I



Institutional Investment in Newly Public Firms

Laura Casares Field

Smeal College of Business

Penn State University

University Park, PA 16802

E-mail: laurafield@psu.edu

Phone: (814) 865-1483

Michelle Lowry

Smeal College of Business

Penn State University

University Park, PA 16802

E-mail: mlowry@psu.edu

Phone: (814) 865-1483

September 30, 2005

We thank Harry DeAngelo, Linda DeAngelo, Amar Gande, Jean Helwege, Raghu Rau, Jay Ritter, Dennis Sheehan, John Wald and workshop participants at the 15th Annual Finance and Accounting Conference, Arizona State University, Binghamton University, Penn State University, the University of Houston, and Vanderbilt University. We thank the Smeal Research Grants Program for generously providing funding to purchase the Spectrum/CDA 13F institutional data. This paper was previously circulated under the title, “How Is Institutional Investment in Initial Public Offerings Related to the Long-Run Performance of These Firms?”

Institutional Investment in Newly Public Firms

Abstract

This paper examines the relation between institutional investment in IPOs and the stock returns associated with these firms. Over both short and long horizons, IPOs with greater institutional shareholdings outperform those with smaller institutional shareholdings. In the short run, the superior returns stem from institutions’ ability to identify venture-backed firms that subsequently outperform. Over the long-run, however, the return difference reflects institutions’ 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 better accounting fundamentals, would enable individuals to avoid much of this underperformance.

I. Introduction

Initial public offerings (IPOs) are an extremely attractive investment opportunity when they first come to market, but less attractive over subsequent years. The average initial return from day 0 to day 1 is approximately 19%, while the average annual raw return over the following five years is only about 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 patterns 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 IPOs between 1980 and 2000. Perhaps even more surprising, they invested in 70% of the worst performing sector, i.e., small, non-venture backed IPOs.[2]

One potential explanation for institutions’ heavy investment 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. 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. This suggests that venture capitalists may provide value-relevant information to institutional investors. However, 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 institutional interest.

These results suggest that individuals 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 higher pre-IPO leverage, lower pre-IPO working capital ratios, and negative pre-IPO earnings. Moreover, these firms’ earnings become significantly more negative in the years after the IPO. We also show that these firms are extremely unlikely to ever garner institutional interest. When we examine long-run stock returns for these firms with only individual investors, we find that they substantially underperform – over a three-year horizon, they earn 16% below size and book-to-market matched firms. Finally, we examine the relation between long-run returns and publicly available information about offer quality. We find that institutional investors place more weight on such quality measures than do individuals, and we show that 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, lower leverage, and positive earnings prior to the IPO. For example, a strategy of investing in firms with below-median leverage prior to the IPO and shorting those with above-median leverage would earn approximately fifty basis points per month over the three years following the offering.

Our results showing that firms with higher institutional investment outperform those with lower levels of institutional investment are consistent with a growing body of literature suggesting that institutions have an advantage over individuals. Gibson, Safieddine, and Sonti (2004) find that 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) find 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 twenty-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 that 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 20 years. Section IV examines the relation between these institutional holdings and IPO long-run performance, while Section V examines the determinants of institutional investment. In Section VI, we focus our attention on firms with only individual investors, while Section VII 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 Loughran and Ritter’s (2004) updated measures of Carter and Manaster’s (1990) underwriter quality to rank each underwriter. Ranks range from 0 to 9.1, 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.[3]

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).[4] 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 receive such a large allocation in the IPO, 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.[5] In cases where sufficient 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 must 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 institutional holdings portfolios toward larger companies, as Gompers and Metrick 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 (2004) methodology, we estimate cross-sectional regressions each year of institutional ownership on size:

[pic] (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.[6] 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.[7] Throughout the remainder of the paper, we refer to residual institutional ownership as just institutional ownership.

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 in the year before the IPO (6.0% for Q1 vs. 11.3% for Q5). The relation between institutional ownership and EBIT is particularly strong, as EBIT increases monotonically across the quintiles. Finally, there is no evidence of significant relations between institutional holding quintile and either book-to-market ratio, underwriter rank, or leverage. Across the entire sample, the average book-to-market ratio is 0.40, the average underwriter rank is 7.1, 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. [8] 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 underperformance 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)).

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: institutions hold an average 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. 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 category.

