Institutional Versus Individual Investment in IPOs: The Importance of Firm Fundamentals

Laura Casares Field

Penn State University E-mail: laurafield@psu.edu

Phone: (814) 865-1483

Michelle Lowry•

Penn State University E-mail: mlowry@psu.edu Phone: (814) 865-1483

November 4, 2005

Abstract: Over both short and long horizons, IPOs with greater institutional shareholdings outperform those with smaller institutional shareholdings. Over a one-quarter horizon, institutions can identify firms that beat market benchmarks. Over the long-run, however, institutions’ advantage lies entirely in their ability to avoid firms that exhibit the worst performance. Institutions appear to rely heavily on readily available firm and offer characteristics when making their investment decisions. In contrast, individual investors are less likely to consider such characteristics and, as a result, they invest disproportionately in poorly performing firms. However, a simple strategy of investing in higher quality firms, for example, firms with positive earnings prior to the IPO, would enable individuals to avoid much of this underperformance.

We thank Harry DeAngelo, Linda DeAngelo, Richard Evans, A mar Gande, Jean Helwege, Tim Loughran, Raghu Rau, Jay Ritter, Dennis Sheehan, John Wald and workshop participants at the 15th Annual Finance and Accounting Conference, the Financial Management Association 2005 meetings, Arizona State University, Binghamton University, Penn State University, the University of Houston, the University of Notre Dame, and Vanderbilt University. We thank the Smeal Research Grants Program for generously providing funding to purchase the Spectrum/CDA 13F institutional data. Previous versions of this paper were titled, “How Is Institutional Investment in Initial Public Offerings Related to the Long-Run Performance of These Firms?” and “Institutional Investment in Newly Public Firms”.

• Corresponding author: Smeal College of Business, Penn State University, University Park, PA 16802. Fax: 814-865-3362.

Institutional Versus Individual Investment in IPOs: The Importance of Firm Fundamentals

Abstract

Over both short and long horizons, IPOs with greater institutional shareholdings outperform

those with smaller institutional shareholdings. Over a one-quarter horizon, institutions can

identify firms tha t beat market benchmarks. Over the long-run, however, institutions’

advantage lies entirely in their ability to avoid firms that exhibit the worst performance.

Institutions appear to rely heavily on readily available firm and offer characteristics when

making their investment decisions. In contrast, individual investors are less likely to

consider such characteristics and, as a result, they invest disproportionately in poorly

performing firms. However, a simple strategy of investing in higher quality firms, for

example, firms with positive earnings prior to the IPO, would enable individuals to avoid

much of this underperformance.

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I. Introduction

Initial public offerings (IPOs) are an extremely attractive investment opportunity when

they first come to market but are less attractive over subsequent years. The average initial

return from day 0 to day 1 is approximately 19%, while the annual raw return over the

following five years averages only 5% (Loughran and Ritter, 2004, 1995). In fact, IPOs have

consistently earned lower returns than the S&P 500 over long horizons, and Brav and

Gompers (1997) show that small, non-venture backed IPOs underperform even size and book-

to-market matched portfolios. The objective of this paper is to examine the investment s of

institutional investors, who are presumably aware of this evidence.1

Despite the poor performance of IPOs relative to various benchmarks, we find that

institutions have been active investors in IPOs. They invested in nearly 90% of them between

1980 and 2000. However, institutions do not invest equally in all types of IPOs. For

example, they invested in a significantly lower proportion of firms (approximately 70%) in

the worst performing sector, i.e., small, non-venture backed IPOs.2

One potential explanation for institutions’ investment patterns in IPOs is that they are

able ex ante to discriminate firm quality. Indeed, not all IPOs are poor investments. Over the

past 20 years, the top 100 IPOs earned over 1000% in the first three years, compared to –99%

for the bottom 100. The challenge for investors is to identify such winners and losers ahead

of time. In the IPO market in particular, institutional investors may have a distinct advantage

over individuals. Institutions have connections to venture capitalists and underwriters, and

they are invited to road shows where they can obtain firm- and offer-specific information.

1 This paper extends Field (1997). In a contemporaneous paper, Dor (2004) also provides evidence on institutional ownership and IPO performance. 2 These investments are measured at least one month after the IPO and, thus, do not include shares that have been flipped.

2

From the San Francisco Chronicle in August 2004, “In a typical road show, large clients of

the lead underwriters are invited to lunch at fancy hotels, where the company going public

spills beans that weren’t included in the prospectus. This supposedly gives the large investors

an edge over the poor schmoes who weren’t invited.”

If institutions possess an informational advantage over individuals, then institutions

may be better able to identify the quality of firms issuing IPOs. Consistent with this

conjecture, newly public firms with larger institutional shareholdings tend to perform better

over several horizons than those with little institutional interest. However, the source of

institutions’ higher returns is different at short versus long investment horizons.

Over short horizons, institutions are able to identify venture-backed firms that

outperform market benchmarks. It is possible that institutions have a particular advantage

within this class of firms because venture capitalists share value-relevant information with

them. In contrast, over longer horizons of one to three years, we find no evidence that

institutions can systematically identify the best performers in any sector of the IPO market.

Over these long-run horizons, the difference in performance between firms with high and low

institutional investment is driven entirely by the significantly negative abnormal returns of

firms with little or no institutional interest.

These results suggest that individual investors experience the greatest IPO

underperformance. To more directly examine this conjecture, we isolate firms with no

institutional presence shortly after the IPO – that is, firms with only individual investors. We

show that these firms are more likely to have negative pre-IPO earnings and lower pre-IPO

working capital ratios. Consistent with having poor pre-IPO fundamentals, these firms do not

do well subsequently: their earnings become significantly more negative in the years after the

IPO and their average stock returns are 16% below size and book-to-market matched firms.

3

Moreover, these firms are extremely unlikely to ever garner institutional interest, suggesting

that it is predominantly individuals who experience this underperformance.3

Finally, we examine the relation between long-run returns and publicly available

information about offer quality. Consistent with Teoh, Welch, and Wong (1998), we find that

publicly available information is significantly related to post-IPO firm performance. Teoh et

al. show that firms with unusually high accruals in the year of the IPO underperform those

firms with low accruals. We find that even simpler measures of firm quality, which

individuals could easily obtain, are significantly related to future firm performance.

Specifically, individuals could avoid the worst performers by simply investing in firms

brought public by higher ranked underwriters and backed by venture capitalists, and in firms

with more working capital and positive earnings prior to the IPO. For example, a portfolio of

firms with above-median ranked underwriters outperforms one with below-median ranked

underwriters by approximately fifty basis points per month over the three years following the

offering. In sum, much of institutions’ advantage over individuals appears to be driven by

institutions ‘doing their homework’ – individuals would benefit greatly by doing theirs.

Our results are consistent with a growing body of literature suggesting that institutions

have an advantage over individuals. Studies of the stocks that institutions and mutual funds

purchase indicate that these agents have significant ability to pick stocks that outperform

benchmarks (e.g., Grinblatt and Titman (1989, 1992), Nofsinger and Sias (1999), and

Wermers (2000)). Parrino, Sias, and Starks (2003) find that institutions tend to sell to

individuals in the year prior to forced CEO turnovers, in part because they are better

information on the prospects of the firm. Gibson, Safieddine, and Sonti (2004) show that

3 While size is an important determinant of whether firms obtain institutional investment, the above-mentioned factors are highly significant even after controlling for size. That is, the firms with only individual investment are not just the smallest firms in our sample.

4

SEO firms with the largest increases in institutional investment around the offering earn

significantly higher abnormal returns than those with the greatest decreases. Chemmanur, He,

and Hu (2005) find that institutions possess private information about SEOs, and they are able

to obtain greater allocations in better offerings. Chen, Harford, and Li (2004) document that

institutions decrease their holdings in firms that subsequently make poor acquisitions. In a

sample of 441 IPOs between 1997 and 2001, Boehmer, Boehmer, and Fishe (2005) find that

underwriters provide institutions with more shares in firms that subsequently perform better.

Our paper contributes to this literature in several ways. First, using a large,

comprehensive sample of IPOs over a 21-year period, we demonstrate that the source of

institutions’ advantage over individuals differs by investment horizon, with institutions

beating market benchmarks only at very short horizons, but successfully avoiding the firms

that tend to perform the worst over longer periods. Second, we find that institutional investors

use publicly available firm and offer characteristics in choosing their IPO investments, but

individuals are more likely to disregard such quality measures. Third, we demonstrate that

the most severe long-run IPO underperformance is concentrated in firms that attract only

individual investors. Finally, our results indicate that while individuals suffer the most

underperformance, this need not be the case – individuals could avoid the worst

underperformers by simply paying closer attention to firm fundamentals.

The paper is organized as follows. Section II describes the data and methodology.

Section III presents evidence on institutional investment patterns in IPOs over the past 21

years. Section IV examines the relation between these institutional holdings and IPO long-

run performance, and Section V examines the determinants of institutional investment. In

Section VI, we focus our attention on firms with only individual investors, and Section VII

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seeks to determine whether individual investors could earn higher returns by paying more

attention to fundamentals. Section VIII concludes.

II. Data and Methodology

Our dataset consists of firms that went public between 1980 and 2000, as listed on the

Securities Data Company (SDC) database. We omit financial institutions (SIC codes 6000-

6999), utilities (SIC codes 4900-4999), closed-end funds, ADRs, unit offerings, and IPOs

with an offer price less than five dollars. Firms are also required to have CRSP data. Our

final sample consists of 5907 IPOs.

