APPROVED: Mazhar Siddiqi, Major Professor James Conover, Committee Member Imre Karafiath, Committee Member Robert Pavur, Committee Member Niranjan Tripathy, Committee Member Marcia Staff, Chair, Department of Finance, Insurance, Real Estate and Law O. Finley Graves, Dean of the College of Business James D. Meernik, Acting Dean of the Robert B. Toulouse School of Graduate Studies THE REASONS FOR THE DIVERGENCE OF IPO LOCKUP AGREEMENTS Fei Gao, B.A., M.B.A. Dissertation Prepared for the Degree of DOCTOR OF PHILOSOPHY UNIVERSITY OF NORTH TEXAS August 2010
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APPROVED: Mazhar Siddiqi, Major Professor James Conover, Committee Member Imre Karafiath, Committee Member Robert Pavur, Committee Member Niranjan Tripathy, Committee Member Marcia Staff, Chair, Department of
Finance, Insurance, Real Estate and Law
O. Finley Graves, Dean of the College of Business
James D. Meernik, Acting Dean of the Robert B. Toulouse School of Graduate Studies
THE REASONS FOR THE DIVERGENCE OF IPO LOCKUP AGREEMENTS
Fei Gao, B.A., M.B.A.
Dissertation Prepared for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
August 2010
Gao, Fei. The reasons for the divergence of IPO lockup agreements. Doctor of
Note: Underwriter ranking (UR) is based on Carter-Manaster (1990), and the higher the score, the higher the reputation. Venture Capital (VC) is the percentage of IPOs backed by venture capital, TECH is the percentage of IPOs that are high-tech firms, and AUDIT is the percentage of IPOs using top six auditors. SIZE is the product of number of shares offered and offer price. Underpricing (UP) is the average percentage price change from offer price to the closing price of the first day after IPO.
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Table 3
Accounting Numbers and Lockup Length
Panel A: Operating Return On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.07 204 -0.05*** 1892 -0.15*** 301 NA NA 0.00
Yr 1 to 3 -0.2*** 183 -0.13*** 1702 -0.31*** 268 NA NA 0.00
Yr 1 to 4 -0.27*** 147 -0.19*** 1309 -0.3*** 209 NA NA 0.03
Panel B: Operating Cash Flow On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.17*** 176 -0.16*** 1617 -0.29*** 235 NA NA 0.02
Yr 1 to 3 -0.40*** 162 -0.27*** 1457 -0.40*** 208 NA NA 0.02
Yr 1 to 4 -0.44*** 130 -0.35*** 1120 -0.42*** 161 NA NA NA
Panel C: Sales
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.3*** 257 0.33*** 2572 0.36*** 533 0.06 NA NA
Yr 1 to 3 0.69*** 229 0.63*** 2276 0.76*** 455 NA NA NA
Yr 1 to 4 1.00*** 180 0.93*** 1739 1.05*** 347 NA NA NA (table continues)
67
Table 3 (continued).
Panel D: Operating Income
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.29*** 204 0.28*** 1892 0.17*** 301 NA NA 0.00
Yr 1 to 3 0.47*** 183 0.45*** 1702 0.21 268 NA NA 0.00
Yr 1 to 4 0.53*** 147 0.56*** 1309 0.25** 209 NA NA 0.06
Panel E: Asset Turnover
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.03 257 0.05*** 2572 0.10*** 533 0.00 NA NA
Yr 1 to 3 0.06 229 0.06*** 2276 0.15*** 455 0.00 0.08 NA
Yr 1 to 4 0.02 180 0.07*** 1739 0.17*** 347 0.00 0.02 NA
Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period.