While institutions invest in significantly fewer IPOs in the small, non-venture backed class, 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 invest in this class at all. The next section examines whether institutions can differentiate a priori the quality of the firm, 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 these 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 five institutional holdings portfolios (as 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. The figures show raw returns and returns net of a matched size/book-to-market portfolio.[9] 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.

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.[10] Tables 3 and 4 show regressions of monthly returns of the high institution 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. 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 significant abnormal returns. 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., large, 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 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 those types of firms that tend to perform better, such as large, venture backed firms. 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, as shown in Figure 4. 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.

As shown in the first panel of Table 5, over a one-quarter horizon, 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.[11]

IV.C. Are Institutions Long-Run Investors in IPOs?

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. Why can institutions identify the ‘winners’ only over short periods? Why can institutions successfully identify and avoid the worst performers over the long-run, but not identify the best performers over similar periods?

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, Figure 3 shows that institutions generally do not hold IPOs for the long-run. Over 60% of institutions have completely divested their holdings within the first year, and almost 80% have divested by the end of the second year. In comparison, only 27% of institutions increase their holdings between the end of the first quarter after the IPO and one year later, and just 16% of institutions own more shares at the end of the second year than they did at the end of the first quarter (not shown in figure).

The fact that institutions tend to hold their IPO investments for relatively short periods is consistent with our evidence in Table 5 that institutions’ ability to identify good performing firms holds only for the short-run. However, the finding that institutions are able to identify – and avoid – the poorest long-run performers has implications for individual investors, who apparently invest disproportionately in newly-public firms that perform the worst over long horizons.

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 will only have 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), 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, liabilities/assets, working capital/assets, log of 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.

Our evidence on the determinants of institutional investment in IPOs, shown in Table 6, is similar to that found by Gompers and Metrick for the entire market. Consistent with Gompers and Metrick, we find that liquidity motives are an important determinant in institutional holdings (as reflected by the significantly positive coefficient on proceeds). In addition, we find that four of our prudence measures – VC backing, underwriter rank, positive EBIT, and working capital/assets – are significant.[12] 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.

Interestingly, institutions appear to use slightly different criteria when evaluating venture versus non-venture backed firms. For non-venture backed firms, underwriter rank is particularly important: the coefficient is twice as large for non-venture backed firms, and the coefficients are significantly different at the 1% level. Apparently, as Megginson and Weiss suggest, venture capital backing provides certification for the offering, such that underwriter reputation is less important when a firm is backed by venture capital. In contrast, firm age appears to be more important for the venture backed sample (coefficients statistically different at the 1% level). In addition, while price run-up is significantly positive for venture-backed firms, the coefficient is insignificant and negative for non-venture backed firms.

Also notable is the fact that publicly available information explains 48% of the variation in institutional investment for non-venture backed firms, compared with only 27% for venture backed firms (as reflected in the adjusted R2s of the regressions for the two groups). 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. However, it is also possible that other publicly available information we have not captured (e.g., VC reputation) explains a disproportionate amount of institutional investments in venture-backed IPOs.

VI. Isolating Firms With No Institutional Investment

The evidence presented in the previous section demonstrates that institutional investors rely heavily on readily available public information when choosing which IPOs to invest in and which to avoid. This suggests that individual investors pay less attention to these publicly-available quality indicators, such as positive earnings, underwriter rank, and offer size. In addition, we know from Table 5 that firms in our low institutional quintile severely underperform in the long-run, suggesting that individuals suffer the greatest IPO underperformance. This section examines the relation between these two findings: individuals’ lack of attention to fundamentals, and the poor long-run returns on those firms in which they invest.

To get the cleanest tests possible of how individuals fare when investing in IPOs, we want to isolate those firms without any institutional investment. Toward that end, rather than utilizing our institutional quintiles, we put firms into two distinct groups: (1) firms with positive institutional investment as of the first post-IPO quarter, and (2) firms with zero institutional investment as of the first post-IPO quarter. We refer to the first group as the “Institutions” group and the second as 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).