For each firm, we collect the offer date, offer price, initial file range, proceeds,

underwriter name(s), whether the issue was backed by a venture capitalist, and the over-

allotment option (if available) from SDC. We use Carter and Manaster’s (1990) measures of

underwriter quality, as updated by Carter, Dark, and Singh (1998) and Loughran and Ritter

(2004), to rank each underwriter. Ranks range from zero to nine, with higher ranks

representing higher quality underwriters. We define the price run-up as the percent difference

between the midpoint of the filing range and the offer price, and we compute the initial return

as the percent difference between the offer price and the first after-market closing price from

CRSP, where this price must be within 14 days of the offer date. We also collect data on the

age for firms in our sample, where age represents the number of years since the company was

founded.4

4 Founding dates for 1980-1984 IPOs come from Jay Ritter’s IPO database and are based on inspection of IPO prospectuses. Founding dates for 1985-1987 IPOs come from Moody’s manuals and Dunn and Bradstreet’s Million Dollar Directory. Founding dates for 1988-1992 IPOs come from inspection of the IPO prospectus and are used in Field and Karpoff (2002). Founding dates for 1993-1995 IPOs come primarily from proxy statements available on Lexis -Nexis, S&P Corporate Descriptions, and Moody’s manuals. For 1996-2000 IPOs, founding dates come from SDC, Moody’s manuals, Dunn and Bradstreet’s Million Dollar Directory, the IPO Reporter, and inspection of IPO prospectuses available on Edgar (some of the prospectus data for 1996-2000 are from Ljungqvist and Wilhelm, 2003). See Appendix 1 of Loughran and Ritter (2004) for a complete description.

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Since 1978, the SEC has required all institutions with more than $100 million of

securities under discretionary management to report holdings of all common stock positions

greater than 10,000 shares or $200,000 on a quarterly basis (at the end of March, June,

September, and December).5 We obtain these data on 13F institutional ownership in

electronic form from CDA/Spectrum for 1980-2000. Specifically, for each IPO firm we

obtain the total number of shares owned by each institution.

Because we are interested in voluntary post-IPO holdings by each institution (as

opposed to initial allocations that institutions receive), we collect the institutional holdings at

least one month after the IPO. Thus, for an IPO on February 21st, we collect institutional

holdings as of the end of March. However, for an IPO on March 3rd, we collect institutional

holdings as of the end of June. Ideally, we would also like to exclude institutions that owned

shares prior to the IPO. Thus, following Dor (2004), we first omit any institution listed as a

venture capitalist on SDC or whose name suggests it is a venture capitalist (e.g., Acacia

Venture Partners). Second, we omit any institution that is listed as owning more than 15% of

the shares offered in the IPO. This is based on the assumption that one entity is extremely

unlikely to obtain such a large stake after the firm goes public, suggesting that it probably

owned these shares prior to the IPO.

We define institutional ownership percentage as the number of shares owned by

institutions divided by the estimated public float. For a recent IPO, the float should be

approximately equal to the total number of shares offered in the IPO, which is equal to shares

offered as listed in the prospectus plus the overallotment option. 6 In cases where sufficient

5 It is not unusual for 13F institutions to report ownership levels that fall below the minimum reporting requirements. Of the 73,930 13F filings by institutions for our IPO sample, 8,337 (or 11%) of them hold fewer than 10,000 shares and an equity position of less than $200,000. 6 For example, shares subject to lock-up provisions and Rule 144A restrictions are not part of the float (see Field and Hanka, 2001).

7

data are available, this is the formula we use to obtain the float. Because SDC does not

provide data on the over-allotment option sold for all issues, in some cases we mus t estimate

it. Based on Aggarwal’s (2000) findings regarding the relation between the initial return and

the size of the over-allotment option, we assume that those issues with an initial return less

than or equal to 5% have a float equal to 105% of shares offered. For those issues with an

initial return greater than 5%, the float equals 115% of shares offered. Using these estimates,

average (median) institutional ownership as a percent of the public float equals 25% (24%).

Figure 1 indicates that institutional ownership in IPOs has increased dramatically over

time. The solid line in Panel A illustrates that institutions have invested in an increasing

number of IPOs over our sample period: they invested in approximately 70% of IPOs in

1980, compared to over 95% in 2000. Panel A also demonstrates that this pattern of increased

institutional interest in IPOs does not appear to be correlated with the volume of IPOs (shown

in the gray bars).

Panel B of Figure 1 shows that the mean and median institutional ownership as a

percent of the public float has also increased dramatically, from less than 10% in 1980 to

approximately 35% in 2000. The finding of dramatic increases in institutional ownership

over time is similar to the pattern documented by Gompers and Metrick (2001) for the overall

market.

In order to compare the performance of firms according to their level of institutional

ownership, we form portfolios based on institutional ownership. The simplest approach

would be to rank all IPOs based on the percent of shares owned by institutions and form

portfolios based on this ranking. However, as indicated by Figure 1, this would bias the high

institutional holding portfolios toward more recent IPOs. In addition, it would likely also bias

the high institut ional holdings portfolios toward larger companies, as Gompers and Metrick

8

show that institutions tend to favor bigger firms. Thus, we want to control for both year and

company size in forming the portfolios. Institutions’ preference for larger companies stems in

large part from their preference for more liquid companies. For a recent IPO, proceeds raised

is likely to be a better estimate of liquidity than market capitalization. The majority of shares

that were outstanding prior to the IPO and not sold in the IPO are restricted under lock-up

agreements, meaning they cannot be traded and do not contribute to firm liquidity. For this

reason, we use proceeds as our size measure.

Following Nagel’s (2005) methodology, we estimate cross-sectional regressions each

year of institutional ownership on size:

,))(log()log(1

log ,2

321

,

,tiii

ti

ti eproceedsproceedsINST

INST+++=

−βββ (1)

where INSTi,t is the institutional holdings for firm i (as a percent of public float) measured at

Quarter 1 and proceedsi is the IPO proceeds of firm i. 7 We use the regression residual for

each firm to group firms into quintiles annually, where Quintile 1 (Q1) represents firms with

the lowest residual institutional ownership, and Quintile 5 (Q5) represents firms with the

highest residual institutional ownership. Finally, we combine quintiles across years to form

our five portfolios, based on institutional ownership net of firm size. Thus, Q1 includes all

IPOs across our 21-year sample period that had the lowest residual institutional ownership in

each year, while Q5 includes all IPOs across the 21-year sample period that had the highest

residual institutional ownership in each year.8 Throughout the remainder of the paper, we

refer to residual institutional ownership as just institutional ownership.

7 Values of INST less than 0.0001 are set equal to 0.0001, and values of INST greater than 0.9999 are set equal to 0.9999. 8 For robustness, we have also performed all tests using an alternative measure of institutional ownership. Specifically, each year we classify IPO firms into one of five quintiles based on market capitalization. Within each of these year-market capitalization portfolios, we place firms into one of five quintiles based on the percent

9

Descriptive statistics for the full sample and for each institutional holding quintile are

provided in Table 1. Over the entire period, institutional investors held an average (median)

of 25.2% (24.0%) of the public float at Quarter 1. There is considerable dispersion in

institutional holdings across the quintiles, with average holdings of 6.7% of the public float

(median=0%) for the smallest quintile, compared to 33.3% (median=31%) for the largest

quintile. In addition, Table 1 indicates that we have successfully controlled for firm size in

our formation of institutional holdings quintiles, as there is no significant difference between

Q1 and Q5 for either proceeds raised or market capitalization.

Table 1 shows several significant differences between the firms with the lowest and

highest institutional holdings. For example, firms with the lowest institutional holdings tend

to be younger on average (10.3 years for Q1 vs. 12.7 years for Q5), are less likely to be

venture backed (32.1% venture backed in Q1 vs. 37.1% in Q5), have higher average initial

returns (22.9% for Q1 vs. 14.7% for Q5), and have a lower median EBIT/TA in the year

before the IPO (6.0% for Q1 vs. 11.3% for Q5). The relation between institutional ownership

and EBIT/TA is particularly strong, as median EBIT/TA increases monotonically across the

quintiles. In addition, firms with lower institutional ownership have lower abnormal returns

over the first year, where abnormal returns are defined as the returns on the IPO firms minus

the return on a matched size, book-to-market portfolio.9 Finally, there is no evidence of

significant relations between institutional holding quintile and either book-to-market ratio,

of shares owned by institutions. Finally, we combine all the high institutional holding groups to form the high institutional holding portfolio, and similarly for the other levels of holdings. The disadvantage of this measure is that it does not entirely control for the effects of firm size. Nonetheless, results are qualitatively similar using this alternative measure. 9 Specifically, to form the size/book-to-market benchmark, all NYSE-listed firms are divided into five quintiles based on size and into five quintiles based on BM. The intersection of these groupings yields 25 size/BM portfolios. Each IPO firm is placed into its appropriate portfolio, and its return is compared to the average returns across all other firms in that portfolio, i.e., all firms on CRSP with size and BM data after excluding firms that have gone public within the past three years.

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underwriter rank, or leverage. Across the entire sample, the average book-to-market ratio is

0.40, the average underwriter rank is 7.0, and median leverage is 66.2%.

III. Institutional Investment Patterns

Stoll and Curley (1970), Ritter (1991), Loughran and Ritter (1995), and Ritter and

Welch (2002) find that IPOs tend to significantly underperform a variety of benchmarks.

