public in bear markets

Proxies variables: 1. SDEBT_TL Short term interest-bearing debts to total liabilities ratio 2. SDEBT_TA Short term interest-bearing debts to total assets ratio 3. MATURITY Long term interest-bearing debts to total interest bearing debts ratio

10

11


What do we do: 2. Are ZERO-LEVERAGED (OVER-LEVERAGED) firms less

(more) likely to go Public in Bear Markets? Zero-Leveraged firms: not interest bearing debt Over-leveraged firms: SDEBT_TL > 75th percentile

3. Does creditworthiness affect the incentives of firms with high

short term debt ratio to adjust capital structure in bear markets?

Two groups : Total assets, ROA, sales growth ratio, and Altman-Z scores

11

12


What do we do: 4. Examine the premise of our hypothesis - Proceed, Offering price 5. Examine the alternative stories -  Urgent cash need -  Urgent need to finance large projects -  Banking sector : Nonperforming debts around 2000

12

13


Literature review: Previous studies have paid attention to hot market IPO. •  Ritter (1984) :

−  Risky companies tend to go public during a specific period (hot market), in which significant underpricing exists due to the high risk nature of IPO companies.

•  Pastor and Veronesi (2005) ‒  Clustering of IPOs during certain periods is attributable to firms’

market timing behaviors.

•  Chemmanur and He (2011): ‒  IPOs can increase market shares in product market, and thus IPO waves

tend to occur in competitive industries.

13

14


Literature review: Premise of our Hypothesis Cost of waiting is higher than benefit

Accept poor condition for equity issuance at IPO

Go public without delaying

High financial distress cost (20% pre-distress firm value) High debt ratio private firms strong incentives to deleverage Limited ways to deleverage -IPO is a vital way to work down debt ratio Pagano, Panetta and Zingales (1998); Baker and Wurgler (2002)

14

15


Hypothesis: Private firms with high short term debt ratio are more likely to

go public in bear markets than do firms with low short term debt ratio to avoid the cost of financial distress

Sample and data: Data source: IPO White Papers Nikkei NEEDS Financial Quest Financial institutions and utilities are removed. Final sample: 1793 IPO companies from 1997-2014 458 BEAR IPOs 1335 Non-BEAR IPOs

15

16


Table1: sample distribution

16

Year BEAR IPOs Non-BEAR IPOs TOPIX (closing price) 1997 40 95 1147.87 1998 41 40 1088.83 1999 9 94 1712.27 2000 68 122 1291.65 2001 97 67 1013.73 2002 66 48 849.25 2003 46 70 1026.24 2004 7 154 1139.41 2005 0 151 1663.75 2006 0 176 1678.91 2007 15 99 1499.94 2008 33 14 854.44 2009 7 12 907.59 2010 4 15 898.80 2011 17 18 728.61 2012 8 36 859.80 2013 0 52 1302.29 2014 0 72 1407.51 Total 458 1335

17


17

BEAR IPOs Non-BEAR IPOS P-value

SDEBT_TL

21.2% [17.5%]

N=457

19.8% [16.9%]

N=1331

0.140 [0.138]

SDEBT_TA

14.5% [11.4%]

N=457

13.4% [10.3%]

N=1331

0.149 [0.126]

MATURITY

48.5% [47.6%]

N=457

53.5% [55.0%]

N=1334

0.006*** [0.006***]

TDEBT_TL

36.3% [35.8%]

N=457

36.2% [36.8%]

N=1331

0.950 [0.760]

LDEBT_TL

15.0% [10.0%]

N=457

16.3% [11.1%]

N=1331

0.159 [0.496]

LBOND_TL

1.1% [0.0%]

N=457

1.7% [0.0%]

N=1331

0.002*** [0.071*]

LEVERAGE

60.0% [64.5%]

N=455

59.8% [62.8%]

N=1320

0.824 [0.705]

Ln(Assets)

8.746 [8.732]

N=455

8.570 [8.451]

N=1320

0.024** [0.013**]

Firm age

22 [19]

N=426

21 [16]

N=1252

0.128 [0.070*]

ROA

11.4% [9.7%]

N=455

12.4% [10.3%]

N=1318

0.075* [0.032**]

SGR

42.1%

[16.5%]

N=449

43.0%

[17.3%]

N=1312

0.848

[0.514]

PPROCEEDS

25.0%

[8.7%]

N=445

41.8%

[15.2%]

N=1303

0.000***

[0.000***]

Price-Operating Income

ratio

15.5

[4.8]

N=457

23.0

[7.5]

N=1331

0.049** [0.000***]

Table 2 Summary statistics

18


Table3: Logit regression Panel A:

18

Model (1) Model (2) Model (3) Model (4)

TDEBT_TL 0.237 (0.75)

SDEBT_TL 0.811** (1.98) 0.801** (1.96)

LDEBT_TL

-0.519 (-1.05) -0.506 (-1.02)

Control variables:

LBOND_TL -5.818*** (-2.82) -5.450*** (-2.70) -4.890** (-2.28) -4.817** (-2.24)

LEVERAGE -0.128 (-0.31) -0.262 (-0.68) 0.139 (0.36) -0.135 (-0.33)