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

Figure 4 shows accounting data for all IPOs delineated by our two groupings, where “Year -1” refers to the fiscal year immediately preceding the IPO, “IPO Year” refers to the fiscal year including the IPO, “Year 1” refers to the first fiscal year after the IPO, and “Year 2” refers to the second fiscal year after the IPO. At each point in time, we look at median EBIT/total assets, the fraction of firms with positive earnings, median retained earnings/total assets, median total liabilities/total assets, and median working capital/total assets. In each graph, the black bars refer to the Individuals Only group whereas the dashed bars refer to the Institutions group.

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, they have higher debt, 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 Panels C and D we see that the Individuals Only firms have median leverage of 75%, versus 65% for the Institutions firms, and median working capital of 12%, versus 22% for the Institutions firms.

These apparent differences in firm quality become much more dramatic after the IPO. While the level of earnings (measured by median EBIT/TA) is not significantly different for the two groups before the IPO, this difference becomes 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/TA experiences a modest drop from 10% to 9% between year -1 to year 0, and the median never becomes negative. 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.

While retained earnings as a fraction of total assets is not significantly different for the two groups before the IPO, 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.

Although the Individuals Only firms have significantly higher leverage and lower working capital before the IPO, neither ratio is significantly different in the IPO year. The influx of capital from the IPO causes leverage to drop and working capital to increase for both groups, but more so for the Individuals Only firms. Over time leverage increases and working capital decreases for both groups, but the changes are especially dramatic for the Individuals Only group. By year 2, the Individuals Only group has median leverage of 52%, compared to 41% for the Institutions group, and median WC/TA of 25%, compared to 33% for the Institutions group.

Overall, the evidence suggests that Individuals Only firms are more likely to have negative earnings, higher leverage and lower working capital before the IPO. While some of these differences lessen in the IPO year, they all reappear in subsequent years. In addition, the level of earnings and retained earnings are also significantly lower for the Individuals Only firms in the years following the IPO. 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 much more pronounced over time.

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 before the IPO and especially after the IPO. In Figure 5, we see similar differences in stock returns. The firms with only individual investment clearly perform substantially worse.[13] 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 5, Figure 6 shows 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 13% of firms with institutional shareholdings. By contrast, Individuals Only firms are significantly less likely to be acquired: 9% versus 23% for the Institutions group.

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 substantially underperform their benchmarks while Institutions firms do not underperform, and that the Individuals Only firms are also 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 7 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. 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 which fail to garner institutional interest at the IPO are unlikely to do so even years later.

Given the evidence in Figure 5 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 8 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, WC/TA, and leverage, 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 nonventure-backing.[14]

Figure 8 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, Figure 8 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. For each characteristic, we form three portfolios: (1) returns on IPOs with above-median characteristic that went public in the past 36 months; (2) returns on IPOs with below-median characteristic that went public within the past 36 months, and (3) the difference between the two portfolios. We regress these returns, net of the risk-free-rate, on the four factors and report the intercept from this regression (similar to Tables 4 and 5).

Figure 8 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 8, 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 these strategies are significantly different at the 1% level. In fact, 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 firms with positive EBIT, higher working capital ratios, and lower leverage prior to the IPO all significantly outperform their counterparts. Further, Table 7 shows that in each of these cases, what we deem the ‘low quality’ characteristic firms (i.e., non-VC backed, negative EBIT, lower WC/TA, and higher leverage) all earn significantly negative abnormal returns. Surprisingly, size of proceeds is not at all predictive of future stock performance.

While firms with positive EBIT, higher WC/TA, and lower leverage perform significantly better, Figure 4 showed that individuals disproportionately invest in firms with negative EBIT, lower WC/TA, and higher leverage. 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. In the short-run, this result is driven by institutional investors earning superior returns in venture-backed firms, while in the longer-run the result is driven by firms with low institutional ownership earning poor returns. That is, firms that institutions avoid tend to perform particularly poorly in the long-run.

We find that institutional investors rely heavily on publicly available information when choosing to invest in IPOs – in particular, institutional investors prefer venture-backed firms, firms taken public by higher quality underwriters, firms issuing larger IPO proceeds, and firms with positive earnings, higher working capital ratios, and lower leverage 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, higher leverage and lower working capital before going public. These results suggest that individuals pay less attention to quality characteristics when choosing to invest in IPOs. Moreover, we find that these firms with only individual investors significantly underperform those with institutional interest.

Finally, we examine the evolution of institutional ownership over time for firms with and without initial institutional presence. 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. Why individual investors continue to invest in IPOs, even though they earn negative abnormal returns on average, is a puzzle we leave to future research.