Brav and Gompers (1997) show that this underperformance is concentrated among small,

non-venture backed IPOs. The first panel of Table 2 confirms that similar patterns also exist

in our sample. Intercepts from four-factor regressions of equally weighted monthly post-IPO

returns over a three-year time horizon indicate that on average, IPOs experience significant

underperformance in the three years after the IPO.10 As shown in the table, this result is

driven by non-venture backed firms. Moreover, the underperformance within the non-venture

backed category is greater for small IPOs than for large IPOs. For the smallest tercile, non-

venture backed IPOs experience average underperformance of 63 basis points per month over

the first three years. Interestingly, the second panel of Table 2 shows that IPO under-

performance is not limited to the long-run: small non-venture backed firms significantly

underperform their benchmarks in the very first quarter.

If institutions are aware of the historical long-run performance of IPOs, then one

might expect them to avoid those types of IPOs that have been shown to perform worst.

Thus, we examine the investment patterns of institutional investors in IPOs by venture capital

backing and size groupings (where firms are classified into small, medium, and large terciles,

based on market capitalization, as done in Brav and Gompers (1997)). 10 Specifically, a firm is included in the regression sample for the first three years after its first institutional reporting date (or until its delisting date if it delists before three years). Monthly returns net of the risk-free rate on this portfolio are regressed on the three Fama-French (1993) factors plus the Carhart (1997) momentum factor. A significantly positive (negative) intercept indicates that the recent IPO firms earned positive (negative) abnormal returns over our sample period.

11

The third panel of Table 2 shows that institutional investors hold a significantly

greater percentage of venture-backed firms (average 28% vs. 24%; median 27% versus 22%).

Moreover, the difference between venture- and non-venture backed IPOs is most substantial

among the smallest firms: on average, institutions hold 20% of small, venture backed IPOs

versus only 13% of small, non-venture backed IPOs (median 17% vs. 7%). These statistics

suggest that institutional investors are aware of the evidence on the poor performance of

small, non-venture backed IPOs, and accordingly, they are more cautious about investing in

this class of firms.

The fourth panel of Table 2 bears this out: institutional shareholders own shares in

85% of non-venture backed IPOs, whereas they hold shares in 96% of venture-backed IPOs.

Looking back at the top two panels of Table 2, this is an interesting observation, as IPO

underperformance – both in the long- and short-run – is concentrated among non-venture

backed firms (particularly small, non-venture backed firms). Clearly, institutional

shareholders seem to recognize that non-venture backed IPO firms do not perform well, and

thus, they are more selective when investing in these firms.

Focusing on size, we see similar patterns when we compare the institutional ownership

of small and large firms to the average performance of these firms. Consistent with small

firms performing more poorly, institutional presence in these firms is significantly lower

(76% for small firms vs. 98% for large ones). Finally, consistent with small, non-venture

firms performing particularly poorly, institutions invest in only 70% of these firms, compared

to over 90% of firms in almost every other VC, size subgroup.

While institutions invest in significantly fewer small, non-venture backed IPOs, it is

perhaps surprising that their presence is as large as it is. Given that these firms experience

such great underperformance, even in the very short-run, one might wonder why institutions

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invest in this class at all. The next section examines whether institutions can differentiate the

quality of the firm a priori, beyond its size and venture backing.

IV. Relation Between Long-Run IPO Returns and Institutional Holdings

If institutions are “informed” investors (Michaely and Shaw (1994) and Badrinath,

Kale, and Noe (1995)), then IPO firms with higher institutional shareholdings should

outperform those with lower institutional shareholdings. As discussed in depth in this section,

our findings suggest that this is, in fact, the case. In light of this evidence, we try to

understand the source of the higher returns for firms with larger institutional investment. For

example, is the significant relation between institutional investment and post-IPO returns

entirely attributable to institutions’ tendency to invest more in those sectors of the IPO market

that perform better? Alternatively, are institutions able to further discriminate firm quality,

and, if so, how do they do this?

IV.A. Descriptive Evidence on Long-Run Returns

We base our empirical tests on the five institutional holdings portfolios described in

Section II, where Quintile 1 (Q1) has the lowest institutional holdings and Quintile 5 (Q5) has

the highest. Figure 2 provides descriptive evidence for a strategy of holding Q5 and shorting

Q1. Specifically, Panel A shows one-quarter, one-year, and three-year buy-and-hold returns

for Q5 minus Q1, and Panel B shows cumulative returns for the same portfolio over the same

horizons.11 The figures show raw returns and returns net of a matched size/book-to-market

portfolio (as defined in Table 1). Figure 2 suggests that a strategy of buying Q5 and shorting

Q1 would earn excess returns at each horizon, using either raw or abnormal returns.

11 Specifically, buy-and-hold returns represent compounded monthly returns, and cumulative returns represent summed monthly returns.

13

IV.B. Four-Factor Regressions and Calendar Time Abnormal Returns

As noted by Fama (1998), cross-correlations between firm returns prevent accurate

significance tests from being conducted on long-run, event time, buy-and-hold and cumulative

abnormal returns. Thus, we rely on four-factor regressions to test the significance of the

relations suggested in Figure 2.12 Tables 3 and 4 show regressions of monthly returns of the

high institutional quintile (Q5) minus the low institutional quintile (Q1) on the three Fama-

French factors plus the Carhart momentum factor. Following Fama and French (1993) and

Carhart (1997), the factors include the market return minus the risk-free rate (RMRF), returns

on a portfolio of small firms minus returns on a portfolio of big firms (SMB), returns on a

high BM portfolio minus returns on a low BM portfolio (HML), and returns on a high

momentum portfolio minus returns on a low momentum portfolio (PR12). To account for the

effects of hot issue markets, regressions are estimated using weighted least squares, where

each monthly return is weighted by the number of IPOs in the portfolio. The intercept from

such a regression can be interpreted as a measure of abnormal performance.

Table 3 shows four-factor regressions over one-quarter, one-year, and three-year time

horizons, meaning that a firm is included in the regression sample for the first three, twelve,

and thirty-six months, respectively, after its first institutional reporting date (or until its

delisting date if delists before this). The results are generally consistent with inferences from

the BHARs and CARs shown in Figure 2. Using equally weighted returns, we find a

significant intercept for all three horizons, suggesting that a strategy of investing in firms with

high institutional holdings and shorting those with low institutional holdings would earn

12 We also test the significance of these relations using calendar time portfolios (as opposed to calendar time regressions, which we report), where the benchmark is one of 25 matched size, book-to-market portfolios. Results (not shown) are similar.

14

significant abnormal returns.13 Using value-weighted returns, intercepts are significant at the

one- and three-year horizons.

Table 3 indicates that institutions do better, on average, on their IPO investments than

individuals. Table 4 attempts to shed light on how institutions achieve their higher returns.

For example, institutions may have a particular advantage within certain classes of firms.

Alternatively, institutions’ higher returns may be driven merely by higher investment in those

types of firms that tend to perform better, e.g., VC-backed firms. To examine these issues, we

form six groups based on VC-backing and size, where the size categorization consists of

terciles based on market capitalization as of the first institutional reporting date. Within each

of these six groups of firms, we regress returns on the Q5 – Q1 portfolio on the four factors

described above (similar to Table 3). Table 4 shows intercepts from each of these regressions

over one quarter, one year, and three year horizons.

Focusing first on the one-quarter results, Table 4 shows that the abnormal returns

shown in Table 3 over this short horizon are driven entirely by the venture backed sample. In

fact, venture backed firms with high institutional ownership outperform those with low

institutional ownership by 3.3% per month in the first quarter. At longer horizons, however,

we see a different pattern emerge. While we continue to find significant intercepts on the

Q5-Q1 portfolios, the source of these abnormal returns is non-venture backed firms. For

horizons of one and three years, returns for non-venture backed firms with the largest

institutional shareholdings are between 7% and 10% per annum higher than those with the

13 Gompers and Metrick (2001) find that greater demand pressure in stocks with the most institutional investment causes these stocks’ returns to be higher. However, in our sample we find that institutional investment in the Q1 firms increases faster than that in the Q5 firms over the first 12 quarters after the IPO. This suggests that the higher returns of the Q5 firms are not driven by heavier institutional buying, and thus greater demand pressure, in these stocks.

15

smallest institutional shareholdings (monthly intercepts between 0.006 and 0.009). In

contrast, we find no such evidence for the venture backed sample at longer horizons.

Finally, Table 4 indicates that institutions’ advantage does not come solely from

heavier investments in the venture, size groupings that tend to perform better. If that was the

case, the Q5-Q1 positive abnormal return would disappear once we controlled for these

factors, meaning we would not see positive abnormal returns within any of the VC, size

subgroups. However, we do see significant alphas for many of these subgroups. For

example, institutions appear to successfully differentiate firm quality within the small tercile

firms at every horizon. At the one-quarter horizon, where institutions have an advantage

among VC-backed firms, we find significantly positive intercepts within the venture backed,

small size tercile. Analogously, at the one- and three-year horizons, where institutions’

advantage lies in non-venture backed firms, we find significant intercepts within the non-VC

backed, small size terciles. Although the Q5-Q1 strategy yields abnormal returns within some

of the other size/venture subgroups, we find no systematic pattern among these other

portfolios.

So why does this strategy of investing in firms with high institutional shareholdings

and shorting those with low institutional shareholdings provide positive abnormal returns?

The positive alphas could come from two different sources: high returns for firms with large

institutional ownership or low returns for firms with small institutional ownership (since our

portfolio measures returns for Q5-Q1). That is, are institutions choosing winners in

Quintile 5, or are they avoiding losers in Quintile 1? Table 5 investigates by providing

intercepts from four-factor regressions for each of the five institutional ownership quintiles

for the full sample and also delineated by venture backing.