Ln(Assets) 0.120** (2.08) 0.125** (2.14) 0.119** (2.05) 0.126** (2.14)

ROA -0.417 (-0.55) -0.276 (-0.37) -0.615 (-0.83) -0.360 (-0.48)

SGR 0.037 (0.52) 0.036 (0.51) 0.031 (0.44) 0.033 (0.46)

Firm age 0.002 (0.37) 0.002 (0.44) 0.002 (0.33) 0.002 (0.44)

HHI -27.632*** (-3.05) -27.587*** (-3.05) -28.252*** (-3.09) -27.929*** (-3.07)

Ln_TOPIX -4.162*** (-14.39) -4.180*** (-14.43) -4.139*** (-14.35) -4.170*** (-14.41)

Constant 39.784*** (8.30) 39.822*** (8.33) 40.005*** (8.27) 39.959*** (8.31)

N 1649 1649 1649 1649

Pseudo R2 0.162 0.164 0.163 0.165

19


Table3: Logit regression Panel B:

19

Model (1) Model (2) Model (3)

SDEBT_TA 1.176* (1.89) 0.248 (0.35)

MATURITY

Control variables:

-0.646*** (-3.16) -0.605** (-2.57)

LBOND_TL -5.401*** (-2.67) -4.771** (-2.37) -4.792** (-2.38)

LEVERAGE -0.415 (-0.98) -0.277 (-0.73) -0.349 (-0.82)

Ln(Assets) 0.125** (2.15) 0.116** (1.99) 0.118** (2.00)

ROA -0.293 (-0.39) -0.351 (-0.47) -0.312 (-0.41)

SGR 0.037 (0.53) 0.037 (0.50) 0.037 (0.51)

Firm age 0.002 (0.46) 0.001 (0.22) 0.001 (0.25)

HHI -27.578*** (-3.05) -28.188*** (-3.14) -28.100*** (-3.12)

Ln_TOPIX -4.170*** (-14.41) -4.193*** (-14.50) -4.195*** (-14.50)

Constant 39.873*** (8.32) 40.789*** (8.57) 40.732*** (8.54)

N 1649 1649 1649

Pseudo R2 0.164 0.167 0.167

20


Table4: Propensity score matching results:

20

TDEBT_TL

1 vs 1 1 vs 3

BERA IPOs (N=325) 36.8% 36.8%

Non-BEAR IPOs (N=1230) 32.9% 34.2%

T-Statistics 1.66* 1.19

SDEBT_TL

BERA IPOs (N=325) 21.8% 21.8%

Non-BEAR IPOs (N=1230) 17.6% 18.5%

T-Statistics 2.74*** 2.43**

LDEBT_TL

BERA IPOs (N=325) 14.9% 14.9%

Non-BEAR IPOs (N=1230) 15.2% 15.9%

T-Statistics -0.26 -0.74

21


Table5: Are ZERO-LEVERAGED (OVER-LEVERAGED) firms less (more) likely to go Public in Bear Markets?

21

Panel A: Proportion test

Bear IPOs Non-BEAR IPOs P-value

ZERO-LEVERAGED 15.5% 19.3% 0.072*

N 457 1331

OVER-LEVERAGED 28.2% 24.6% 12.2

N 457 1331

Panel B: Logit regression

ZERO-LEVERAGED -0.526***

(-2.59)

OVER-LEVERAGED 0.312**

(2.00)

N 1649 1649

Pseudo R2 0.166 0.164

Panel C: PSM matching results

1 vs 1 Bear IPOs Non-BEAR IPOs T-Statistics

ZERO-LEVERAGED 14.8% 25.2% -2.96***

N 325 1230

OVER-LEVERAGED 29.6% 19.1% 2.80***

N 325 1230

22


Table6: Creditworthiness, default risk, and bear market IPOs

22

Panel A

Ln(Assets) ROA SGR Altman-Z scores

Small Large Low High Slow Fast Low High

SDEBT_TL 1.470*** 0.217 1.363** -0.077 0.159 1.211** 2.095*** -1.427

(2.78) (0.31) (2.45) (-0.12) (0.23) (2.23) (2.94) (-1.35)

N 1009 635 721 922 674 959 581 555

Pseudo R2 0.178 0.175 0.159 0.198 0.208 0.154 0.198 0.218

Panel B



SDEBT_TA 2.250*** 0.407 1.723** -0.088 -0.145 2.013** 3.106*** -2.134

(2.61) (0.42) (2.12) (-0.08) (-0.14) (2.34) (2.78) (-1.37)

N 1009 635 721 922 674 959 581 555

Pseudo R2 0.177 0.176 0.158 0.198 0.208 0.155 0.198 0.218

Panel C



MATURITY -0.790*** -0.660* -1.026*** -0.453* -0.580 -0.765*** -1.549*** -0.193

(-3.09) (-1.75) (-2.80) (-1.69) (-1.64) (-2.96) (-3.46) (-0.51)

N 1009 635 721 922 674 959 581 555

Pseudo R2 0.180 0.179 0.163 0.201 0.212 0.157 0.206 0.214

23


Following sections: -Examine our Premise and alternative stories based on PSM

matched sample - Specifically, Model (2) in Panel A of Table 3 in the first stage

logit regressions. We adopt the Non-BEAR IPOs which is closest in the likelihood to conduct IPO in bear markets as the matching Non-BEAR IPOs.

