HOT and COLD Periods in Public and Private Markets for Biotech IPOs, 1970 - 2001
Irvin W. Morgan Jr and John R. Norsworthy
Lally School of Management
Rensselaer Polytechnic Institute
110 8 th Street
Troy, New York 12180
Morgai2 @rpi.edu
(518) 276 - 4414 randy@norsworthy.net
(518) 276 – 6649
January, 13, 2004
ABSTRACT
We investigate the relationship between the level of activity in the public and private capital markets for Biotech ventures with the level of activity in the general capital markets. We find that there are several HOT periods for the Biotech industry that do not coincide with HOT periods for the general capital markets. We also investigate the relationship between the levels of financing activity in the public capital markets with those for the private capital markets for the Biotech industry and find that HOT periods usually coincide between public and private markets. However, when exiting HOT periods, the public markets for the Biotech industry experience more severe drop-off in financing activity. Private financing in the Biotech industry usually drops off slower and remains in a “normal” state when public Biotech financing becomes COLD. We label this the “Redeployment Effect”. This lagged response in the private markets extends up to 24 months beyond the public market activity levels.
This paper is based on findings from Professor Morgan’s dissertation at Rensselaer
Polytechnic Institute. He thanks the Severino Center for Entrepreneurship and Center for
Financial Technology at the Lally School of Management of RPI for financial assistance in obtaining databases.
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1.1. Introduction
This study is a longitudinal investigation of financing decisions in the Biotech Industry between 1970 and 2001. The study focuses on the Biotech industry because it: a) is an essential part of future economic development of this country; b) possesses characteristics that provide a fertile environment for an investigation of financing patterns; and c) has unique characteristics that influence financing.
1.2. Industry Effect
As McGahan and Porter (1997) state in their paper: Industry does matter. The industry in which a firm operates affects the firm’s overall profitability, which ultimately impacts the firm’s long-term financing patterns. Damodaran (1997) shows that there is a substantial variation in the liquidity level as a percent of assets across industry groupings. Bradley,
Jarrell and Kim (1984) find that average leverage ratios are strongly related to industry classification, and that this relation remains strong even after exclusion of regulated firms. They show that almost 54% of the cross-sectional variance in firm leverage ratios can be explained by industrial classification. Due to the significance of industry characteristics, our approach is to look at an industry individually in order to control for its effect on financing decisions and leverage levels in general. Therefore, we have selected a single industry for this study. We do understand that focusing on a single industry reduces the ability to generalize results. However, the Biotech industry’s characteristics make an investigation very dynamic and worthy of isolation. Some characteristics that make the Biotech industry attractive are as follows:
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a) The Biotech industry is very active with many new entrants and has gone through several cycles of strong (HOT) and weak (COLD) public markets; b) It has long lead-times to profitability, often 12-16 years requiring multiple financings before and after firms have gone public; c) The high level of intangible assets, created by significant R&D spending, affects the type of financing and the degree to which public and private capital is obtained; d) The industry has significant Venture Capitalist participation; e) The industry has an active strategic alliance segment.
The Biotech industry is vibrant with investment and many new entrants. In this panel of
323 firms, one hundred and forty-nine (149) firms went “public” within the last 7 years.
Because our panel includes this substantial portion of young firms, it provides a set of unique obstacles when these firms attempt to obtain financing.
2.1. Background - Industry Review
Since its inception in the 1970s, the Biotech Industry has provided significant growth and innovation to the U.S. economy. During the past 25+ years, there have been over 130+ new biotech products approved by the FDA. Significant products in both human and agricultural areas have been introduced. Biotechnology products have impacted the treatment of cancer, AIDS, heart disease, multiple sclerosis, hepatitis, adolescent growth deficiency and other infectious diseases. Biotech products have also revolutionized our agricultural industry by improving crop yields and making produce resistant to insects
3