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Results in the first panel of Table 5 suggest that over short horizons, institutions have

some ability to identify both the worst and the best performers. Looking at returns measured

over one quarter, firms with the lowest levels of institutional investment (Q1) earn

significantly negative abnormal returns, indicating that institutions successfully avoid the

worst performers. In addition, venture-backed IPOs with the highest levels of institutional

investment (Q5) earn significantly positive abnormal returns. That is, among venture-backed

IPOs, institutions are able to identify firms that significantly outperform market benchmarks

over one quarter.

However, at longer horizons (see the second and third panels of Table 5), none of the

quintiles earn positive abnormal returns. The positive returns of Q5 minus Q1 over the one-

year and three-year periods are driven entirely by the poor performance of the Q1 firms,

particularly for non-venture backed firms. Thus, institutional investors do not seem to have

any ability to choose firms that perform extraordinarily well over the long-run. Rather, the

difference in long-run returns between firms with high and low institutional interest reflects

the fact that institutions invest less in firms that subsequently suffer the worst long-run

underperformance.14

IV.C. Discussion of Returns Evidence

Results in the previous section suggest that institutional investments shortly after the

IPO contain information regarding both the short-run and long-run performance of these

firms. However, the information content differs according to the horizon. These findings

14 We have also estimated similar regressions for other intervals, for example two quarters and two years. The two quarter horizon results are similar to the one-quarter results, with the Q5-Q1 strategy producing significant abnormal returns, which are driven by institutions successfully identifying both the best and the worst performers. In contrast, the two-year results are similar to the one- and three-year results, with the Q5-Q1 strategy again producing significant abnormal returns, but in this case being driven solely by the significant negative performance of those firms with the least institutional interest (Q1).

17

lead to the following questions. First, why can institutions identify the ‘winners’ only over

short periods? Second, can investors benefit from our findings? We discuss each of these

questions in turn.

To shed light on the first question, we examine the length of time institutions tend to

hold their IPO investments. If institutions identify ‘winners’ over only short periods, perhaps

they tend to hold short-term positions in IPOs. In a study of mostly seasoned firms, Wermers

(2000) finds that institutions frequently divest their positions after short periods, suggesting

that they may expend more effort in forecasting firm performance over relatively short

horizons. Consistent with Wermers’ evidence regarding more seasoned firms and our

findings in Table 5 showing that institutions invest in better performing firms only over the

short-run, we find that institutions generally do not hold IPOs for long periods. Over 60% of

institutions divest their initial holdings before the end of the first year and almost 80% have

divested by the end of the second year. In comparison, only 27% of institutions increase their

initial holdings between the end of the first quarter after the IPO and one year later, and just

16% by two years later (results not tabulated).

Turning to our second question of whether investors could benefit from our findings,

the significant abnormal returns from a strategy of going long Q5 and shorting Q1seem to

suggest an arbitrage opportunity. In fact, however, it is only an actual arbitrage opportunity if

one can short Q1. Notably, D’Avolio’s (2002) findings suggest that a lack of institutional

interest in these Q1 firms may result in a scarcity of shares available to short. For the one-

and three-year horizons, the abnormal returns from the Q5-Q1 strategy are entirely driven by

the low performance of the Q1 firms. Thus, it is unlikely that our findings provide investors

with a strategy to earn positive abnormal returns over these periods. For the one-quarter

horizon, the significantly positive performance of the Q5 firms contributes to the Q5-Q1

18

returns. Thus, an investor could benefit if he could identify these Q5 firms. However,

institutional ownership data are generally not available until 45 days after the beginning of the

quarter, i.e., midway through the quarter in which we find evidence of abnormal returns.

While it is unlikely that one could implement the Q5-Q1 strategy to earn arbitrage

profits, it is possible that there are other ways that investors could benefit from our results.

For example, the finding that institutions are able to identify – and avoid – the poorest long-

run performers potentially has implications for individual investors, who apparently invest

disproportionately in newly-public firms that perform the worst over long horizons. Sections

5 and 6 focus on this issue.

V. How Do Institutions Choose Their IPO Investments?

Institutions potentially have both private and public information available to them

when making IPO investments, but the majority of individuals have access only to public

information. By focusing solely on readily available public information, this section provides

insight into the extent to which individuals might be able to avoid those IPOs that exhibit the

poorest long run performance.

Following Gompers and Metrick (2001), we consider three types of public information

that tend to influence cross-sectional variation in institutional ownership of firms: (i) the legal

environment institutions face as fiduciaries (“prudence,” see also Del Guercio, 1996),

(ii) liquidity and transaction cost motives, and (iii) historical return patterns.

Based on evidence presented in Del Guercio (1996), Muscarella, Peavy and

Vetsuypens (1990), Megginson and Weiss (1991), and Carter and Manaster (1990), we

include firm age, venture capital backing, and underwriter rank as proxies for prudence. We

also include the following accounting information as measures of prudence: sales/assets,

19

liabilities/assets, working capital/assets, and a dummy variable equal to one for firms with

positive EBIT, all measured the year before the IPO. To determine whether liquidity and

transaction cost motives are important for institutions, we include the log of real IPO proceeds

measured in $1983 (similar in spirit to the firm size variable used by Gompers and Metrick).

Finally, to determine whether historic return patterns are important for institutions, we include

price run-up as a measure of momentum. We also include yearly dummies to account for the

overall increase in institutional ownership over time (coefficients not reported in table).

Table 6 shows two measures of institutional ownership regressed on the above factors.

In the first column, the dependent variable equals institutional ownership as a percent of the

public float, and in the second column the dependent variable is a dummy variable, equal to

one if the firm has institutional ownership and zero otherwise. In both cases, institutional

ownership is measured between one and four months after the IPO (consistent with earlier

tables).

The findings in Column 1 of Table 6 are similar to the results reported by Gompers

and Metrick for the entire market. Consistent with Gompers and Metrick, we find that

liquidity motives are an important determinant of institutional holdings (as reflected by the

significantly positive coefficient on proceeds). In addition, we find that four of our prudence

measures – underwriter rank, VC backing, positive EBIT, and working capital/assets – are

significant.15 A comparison of our findings with those of both Gompers and Metrick and Del

Guercio suggests that prudence motives are slightly more important for IPO firms, perhaps

because these firms are so much riskier than seasoned firms.

15 Although we find positive earnings to be an important determinant in institutional investment, we do not find that the magnitude of earnings matters: when we include EBIT/TA, either in addition to or instead of the positive EBIT dummy, we find that EBIT/TA is not a significant determinant of institutional investment.

20

Interestingly, institutions appear to use slightly different criteria when evaluating

venture versus non-venture backed firms. We estimate this same Table 6 regression for

venture-backed and for non-venture backed IPOs (results not reported). The variables that

were most significant across the whole sample are also significant for the two subsamples.

However, these nine publicly available measures explain 48% of the variation in institutional

investment for non-venture backed firms, compared with only 28% for venture backed firms.

This evidence, in combination with the evidence provided earlier that institutions earn

positive abnormal returns in the short-run for venture backed investments, suggests that

institutions may be privy to information more proprietary in nature for these firms, possibly

gleaned through ongoing relationships with venture capitalists.

Finally, comparing the OLS regression in Column 1 with the logit regression in

Column 2, we see that the determinants of whether an institution invests in an IPO company

are similar to the determinants of the magnitude of institutional ownership. The biggest

difference between the two regressions is price run-up: firms with a larger price run-up are

less likely to obtain institutional ownership, but price run-up is not significantly related to the

magnitude of institutional ownership. Other than price run-up, the most important factors in

both regressions are proceeds, underwriter rank, and earnings: institutions are less likely to

invest in firms with smaller proceeds, lower-ranked underwriters, and negative earnings.16

16 We have also estimated this regression including market capitalization immediately after the IPO, but it is not significant. The finding that institutions focus on proceeds rather than market capitalization is consistent with institutions being most concerned with liquidity. Because many of the pre -IPO shares are locked-up following the offering, proceeds is a better metric of liquidity than market capitalization.

21

VI. Isolating Firms With No Institutional Investment

The evidence presented thus far indicates that firms with high institutional investment

outperform those with low institutional investment and that institutional investments are

strongly related to readily available public information. The remainder of the paper seeks to

determine whether individuals could do better by paying more attention to similar public

information measures. As a first step, this section examines the relation between individuals’

apparent lack of attention to fundamentals and the poor long-run returns of those firms in

which they invest.

To get the cleanest tests possible of how individuals fare when investing in IPOs, we

isolate those firms without any institutional investment. Toward that end, rather than utilizing

our institutional quintiles, we put firms into two distinc t groups: (1) firms with positive

institutional investment as of the first institutional reporting date (the “Institutions” group),

and (2) firms with zero institutional investment as of the same date (the “Individuals Only”

group). The Institutions group consists of 5,256 firms (89% of total IPO sample), while the

Individuals Only group consists of 651 firms (11% of total IPO sample). As one might

expect, the average market capitalization of the “Individuals Only” firms is significantly

smaller than tha t of the “Institutions” group. However, for purposes of our analysis, it is

perhaps more important to note that the “Individuals Only” firms do not solely represent the

smallest firms in our sample. For example, the median size of the “Individuals Only” group is

$15.6 million, and 341 firms have a market capitalization below this. Notably, a similar

number (349) of “Institutions” firms have a market capitalization below this same cutoff.