Premise: Do Bear IPOs have to accept poor market condition

for equity issuance in bear markets?

23

24


Table 7:Primary proceeds, offering price multiples

24

Model (1) Model (2) Model (3)

BEARIPOD

Control variables:

-0.079*** (-2.66) -2.634*** (-3.82) -12.843** (-2.24)

LBOND_TL 0.338 (0.59) 33.751 (1.40) 163.001 (0.89)

LEVERAGE -0.225*** (-2.61) -5.289*** (-2.83) -31.140** (-2.10)

Ln(Assets) -0.078*** (-4.86) -0.780** (-1.97) -5.160* (-1.68)

ROA -0.277 (-0.87) -24.481*** (-4.03) -173.331*** (-3.78)

SGR 0.105*** (3.65) 1.725* (1.84) 17.031* (1.96)

Firm age -0.004*** (-4.27) -0.054*** (-2.85) -0.480*** (-2.83)

HHI -6.285*** (-2.83) -276.145*** (-2.81) -118.971 (-0.39)

Ln_TOPIX 0.290*** (4.68) 2.800 (1.61) 44.726*** (4.15)

Constant 1.357 (1.37) 98.724** (2.14) -147.546 (-0.95)

N 838 838 802

Pseudo R2 0.329 0.246 0.097

25


Alternative story 1: Urgent cash needs DeAngelo, DeAngelo and Stulz (2010)

25

Year 0 Year 1 Year 2

Bear IPOs 17.6% [13.4%] N= 418(Negative: 13.6%)

17.0% [12.6%] N=410 (Negative: 15.3%)

17.4% [12.7%] N=405 (Negative: 13.5%)

Non-BEAR IPOs 19.1% [14.7%] N= 419 (Negative: 13.1%)

17.5% [12.3%] N=404 (Negative: 12.6%)

17.2% [14.5%] N=379 (Negative: 12.1%)

P-value (Diff. test) 0.177 [0.187] 0.666 [0.654] 0.825 [0.979]

P-value (Proportion diff. test) 0.839 0.278 0.546

26


Alternative story 2: Urgent need for large investment projects - Capital investment around IPO Alternative story 3: Banking sector: Nonperforming debt -Non-performing debt ratio and BIS capital ratio of the firm’s

main bank. *No supportive evidence for alternative stories

26

27


Conclusion and contribution: Conclusion: Private firms with high waiting cost (cost of financial distress)

are willing to go public even in bear markets. Contribution: 1.  The first research to examine characteristics of companies

going public in bear markets. 2.  Highlights the cost of financial distress as an important

determinant of IPO timing. 3.  Indicating high cost of financial distress. 4.  Indicate the benefits of Zero-leveraged firms.

27

28 28

Thank you very much for your kind attention !

ありがとうございました！

29


Appendix

29

Variable Definition BEARIPOD Dummy variable which takes a value of 1 for BEAR IPOs and 0 for Non-BEAR IPOs SDEBT_TL Total short term interest-bearing debts divided by total liabilities

SDEBT_TA Total short term interest-bearing debts divided by total assets

MATURITY Total long term interest-bearing debts divided by total interest-bearing debts

TDEBT_TL Total interest-bearing debts divided by total liabilities

LDEBT_TL Total long term interest-bearing debts divided by total liabilities

LDEBT_TA Total long term interest-bearing debts divided by total assets

LBOND_TL Long term bond deflated by total liabilities

LEVERAGE Total liabilities divided by total assets Ln(Assets) Natural logarithm of the book value of total assets Firm age Firm age at the time of IPO ROA Operating income divided by total assets SGR Percentage sales growth ratio from previous year Ln_TOPIX Natural logarithm of TOPIX (the stock price index of the Tokyo Stock Exchange) at the

date of IPO

HHI

Herfindahl-Hirschman index (HHI), calculated by squaring the market share of each firm competing in an industry and in a given year and then summing the resulting numbers by industry and year

SD_Margin Standard deviation of gross margin by industry, where gross margin is calculated as gross profit divided by sales

ZERO-LEVERAGED A dummy variable takes a value one for firms without interest-bearing debts and zero otherwise

OVER-LEVERAGED A dummy variable takes a value of one for firms with SDEBT_TL higher than the 75th percentile and zero otherwise

PPROCEEDS Primary proceeds / Total assets in Year -1, where Year 0 indicates IPO year

Price -Operating Income ratio Offering price, deflated by operating income per share

Altman-Z scores Altman-Z scores = ([Working Capital / Total Assets] x 0.717) + ([Retained Earnings / Total Assets] x 0.847) + ([Operating Earnings / Total Assets] x 3.107) + ([Book Value of Equity / Total Liabilities] x 0.420) + ([Sales / Total Assets] x 0.998)

going public in bear markets motivation...high financial distress cost (20% pre-distress firm value)...

Documents