and disease. This industry has spawned companies, which employ over 180,000 in the
U.S.
At the end of 2001, there were approximately 350+ public and nearly 2000 private
Biotech firms. However, because there are only a few profitable firms and this industry consumes nearly $14 billion per year exclusively in R&D, many firms require multiple financings in order to complete product trials and commercialization. Continuous access to capital markets is therefore essential to a firm’s long-term survivability. This industry has been fortunate enough during the past 25 years to obtain in excess of $141 billion in public and private financings.
Prior to 2000, many Biotech firms were nearing the exhaustion of their cash balances.
However, the HOT public market in mid-2000 enabled many firms to raise sufficient capital (64 went public in 2000) to cover their cash consumption (“burn”) levels for the next five years. The impact of the 2000 HOT market was to delay the need for industry consolidation, which had been a factor in this industry in the late 1990s.
The Biotech Industry has experienced several cycles of HOT and COLD public equity markets during its 25-30 year history. Although there are presently roughly 350+ public biotech firms, there have been over 1430 firms that were public at some time during this study period. This high turnover reflects the unpredictability of Biotech research along with the high investment levels required for product commercialization. Today, it requires in excess of 10 years and a total cost between $500–800 million (DiMasi,
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Hansen and Grabowski, 2003) to bring a new Biotech product from idea through trials to manufacture and commercialization. These requirements generate a need for continuous access to financial markets and when those markets are not available, create a strong environment for mergers and acquisitions. This active merger and acquisition environment is illustrated by the industry’s high turnover rate. The enormous financing requirements combined with product uncertainty are the principle reasons why many start-ups are subsequently acquired or merged while others fail and are de-listed.
Our industry population has approximately 1430 firms with 3330 financings during the
1970 - 2001 timeframe. These 3330 financings generated over $141 billion in additional capital since 1970. Table 1 analyzes the total financings between public and private markets. The 1430 firms had approximately 2.5 financings per firm averaging over $46.4 million each.
1
1
Using the SDC Database from Thomson Financial, Compustat, CRSP, BIOSCAN and Gales Company Resources
Databases, a panel of 323 IPOs was selected between 1970 and 1999. For this panel, we obtained available daily stock pricing and trading volume data from CRSP and quarterly financial statement information from Compustat and Primark
Disclosure databases. Additionally, pre-IPO and venture capital data was acquired from SDC-Venture Expert (Venture
Capital financings), SEC S-1 databases and BioScan. Company statistics were cross-validated with information on the
Bioscan and Gales Company Resources databases. We are thankful for financial support provided for databases by the
Severino Center for Entrepreneurship and Center for Financial Technology at Rensselaer Polytechnic Institute.
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Table 1: Summary Total Population Statistics – Biotech Study
Biotech Study
($ Millions)
Public Private Other Total
No. of Financings
No. of Companies
Ave. Financings / firm
1842 1472 16 3330
1430
2.5
Total Amt Raised ($Bil) 85.4 42.7
14.5
141.6
Ave. Amt Raised ($Mil) 46.4
28.2
907.6
42.5
Source: Author’s dataset: unless otherwise noted, all Tables and Figures are from the
Author’s dataset
Of the 3330 financings, there were 1842 public market financings (including 976 IPOs) raising over $85.4
4 billion and averaging $46.4 million per financing. In the private markets, there were 1472 financings raising $41.7 billion and averaging $28.2 million per financing. Additionally, there were 16 debt financings in the European markets (Medium
Term Notes) that averaged over $900 million per financing for a total of $14.5 billion.
These have been highlighted, because they are not characteristic of the industry in general. Table 2 and 3 summarize the number of financings and nominal dollar values by year. Several points of interest that are illustrated in these tables are: 1) the cyclical nature of financing in this industry There are at least 5 periods of high public market volume:
1980-81, 1983, 1986, 1991, 1996, and 2000. (We shall discuss these HOT periods later in this paper); and 2) the growth in the number and gross annual proceeds of Biotech financings from the mid-1970s to the late-1990s.
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Table 2: Yearly Biotech Financings (#): Private vs. Public Markets
1994
1995
1996
1997
1998
1999
2000
2001
1986
1987
1988
1989
1990
1991
1992
1993
1978
1979
1980
1981
1982
1983
1984
1985
1970
1971
1972
1973
1974
1975
1976
1977
TOTAL 1842
Source: Author's dataset
#
124
84
125
206
122
60
48
96
38
46
47
157
141
34
92
77
28
Issues
17
17
29
4
2
8
3
37
26
101
30
11
25
3
4
BIOTECH STUDY
Summary of All Financings - Number of Issues
Public yr/yr chg % chg
17
0 0.0%
12 70.6%
-21 -72.4%
-5 -62.5%
1 33.3%
-2 -50.0%
1 50.0%
1 33.3%
7 175.0%
14 127.3%
12 48.0%
-11 -29.7%
75 288.5%
-71 -70.3%
4 13.3%
58 170.6%
-15 -16.3%
-49 -63.6%
18 64.3%
1 2.2%
110 234.0%
-16 -10.2%
-17 -12.1%
-40 -32.3%
41 48.8%
81 64.8%
-84 -40.8%
-62 -50.8%
-12 -20.0%
48 100.0%
-58 -60.4%
#
Issues
1
1
2
2
6
20
21
30
33
38
53
44
39
49
32
83
79
106
96
131
141
132
130
65
54
100
Private yr/yr chg % chg
1
-1 -100.0%
0
0
0
1
1 100.0%
0
9
3
5
15
27
-10
35
10
-9
-2
0.0%
-2 -100.0%
6
14 233.3%
1 5.0%
42.9%
10.0%
15.2%
39.5%
-9 -17.0%
-5 -11.4%
10 25.6%
-17 -34.7%
51 159.4%
-4 -4.8%
34.2%
-9.4%
36.5%
7.6%
-6.4%
-1.5%
-65 -50.0%
-11 -16.9%
46 85.2%
#
Total yr/yr
Issues
17
18
29 chg % chg
17
1 5.9%
11 61.1%
8 -21 -72.4%
3 -5 -62.5%
1 33.3%
5
6
4
3 -1 -25.0%
2 66.7%
1 20.0%
5 83.3% 11
31
57
20 181.8%
26 83.9%
47 -10 -17.5%
131 84 178.7%
63 -68 -51.9%
72
145
9 14.3%
73 101.4%
121 -24 -16.6%
67 -54 -44.6%
95 28 41.8%
79 -16 -16.8%
240 161 203.8%
220 -20 -8.3%
230 10 4.5%
180 -50 -21.7%
256
347
76 42.2%
91 35.5%
254 -93 -26.8%
190 -64 -25.2%
113 -77 -40.5%
150 37 32.7%
138 -12 -8.0%
1488 3330
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Table 3: Yearly Biotech Financings ($mil): Public vs. Private Markets
BIOTECH STUDY
Summary of All Financings - Dollars per Year ($ millions)
Public yr/yr
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
$ mil
82.1
82.1
84.2
93.5
chg % chg
2.1
2.6%
9.3
11.0%
81.4
-12.1 -12.9%
96.4
15.0
18.4%
75.0
-21.4 -22.2%
15.3
-59.7 -79.6%
22.2
26.8
6.9
45.1%
4.6
20.7%
102.6
75.8 282.8%
270.1
167.5 163.3%
494.8
224.7
83.2%
278.7 -216.1 -43.7%
1983 1722.5 1443.8 518.0%
1984
1985
282.4 -1440.1 -83.6%
754.9
472.5 167.3%
1986 2289.7 1534.8 203.3%
1987 1417.7 -872.0 -38.1%
1988 546.6 -871.1 -61.4%
1989 1066.5
519.9
95.1%
1990 1612.7
546.2
51.2%
1991 5366.4 3753.7 232.8%
1992 5950.3
583.9
10.9%
1993 4353.6 -1596.8 -26.8%
1994 2893.2 -1460.4 -33.5%
1995 4440.8 1547.6
53.5%
1996 8325.6 3884.8
87.5%
1997 6943.2 -1382.4 -16.6%
1998 4375.8 -2567.4 -37.0%
1999 8200.8 3825.0
87.4%
2000 16553.6 8352.8 101.9%
2001 6607.7 -9945.9 -60.1%
Private yr/yr
Total yr/yr
$ mil chg % chg
3.4
3.4
-3.4 -100.0%
0.0
0.0
0.0
20.9
20.9
2.0 -18.9 -90.4%
22.0
20.0 1000.0%
-22.0 -100.0%
121.2 121.2
255.9 134.7 111.1%
184.6 -71.3 -27.9%
400.3 215.7 116.8%
338.2 -62.1 -15.5%
263.5 -74.7 -22.1%
773.0 509.5 193.4%
$ mil chg % chg
82.1
82.1
87.6
93.5
5.5
6.7%
5.9
6.7%
81.4
-12.1 -12.9%
96.4
15.0
18.4%
75.0
-21.4 -22.2%
36.2
-38.8 -51.7%
24.2
-12.0 -33.1%
48.8
24.6 101.7%
102.6
53.8 110.2%
391.3
288.7 281.4%
750.7
359.4
91.8%
463.3
-287.4 -38.3%
771.9
-1.1
-0.1%
842.8
70.8
9.2%
1625.2 782.5
92.8%
786.5 -838.8 -51.6%
1549.8 763.3
97.1%
1704.8 155.0
10.0%
2580.0 875.2
51.3%
2094.2 -485.8 -18.8%
2122.8 1659.5 358.2%
620.6 -1502.2 -70.8%
1018.4
397.8
64.1%
3062.7 2044.3 200.7%
2189.6
-873.0 -28.5%
1389.4
-800.3 -36.5%
2691.7 1302.4
93.7%
2399.2
-292.6 -10.9%
6916.2 4517.1 188.3%
7655.1
738.9
10.7%
6933.6
-721.5
-9.4%
4987.4 -1946.2 -28.1%
4467.1 2373.0 113.3% 8908.0 3920.6
78.6%
3700.0 -767.2 -17.2% 12025.6 3117.6
35.0%
6760.6 3060.6
82.7% 13703.8 1678.2
14.0%
6317.8 -442.8
-6.6% 10693.6 -3010.2 -22.0%
5417.4 -900.4 -14.3% 13618.2 2924.7
27.3%
7238.0 1820.6
33.6% 23791.6 10173.4
74.7%
7949.5 711.5
9.8% 14557.2 -9234.4 -38.8%
56190.2
141617 TOTAL 85427.1
Source: Author's dataset
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The industry five (5) periods of high and expanding, HOT, new issue volume are explored in detail in sections 3 thru 6. Conversely, the Biotech industry has also experienced several major (COLD) cycles of declining volume. As noted in earlier research, when the public markets are COLD, firms use strategic alliances as a source of funding. Others had asserted that the private capital markets are also used when public markets are tight. We show later in this paper an alternative theory that focuses on the complementary nature between public and private markets. The calendar year 2000 is an example of the inverse relations between the Biotech firm’s ability to raise funds in the capital markets and their subsequent need to participate in strategic alliances
During 2000 there were 12 strategic alliances valued at over $1.6 billion in cash payments Although one deal, CuraGen / Bayer, had the potential value in royalties of
$1.5 billion over a 15 year period, the up-front cash payment was only $124 million. Due to the active public equity markets in mid-2000, the Biotech industry going into 2001 had substantial cash on their balance sheets. This should result in a reduction in the number of early stage strategic alliances during the next two - three years.
An interesting point is shown on Table 4. Although the mean dollar amount per financing has increased dramatically since 1970 in both the public and private markets
(public $61.1 vs. $7.2; private $44.2 vs. $8.1), the respective medians per financing remain around $10 and $28 million, illustrating that this industry continues to be principally funded by small investments. As noted in chapter one, this low median dollar amount for the private market becomes problematic for major institutional investors. We
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show that at IPO, the average market value of equity ($88.36 million) for the entire panel is less than the $500 million desired level by institutional investors.
Table 4: Summary of Financings by Decade
BIOTECH STUDY
PRIVATE MARKET FINANCINGS
Amount Raised ($Mil)
Years
1970 - 1979
1980 - 1989
#
8
331
1990 - 2001 1149
Sub-total 1488
Mean Median Stddev
8.1
16.9
6.3
7.3
7.9
37.5
Total
54.3
5570.2
44.2
10.0
194.1 50559.4
37.8
10.0
172.0 56183.9
PUBLIC MARKET FINANCINGS
Amount Raised ($ Mil)
Years
1970 - 1979
#
97
1980 - 1989 496
1990 - 2001 1249
Sub-total 1842
Mean Median Stddev
7.2
3.6
10.2
Total
679.5
19.1
10.0
28.9
9123.8
61.1
28.0
156.9 75629.8
47.4
20.3
132.2 85433.1
The growth of Biotech financing over the past thirty years is illustrated in Table 2. There have been significant swings in volume from highs in the 1980-1981, 1983, 1986, 1991 and 1996 for the public markets to lows in volume in 1982, 1984, 1994 and 1997. Table 2 above shows that during public market volume peaks, there were corresponding private markets peaks in years 1980-1981, 1983, 1986, 1991 and 1996.
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As illustrated in Tables 2 and 3 above, the number of issues and amounts increased significantly in both public and private markets. Peak years in 1980-81, 1983, 1986, 1991 and 1996 were followed by declines in the public markets. The private markets saw reduced growth rates for new issues in 1982, 1984 and 1992 and actual declines in 1987 and 1997. The yearly gross proceeds in public markets experienced declines from their prior years’ HOT periods in 1982, 1984, 1987 and 1997. The 1992 COLD period continued to increase in dollar volume although the number of new issues declined. The private yearly gross dollar volume declined in 1982, 1984, and 1987, but experienced increased gross dollar volume in 1992 and 1997. The dollar volume trend reflects the impact of higher average amounts raised per financing in the 1990s in comparison to the
1970s and 80s.
Overall, these markets are very positively correlated. We show later in this paper that the relationship between the public and private segments of the Biotech industry work in concert with one another, rather than in opposition as some earlier researchers have asserted.
The following are some findings of this study: 1) there are several Biotech HOT and
COLD public periods that coincide with those HOT and COLD periods for the general market. However, there are HOT and COLD periods that are unique to the Biotech industry. 2) Within the Biotech industry, public and private financing cycles principally coincide. However, the Biotech private transitions from HOT to COLD states are slower than the Biotech public markets. We label this our “Redeployment Effect”.
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This paper is organized as follows: Section 3 introduces the recent literature on HOT and
COLD equity markets. Section 4 introduces how HOT markets are defined and the three research questions addressed in this paper. Sections 5-7 provide our findings for each of the three research questions. Our conclusions and implications are included in Section 8.
Appendix 1 provides the distributed lag models and the table of results for the regressions on the public and private markets.
3. Literature Review: Hot and Cold Markets
Ibbotson (1975), Ibbotson and Jaffe (1975) and Ritter (1984) define HOT markets as periods of unusually high initial returns (underpricing). Allen and Faulhaber (1989) assert that HOT markets occur when there are positive shocks to expected profitability of firms.
They conclude that these states will exist only as long as it takes competition to drive down industry profits. Chemmanur and Fulghieri (1994) define HOT markets as a large number of offerings by unusually profitable firms. They view HOT markets as being characterized by firms that choose to go public earlier than “normal”. The Biotech industry has not had many firms that were profitable at the time of their IPOs. Of the 323
IPOs in this panel only 59 (18.3%) firms were profitable in the quarter before their IPO.
HOT markets do impact the firm’s IPO decision.
2 In this panel, 222 of 323 (68%) firms went public during HOT markets.
Choe, Masulis and Nanda (1993) state that macroeconomic conditions contribute to HOT periods. In economic expansion there is an expectation of higher cash flow resulting in a reduction of the information cost for equity. We include a GDP dummy variable (1 or 0)
2 See Chapter 5 in Morgan (2003).
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in this model to reflect an expanding versus contracting economy. The percent growth in
GDP is not used because there are significant revisions to this number from original government estimates. Inclusion would distort the ex ante information that had been available at the time the firm’s management decided to raised additional capital.
However, the dummy variable (GDPSTE) is not significant in any of our underpricing regressions.
Bayless and Chaplinsky (1996) argue that HOT IPO markets need not occur solely because of swings in GDP, but can be created because the cost of issuing equity is lower for all firms during high IPO volume periods, resulting in a smaller “lemons” premium.
This smaller premium is a function of lower prediction errors due to the reduction of information costs associated with the clustering of certain types of firms in high and low volume periods. This reduction in information costs results in lower underpricing. Their measure of a HOT market is based upon the 3-month average where equity volume exceeds the upper quartile, while COLD periods are determined when the 3-month moving average of the issue volume falls below the lower quartile. They include three measures of volume: nominal dollar, real dollar and scaled issue volume as the determinants for market states. Their research generates three HOT periods during the
1968 –1990 timeframe. As shown in Table 5, the three HOT periods defined by Bayless and Chaplinsky (1996) are November 1980 – February 1984, July 1985 – August 1987 and April 1988 – September 1988, respectively.
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Bailsford, Heaney, Powell and Shi (2000) use a Markov chain regime-switching technique to establish breaks for HOT and COLD new issue periods from 1976 to 1998.
They use the means and standard deviations for four monthly variables: IPO volume, average and total underpricing, and gross proceeds as the basis for determining market states. The market’s switching points were determined by the probability of a certain state being above .5 for six consecutive months. Their results based upon all four measures show a final HOT period that extends from May 1991 – Jun 1998. This extended HOT period reflects the generally higher overall volume of IPOs in the market and expanding economy during the 1990’s. Using vector autoregressive (VAR) models they establish relationships between IPO volume and value weighted underpricing to lagged changes in
IPO volume, value weighted total underpricing, spread in term premium (10 year yield –
3 month yield on Treasury issues), changes in S&P index and GDP growth indices. Their results show that IPO volume is a positive function of lagged underpricing by up to six months.
Lowry and Schwert (2002) find that average initial returns and IPO volume are highly autocorrelated. More companies tend to go public following periods of high initial returns. They also find that there are lead-lag relations between high initial returns and
IPO volume. They find that underpricing of initial returns leads volume of IPOs by between 3 and 6 months, which are followed by lower initial returns (underpricing).
Loughran and Ritter (2002) argue that underpricing is related to information that becomes available during the registration period. The information is only partially incorporated
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into the offer price through revision. However, due to overlapping registration periods during periods of high offering volume, this new information results in market-wide returns that have high levels of underpricing. This combined period of high initial returns and high issue volume generates what is termed a HOT market. The Loughran and
Ritter’s model includes an aftermarket variable as a determinant of high initial returns.
They used the market return during the 15 days prior to the IPO as a measure of the level of the expected return in the public market.
4. HOT Market Definition
A primary reason for selecting the Biotech industry is because it has experienced several cycles of strong (HOT) and weak (COLD) public equity market periods. These HOT and
COLD periods are important because their presence affects firm financing behavior. Due to these cycles, the Biotech industry has gone through periods of excess cash and periods of cash shortages.
We investigate three research questions regarding the effects of HOT and COLD periods.
The first question examines the relationship between HOT or COLD periods in the
Biotech industry with those in the general market. The second addresses the relationship between HOT and COLD periods in public compared to private markets in the Biotech industry. The third research question tests the timing and lead-lag relationship of the public and private Biotech markets. This section addresses earlier literature that states that private and public markets are counter cyclical. The literature asserts that when the public market is HOT, the private market is not. It is stated (e.g., Lerner 1994) that many
15