Thus, the substantial size difference is primarily driven by the fact that nearly all large firms

22

have institutions ; notably, however, many (but not all) small firms also have institutional

investors.

VI.A. Accounting Fundamentals for Firms With and Without Post-IPO Institutional Investment

Figure 3 shows accounting data for all IPOs delineated by institutional presence,

where “Year -1” refers to the fiscal year immediately preceding the IPO, “IPO Year” refers to

the fiscal year including the IPO, and “Year 1” and “Year 2” refer to the first two fiscal years

after the IPO. At each point in time, we look at median EBIT/total assets, median retained

earnings/total assets, median working capital/total assets, and the fraction of firms with

positive earnings for our two groups.

Focusing first on firm characteristics prior to the IPO, there is some indication that

Individuals Only firms have poorer fundamentals than Institutions firms – they are less likely

to have positive earnings before the IPO, and they have less working capital. Specifically,

Panel A shows that only 49% of firms in the Individuals Only group have positive earnings,

compared to 62% in the Institutions group. In Panel D we see that the Individuals Only firms

have median working capital/total assets of 12%, versus 22% for the Institutions firms.

These apparent differences in firm quality become much more dramatic after the IPO.

Looking at the fraction of firms with positive earnings, we see a drop over time for both

groups, but the Institutions group always contains significantly more firms with positive

earnings. Moreover, differences in the level of earnings (median EBIT/TA) between the two

groups become highly significant starting in the year of the IPO. The Individuals Only firms’

EBIT/TA drops from a median of 9% before the IPO to only 2% during the IPO year and then

becomes negative after that. In contrast, the Institutions firms’ median EBIT/total assets

experiences a modest drop from 10% to 9% between year -1 and year 0, and the median never

23

becomes negative. Consistent with these patterns in earnings, Panel C shows that the

Institutions group’s retained earnings tends to increase over time, while that for the

Individuals Only group decreases rapidly. As a result, the difference between the two groups

is significantly different in every period after year -1. Finally, similar to the patterns observed

in year -1, Individuals Only firms have significantly lower working capital in years 1 and 2.

Overall, these accounting ratio results demonstrate that, along some dimensions,

Individuals Only firms are of lower quality before the IPO, and the differences in quality

become even more pronounced over time.17

VI.B Stock Returns and Firm Status for Firms With and Without Post-IPO Institutional Investment

The previous section shows that Individuals Only firms have significantly poorer

accounting fundamentals both before and after the IPO. In Figure 4, we see similar

differences in stock returns. The firms with only individual investment clearly perform

substantially worse.18 In the three years post-IPO, the Individuals Only firms substantially

underperform, earning 16% less than their size and book-to-market matched counterparts after

three years.

Consistent with the returns evidence presented in Figure 4, we also find that

Individuals Only firms are substantially more likely to be delisted than are Institutions firms:

33% of all Individuals Only firms delist within five years of the IPO, compared with only

17 We have also estimated all the above relations on a sample of “Individuals Only” and “Institutions” firms that are more similar in size. Specifically, we base our tests on all firms with a market capitalization below $15.6 million (the median market capitalization of the “Individuals Only” firms). This results in a sample of 341 “Individuals Only” firms and 349 “Institutions” firms. Findings with respect to median EBIT/TA, the percent of firms with positive EBIT/TA and median RE/TA are all similar. We do not find significant differences in WC/TA using this alternative sample. 18 We also estimate four-factor regressions (not reported), where our dependent variable equals returns on a portfolio of firms with institutional investment minus returns on a portfolio of firms without institutional investment. Consistent with Figure 5, we obtain a significantly positive intercept, indicating that the firms with institutional ownership perform significantly better.

24

13% of firms with institutional shareholdings.

VI.C Do the Individuals Only Firms Ever Attract the Attention of Institutional Investors?

The evidence presented in the previous section – that the Individuals Only firms

experience substantially lower abnormal returns and are significantly more likely to be

delisted – suggests that individuals bear the brunt of IPO underperformance. However, the

evidence presented thus far is merely suggestive, as we have not investigated the possibility

that institutional investors later buy into the Individuals Only firms.

Figure 5 shows the evolution of institutional ownership over three years for the

Institutions group and the Individuals Only group. As shown in Panel A, 12% of the average

Institutions firm is owned by institutional investors in the first post-IPO quarter and that

number gradually increases through the first two years, until it stabilizes at around 19% of

shares outstanding.19 By contrast, the Individuals Only group starts with zero institutional

holdings (by construction), but even three years later, institutional investors own only an

average of 5% of the shares outstanding for those firms remaining in the sample. The

evidence in Panel B, which shows median institutional ownership over time, provides even

more insight. At Quarter 1, the median Institutions firm has 10% of its shares owned by

institutional investors; by three years out, the median is 15%. Interestingly, the majority of

Individuals Only firms have no institutional investors even seven quarters after the IPO. By

three years post-IPO, the median Individuals Only firm has less than 1% institutional

investment. This evidence demonstrates that firms failing to garner institutional interest at the

IPO are unlikely to do so even years later.

19 This figure depicts total shares owned by all institutions (i.e., not just the ‘original institutions’, who owned shares at the end of the first quarter following the IPO), calculated as a percent of shares outstanding rather than as a percent of float, as in Table 1 (since we are following investments over time).

25

Given the evidence in Figure 4 that the Individuals Only firms substantially

underperform those with institutional presence, as well as the finding that institutions are

unlikely to ever invest in this class of firms, it is clear that individual investors suffer the

worst IPO long-run underperformance.

VII. Can Individuals Do Better?

By isolating newly-public firms with only individual investors, we find direct

evidence that individuals are more likely to invest in firms with poorer accounting

fundamentals and lower long-run returns. In this section we examine the direct relation

between these accounting fundamentals of IPO firms and their post-IPO returns. That is, how

much better could individuals do by simply paying more attention to readily available firm-

and offer-specific information known at the IPO?

Figure 6 and Table 7 examine average post-IPO returns, based upon the factors

institutions seem to use in making their IPO investment decisions. From Table 6, we know

that institutions prefer venture capital backed IPOs, IPOs issued by higher ranked

underwriters, IPOs with larger proceeds, firms with positive pre-IPO earnings, and firms with

higher pre-IPO working capital. Thus, we bifurcate our sample based on each of these

dimensions and then compare returns for each of the groups. Specifically, for underwriter

rank, IPO proceeds, and WC/TA, we determine the median of companies going public in each

year, and rank firms above or below that yearly median. We also examine the same returns

measures for firms with positive versus nonpositive earnings in the year prior to the IPO, and

venture versus non-venture backing.

Figure 6 provides descriptive evidence on the long-run returns of IPO firms based on

these characteristics, and Table 7 tests the significance of these relations. Specifically,

26

Figure 6 shows buy-and-hold abnormal returns, relative to size- and book-to-market matched

firms, for quarterly horizons of one quarter through twelve quarters after the IPO. Because

significance tests cannot be conducted on these event-time buy-and-hold returns, Table 7

shows intercepts from 4-factor regressions, where the dependent variable equals returns on

various portfolios of firms that have gone public within the past 36 months. Specifically, for

each characteristic, we form three portfolios: (1) a long position in firms with an above-

median characteristic ; (2) a long position in IPOs with a below-median characteristic, and (3)

a long position in IPOs with an above-median characteristic and a short position in IPOs with

a below-median characteristic. We regress the returns on each of these portfolios, net of the

risk-free-rate, on the four factors and report the intercept from this regression (similar to

Tables 4 and 5).

Figure 6 and Table 7 show that institutions are correct in paying attention to these

readily available measures of firm and offer quality. For example, looking at Panel A of

Figure 6, a simple strategy of investing in firms with higher-ranked underwriters dominates a

strategy of investing in firms with lower-ranked underwriters. Table 7 confirms that the

difference is significant. Specifically, a portfolio of firms with above-ranked underwriters

outperforms a portfolio of firms with below-ranked underwriters by approximately 50 basis

points per month over the three years following the IPO. Table 7 also shows that firms with

below-median underwriters significantly underperform market benchmarks, while firms with

above-median underwriters do not exhibit underperformance during our sample period.

Similarly, VC-backed firms yield significantly higher returns than non-VC backed

firms, and as shown in Panel B of Figure 6, firms with positive EBIT, and higher working

capital ratios prior to the IPO also significantly outperform their counterparts. Further, Table

7 shows that in each of these cases, what we deem the ‘low quality’ characteristic firms (i.e.,

27

non-VC backed, negative EBIT, and lower WC/TA) all earn significantly negative abnormal

returns. Surprisingly, size of proceeds is not at all predictive of future stock performance.

While firms with positive EBIT and higher WC/TA perform significantly better,

Figure 3 showed that individuals disproportionately invest in firms with negative EBIT and

lower WC/TA. Similarly, while firms that are VC backed and have higher ranked

underwriters are also more likely to perform better, findings in Table 6 suggest that

individuals are more likely to invest in firms that are not VC backed and that have lower

ranked underwriters. Individuals are disproportionately investing in the types of firms that,

according to Table 7, earn significantly negative abnormal returns over the long-run. This in

large part explains why the Individuals Only firms perform so poorly. In sum, it would

behoove individual investors to pay more attention to these readily available firm and offer

characteristics when making long-run investments in IPOs.

VIII. Conclusion

This paper examines institutional investments shortly following the IPO. We find that

newly public firms with high institutional shareholdings outperform those with low

institutional shareholdings at various investment horizons. Much of institutions’ advantage

lies in their ability to avoid the worst-performing firms, and we observe that firms with the

lowest levels of institutional investment significantly underperform over all horizons. In

addition, institutions successfully identify the firms that perform best over short intervals.