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Table 4
Regression for Length of Lockup (OLS)
Estimated Coefficient P-Value
Operating Retn 1-3 Year Growth Rate -0.118 0.003
Cash Flow 1-3 Year Growth Rate 0.005 0.813
Sales 1-3 Year Growth Rate -0.039 0.347
Opera Income 1-3 Year Growth Rate 0.064 0.164
Asset Turnover 1-3 Year Growth Rate 0.069 0.048
Size -0.063 0.021
Age -0.017 0.453
High-tech -0.037 0.105
Underwriter Ranking -0.335 0.000
Venture Capital Backing -0.094 0.000
Auditor Ranking -0.054 0.013
Adjusted R Square 18.10
Note: Number of lockup days is the dependent variable. Independent variables are listed in the table. Size is the natural logarithm of the product of the number of shares offered and offer price. Age is defined as the years from a firm‟s inception till IPO. High-tech is a dummy variable, and it equals 1 for high-tech firms and 0 otherwise. Venture Capital Backing is a dummy variable, and it equals 1 for firms with venture capital backing and 0 otherwise. Auditor Ranking is a dummy variable, and it equals 1 for firms use top six auditors and 0 otherwise. All other independent variables are defined as before. The sample includes firms with lockup lengths longer than 180 days, equal to 180 days, and shorter than 180 days. Ordinary Least Square (OLS) is used.
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Table 5
Regression for Length of Lockup -- Binary Logistic
Estimated Coefficient P-Value Exp(B)
Operating Retn 1-4 Year Growth Rate -0.096 0.647 0.908
Cash Flow 1-4 Year Growth Rate -0.017 0.602 0.983
Sales 1-4 Year Growth Rate -0.052 0.629 0.949
Opera Income 1-4 Year Growth Rate 0.075 0.362 1.077
Asset Turnover 1-4 Year Growth Rate 0.710 0.076 2.033
Size 0.465 0.044 1.592
Age -0.004 0.649 0.996
High-tech -0.405 0.226 0.667
Underwriter Ranking -0.436 0.000 0.647
Venture Capital Backing -1.243 0.000 0.288
Auditor Ranking 0.220 0.513 1.246
Adjusted R Square 35.30
Note: The sample only includes firms with lockup lengths other than 180 days. Binary logistic test is used. Dependent variable is 0 for firms with a lockup length shorter than 180 days, and 1 for firms with a lockup length longer than 180 days. Independent variables are the same as defined before. Exp(B), or odds ratio, is calculated by raising e to the power of logistic coefficient.
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Table 6
Regression for Length of Lockup -- Multinomial Logistic
Panel A: Compare Length <180 and =180
Estimated Coefficient P-Value Exp(B)
Operating Retn 1-3 Year Growth Rate -0.008 0.503 0.992
Cash Flow 1-3 Year Growth Rate 0.010 0.190 1.010
Sales 1-3 Year Growth Rate 0.001 0.992 0.959
Opera Income 1-3 Year Growth Rate 0.005 0.582 1.005
Asset Turnover 1-3 Year Growth Rate 0.026 0.339 1.027
Size -0.851 0.000 0.427
Age 0.002 0.582 1.002
High-tech 0.330 0.027 1.390
Underwriter Ranking 0.032 0.472 1.033
Venture Capital Backing 0.001 0.992 1.001
Auditor Ranking -0.168 0.306 0.845
Panel B: Compare Length >180 and =180
Estimated Coefficient P-Value Exp(B)
Operating Retn 1-3 Year Growth Rate -0.006 0.516 0.994
Cash Flow 1-3 Year Growth Rate -0.001 0.903 0.999
Sales 1-3 Year Growth Rate 0.007 0.757 1.007
Opera Income 1-3 Year Growth Rate 0.004 0.671 1.004
Asset Turnover 1-3 Year Growth Rate 0.010 0.710 1.010
(table continues)
71
Table 6 (continued).
Estimated Coefficient P-Value Exp(B)
Size -0.959 0.000 0.383
Age -0.008 0.077 0.992
High-tech -0.137 0.334 0.872
Underwriter Ranking -0.379 0.000 0.682
Venture Capital Backing -0.540 0.000 0.583
Auditor Ranking -0.330 0.015 0.719
Note: The sample is partitioned into three groups – firms with lockup length equal to 180 days, shorter than 180 days, and longer than 180 days. The group with a 180-day lockup length is a reference group, and its characteristics are compared to those of the other two groups. Dependent variable is 0, 1, and 2, representing each of the three groups, respectively. Independent variables are defined as before. Multinomial logistic regression is conducted. Exp(B), or odds ratio, is calculated by raising e to the power of logistic coefficient.