of the principal participants use the private capital market as an alternative to the public markets. We find a different result: for the Biotech industry, there is primarily concurrence between HOT public and private markets. We conclude that there when capital is available, it is available in both markets. However, as discussed below, we find that at the end of a HOT period, private markets decline at a slower pace than their public market counterparts. We label this lagged pattern between private and public markets as a redeployment effect.
These three research questions address overall market activity.
Summary of Research Questions Concerning the Impact of HOT Markets:
1.
Have HOT Biotech IPO markets coincided with HOT general markets?
2.
Does the presence of a HOT public equity market affect private market financing?
3.
Does the Biotech public market lead or lag its private counterpart?
HOT markets have been defined using one or more of the following four measures:
Number of new issues (IPOs); Total Gross Proceeds; Gross Underpricing; and, Average
Underpricing. We include both equally weighted and value-weighted underpricing because the smaller firms in this study may distort the equally weighted underpricing measure. This panel has average equally weighted and value weighted underpricings of
9.5% and 10.5%, respectively.
5. Have HOT Biotech IPO markets coincided with HOT general markets?
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The results for Bailsford, Heaney, Powell and Shi (2000) and Bayless and Chaplinsky
(1996) reveal up to five and three HOT markets during their respective sample periods.
The outer bounds of the HOT periods are:
Table 5: HOT periods range (Overall Market)
HOT Periods (Overall Market versus Biotech Market)
Overall
(Bailsford et.al
Overall (Bayless and Chaplinsky
[2000])
Biotech (This
Study)
Sept 77 - Oct 78
Aug 80 - Dec 81
Nov 82 - Sept 84
Feb 90 - Mar 92
Mar 91 - Jun 98
Apr 88 - Sept 88
Nov 80 - Feb 84
July 84 - Aug 87
May 81 - July 81
Jun 83 - Mar 84
Jun 86 - Oct 87
Apr 91 - May 93
Nov 93 - Oct 94
Oct 95 - Mar 97
Jul 97 - Dec 97
Apr 98 - Jul 98
May 00 - Nov 00
5.1. Biotech Study Results
Using the methodology of Bayless and Chaplinsky (1996) for three variables, we determined HOT and COLD periods for the Biotech industry IPOs. We define a HOT period as the three-month moving average exceeding the upper quartile for at least three consecutive months. Alternatively, COLD periods are defined as the three-month moving average that remains below the lower quartile for at least three consecutive months. As illustrated in Table 6, there are several general market HOT periods that coincide with
Biotech HOT periods, May 81 – July 81, June 83 – March 84 and June 86 – October 87.
When unit volume is separated during the expanding 1990s, there are 5 distinct HOT regimes for Biotech: (1) April 91-May 93, (2) November 93-October 94, (3) October 95-
March 97, (4) July 97-December 97 and (5) April 98-Jul 98. Bailsford et al (2000) define
17