Specifically, venture-backed firms with the highest levels of institutional investments beat

market benchmarks over a one quarter interval after the IPO.

We find that institutional investors rely heavily on publicly available information

when choosing the IPOs in which they invest – in particular, institutional investors prefer

28

venture-backed firms, firms taken public by higher quality underwriters, firms issuing larger

IPO proceeds, and firms with positive earnings and higher working capital ratios prior to

the IPO.

To better understand the choices of individual investors, we isolate those firms with no

institutional ownership. We find that such firms are more likely to have negative pre-IPO

earnings and lower working capital before going public. These results suggest that

individuals pay less attention to quality characteristics when choosing which IPOs to invest

in. Moreover, we find that those firms with only individual investors significantly

underperform those with institutional interest.

An examination of institutional ownership over time for firms with and without initial

institutional presence provides additional evidence on individuals’ investment returns.

Although the typical firm with institutional investors after the IPO continues to attract more

institutional investment, most firms lacking initial institutional interest fail to garner the

interest of institutional investors even years later. Together, these results imply that it is

individual investors who bear the brunt of IPO underperformance.

Finally, we find that individuals could do substantially better in their IPO investments

by paying more attention to readily available public information. Firms with positive EBIT

and with lower working capital prior to the IPO, with higher ranked underwriters, and that are

backed by venture capitalists significantly outperform their counterparts. It is puzzling why

individuals continue to invest in these types of firms that perform so poorly. In fact, it is

puzzling why these types of firms are able to go public at all.

29

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Table 1. Firm Characteristics by Institutional Ownership

The sample consists of 5907 IPOs between 1980 and 2000, which we classify into institutional ownership quintiles as follows. Each year, we estimate cross-sectional regressions of institutional ownership on IPO proceeds:

t,i2

t,i3t,i21t,i

t,i e))oceeds(log(Pr)oceedslog(PrInst1

Instlog +β+β+β=

−. We use the regression residual for each

firm to group firms into quintiles, where Quintile 1 (Q1) represents firms with the lowest residual institutional ownership, and Quintile 5 (Q5) represents firms with the highest residual institutional ownership. Age is the age of IPO firms as they go public. Proceeds are the proceeds raised in the IPO, and Market Cap is the market capitalization, measured on the institutional holdings report date, both measured in 2000 million dollars. The book-to-market ratio is book value divided by market cap, where book value is measured as book value at the end of the first fiscal year after the IPO; Fraction Venture-Backed shows the percent of IPO firms with venture-capital backing; Underwriter Rank is the average Carter-Manaster (1990) underwriter ranking score, as updated by Loughran and Ritter (2004); Initial Return is the percent difference between the offer price and the first after-market closing price, as listed on CRSP; EBIT/TA is defined as earnings before interest and taxes during the fiscal year ending prior to the IPO, divided by total assets at the end of that fiscal year; and leveraget-1 is defined as total debt over total assets at the end of the fiscal year prior to the IPO. Abnormal returns equal annual buy-and-hold returns, net of size and book-to-market matched portfolios (i.e., compounded monthly returns over the first year). Medians are reported for EBIT and leverage, and all other numbers represent means.

Institutional Quintile

Firm Characteristic

Full Sample

1 (lowest)

2 3 4 5 (highest)

Institutional Ownership as Percent of Public Float

25.2% 6.7% 22.0% 30.8% 33.2% 33.3%***

Proceeds ($2000 million) $56.6 45.9 65.6 60.5 58.3 52.6

Market Cap ($2000 million) $362.0 303.2 401.8 375.4 344.7 383.3

Book-to-Market Ratio 0.40 0.41 0.42 0.39 0.38 0.39

Age 13.3 10.3 14.9 14.4 13.7 12.7***

Fraction Venture-Backed 41.0% 32.1% 42.3% 43.5% 47.3% 37.1%**

Underwriter Rank 7.0 6.0 7.7 7.8 7.4 6.2

Initial Return 19.9% 22.9% 19.2% 22.5% 20.2% 14.7%***

EBIT/TA t-1 (median) 9.7% 6.0% 9.2% 10.1% 10.5% 11.3%***

Leveraget-1 (median) 66.2% 69.5% 66.3% 65.0% 65.3% 65.7%

Abnormal annual returns -4.2% -10.8% -3.9% -2.3% -2.2% -2.3%

Number of Firms 5907 1173 1188 1183 1187 1176

***,**Indicates that the mean (median for EBIT) for the lowest institutional ownership quintile (Q1) is significantly different from that for the highest institutional ownership quintile (Q5) at the 1% and 5% level, respectively.

33

Table 2. Post IPO-Returns and Institutional Holdings The sample consists of 5907 IPOs between 1980 and 2000. We also separate the sample based on venture capital backing and size. Specifically, we place firms into groupings based on venture backing and also based on size, where firms are placed into one of three terciles, based on market capitalization at the institutional reporting date that occurs between one and four months after the IPO. Firms in the smallest tercile are labeled as small firms, and those in the largest tercile are labeled as large firms. For returns based on a three year (one quarter) horizon, we estimate weighted least squares regressions (where the weight equals the number of firms in the portfolio in each month) of monthly returns net of the risk-free rate on all firms that went public within the prior three years (one quarter) on four factors: the market return minus the risk-free rate (RMRF), returns on a portfolio of small firms minus returns on a portfolio of big firms (SMB), returns on a high BM portfolio minus returns on a low BM portfolio (HML), and returns on a high momentum portfolio minus returns on a low momentum portfolio (PR12). Intercepts from these regressions are shown in the first two panels of the table. For the third panel, 13f institutional holdings are based on shares held by institutions, after excluding venture capitalists and institutions that own more than 15% of the public float. These shares are divided by the public float, equal to offering size including the estimated over-allotment option, to obtain percent institutional holdings. These holdings are similarly measured at the first institutional holdings reporting date.

All Firms Venture Backed Non Venture Backed

Four Factor Regression Alphas Across All IPO Firms, for Three Year Horizon Returns: (Statistical significance of each estimate of alpha from zero denoted with asterisks)

All Firms -0.0047*** -0.0028 -0.0057***

Small Firms -0.0052** -0.0023 -0.0063**

Medium Firms -0.0041** -0.0013 -0.0062***

Large Firms -0.0046*** -0.0041* -0.0044***

Four Factor Regression Alphas Across All IPO Firms, for One Quarter Horizon Returns: (Statistical significance of each estimate of alpha from zero denoted with asterisks)

All Firms -0.0028 -0.0026 -0.0029

Small Firms -0.0080** -0.0011 -0.0108***

Medium Firms 0.0021 0.0007 0.0033

Large Firms -0.0011 -0.0064 0.0024

Average 13f Institutional Holdings at Quarter 1: (Statistical significance of difference between venture and non-venture backing denoted with asterisks)

All Firms 25.2% 27.8% 23.5%***

Small Firms 15.0% 19.7% 12.9%***

Medium Firms 27.2% 27.7% 26.7%

Large Firms 33.9% 33.6% 34.2%

Percentage of IPO Firms with 13f Institutional Shareholders at Quarter 1: (Statistical significance of difference between venture and non-venture backing denoted with asterisks)

All Firms 89.4% 95.8% 85.1%***

Small Firms 75.7% 89.0% 69.7%***

Medium Firms 95.1% 97.3% 93.1%***

Large Firms 97.5% 98.9% 96.5%***

***, **, * For the first and second panels, indicates significance at the 1%, 5% , and 10% level, respectively. For the third and fourth panels, indicates that the differences between the means for the venture backed and non-venture backed samples are significant at the 1%, 5%, and 10% level, respectively.

34

Table 3. Four-Factor Regressions for the Highest minus Lowest Institutional Ownership Quintiles This table shows weighted least squares regressions of monthly returns net of the risk-free rate on four factors: the market return minus the risk-free rate (RMRF), returns on a portfolio of small firms minus returns on a portfolio of big firms (SMB), returns on a high BM portfolio minus returns on a low BM portfolio (HML), and returns on a high momentum portfolio minus returns on a low momentum portfolio (PR12). Weights equal the number of IPOs each month. To form the institutional ownership quintiles, we estimate cross-sectional regressions of institutional ownership on IPO proceeds each year as follows:

t.i2

t,i3t,i21t,i

t,i e))oceeds(log(Pr)oceedslog(PrInst1

Instlog +β+β+β=

−. We use the regression residual for each firm to group firms into quintiles, where

Quintile 1 (Q1) represents firms with the lowest residual institutional ownership and Quintile 5 (Q5) represents firms with the highest residual institutional ownership . The dependent variable equals returns on the high institutional holdings quintile minus returns on the low institutional holdings quintile, monthly, for firms that have gone public within the past one quarter, one year, and three years. Both equal-weighted and value-weighted regressions are shown. T-statistics are shown in parentheses.