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Table 7
Accounting Numbers and Lockup Length -- Opaque Firms
Panel A: Operating Return On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.15* 19 -0.09 39 -0.40*** 89 NA NA 0.00
Yr 1 to 3 -0.29** 17 -0.17 36 -0.52** 74 NA NA 0.06
Yr 1 to 4 -0.32** 16 -0.48*** 31 -0.47*** 58 NA NA NA
Panel B: Operating Cash Flow On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.40 15 -0.08 36 -0.57*** 65 NA NA 0.00
Yr 1 to 3 -0.48* 14 -0.11 33 -0.58*** 53 NA NA 0.06
Yr 1 to 4 -0.30** 13 -0.73*** 29 -0.52*** 39 NA NA NA
Panel C: Sales
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.39*** 28 0.22*** 61 0.35*** 180 NA NA NA
Yr 1 to 3 0.98*** 22 0.46*** 50 0.76*** 144 NA NA NA
Yr 1 to 4 1.73*** 21 0.79*** 43 1.12*** 107 NA NA NA
(table continues)
73
Table 7 (continued).
Panel D: Operating Income
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.25 19 0.27* 39 -0.22 89 NA NA 0.03
Yr 1 to 3 0.43** 17 0.54** 36 0.04 74 NA NA 0.02
Yr 1 to 4 0.24 16 0.32 31 0.00 58 NA NA NA
Panel E: Asset Turnover
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.03 28 0.01 61 0.19*** 180 0.05 NA NA
Yr 1 to 3 0.07 22 -0.09 50 0.29*** 144 0.06 NA NA
Yr 1 to 4 -0.03 20 -0.13*** 43 0.39*** 107 0.04 NA NA
Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period.
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Table 8
Regression for Length of Lockup -- Opaque Firms
Estimated Coefficient P-Value
Operating Retn 1-3 Year Growth Rate -0.448 0.002
Cash Flow 1-3 Year Growth Rate -0.152 0.257
Sales 1-3 Year Growth Rate -1.166 0.002
Opera Income 1-3 Year Growth Rate 0.602 0.007
Asset Turnover 1-3 Year Growth Rate 0.960 0.007
Size -0.189 0.207
Age -0.078 0.443
High-tech 0.045 0.733
Underwriter Ranking -0.226 0.133
Venture Capital Backing -0.043 0.674
Auditor Ranking -0.182 0.146
Adjusted R Square 24.20
Note: The regression is for opaque firms that include lockup lengths equal to 180 days, longer than, and shorter than 180 days. OLS is used. All variables are defined as before.
75
Table 9
Accounting Numbers and Lockup Length -- High-tech Firms
Panel A: Operating Return On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.08 76 -0.01 523 -0.20 64 NA NA 0.00
Yr 1 to 3 -0.19* 72 -0.13** 468 -0.42*** 58 NA NA 0.00
Yr 1 to 4 -0.43** 60 -0.37*** 360 -0.55** 45 NA NA NA
Panel B: Operating Cash Flow On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.25*** 70 -0.11*** 467 -0.24*** 53 NA NA NA
Yr 1 to 3 -0.46*** 67 -0.28*** 419 -0.46*** 47 NA NA NA
Yr 1 to 4 -0.57*** 56 -0.58*** 319 -0.64*** 36 NA NA NA
Panel C: Sales
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.35*** 98 0.37*** 817 0.37*** 131 NA NA NA
Yr 1 to 3 0.76*** 91 0.68*** 705 0.66*** 108 NA NA NA
Yr 1 to 4 1.17*** 72 0.99*** 539 1.04*** 80 NA NA NA
(table continues)
76
Table 9 (continued).
Panel D: Operating Income
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.29* 76 0.34*** 522 0.17* 64 NA NA 0.03
Yr 1 to 3 0.58** 72 0.43*** 468 0.09 58 NA NA 0.02
Yr 1 to 4 0.02 60 0.21*** 360 0.10 45 NA NA NA
Panel E: Asset Turnover
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.04 98 0.13*** 817 0.14*** 131 0.01 0.00 NA
Yr 1 to 3 0.09 91 0.16*** 705 0.22*** 108 0.03 0.00 NA
Yr 1 to 4 0.10 72 0.19*** 539 0.30*** 80 0.02 0.02 NA
Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period.