the entire period, without breaks, from 1991 to 1998 as a HOT period. HOT and COLD periods based upon IPO volume and Gross Proceeds (nominal and 1999 constant dollars) are consistent with HOT period segments defined by Bayless and Chaplinsky (1996).
Exceptions are September 77 - October 78, all of 1985 and 1988 periods, which were identified as HOT by Bailsford et al (2000) or Bayless and Chaplinsky (1996) but the
Biotech industry is in COLD or normal states according to our analysis. Finally, the
HOT period March 2000 – November 2000 was generated by an increase in expectations of the industry profitability (consistent with Allen and Faulhaber, 1989) associated with announcement of the technological breakthrough of the complete mapping of the human genome sequence.
Nominal Gross monthly proceeds (upper quartile = $99.8 million) or Real Dollar (1999
=100) Gross Proceeds (upper quartile = $111.7 million) show several more HOT cycles generated because of the increasing size of the typical offering. An increase in first-day underpricing is generally corresponding to high new issue volume. Monthly new issue volumes and value weighted and equally weighted monthly underpricing are illustrated in
Figure 1. The complete monthly underpricing trends are not necessarily reflected in the panel data due to the occurrence of the selected firm’s IPO within a particular HOT or
COLD period. If the firm’s IPO is at the beginning of a new cycle, then its underpricing level may be lower than the overall monthly underpricing and not reflect the upward trend. If the panel firm IPO occurs at the end of a HOT period, then again the firm’s underpricing would be below the mean for that HOT cycle. A clearer view of the
18

correlation between increasing underpricing and changes in volume is illustrated in the annual data of Figure 2.
Figure 1: Biotech Monthly IPO Volume: Total Population vs. Panel Underpricing
2.00
1.50
1.00
.50
Total Biotech IPO Population versus Sample Underpricing
UNPRC1_1
VWUNDER
FINSCN_1
18.00
16.00
14.00
12.00
10.00
8.00
.00
1
-.50
37 73 109 145 181 217 253 289 325
6.00
4.00
2.00
-1.00
.00
Months
Figure 2: Yearly Underpricing and Change in Total Volume (#s, $s)
Biotech Panel
1400% 70.00%
1200%
60.00%
50.00%
1000%
40.00%
% ch g yr to yr
800%
600%
400%
30.00%
20.00%
10.00%
0.00%
200%
-10.00%
0%
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
-200% years
-20.00%
-30.00%
%
Un der pri cin g
%chg #
%chg $
UNDRPR1
19

Table 6: Biotech Study HOT and COLD IPO Periods
BIOTECH STUDY
HOT AND COLD IPO MARKETS
3 MONTH MOVING AVERAGE
(# IPOS) Gross Proceeds
Nominal 1999 Real Dollars
COLD PERIODS
1/71 - 5/71
Cold definition:
No. of IPOs per month on a 3 month moving average <1.08;
Gross Proceeds on a nominal basis
< $6.78mil per month; Gross
Proceeds in 1999 real dollars <
13.67mil 3mo
Moving average.
2/73 - 8/73
10/78 - 5/79
1/80 - 5/ 80
9/82 - 11/82
10/98 - 1/99
10/99 - 2/00
1/01 - 6/01
4/70 - 6/71
10/71 - 4/72
12/72 - 8/73
9/78 - 5/79
1/80 - 3/80
7/80 - 9/80
3/88 - 5/88
1/01 - 6/01
HOT PERIODS
1999 Real dollar basis > $111.7mil.
N = 976 IPOs
5/81 - 7/81
6/83 - 3/84
6/86 - 10/87
6/83 - 8/83
Hot period definition: No. of
IPOs on a 3 mo
MA >4.33 units;
Gross proceeds on a nominal basis
3moMA >
$99.8mil; Gross
Proceeds on a
4/91 - 5/93 4/91 - 5/92
7/92 - 8/93
11/93 - 10/94 12/93 - 2/94
6/94 - 7/94
12/94 - 2/95
10/95 - 3/97 11/95 - 8/96
10/96 - 1/97
7/97 - 12/ 97 8/97 - 12/97
4/98 - 7/98 4/98 - 8/98
4/99 - 8/99
3/00 - 11/00
Source: Author
5/70 - 2/71
10/71 - 3/72
2/73 - 8/73
9/78 - 5/79
1/80 - 9/80
2/82 - 5/82
9/82 - 11/82
7/84 - 2/85
1/86 - 3/86
3/88 - 5/88
2/89 - 4/89
1/01 - 6/01
6/83 - 9/83
6/86 - 8/86
4/91 - 5/92
7/92 - 8/93
11/93 - 2/94
11/95 - 8/96
10/96 - 1/97
7/97 - 12/97
4/98 - 8/98
4/99 - 8/99
3/00 - 11/00
20

The early COLD periods shown on Table 6 reflect low volumes in an emerging industry during the first half of the 1970s decade. These IPOs are principally clinical laboratories.
The monthly equally-weighted and value-weighted underpricing associated with the 323 panel firms is included in Figure 2, which illustrates that during increasing IPO volume, underpricing has led volume or is increasing with volume. These HOT and COLD periods based upon IPO volume and Gross Proceeds (nominal and 1999 constant dollars) are consistent with segments of Bayless and Chaplinsky (1996) defined HOT periods.
However, there are exceptions. Biotech industry IPOs decline to into normal volumes in
1985 and 1988, where Bayless and Chaplinsky (1996) have the overall market in a HOT period. As noted, Bailsford et al. (2000) define a HOT period for the entire 1991-1998 timeframe, where the Biotech industry goes through five distinct HOT and several normal periods based upon volume and gross proceeds.
Prior to 2000, when the mapping of the human genome was announced, the Biotech industry had experienced a severe contraction in available funds in both public and private markets since the 1996-97 HOT market. There were some characteristic shifts in the market during the 1990s that affected the industry’s ability to raise funds. The
Biotech industry’s primary sources of funding, other than venture capitalists (VCs), had been institutional investors. However, the run-up in equity prices in the 1990s and the massive influx of cash to mutual funds resulted in a need for a higher average investment per firm ($50 million vs. $15.3 million) for private placements. Consequently, the desired mutual fund deal size grew to $50 million, which exceeds the median size of private equity investment in the Biotech industry ($7.8 million) by over six times (see Table 7).
21