One Quarter One Year Three Years

Variable Equal Weighted

Value Weighted

Equal Weighted

Value Weighted

Equal Weighted

Value Weighted

Intercept 0.015*** (2.56)

0.010 (1.12)

0.010*** (3.18)

0.012** (2.19)

0.006** (2.31)

0.008* (1.71)

RMRF -0.001** (-0.81)

-0.002 (-1.04)

-0.002** (-2.06)

-0.003* (-1.93)

-0.001** (-2.24)

-0.002** (-2.11)

SMB -0.002 (-0.83)

-0.003 (-1.02)

-0.0001 (-0.11)

-0.003 (-1.27)

-0.0001 (-0.13)

-0.004** (-2.50)

HML -0.002*** (-0.75)

-0.001 (-0.28)

-0.003*** (-2.79)

-0.004* (-1.96)

-0.002* (-1.87)

-0.004** (-2.34)

PR12 0.005*** (2.91)

0.006** (2.14)

0.003*** (2.64)

0.005*** (2.85)

0.002** (1.97)

0.004** (2.53)

Adjusted R-Squared 0.05 0.02 0.12 0.08 0.06 0.09

***, **, * Significantly different from zero at the 1%, 5%, and 10% level, respectively.

35

Table 4. Intercepts from Equal-weighted Four-Factor Regressions for the Highest minus Lowest Institutional Ownership Quintiles, by Firm Size Tercile and Venture Backing

The sample consists of 5907 IPOs between 1980 and 2000. Each year, we estimate cross-sectional regressions of

institutional ownership on firm size: t.i2

t,i3t,i21t,i

t,i e))oceeds(log(Pr)oceedslog(PrInst1

Instlog +β+β+β=

−. We

use the regression residual for each firm to group firms into quintiles, where Quintile 1 (Q1) represents firms with the lowest residual institutional ownership, and Quintile 5 (Q5) represents firms with the highest residual institutional ownership. The first column represents all IPO firms, while the second and third represent IPOs that are venture backed and non-venture backed, respectively. We also separate the sample on size. Specifically, we categorize firms into terciles, based on market capitalization at the institutional reporting date. For the one-quarter horizon, we regress monthly equal-weighted returns on the high institutional holdings quintile minus the low institutional holdings quintile for firms that went public within the prior three months on four factors: the market return minus the risk-free rate (RMRF), returns on a portfolio of small firms minus returns on a portfolio of big firms (SMB), returns on a high BM portfolio minus returns on a low BM portfolio (HML), and returns on a high momentum portfolio minus returns on a low momentum portfolio (PR12). Intercepts from these regressions are shown in the table. For the one- and three-year horizons, regression samples include firms that have gone public within the past 12 and 36 months, respectively. T-statistics are shown in parentheses.

Returns Measured Over:

Full Sample

Venture Backed Firms

Non-Venture Backed Firms

One Quarter Horizon: All Size Terciles 0.015**

(2.55) 0.033***

(2.95) 0.004 (0.66)

Smallest Size Tercile 0.016** (2.08)

0.048** (2.55)

0.006 (0.73)

Middle Size Tercile 0.017 (1.56)

0.033* (1.85)

0.001 (0.05)

Largest Size Tercile 0.020 (1.21)

0.035 (0.99)

-0.019 (-0.94)

One Year Horizon: All Size Terciles 0.010***

(3.18) 0.009 (1.46)

0.009** (2.50)

Smallest Size Tercile 0.013*** (2.96)

0.003 (0.33)

0.013*** (2.74)

Middle Size Tercile 0.008* (1.68)

0.020** (2.39)

-0.002 (-0.32)

Largest Size Tercile 0.014** (2.03)

0.021 (1.61)

0.011 (1.45)

Three Year Horizon: All Size Terciles 0.006**

(2.32) 0.005 (1.27)

0.006** (2.14)

Smallest Size Tercile 0.008** (2.10)

0.006 (0.80)

0.008* (1.94)

Middle Size Tercile 0.005 (1.33)

0.007 (1.22)

0.002 (0.44)

Largest Size Tercile 0.007 (1.62)

0.004 (0.50)

0.009** (2.09)

***, **, * Significantly different from zero at the 1%, 5% , and 10% level, respectively.

36

Table 5. Intercepts from Four-Factor Regressions by Institutional Quintile, One Quarter, One Year, and Three Year Horizons

The sample consists of 5907 IPOs between 1980 and 2000. See Table 4 for description of quintile formation. For the one quarter horizon, we regress equal-weighted monthly returns net of the risk-free rate on firms in each of the institutional holdings for firms that went public within the prior three months on four factors: the market return minus the risk-free rate (RMRF), returns on a portfolio of small firms minus returns on a portfolio of big firms (SMB), returns on a high BM portfolio minus returns on a low BM portfolio (HML), and returns on a high momentum portfolio minus returns on a low momentum portfolio (PR12). Intercepts from these regressions are shown in the table. For the one year and three year horizons, regression samples include firms that have gone public within the past 12 and 36 months, respectively. T-statistics are shown in parentheses.

Returns Measured Over:

Quintile 1 (lowest)

Quintile 2 Quintile 3 Quintile 4 Quintile 5 (highest)

Difference (High – Low)

One Quarter:

Full Sample -0.012** (-2.49)

0.0001 (0.022)

-0.002 (-0.53)

0.004 (0.92)

0.003 (0.61)

0.015** (2.55)

Venture Backed Firms -0.016* (-1.91)

-0.0010 (-0.17)

-0.003 (-0.39)

0.005 (0.73)

0.014** (2.07)

0.033*** (2.95)

Non-Venture Backed Firms -0.008 (-1.60)

0.0002 (0.05)

-0.004 (-0.90)

-0.0001 (-0.02)

-0.003 (-0.65)

0.004 (0.66)

One Year:

Full Sample -0.017*** (-5.04)

-0.006** (-2.36)

-0.005* (-1.84)

-0.005* (-1.87)

-0.006** (-2.34)

0.010*** (3.18)

Venture Backed Firms -0.013** (-2.34)

-0.006 (-1.61)

-0.003 (-0.76)

-0.002 (-0.55)

-0.002 (-0.43)

0.009 (1.46)

Non-Venture Backed Firms -0.017*** (-5.21)

-0.005* (-1.87)

-0.006** (-2.36)

-0.007*** (-2.83)

-0.009*** (-3.16)

0.009** (2.50)

Three Years:

Full Sample -0.009*** (-2.79)

-0.004** (-2.40)

-0.005*** (-2.78)

-0.004** (-2.22)

-0.002 (-1.09)

0.006** (2.32)

Venture Backed Firms -0.006 (-1.42)

-0.001 (-0.48)

-0.002 (-0.94)

-0.001 (-0.51)

-0.001 (-0.22)

0.005 (1.27)

Non-Venture Backed Firms -0.009*** (-2.89)

-0.006*** (-3.00)

-0.006*** (-3.24)

-0.006*** (-3.22)

-0.003 (-1.40)

0.006** (2.14)

***, **, * Significantly different from zero at the 1%, 5%, and 10% level, respectively.

37

Table 6. What Factors Attract Institutional Investment in IPOs?

The sample consists of 5907 IPOs between 1980 and 2000. In the first column, percent institutional holdings is regressed on various firm and offer characteristics. Percent institutional holdings equals shares held by institutions, after excluding venture capitalists and institutions that own more than 15% of the public float, divided by the public float, equal to offering size including the estimated size of the over-allotment option. These holdings are measured at the first institutional holdings reporting date that occurs between one and four months after the IPO. In the second column, the dependent variable is a dummy variable, equal to one of the firm had any institutional holdings as of this same date, and zero otherwise. Explanatory variables in these regressions represent information known at the time of the offering. Proceeds are the proceeds raised in the IPO and given in year 2000 million dollars. Firm age is the age of each firm at the time of the IPO. Underwriter Rank is the average Carter-Manaster underwriter ranking score, as updated by Loughran and Ritter (2004). The dummy variable for venture capital backing equals one if the firm received venture capital investments before the IPO, and zero otherwise. The positive EBIT dummy equals one if EBIT in year t -1 is positive, and zero otherwise. Sales/assets , liabilities/assets, and working capital/assets are all measured at the end of the fiscal year ending prior to the IPO. Price Run-Up is the percent difference between the midpoint of the filing range and the offer price. Yearly dummies are included as additional explanatory variables but are not reported. T-statistics are given in parentheses.

OLS Dependent Variable:

% Institutional Ownership

Logit Dependent Variable:

Institutional Ownership Dummy

Intercept -27.43*** (-10.31)

-4.31*** (5.79)

Log Proceeds 6.98*** (22.22)

2.04*** (14.01)

Firm Age 0.51** (2.25)

0.17** (2.09)

Underwriter Rank 1.55*** (11.5)

0.18*** (4.67)

VC Dummy 1.15** (2.55)

0.32* (1.87)

Positive EBIT Dummyt-1 4.10***

(7.54) 0.57***

(3.21)

Sales/Assets t=-1 0.14

(0.84) -0.04 (0.65)

Liabilities / Assetst=-1 0.40

(0.93) 0.14

(1.02)

Working Capital / Assetst=-1 1.01*

(1.89) 0.29*

(1.80)

Price Run-Up 1.44 (1.49)

-0.97** (2.5)

Adjusted R2 (Pseudo R2) 0.40 0.43

***, **, * Significantly different from zero at the 1%, 5% and 10% level, respectively.

38

Table 7. Intercepts from Four-Factor Regression by Firm Characteristics Three Year Horizon

The sample consists of 5907 IPOs between 1980 and 2000. For underwriter rank, proceeds, and working capital (scaled by total assets), we divide the sample annually into two equal-sized groups, one group with an above-median score on the characteristic and the other with a below-median score on the characteristic. For venture capital backing, we break the sample into venture backed firms and non-venture backed firms. For earnings, we break firms into groups based on positive earnings (EBIT = 0) and negative earnings (EBIT < 0) in the year before the IPO. For underwriter rank, we form two portfolios: firms that have gone public within the past 36 months with an above-median underwriter, and firms that have gone public within the past 36 months with a below-median underwriter. We regress the monthly returns net of the risk-free rate on each of these portfolios on the three Fama -French factors and the Carhart momentum factor. Intercepts from these regressions, with t-statistics in parentheses, are shown in the first two columns. Finally, the last column shows intercepts from a similar regression where the dependent variable is returns on the first portfolio minus returns on the second portfolio. Regression portfolios are constructed similarly for the other variables (VC backing, positive EBIT, etc.).