77
Table 10
Regression for Length of Lockup (High-tech Firms)
Estimated Coefficient P-Value
Operating Retn 1-3 Year Growth Rate -0.005 0.935
Cash Flow 1-3 Year Growth Rate 0.010 0.813
Sales 1-3 Year Growth Rate -0.118 0.063
Opera Income 1-3 Year Growth Rate 0.050 0.531
Asset Turnover 1-3 Year Growth Rate 0.062 0.164
Size -0.136 0.004
Age -0.024 0.563
Underwriter Ranking -0.260 0.000
Venture Capital Backing -0.141 0.000
Auditor Ranking -0.083 0.041
Adjusted R Square 16.3
Note: The regression is for opaque firms that include lockup lengths equal to 180 days, longer than, and shorter than 180 days. OLS is used. All variables are defined as before.
78
Table11
Accounting Numbers and Lockup Length -- High λ Firms
Panel A: Operating Return On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.41** 17 0.02 340 -0.14** 25 NA NA 0.00
Yr 1 to 3 0.05 17 -0.09* 301 -0.24** 23 NA NA 0.00
Yr 1 to 4 -0.74** 11 -0.18*** 265 -0.28** 17 NA NA NA
Panel B: Operating Cash Flow On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.20* 17 -0.02 290 -0.02 20 NA NA NA
Yr 1 to 3 -0.34** 17 -0.16** 257 -0.15* 18 NA 0.04 NA
Yr 1 to 4 -1.02*** 11 -0.31*** 215 -0.30** 13 NA 0.06 NA
Panel C: Sales
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.58** 25 0.30*** 542 0.35** 46 NA NA NA
Yr 1 to 3 1.45*** 21 0.54*** 463 0.65*** 39 NA NA NA
Yr 1 to 4 2.24*** 12 0.68*** 401 0.67*** 28 NA NA NA
(table continues)
79
Table11 (continued).
Panel D: Operating Income
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 1.20** 17 0.26** 340 0.07 25 NA NA 0.03
Yr 1 to 3 1.82** 16 0.40*** 301 0.21** 23 NA NA 0.02
Yr 1 to 4 0.63* 11 0.35*** 265 -0.05 17 NA NA 0.07
Panel E: Asset Turnover
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.07 25 0.07** 542 0.10** 46 0.05 NA NA
Yr 1 to 3 0.00 21 0.08** 463 0.23** 39 0.03 0.06 NA
Yr 1 to 4 -0.19 12 0.09** 401 0.13** 28 0.02 0.02 NA
Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period. High λ firms are defined as those firms with top 20% of λ calculated in the one month period after firms‟ lockup expiry.
80
Table 12
Regression for Length of Lockup -- high λ
Estimated Coefficient P-Value
Operating Retn 1-4 Year Growth Rate -2.052 0.042
Cash Flow 1-4 Year Growth Rate 0.370 0.269
Sales 1-4 Year Growth Rate -0.140 0.597
Opera Income 1-4 Year Growth Rate 1.110 0.095
Asset Turnover 1-4 Year Growth Rate 0.867 0.007
Size 0.584 0.465
Age -0.071 0.694
High-tech -0.415 0.064
Underwriter Ranking 0.325 0.489
Venture Capital Backing -0.432 0.285
Auditor Ranking -0.241 0.257
Adjusted R Square 61.3
Note: The OLS regression excludes firms with lockup length equal to 180 days and includes firms with top 20% λ.
81
Table13
Accounting Numbers and Lockup Length -- Low λ Firms
Panel A: Operating Return On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.09 38 -0.07 387 -0.06 78 NA NA NA
Yr 1 to 3 -0.09 35 -0.14** 360 -0.19** 68 NA NA NA
Yr 1 to 4 -0.25** 33 -0.15** 292 -0.20** 60 NA NA NA
Panel B: Operating Cash Flow On Asset
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.17** 32 -0.17*** 336 -0.04 62 NA NA NA
Yr 1 to 3 -0.44** 29 -0.25*** 310 -0.23** 52 0.08 0.04 NA
Yr 1 to 4 -0.43*** 28 -0.27*** 250 -0.20** 45 NA 0.03 NA
Panel C: Sales
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.26*** 45 0.28*** 477 0.37*** 134 NA NA NA
Yr 1 to 3 0.69*** 40 0.60*** 439 0.73*** 121 NA NA NA
Yr 1 to 4 0.66*** 33 0.94*** 355 1.05*** 99 0.05 0.02 NA
(table continues)
82
Table13 (continued).