These lower Biotech investments are a function of smaller market capitalization by the average firm (less than $500 million). Consequently, in the late 1990s many biotech firms no longer had access to institutional investors, which had previously been their principal source of financing for the Biotech industry.
Table 7: Financings by Type and Market – All Groups
BIOTECH STUDY
ALL GROUPS FINANCINGS BY TYPECODE
TYPECODE
Description
0 Venture Capital
1 Common Stock
2 Cvt Preferred
3 Cvt Debt
4 Preferred Stock
5 Debt
7 Warrants
Yrfnd
PRIVATE MARKETS
Dollar Amount ($ Mils)
Sbf
(Mn # Mn Mdn Stdv Total
1986
1985
1984
1983
1983
1965
1984
Private sub-total 1982
0.2
3.3
2.5
4.9
1.9
2.5
4.7
209
669
161
99
75
204
55
21.8
15.3
13.4
120.3
14.5
57.7
10.9
2.8
1472 37.9
13.7
7.8
7.0
25.0
9.9
26.0
6.7
10.0
40.6
38.1
18.1
283.3
15.6
96.3
15.4
4526.4
10165.7
2152.7
11914.1
1090.9
11220.5
597.8
41668.1
5 European LYONS
3.6
16 907.6
450.0
1133.6
14521.8
TYPECODE
Description
0 Venture Capital
1 Common Stock
2 Cvt Preferred
3 Cvt Debt
4 Preferred Stock
5 Debt
7 Warrants
9 ADRs
Public sub-total
Yrfnd
PUBLIC MARKETS
DOLLAR AMOUNT ($ Mils)
Sbf
(MN # Mn Mdn Stdv Total
1981
1975
1971
1970
1954
1984
1969
-
2.1
1603 41.8
2.5
22 44.7
3.6
3.0
3.5
2.4
1.7
57
3
69
49
39
67.6
175.0
149.7
12.0
88.3
1979 2.2
1842 47.2
-
19.2
20.0
37.0
200.0
100.0
9.4
37.5
20.3
-
130.5
103.2
80.7
139.2
172.7
9.8
181.8
-
65829.5
982.3
3855.3
525.0
10328.1
552.6
3354.3
85427.1
The 2000 HOT market enabled many Biotech firms to issue large equity offerings, which would be expected to defer a cash crisis for several years. Prior to 2000, there was
22

considerable discussion in the industry about the need for consolidation, to create sufficiently large entities in market capitalization that would attract continued institutional investments.
6. Does a HOT public equity market create a HOT private market ?
There has been less volatility in Biotech industry private market compared to public market placements. Earlier studies (e.g. Lerner 1994) have stated that this results from venture capitalists using private markets as a source of funding when the public markets are COLD. Using methodology from Bayless and Chaplinsky (1996) as well as distributed lags, we test if there are concurrent HOT or COLD periods of private and public markets for the Biotech industry. Variables include: 3-month moving averages for number of new issues; gross monthly proceeds (in nominal $ and 1999 real $), and equally weighted; and gross underpricing to determine if the markets are in a HOT or
COLD state. We use new issue volume along with nominal and 1999 constant dollar gross monthly proceeds for all groups to determine the HOT and COLD periods for the
1970-2001 timeframe. Upper and lower quartiles for the number of issues in nominal and
1999 constant dollar proceeds are shown in Table 8. If we focus on the periods after
1979, we find several common Biotech HOT periods (Table 9) in both public and private markets: July 91 – April 92; July 93 - April 94; May 95 – May 98; February 00 – May 00 and December 2000 – December 2001. These common HOT periods imply that when capital is available to an industry, it is available in both the private and public markets, reflecting a faddish nature of capital markets.
23

Another explanation is that, because venture capitalists and other private corporate and institutional investors are so prevalent in this industry, when the public market is HOT it allows these investors to cash out earlier investments, thereby providing new capital to be redeployed into new ventures. This pattern is consistent with the redeployment effect defined above between public and private markets. It is also supported by our distributed lag findings discussed below.
This latter interpretation may explain a portion of the activity in this industry. However, there is a significant portion of the VC backed firms whose final VC investment occurs years after the IPO. Therefore, it is inconsistent to think that these VCs would simultaneously cash out and re-invest in the same venture. It is more logical to reason that these are sub-pools of the VC segment. There is one segment that prepared its venture firm for IPO and used the IPO as an exit strategy, while another segment used the
IPO as a financing event within a series of investments that extended beyond the IPO.
3
The COLD Biotech periods (Table 10) show a significantly different pattern than their
HOT counterparts. There are only two COLD periods common to the public and private markets: February 70 – December 79 and January 80 – December 80. Instead of having common COLD periods, the Biotech private markets are in a normal state when the public markets go COLD in 1982, 1987-88, 1998-99, and March 01 thru May 01. This supports earlier conclusions that venture backed firms use the private markets when the public markets are not receptive to offerings and the redeployment effect noted above.
3 These performance differences are explored in Morgan, 2003 Ch. 5 and 6.
24

The public market’s dollar volume decline in 1987 was exceeded by the private market’s percentage drop during 1987. However, both markets continued to decrease in the following year, 1988. The three-year regime shows that the combined reduction percentages in the public market follow the patterns of other years. Overall, there is a positive correlation (r = .76) between the volume in the public and private markets showing that these markets are positively connected. This supports the theory of concurrent availability of funds, since several common HOT periods occur among private and public markets. There are some interesting trends, however, in that generally the fall off in the public market may coincide with a small increase in the private market or a fall off that is not as large, which supports a lagged re-deployment of funds theory.
25

Figure 3: Log-scale of Public – Private monthly financings (Number per month)
BIOTECH STUDY- ALL GROUPS # of financings per month
100 pubcnt privcnt
10
1
1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313
Months
Figure 4: Log Scale of Total monthly ($ millions): Private vs. Public Markets
BIOTECH - ALL GROUPS Public versus Private Financings ($Millions) amtpriv amtpub
10000
1000
Jul-69 Apr-80 Jun-85 Jun-90 Jun-95 Jun-00
1
0.1
100
10
Months
26

Table 8: Public vs. Private Descriptive Statistics – Monthly
BIOTECH STUDY
Monthly Statistics Private versus Public markets
Statistics
N
Private Market Public Market
Gross Proceeds Gross Proceeds
# Issues Nominal 1999 $s # Issues Nominal 1999 $s
323
0
323
0
323
0
323
0
323
0
323
0
Mean
Std. Error of Mean
4.5
0.3
127.6
15.6
133.6
15.6
5.7
0.3
260.7
28.6
276.8
28.1
Median
Mode
Std. Deviation
Variance
Skewness
3.0
0.0
4.5
20.6
1.1
Std. Error of Skewnes 0.1
Kurtosis 0.6
Std. Error of Kurtosis 0.3
Range
Minimum
Maximum
Sum
Quartiles
25
50
75
20.0
0.0
20.0
1438.0
41204.4
43143.4
1831.0
84208.0
89418.5
1.0
3.0
7.0
41.0
0.0
280.2
78529.3
5.7
0.1
44.4
0.3
3032.0
0.0
3032.0
1.1
41.0
138.6
47.3
0.0
280.4
78634.4
5.8
0.1
47.9
0.3
3131.2
0.0
3131.2
1.3
47.3
156.0
4.0
1.0
5.6
31.1
1.6
0.1
2.8
0.3
31.0
0.0
31.0
2.0
4.0
8.0
70.2
0.0
513.4
95.9
0.0
505.6
263624.4 255583.6
5.1
0.1
35.5
0.3
4940.6
0.0
4940.6
13.3
70.2
296.9
4.8
0.1
32.6
0.3
4821.4
0.0
4821.4
22.4
95.9
313.0
27