High Quality Low Quality High minus Low Quality

Underwriter Rank (high quality = high rank)

-0.002 (-1.59)

-0.007*** (-3.38)

0.005*** (3.08)

VC Backing (high quality = VC backed)

-0.002 (-0.90)

-0.006*** (-3.85)

0.004** (2.38)

Proceeds (high quality = high proceeds)

-0.005*** (-3.38)

-0.005** (-2.05)

-0.0005 (-0.27)

Positive EBITyr -1 (high quality = positive EBIT)

-0.002* (-1.73)

-0.007** (-2.33)

0.005* (1.67)

WC / TA yr -1 (high quality = high WC/TA)

-0.001 (-0.73)

-0.007*** (-3.86)

0.006*** (4.02)

***, **, * Significantly different from zero at the 1%, 5% and 10% level, respectively.

39

Figure 1. Descriptive Statistics on Institutional Ownership

Panel A. Number of IPOs and Fraction with Institutional Ownership Over Time The gray bars show the number of IPOs each year, and the scale for this series is shown on the right hand side of the graph. The solid line shows the fraction of IPOs each year with institutional ownership, and the scale for this series is shown on the left hand side of the graph.

60%

65%

70%

75%

80%

85%

90%

95%

100%

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

% W

ith In

stitu

tiona

l Ow

ners

hip

0

50

100

150

200

250

300

350

400

450

500

550

600

650

Num

ber

of IP

Os

Number of IPOs

Fraction With Institutional Ownership

Panel B. Average and Median Institutional Owners hip of Public Float over Time

The solid line shows the average institutional ownership, as a percent of the public float, and the dotted line shows the median institutional ownership, as a percent of the public float.

0%

5%

10%

15%

20%

25%

30%

35%

40%

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Average Institutional Ownership

Median Institutional Ownership

40

Figure 2. Difference in Returns for Highest Institutional Quintile Portfolio Minus Lowest Institutional Quintile Portfolio

The sample consists of 5907 IPOs between 1980 and 2000. Each year, we estimate cross-sectional regressions of

institutional ownership on firm size: t.i2

t,i3t,i21t,i

t,i e))oceeds(log(Pr)oceedslog(PrInst1

Instlog +β+β+β=

−.

We use the regression residual for each firm to group firms into quintiles, where Quintile 1 (Q1) represents firms with the lowest residual institutional ownership, and Quintile 5 (Q5) represents firms with the highest residual institutional ownership. Finally, we calculate buy-and-hold and cumulative returns on Q5-Q1, over one quarter, one year, and three years following first the institutional holdings report date between one and four months after the IPO. Both raw returns and abnormal returns net of size and book-to-market matched portfolios are shown.

Panel A. Buy-and-Hold Returns (High Inst – Low Inst)

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

Raw Returns Size, BM

Buy

and

Hol

d R

etur

ns

One QuarterOne YearThree Year

Panel B. Cumulative Returns (High Inst – Low Inst)

0%

3%

6%

9%

12%

15%

18%

21%

Raw Returns Size, BM

Cum

ulat

ive

Abn

orm

al R

etur

ns

One QuarterOne Year

Three Year

41

Figure 3. Accounting Performance for Firms With and Without Institutional Ownership at Quarter 1

The sample consists of 5907 IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured between one and four months after the IPO. In each figure, the black bars show accounting variables for the 651 firms with no initial institutional shareholders, while the grey bars show the same ratios for the 5,256 firms with institutional shareholders. The accounting variables shown are the percent of firms with positive EBIT, median EBIT/Total Assets (EBIT/TA), median Retained Earnings/Total Assets (RE/TA), and median Working Capital/Total Assets (WC/TA), measured the year before the IPO (Year -1), the year the firm went public (IPO Year), the year after the IPO (Year 1), and two years after the IPO (Year 2). *** indicates a significant difference at the 1% level of the variable in question between the groups with and without institutional shareholders. Panel A: Percent of Firms with EBIT > 0 Panel B: Median EBIT/TA

62%67%

57%

49%49%*** 47%***

39%***35%***

0%

10%

20%

30%

40%

50%

60%

70%

80%

Year -1 IPO Year Year 1 Year 2

Initial Inst Ownership = 0Initial Inst Ownership > 0

9%10%

9%

7%

5%

-2%***-2%***

2%***

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

Year -1 IPO Year Year 1 Year 2

Initial Inst Ownership = 0Initial Inst Ownership > 0

Panel C: Median RE/TA Panel D: Median WC/TA

-11%***

-7%***

-1%***

0%

-1%

2%3%

3%

-12%

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

Year -1 IPO Year Year 1 Year 2

Initial Inst Ownership = 0Initial Inst Ownership > 0

22%

45%

36%33%

44%

25%***

31%***

12%***

0%

10%

20%

30%

40%

50%

Year -1 IPO Year Year 1 Year 2

Initial Inst Ownership = 0Initial Inst Ownership > 0

42

Figure 4. Three Year Buy-and-Hold Abnormal Returns for Newly Public Firms With and Without Institutional Ownership at Quarter 1

The sample consists of 5907 IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured between one and four months after the IPO. The broken line shows buy-and-hold abnormal returns for the 651 firms with no initial institutional shareholders at Quarter 1, while the solid line shows buy-and-hold abnormal returns for the 5,256 firms with institutional shareholders at Quarter 1. Buy and hold returns are net of size and book-to-market matched firm returns.

-17%

-14%

-11%

-8%

-5%

-2%

1%

4%

7%

10%

13%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Quarter Since IPO

BH

Abn

orm

al R

etur

ns

Initial Inst Ownership > 0

Initial Inst Ownership = 0

43

Figure 5. Institutional Ownership Over Time for Newly Public Firms With and Without Institutional Ownership at Quarter 1

The sample consists of 5907 IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured within one and four months after the IPO (referred to as Quarter 1). In Panel A, the black bars show average institutional ownership over the first 12 quarters post-IPO for the 651 firms with no initial institutional shareholders at Quarter 1, while the grey bars show average institutional ownership for the 5,256 firms with institutional shareholders at Quarter 1. In Panel B, the black bars show median institutional ownership over the first 12 quarters post-IPO for the 651 firms with no initial institutional shareholders at Quarter 1, while the grey bars show median institutional ownership for the 5,256 firms with institutional shareholders at Quarter 1.

Panel A: Average Institutional Ownership Over Time

12%

14%

15%16%

17%17%

18%19% 19%19% 19% 19%

0%1% 1%

2% 2% 3%3% 3%

4% 4% 5%5%

0%

5%

10%

15%

20%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Quarter Since IPO

Per

cent

of S

hare

s O

utst

andi

ng

Initial Inst Ownership > 0Initial Inst Ownership = 0

Panel B: Median Institutional Ownership Over Time

10%11%

12%12%

13%13% 14%

14% 15% 15% 15% 15%

0% 0% 0% 0% 0% 0% 0% 0.2% 0.4% 0.5% 0.7% 0.9%

0%

5%

10%

15%

20%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Quarter Since IPO

Per

cent

of S

hare

s O

utst

andi

ng

Initial Inst Ownership > 0Initial Inst Ownership = 0

44

Figure 6. Abnormal Buy and Hold Returns for Newly Public Firms Based on Firm Characteristics Known at the IPO

These figures show mean abnormal buy-and-hold returns, net of size and book-to-market matched portfolios, for 5907 IPOs between 1980 and 2000 for investment horizons of one quarter through 12 quarters post-IPO. For underwriter rank, proceeds, and working capital (scaled by total assets), we divide the sample annually into two equal-sized groups, one group with above-median score on the characteristic and the other with a below-median score on the characteristic. For venture capital backing, we break the sample into venture backed firms and non-venture backed firms. For earnings, we break firms into groups based on positive earnings (EBIT = 0) and negative earnings (EBIT < 0) in the year before the IPO.

Panel A. Offer Characteristics Known Before the IPO

-17%

-14%

-11%

-8%

-5%

-2%

1%

4%

7%

10%

13%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Quarter Since IPO

BH

Abn

orm

al R

etur

n

Low Rank Underwriter

High Rank Underwriter

-17%

-14%

-11%

-8%

-5%

-2%

1%

4%

7%

10%

13%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Quarter Since IPO

BH

Abn

orm

al R

etur

n

Non-VC Backed

VC Backed

-17%

-14%

-11%

-8%

-5%

-2%

1%

4%

7%

10%

13%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12Quarter Since IPO

BH

Abn

orm

al R

etur

n

Low Proceeds

High Proceeds

45

Figure 6. Abnormal Buy and Hold Returns for Newly Public Firms Based on Firm Characteristics Known at the IPO (continued)

Panel B: Accounting Data Measured in the Year Prior to the IPO

-17%

-14%

-11%

-8%

-5%

-2%

1%

4%

7%

10%

13%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Quarter Since IPO

BH

Abn

orm

al R

etur

n

EBIT < 0

EBIT = 0

-17%

-14%

-11%

-8%

-5%

-2%

1%

4%

7%

10%

13%

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Quarter Since IPO

BH

Abn

orm

al R

etur

n

Low WC/TA

High WC/TA