Panel D: Operating Income
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 0.21** 39 0.22** 387 0.22* 78 NA NA NA
Yr 1 to 3 0.49*** 35 0.49*** 360 0.31** 68 NA NA 0.06
Yr 1 to 4 0.62*** 29 0.67*** 292 0.71*** 60 NA NA NA
Panel E: Asset Turnover
GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)
3&1 2&1 2&3
Yr 1 to 2 -0.02 45 0.02 477 0.12*** 134 0.01 NA NA
Yr 1 to 3 0.00 40 0.02 439 0.13** 121 0.01 NA NA
Yr 1 to 4 -0.03 37 0.05** 355 0.17*** 99 0.02 NA NA
Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period. High λ firms are defined as those firms with bottom 20% of λ calculated in the one month period after firms‟ lockup expiry.
83
Table 14
Regression for Length of Lockup (Low λ)
Estimated Coefficient P-Value
Operating Retn 1-3 Year Growth Rate -0.391 0.220
Cash Flow 1-3 Year Growth Rate 0.165 0.305
Sales 1-3 Year Growth Rate -0.030 0.856
Opera Income 1-3 Year Growth Rate 0.118 0.711
Asset Turnover 1-3 Year Growth Rate 0.248 0.036
Size -0.119 0.353
Age -0.174 0.086
High-tech -0.093 0.384
Underwriter Ranking -0.281 0.032
Venture Capital Backing -0.190 0.096
Auditor Ranking 0.058 0.581
Adjusted R Square 27.10
Note: The OLS regression excludes firms with a 180-day lockup period, and includes firms with bottom 20% of λ.
84
Table 15
Long-run Returns for All IPO Firms
Panel A: Univariate Test
Long-run Returns (Median)
Return Period <180 N =180 N >180 N Difference(p-value)
Note: Long-run returns are defined as the 6-month, 1-year, 2-year, and 3-year holding period return following a firm‟s IPO. All the returns are calculated starting at the 26th day after firms‟ IPO to avoid the effect of earlier aftermarket activities such as stabilization and quiet period (Brau et al. 2007). Value-weighted and equally-weighted (not shown) market-adjusted excess returns are calculated. Market adjusted return (MAR) is defined as the firm‟s buy and hold return (BAH) minus the market return from CRSP. Buy and
hold return is defined as the geometrically compounded return BAH = (1 +𝑀𝑡=𝑗 ri,t )-1,
where ri,t is the daily return for stock I on day t, j is the starting day and M is the ending
day for a calculating period. Market adjusted return is calculated as MAR = (1 +𝑀𝑡=𝑗 ri,t )
– (1 +𝑀𝑡=𝑗 rm,t ), where rm,t is the equally-weighted or value-weighted daily market return
from the CRSP. In the OLS regression, 3-year long-run return is the dependent variable, and all the independent variables are defined as before.
86
Table 16
Abnormal Return around Lockup Expiry
Panel A: All Data
Short-run Returns (Mean, %)
Return Period <180 N =180 N >180 N Difference(p-value)
Day (-3,3) -1.4*** 280 -1.85*** 2391 -1.91*** 578 0.12
Day (-4,4) -0.91** 280 -1.79*** 2391 -2.52*** 578 0.16
Panel B: Opaque and Transparent
Short-run Return (Mean, %)
Return Period Opaque N Transparent N Difference(P-value)
Day(-3,3) -1.64* 402 -2.23*** 925 0.058
Day(-4,4) -2.31 402 -2.1*** 925 0.16
Panel C: High-tech and Non-high-tech
Short-run Return (Mean, %)
Return Period High-tech N Non-high-tech N Difference(P-value)
Day(-3,3) -2.82*** 1104 -1.28*** 2503 0.00
Day(-4,4) -3.20*** 1104 -1.19*** 2503 0.00
Panel D: Top 20 Adverse Selection
Short-run Return (Mean, %)
Return Period <180 N >180 N Difference(P-value)
Day(-3,3) 3.62* 30 -0.27 53 0.058
Day(-4,4) 5.33** 30 -0.50 53 0.16
(table continues)
87
Table 16 (continued).