Table 9: HOT Periods: Public vs. Private Markets – All Issues
BIOTECH STUDY
Comparison of HOT Periods for Private & Public Markets - All Issues
Private Markets
# Issues
Gross Proceeds
Nominal Real 1999$
3/89 - 6/89 4/89 - 6/89
10/89 - 12/89 10/89 - 12/89
7/93 - 3/92 10/91 - 4/92 10/91 - 4/92
7/93 - 6/94 8/93 - 2/94 8/93 - 2/94
8/94 - 12/94
5/95 - 9/98 3/96 - 9/98 3/96 - 5/97
7/97 - 9/98
2/00 - 4/00
12/98 - 4/99 12/98 - 4/99
6/99 - 9/00 6/99 - 9/00
5/01 - 12/01 12/00 - 12/01 12/00 - 12/01
Public Markets 6/83 - 10/83
6/86 - 9/86
4/87 - 8/87
4/91 - 7/92
12/92 - 5/93
1/91 - 4/93
7/86 - 8/86
1/91 - 8/91
10/91 - 4/93
10/93 - 4/94 10/93 - 2/94 10/93 - 2/94
6/94 - 8/94 7/94 - 9/94 7/94 - 9/94
8/95 - 1/97
3/97 - 5/97
7/97 - 1/98
8/95 - 1/97
3/97 - 9/98
9/95 - 1/97
3/97 - 9/98
3/98 - 5/98
3/00 - 5/00
2/99 - 4/99
6/99 - 7/01
2/99 - 4/99
6/99 - 7/01
10/00 - 12/00 11/01 - 12/01 11/01 - 12/01
# Issues = the total monthly number of new issues. HOT periods are determined by the volume or gross proceeds equal to or greater than the upper quartile of the monthly distribution from 1970 -2001. Gross proceeds in real 1999 dollars were inflated using the monthly PPI index for industrials.
28

Table 10: COLD Periods: Public vs. Private Markets -All Issues
BIOTECH STUDY
Comparison of COLD Periods for Private and Public Markets
Gross Proceeds
# Issues Nominal Real 1999$
Private Markets 2/70 - 12/79 2/70 - 10/75 2/70 - 6/71
1/77 - 12/77 10/71 - 6/75
6/79 - 12/79 6/79 - 9/79
4/80 - 12/80 5/80 - 8/80 4/80 - 7/80
Public Markets 2/70 - 4/71 2/70 - 5/70 11/70 - 2/71
11/70 - 4/71
9/71 - 3/72 9/71 - 10/72 10/71 -3/72
12/72 - 9/73
6/75 - 9/79 11/75 - 5/79 11/75 - 5/79
7/79 - 9/79
12/79 - 9/80 1/80 - 8/80
2/82 - 5/82 2/82 - 7/82
1/80 - 8/80
2/82 - 7/82
7/84 - 9/85 9/84 - 2/85 9/84 - 2/85
12/87 - 5/88 1/88 - 5/88 12/87 - 5/88
10/88 - 12/88
10/98 -1/99
3/01 - 5/01
# Issues = the total monthly number of new issues. Cold periods are determined by the volume or gross proceeds equal to or less than the lower quartile of the monthly distribution from 1970 -2001. Gross proceeds in real 1999 dollars were inflated using the monthly PPI index for industrials.
29

7. Does the biotech public market lead or lag its private counterpart?
7.1. The Distributed Lag Model
We test the relationship between public and private biotech financings using distributed lag regressions. We investigate the following six variables:
1) number of monthly financings - private, AMT9;
2) number of monthly financings- public, AMT10;
3) total nominal dollars per month-private, AMT2
4) total nominal dollars per month - public, AMT6;
5) total dollars (1999 Real) per month–private, AMT14;
6) total dollars (1999 Real) per month-public, AMT18.
Our objective is to determine the lead-lag relationship between public and private biotech financial markets. Our timeframe runs from 1980 to 2001 because earlier data on private financings is somewhat unreliable. One reason is that earlier financing history for individual firms after they have been acquired or merged is usually not identified as that firm’s activity.
4
Our distributed lag regressions correlated the activity in each market to:
1) their prior activity up to six months;
2) the alternative market prior activity (up to 24 months);
4 We used several databases to cross-validate this information. However, we judge that the earlier data are still too unreliable to be included.
30

3) investor sentiment as measured by the 10 year – 3 month treasury yield spread for the prior month;
4) prior six months of S&P 500 returns;
5) the state of the economy.
The following equation was used to estimate the number of monthly financings. The regression models for the remaining variables are included in Appendix 1.
AMT9
+ *
T
= A
0
+ j
6
∑
= 1
B j
* AMT9 + k
3
∑
= 1
C k
* AMT10 t − 1
+
6
∑ m = 0
F m
* AMT12 + *
+ l
23
∑
= 0
D l
* AMT6 t
+
t
Where,
AMT9 t
= number of private Biotech financings in month t.
AMT10 t
= number of public Biotech financings in month t.
AMT6 t
= total nominal dollars of public Biotech financings in month t.
AMT11 t
= 10 year minus 3 month treasury yield spread in month t.
AMT12 t
= S&P 500 return for month t.
GDPSTE t
= dummy variable for the state of the economy.
GDPSTE equals 1, if in expansion and 0, if in a recession.
The first regression (EQ. 1F: Adj. RSQ = .427) for the number of private (AMT9) financings (EQ. 1F: Appendix 1) per month is significantly positively related to its prior two months activity (t = 4.03: p .000 and t = 1.86: p .064) and to lagged dollar volume in the public markets (t =2.69: p= .008) up to 23 months. The number of private financings
31

per month is not significantly related to investor sentiment in the month prior, the S&P
500 returns for the prior 5 months or the state of the economy in the month of the financing. These results support the assumptions that markets are autocorrelated with cycles of high and low activity. The regressions reveal at least three months (t = 0, -2) of highly correlated activity. The same model also shows strong correlation between the monthly offerings in the private market with prior monthly dollar volume in the public market. The strength of this relationship runs up to the previous 23 months. This implies that the markets are very closely related in that the early lag months are also significant.
The strength of the relationship between the public and private market extends to 23 months with positive coefficients indicates that public market activity leads and is a determinant of private market volume. This conclusion is also supported by the second regression on public market monthly unit volume.
Our second regression (EQ. 2F: ADJ RSQ = .529) of the number of public financings
(AMT10) per month finds that AMT10 is significantly positively autocorrelated (t = 7.67: p = .000, and lag (–4), t =2.38: p = .018.) From the HOT and COLD literature, we understand that these cycles run between 3 to 6 months. Additionally, recent findings by
Lowry and Schwert (2002) and Loughran and Ritter (2002) discuss the autocorrelation in volume and underpricing, and show that underpricing leads new issue volume by up to 6 months. When we take this into consideration with the fact that average registration period ranges between 65 and 109 days, we understand the gap in the lags for public financings. Our regressions show that the number of public financings per month is positively correlated (t = 2.04: p = .043) to the prior month’s financings in the private
32

markets. This supports the theory that the two markets move together as opposed to a supplemental theory that says when one market is HOT the other market is NOT.
Equation 2F does confirm that activity in the public market is not determined by that in the private market. The 24 monthly lags starting in the current period of private market nominal dollar volume are not significant at any stage. The monthly public market offerings are significantly positively related to the current and each of the prior five months of S&P 500 returns, which implies that general market conditions impact the level of public market offerings. The strongest correlations are at lags 2 (t =3.73) and 3 (t
= 3.59). The strong correlations at lags 2 and 3 again reflect the impact of a registration window ranging from 65 to 100 days. Finally, monthly public volume (number of issues) is not significantly related to either investor sentiment or the state of the economy in the month of the financing.
The third regression (EQ. 3F: ADJ RSQ = .188) has private total dollar amount per month (AMT2) as the dependent variable. These results find significant (t =1.84: p =
.067) positive relations between private dollar volume and its one period lagged values.
As illustrated in equation 1F, there is also a significant positive relation (t = 3.42: p =
.001) between private dollar volume per month and their lagged public market counterpart, which extends up to 23 months. However, monthly private market dollar volume is not significantly related to investor sentiment, S&P 500 monthly returns, or the state of the economy.
33

Our regression on monthly public market dollar volume (AMT6) (EQ. 4F: ADJ RSQ =
.232) exhibits a negative relation to prior months public market dollar volume at lags 2 (t
= -1.66: p = .099) and 4 (t = -1.76: p = .08) and significantly positive relations to the fifth month lag. In future research, we will investigate this pattern to examine the decisionmaking associated with this change in sign. Monthly public market dollar volume is also significantly positively related (t = 2.24: p = .026) with the one-month lag in public market unit volume. This supports the literature’s finding that issues come in clusters.
There is also a strong and positive relation between monthly public market dollar proceeds and their private market counterparts. We found above that the number of offerings was not strongly related to private market dollar volume. However, we find that public market dollar volume is significantly related (t = 3.78: p = .000) to private market dollar volume. Because both private and public monthly dollar volumes are strongly related in each regression, we cannot determine the direction of a lead or lag relationship.
This finding also supports a complementary theory: when capital is available, it is available in both markets.
The final two regression equations use monthly proceeds in 1999 real dollars. These were adjusted for inflation by the Producer Purchasing Index (PPI) as the deflator. The private market dollar volume (EQ. 5F: ADJ RSQ = .169) equation shows positive relations to the one-month lag of dollar volume (t = 1.88: p = .061), and to all 24 months of lagged monthly public proceeds in 1999 real dollars. Monthly proceeds in 1999 real dollars (Eq.
6F: ADJ RSQ = .21) for the public market shows significantly positive relations to its lags for month 1, 3 and 5; all 24 months lags for private market proceeds in 1999 real
34

dollars; and the S&P 500 returns for lags 2, 3 and 4. These final regressions confirm the complementary nature of the public and private market segments and that activity in both markets occur in clusters.
8. Conclusions.
The three questions investigated have clear answers based on our empirical results. a.
b.
Have HOT Biotech IPO markets coincided with HOT general markets?
Does the presence of a HOT public equity market affect private market financing? c.
Does the Biotech public market lead or lag its private counterpart?
First, in the biotech industry, HOT markets for IPOs have been somewhat associated with the general market for IPOs, but the industry shows its own uniqueness. Our method for investigating this question corresponds generally to those applied earlier: however, we find less strong association between the Biotech and general industry IPO activity. The sources of the differences may lie in the payoffs to R&D activity. The announcement of the mapping of the human genome stands out as a particular cause, but a more detailed investigation may reveal others.
Second, the public equity market appears to be the market of choice for IPOs in the
Biotech industry – the cost of funds is lower there – but private market financings are more stable, and less sensitive to cycles than the public market. There is an effect: funds
35

availability in the private market implies availability in the private market, but the reverse relationship is weaker.
Finally, the lead-lag relationship between financings in public and private markets is fuzzy. Clearly IPO financings in the two markets occur in clusters that are rather coincident in time. A finer determination of the direction of the relationship might be revealed by additional research that considered the total financings, initial and seasoned, in the industry. This kind of investigation might also shed further light on the availability story addressed with regard to the second research question above.
Perhaps most compelling finding for the direction of future research is the suggestion that the financing environment of the Biotech industry is different from that of the general market. Particularly because the industry is so R&D intensive, with long lead times to payoffs from research, its financing profile is distinct: one set of explanations and relationships in the determinants of financing decisions is unlikely to fit all industries.
36

References:
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1&2, 35-68.
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Choe, H., R. Masulis, and V. Nanda, 1993, “Common stock offerings across business cycle.” Journal of Empirical Finance , 1: 1-29.
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185.
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Financial Economics , 2: 235-272.
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37

Lerner, Josh, 1994, “Venture capitalists and the decision to go public.” Journal of
Financial Economics , 35: 293-316.
Loughran, T. and Ritter, J. 1995. “The new issues puzzle.” Journal of Finance , 50: no. 1,
23-52.
Loughran, T, and Ritter, J., 2002, “Why don’t issuers get upset about leaving money on the table in IPOs?” Review of Financial Studies , 15: 413-443.
Lowry, M., and G.W. Schwert, 2002, “IPO market cycles: bubbles or sequential learning?” Journal of Finance , 57: no. 3, 1171-1200.
McGahan, A.M., and M.E. Porter, 1997, “How much does industry matter, really?”
Strategic Management Journal , 18: 15-30.
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Ritter, J.R., 1984, “The HOT issue market of 1980.” Journal of Business , 57: 215-240.
38

APPENDIX 1
Public and Private Market Distributed Lag Equations and Variables
1F. Number of private financings per month
AMT 9
T
= A
0
+ j
6
∑
= 1
B j
*
AMT 9 t − j
+ k
3
∑
= 1
C k
* AMT 10 t − k
+
23
∑ l = 0
D l
* AMT 6 t − l
+ E
1
* AMT 11 t − 1
+
6 ∑ m = 0
F m
* AMT 12 t − m
+ G
1
* GDPSTE t
2F. Number of public financings per month
+ ε t
AMT 10
T
= A
0
+ j
6
∑
= 1
B j
* AMT 10 t − j
+ k
3
∑
= 1
C k
* AMT 9 t − k
+ E
1
* AMT 11 t − 1
+
6 ∑ m = 0
F m
* AMT 12 t − m
+ G
1
* GDPSTE t
+ ε t
3F. Total nominal dollars of private financings per month
+ l
23
∑
= 0
D l
* AMT 2 t − l
AMT 2
T
= A
0
+ j
6
∑
= 1
B j
* AMT 2 t − j
+ k
3
∑
= 1
C k
* AMT 9 t − k
+ l
23
∑
= 0
D l
* AMT 6 t − l
+ E
1
* AMT 11 t − 1
+
6
∑ m = 0
F m
* AMT 12 t − m
+ G
1
* GDPSTE t
+ ε t
4F. Total nominal dollars per month of public financings
AMT 6
T
= A
0
+ j
6
∑
= 1
B j
* AMT 6 t − j
+ k
3
∑
= 1
C k
* AMT 10 t − k
+ E
1
* AMT 11 t − 1
+
6 m
∑
= 0
F m
* AMT 12 t − m
+ G
1
* GDPSTE t
+ ε t
+
23 l
∑
= 0
D l
* AMT 2 t − l
39

5F. Total 1999 real dollars per month of private financings
AMT 14
T
= A
0
+ j
6
∑
= 1
B j
* AMT 14 t − j
+ k
3
∑
= 1
C k
* AMT 9 t − k
+
+ E
1
* AMT 11 t − 1
+ m
6
∑
= 0
F m
* AMT 12 t − m
+ G
1
* GDPSTE t
+
6F.
Total 1999 Real dollars per month of public financings
ε t
23 l
∑
= 0
D l
* AMT 18 t − l
AMT 18
T
= A
0
+ j
6
∑
= 1
B j
* AMT 18 t − j
Where,
+ E
1
* AMT 11 t − 1
+ m
6
∑
= 0
F m
* AMT 12 t − m
+ k
3
∑
= 1
C k
* AMT 10 t − k
+ G
1
* GDPSTE t
+ ε t
+
23 l
∑
= 0
D l
* AMT 14 t − l
AMT2 t
= total nominal dollars of private biotech financings in month t.
AMT6 t
= total nominal dollars of public biotech financings in month t.
AMT9 t
= number of private biotech financings in month t.
AMT10 t
= number of public biotech financings in month t.
AMT11 t
= 10 year minus 3 month treasury yield spread in month t.
AMT12 t
= S&P 500 return for month t.
AMT14 t
= Total $’s monthly: Private-1999 real
AMT18 t
= Total $ monthly: public-1999 real
GDPSTE t
= dummy variable for the state of the economy equals to
1, if in expansion, and 0, if in a recession.
40