Note: The market model is specified as follows: Rit = αi + βi Rmt + εit, where Rit is the return for firm I on day t in estimation period; Rmt is the average return for all firms in the stock market on day t (CRSP value-weighted index is used as the market index); αi and βi are the intercept and the slope parameters for firm I; αi and βi will be estimated over T trading days in the estimation period, where T varies according to the length of lockup. For IPOs having a lockup period between 3 to 5 months, the estimation period will start at the first day of its IPO, and end 10 days before the event day (lockup expiry). If an IPO has a 6 month or longer lockup period, the estimation period will start 130 days before the event day and end 10 days before the event day. The average 7-day and 9-day abnormal returns (3 days and 4 days before and after the IPO lockup expiration day) are calculated.
88
Table 17
Percentage of Shares Locked
Percent of Shared Locked (%)
High Agency Low Agency Difference (P-value)
Mean 60.28 60.02 0.39
Median 67.25 64.88 0.21
<180 >180
Mean 50.45 52.29 0.19
Median 57.58 56.79 0.3
Note: Free cash flow, growth rate, expense ratio, asset utilization ratio, and the amount of debt at the time of a firm‟s IPO are used as proxies for agency cost to partition the sample into high and low agency firms. Percent of share locked is defined as number of shares locked in the lockup agreement divided by the number of shares outstanding after a firm‟s IPO.
89
Table 18
Agency Problem and Long-run Return
Panel A: Univariate Test
Long-run Return (Median, %)
Return Period Low Agency N High Agency N Difference(P-value)
6-month 0.014 545 -0.25*** 292 0.00
1-year -0.05* 545 -0.48*** 292 0.00
2-year -0.28*** 545 -0.64*** 292 0.00
3-year -0.42*** 545 -0.73*** 292 0.00
Panel B: Regression Results
Estimated Coefficient P-value
Agency Score
-0.232
0.000
Underpricing
-0.071
0.138
Lockup Length
-0.061
0.228
Size
-0.068
0.231
Age
-0.09
0.076
High-tech
0.092
0.064
Underwriter
0.011
0.856
Venture Capital
-0.035
0.522
Auditor
0.007
0.881
Adjusted R Square 4.4
(table continues)
90
Table 18 (continued).
Note: The sample is partitioned into high and low agency groups by using the scoring scheme discussed. The dependent variable is the 1-year stock return. Six-month, 2-year, and 3-year returns give similar results. Higher agency score indicates a higher agency problem. Other variables are defined as before. OLS is used.
91
Table 19
Long-run Returns and Underwriter Reputation
Panel A: High and Low Reputation
Long-run Return (Median, %)
Return Period High N Low N Difference(P-value)
6-month 0.002 290 -0.24*** 292 0.00
1-year -0.11** 290 -0.55*** 292 0.00
2-year -0.27*** 290 -1.01*** 292 0.00
3-year -0.51*** 290 -1.23*** 292 0.00
Panel B: High Reputation
Long-run Return (Median, %)
Return Period <180 N >180 N Difference(P-value)
6-month 0.026 154 -0.019 135 0.48
1-year -0.054 154 -0.18*** 135 0.09
2-year -0.26*** 154 -0.28*** 135 0.31
3-year -0.52*** 154 -0.51*** 135 0.29
Panel C: Low Reputation
Long-run Return (Median, %)
Return Period <180 N >180 N Difference(P-value)
6-month -0.22 39 -0.24*** 306 0.11
1-year -0.33** 39 -0.55*** 306 0.06
2-year -0.65** 39 -1.05*** 306 0.00
3-year -0.86** 39 -1.26*** 306 0.00
(table continues)
92
Table 19 (continued).
Note: If an underwriter has a ranking of 8 or above, I define it as a high reputation underwriter. If an underwriter has a ranking of 4 or below, I define it as a low reputation underwriter.