41

Biotech Study - Distributed Lag Results (Public versus Private Markets)
EQ. 1F
R Square
0.459
Coefficients
P Values
Dependent Variable equals total number of private financings - monthly (AMT9)
Lagged Variable (Months 0 thru -6)
Constant
0.079
[.908]
AMT9(-1) AMT9(-2) AMT9(-3) AMT9(-4)
0.272
[.000]
0.130
[.064]
0.106
[.137]
0.052
[.465]
Coefficients
P Values
AMT9(-5) AMT9(-6) AMT10(-1 AMT10(-2)AMT10(-3)
0.052
[.460]
0.070
[.316]
0.058
[.285]
-0.019
[.755]
-0.020
[.718]
Coefficients
P Values
Coefficients
P Values
AMT11(-1) AMT12(0) AMT12(-1 AMT12(-2)AMT12(-3)
0.060
5.152
4.124
[.740] [.182] [.168]
AMT12(-4) AMT12(-5)GDPSTE
1.042
[.738]
0.014
[.997]
0.778
[.299]
3.097
[.210]
2.069
[.411]
Lagged Public monthly nominal dollars (0 thru - 23 months)
AMT6(0) AMT6(-1) AMT6(-2) AMT6(-3) AMT6(-4) AMT6(-5)
0.00002
[.008]
0.00005
[.008]
0.00007
[.008]
0.00008
[.008]
0.00010
[.008]
0.00011
[.008]
AMT6(-6) AMT6(-7) AMT6(-8) AMT6(-9) AMT6(-10) AMT6(-11)
0.00013
[.008]
0.00014
[.008]
0.00014
[.008]
0.00015
[.008]
0.00015
[.008]
0.00015
[.008]
AMT6(-12) AMT6(-13) AMT6(-14) AMT6(-15) AMT6(-16) AMT6(-17)
0.00015
[.008]
0.00015
[.008]
0.00015
[.008]
0.00014
[.008]
0.00014
[.008]
0.00013
[.008]
AMT6(-18) AMT6(-19) AMT6(-20) AMT6(-21) AMT6(-22) AMT6(-23)
0.00011
[.008]
0.00010
[.008]
0.00008
[.008]
0.00007
[.008]
0.00005
[.008]
0.00002
[.008]
EQ. 2F
R Square
0.556
Coefficients
P Values
Constant
0.821
0.333
Dependent Variable equals total number of public financings - monthly (AMT10)
Lagged Variable (Months 0 thru -6)
AMT10(-1)AMT10(-2 AMT10(-3)AMT10(-4)
0.501
[.000]
-0.006
[.934]
0.021
[.771]
0.170
[.018]
Lagged Private monthly nominal dollars (0 thru - 23 months)
AMT2(0) AMT2(-1) AMT2(-2) AMT2(-3) AMT2(-4) AMT2(-5)
-0.00001
[.509]
-0.00003
[.509]
-0.00004
[.509]
-0.00005
[.509]
-0.00006
[.509]
-0.00006
[.509]
Coefficients
P Values
Coefficients
P Values
Coefficients
P Values
AMT10(-5) AMT10(-6)AMT9(-1) AMT9(-2) AMT9(-3)
-0.001
-0.008
0.160
-0.047
0.093
[.990]
[.896] [.043] [.563] [.244]
AMT11(-1) AMT12(0) AMT12(-1 AMT12(-2)AMT12(-3)
0.094
10.927
10.943
10.960
10.977
[.674] [.015] [.002] [.000] [.000]
AMT12(-4) AMT12(-5)GDPSTE
10.994
[.004]
11.010
[.024]
-0.375
[.664]
AMT2(-6) AMT2(-7) AMT2(-8) AMT2(-9) AMT2(-10) AMT2(-11)
-0.00007
[.509]
-0.00008
[.509]
-0.00008
[.509]
-0.00008
[.509]
-0.00009
[.509]
-0.00009
[.509]
AMT2(-12) AMT2(-13) AMT2(-14) AMT2(-15) AMT2(-16) AMT2(-17)
-0.00009
[.509]
-0.00009
[.509]
-0.00008
[.509]
-0.00008
[.509]
-0.00008
[.509]
-0.00007
[.509]
AMT2(-18) AMT2(-19) AMT2(-20) AMT2(-21) AMT2(-22) AMT2(-23)
-0.00006
-0.00006
-0.00005
-0.00004
-0.00003
-0.00001
[.509] [.509] [.509] [.509] [.509] [.509]
42

43

Biotech Study - Distributed Lag Results (Public versus Private Markets)
EQ. 5F Dependent Variable equals total monthly dollars of private financings - 1999 Real (AMT14)
R Square
0.210
Coefficients
P Values
Lagged Variable (Months 0 thru -6)
Constant
AMT14(-1)AMT14(-2 AMT14(-3)AMT14(-4)
22.28
0.119
-0.066
0.113
0.054
[.699]
[.094] [.358] [.114] [.435]
Coefficients
P Values
AMT14(-5) AMT14(-6)AMT9(-1) AMT9(-2) AMT9(-3)
0.039
0.064
1.692
5.127
-4.608
[.575] [.356] [.769] [.391] [.422]
AMT11(-1) AMT12(0) AMT12(-1 AMT12(-2)AMT12(-3)
Coefficients
-13.738
269.950
225.310
180.670
136.040
P Values
[.378] [.393] [.364] [.384] [.521]
Coefficients
P Values
AMT12(-4) AMT12(-5)GDPSTE
91.401
46.764
-1.132
[.724]
[.887] [.985]
Lagged Public monthly dollars- 1999 Real (0 thru - 23 months)
AMT18(0) AMT18(-1) AMT18(-2) AMT18(-3) AMT18(-4) AMT18(-5)
0.003
[.001]
0.005
[.001]
0.008
[.001]
0.010
[.001]
0.012
[.001]
0.014
[.001]
AMT18(-6) AMT18(-7) AMT18(-8) AMT18(-9) AMT18(-10)AMT18(-11)
0.015
[.001]
0.016
[.001]
0.017
[.001]
0.018
[.001]
0.018
[.001]
0.019
[.001]
AMT18(-12)AMT18(-13)AMT18(-14 AMT18(-15 AMT18(-16)AMT18(-17)
0.019
0.018
0.018
0.017
0.016
0.015
[.001] [.001] [.001] [.001] [.001] [.001]
AMT18(-18)AMT18(-19)AMT18(-20 AMT18(-21 AMT18(-22)AMT18(-23)
0.014
[.001]
0.012
[.001]
0.010
[.001]
0.008
[.001]
0.005
[.001]
0.003
[.001]
EQ. 6F Dependent Variable equals total monthly dollars of public financings - 1999 Real (AMT18)
R Square Lagged Variable (Months 0 thru -6)
0.269
Constant
AMT18(-1)AMT18(-2 AMT18(-3)AMT18(-4)
Coefficients
-131.44
0.040
-0.128
0.096
-0.112
P Values [.218]
[.607] [.105] [.219] [.102]
Coefficients
P Values
AMT18(-5) AMT18(-6)AMT10(-1 AMT10(-2)AMT10(-3)
0.208
0.010
22.911
-1.061
-2.830
[.002] [.883] [.014] [.917] [.759]
Coefficients
P Values
AMT11(-1) AMT12(0) AMT12(-1 AMT12(-2)AMT12(-3)
-2.1928
405.420
426.110
446.810
467.500
[.936]
[.455] [.313] [.204] [.198]
Coefficients
P Values
AMT12(-4) AMT12(-5)GDPSTE
488.19
508.890
83.080
[.276]
[.377] [.429]
Lagged Private monthly dollars - 1999 Real (0 thru - 23 months)
AMT14(0) AMT14(-1) AMT14(-2) AMT14(-3) AMT14(-4) AMT14(-5)
0.014
[.000]
0.026
[.000]
0.037
[.000]
0.047
[.000]
0.057
[.000]
0.064
[.000]
AMT14(-6) AMT14(-7) AMT14(-8) AMT14(-9) AMT14(-10)AMT14(-11)
0.071
[.000]
0.077
[.000]
0.081
[.000]
0.085
[.000]
0.087
[.000]
0.088
[.000]
AMT14(-12)AMT14(-13)AMT14(-14 AMT14(-15 AMT14(-16)AMT14(-17)
0.088
[.000]
0.087
[.000]
0.085
[.000]
0.081
[.000]
0.077
[.000]
0.071
[.000]
AMT14(-18)AMT14(-19)AMT14(-20 AMT14(-21 AMT14(-22)AMT14(-23)
0.064
0.057
0.047
0.037
0.026
0.014
[.000] [.000] [.000] [.000] [.000] [.000]
44