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Table 20
Venture Capital Backing and Long-run Returns
Panel A: With and without VC Backing
Long-run Return (Median, %)
Return Period VC N No VC N Difference(P-value)
6-month -0.10** 253 -0.13*** 687 0.05
1-year -0.32*** 253 -0.36*** 687 0.04
2-year -0.61*** 253 -0.74*** 687 0.03
3-year -0.87*** 253 -0.93*** 687 0.03
Panel B: VC Backing
Long-run Return (Median, %)
Return Period <180 N >180 N Difference(P-value)
6-month -0.01 129 -0.15*** 124 0.02
1-year -0.14** 129 -0.43*** 124 0.00
2-year -0.29*** 129 -0.84*** 124 0.00
3-year -0.60*** 129 -1.07*** 124 0.00
Panel C: No VC Backing
Long-run Return (Median, %)
Return Period <180 N >180 N Difference(P-value)
6-month -0.07** 165 -0.16*** 521 0.03
1-year -0.19*** 165 -0.42*** 521 0.00
2-year -0.34*** 165 -0.85*** 521 0.00
3-year -0.64*** 165 -1.06*** 521 0.00
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Table 21
Auditor Reputation and Long-run Return
Panel A: High and Low Ranking Auditor
Long-run Return (Median, %)
Return Period High N Low N Difference(P-value)
6-month -0.10*** 602 -0.19*** 322 0.01
1-year -0.27*** 602 -0.47*** 322 0.00
2-year -0.59*** 602 -0.88*** 322 0.00
3-year -0.84*** 602 -1.04*** 322 0.02
Panel B: High Ranking Auditor
Long-run Return (Median, %)
Return Period <180 N >180 N Difference(P-value)
6-month -0.08* 384 -0.10*** 220 0.11
1-year -0.20*** 384 -0.32*** 220 0.03
2-year -0.37*** 384 -0.71*** 220 0.01
3-year -0.63*** 384 -0.98*** 220 0.00
Panel C: Low Ranking Auditor
Long-run Return (Median, %)
Return Period <180 N >180 N Difference(P-value)
6-month -0.006 70 -0.25*** 251 0.01
1-year -0.14** 70 -0.61*** 251 0.00
2-year -0.28* 70 -0.98*** 251 0.00
3-year -0.61*** 70 -1.13*** 251 0.00
(table continues)
95
Table 21 (continued).
Note: The top six auditors are defined as reputable auditors, and the remaining auditors are defined as low ranking auditors.
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Table 22
Short-run Return and Agency Problem
Panel A: Low and High Agency
Short-run Return (Mean, %)
Return Period Low Agency N High Agency N Difference(P-value)
Day(-3,3) -0.58 529 -3.34*** 281 0.00
Day(-4,4) -0.53 529 -3.13*** 281 0.00
Panel B: Regression Results
Estimated Coefficient P-value
Agency Score
0.24
0.666
Size
0.096
0.087
Insider Holding Before IPO
0.053
0.276
High-tech
-0.065
0.201
Underwriter
-0.045
0.433
Venture Capital
-0.147
0.009
Adjusted R Square 2.7
Note: Low and high agency firms are defined as before. OLS regression is used in panel B. Dependent variable is the short-run return around lockup expiry, and independent variables are some factors that may affect the short-run abnormal returns.
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Figure 1: Long-run Return for IPO Firms. The figure shows the long-run returns for firms with lockup lengths shorter than, equal to, and longer than 180 days. Horizontal axis is the number of observations, and the vertical axis is the median 1-year returns.
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CHAPTER 6
CONCLUSION AND DISCUSSION
This dissertation investigates the reasons for the divergence of initial public
offering (IPO) lockup agreements. Previous studies exploring this topic chose
inappropriate proxies for firm quality, information asymmetry, and agency problems.
They also ignored long-term stock returns after firms‟ IPO, and short-term stock returns
after firms‟ lockup expiry. These return behaviors may give us some insight about the
reasons for the existence of IPO lockup agreements. In this dissertation, I try to fill in
some of the gaps in existing literature.
I use the growth rate of IPO firms‟ operating performance as a proxy for firm
quality to examine whether there is a relationship between lockup length and firm quality.
I also study this relationship for firms with different levels of information asymmetry. I
partition the sample into firms with high and low information asymmetry by using two
new proxies for information asymmetry – high-tech firms and high-adverse selection
firms.
I find that, among the five accounting variables used, only asset turnover shows a
positive relationship with lockup length for some time periods. This is true for both the
whole sample and for sub-samples containing only firms with high information
asymmetry. Since there are not consistently strong positive relationships between
operating performance and lockup length, I conclude that there is only weak evidence to
support the notion that lockup length is used to signal firms‟ quality. In other words,
there is weak evidence that high-quality firms use a longer lockup length to differentiate
their quality from low-quality firms. On the other hand, I do not find a significant negative
relationship between operating performance and lockup length. Thus, based on
100
operating performance, there is no evidence to support the notion that lockup length is
used to differentiate firms with low agency problems from firms with high agency
problems.
There are two possible reasons why I do not find a strong relationship between
lockup length and firm quality. First, even though the growth rate of operating
performance has been used as a proxy for firm quality in the literature, it may not be a
good one. Further, choosing different accounting variables may give different results.
Second, for some of the tests, the number of observations is too small to get a
significant result due to missing data or negative accounting values in the base year.
Future research should focus on searching for better proxies for firm quality and test
their relationship with lockup length.
I then examine the long-run stock returns for IPO firms. For the whole sample, I
find that IPOs with short lockups experience a much better long-run return than that for
IPOs with long lockups. For instance, the median 2-year stock return for firms with short
lockups is -33%, while it is -84% for firms with long lockups. The difference is significant
at the 1% level. This result rejects the signaling hypothesis, which predicts no difference
between the long-run returns of the two groups, and is consistent with the agency
hypothesis. According to the agency hypothesis, as insiders of high agency firms
continuously cause agency problems after lockup expiry, firms‟ operating performance
will deteriorate. As a result, more investors will sell the firms‟ shares. Thus, this high
agency cost will lead to poor long-run returns for firms with long lockup periods.
Further, I find that among firms with low-reputation underwriters, the long-run
returns of short lockups are consistently higher than long-run returns for long lockups.
Among firms with high-reputation underwriters, on the other hand, I find no difference
101
between long-run returns for short and long lockups. This finding is consistent with the
agency explanation and suggests strongly that underwriter reputation and lockup length
are substitute methods for controlling the agency problem.
When I examine long-run returns for firms that have venture capital backing and
for firms that use a top six auditor, I find no relationship between lockup length and long-
run returns. Unlike underwriter reputation, venture capital backing and auditor quality do
not appear to be substitutes for lockup length in controlling agency problems.
I further contribute to the IPO long-run return literature by finding that firms with
high agency problems experience much worse long-run returns than firms with low
agency problems. The sample is partitioned into high and low agency firms by using five
agency variables from the literature: free cash flow, growth rate, expense ratio, asset
utilization ratio, and the amount of debt. The results show that firms with low agency
problems have a median 3-year stock return of -42%, which is significantly higher than
the -73% for firms with high agency problems.
Finally, I investigate the short-run returns for IPO firms around their lockup
expiration day. For the whole sample, I find that firms with long lockups and short
lockups do have significant negative abnormal returns, even though they are not
significantly different from each other. Thus, I reject the signaling hypothesis which
predicts that there should be no abnormal returns. However, the results do not fully
support the agency hypothesis either. The agency hypothesis predicts that short-run
returns for short lockups should be better than those for long lockups. Consistent with
the literature, I find that high-tech firms experience a much worse short-run abnormal
return than non-high-tech firms, and venture capital backed firms experience a much
worse short-run abnormal return than non-venture-backed firms. I, like previous
102
researchers, am unable to provide an explanation for these unusual returns. In sum, the
evidence from the short-run returns around the lockup expiration date rejects the
signaling hypothesis while partially supporting the agency hypothesis (negative short-
run returns at lockup expiry). The possible reason for not finding a full support for the
agency hypothesis is that there may be other unknowns, and therefore factors not
controlled for that also affect the short-term stock behavior. Future research can be
focused on the possible reasons for the lack of significant difference in short-run returns
for short and long lockups at lockup expiry.
My examination of short-run returns at lockup expiry also shows that firms with
high agency problems experience a much worse short-run return than firms with low
agency problems. For instance, the average 7-day abnormal return around lockup
expiry for firms with low agency problems is -0.58%, which is insignificant different from
zero. This is significantly better than the -3.34% for firms with high agency problems.
However, in the regression analysis, the agency variable is not significant while venture
capital is significantly negatively related to short-run returns.
Interestingly, I do not find a significant relationship between the percentage of
shares locked and lockup length. But I do find that firms with a 180-day lockup period
have bigger size, which is proxied by the proceeds from the IPO. In other words, firms
with a 180-day lockup period raise more money in their IPOs than firms with a lockup
period shorter or longer than 180 days. The average proceeds for firms with a 180-day
lockup period is $68 million, compared to $30 million for long lockups and $45 million for
short lockups.
103
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