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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: [pic]. 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 average 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 1983 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; the 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 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. Medians are reported for EBIT and leverage, and all other numbers represent means.

| | |Institutional Quintile |

| |Full Sample | |

|Firm Characteristic | | |

| | |1 (lowest) |2 |3 |4 |5 (highest) |

|Institutional Ownership as Percent of Public|25.2% |6.7% |22.0% |30.8% |33.2% |33.3%*** |

|Float | | | | | | |

|Proceeds ($1983 million) |$ 33.53 |27.2 |38.9 |35.8 |34.5 |31.1 |

|Market Cap ($1983 million) |$214.64 |180.7 |238.0 |222.4 |204.2 |227.0 |

|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 |7.1 |6.1 |7.7 |7.8 |7.5 |6.3 |

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

|EBITt-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% |

|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.

Table 2. Post-IPO Returns and Institutional Holdings, by Institutional Ownership

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. Firms in the smallest tercile are labeled small firms, and those in the largest tercile are labeled large firms. For returns based on a three year (one quarter) horizon, we regress 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 measured at the first institutional holdings reporting date that occurs between one and four months after the IPO.

| |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.

Table 3. Four-Factor Regressions for the Highest minus Lowest Institutional Ownership Quintiles, One Quarter Horizon

This table shows weighted least squares regressions of monthly returns 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 dependent variable, we estimate cross-sectional regressions of institutional ownership on IPO proceeds each year as follows: [pic]. 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, over one-quarter, one-year, and three-year horizons. Both equal-weighted and value-weighted regressions are shown.

| |One Quarter |One Year |Three Years |

|Variable | | | |

| |Equal Weighted |Value Weighted |Equal Weighted |Value Weighted |Equal Weighted |Value Weighted |

|Intercept | 0.015*** |0.010 | 0.010*** | 0.012** | 0.006** | 0.008* |

| |(2.56) |(1.12) |(3.18) |(2.19) |(2.31) |(1.71) |

|RMRF | -0.001** |-0.002 | -0.002** | -0.003* | -0.001** | -0.002** |

| |(-0.81) |(-1.04) |(-2.06) |(-1.93) |(-2.24) |(-2.11) |

|SMB |-0.002 |-0.003 |-0.0001 |-0.003 |-0.0001 | -0.004** |

| |(-0.83) |(-1.02) |(-0.11) |(-1.27) |(-0.13) |(-2.50) |

|HML | -0.002*** |-0.001 | -0.003*** | -0.004* | -0.002* | -0.004** |

| |(-0.75) |(-0.28) |(-2.79) |(-1.96) |(-1.87) |(-2.34) |

|PR12 | 0.005*** | 0.006** | 0.003*** | 0.005*** | 0.002** | 0.004** |

| |(2.91) |(2.14) |(2.64) |(2.85) |(1.97) |(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.

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: [pic]. 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 returns on the high institutional holdings quintile minus the low institutional holdings quintile for firms that went public within the prior twelve 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.

|Returns |Full |Venture Backed Firms |Non-Venture Backed Firms |

|Measured Over: |Sample | | |

|One Quarter Horizon: |

|All Size Terciles | 0.015** | 0.033*** | 0.004 |

| |(2.55) |(2.95) |(0.66) |

|Smallest Size Tercile | 0.016** | 0.048** | 0.006 |

| |(2.08) |(2.55) |(0.73) |

|Middle Size Tercile |0.017 | 0.033* | 0.001 |

| |(1.56) |(1.85) |(0.05) |

|Largest Size Tercile |0.020 |0.035 |-0.019 |

| |(1.21) |(0.99) |(-0.94) |

|One Year Horizon: |

|All Size Terciles | 0.010*** |0.009 | 0.009** |

| |(3.18) |(1.46) |(2.50) |

|Smallest Size Tercile | 0.013*** |0.003 | 0.013*** |

| |(2.96) |(0.33) |(2.74) |

|Middle Size Tercile | 0.008* | 0.020** |-0.002 |

| |(1.68) |(2.39) |(-0.32) |

|Largest Size Tercile | 0.014** |0.021 | 0.011 |

| |(2.03) |(1.61) |(1.45) |

|Three Year Horizon: |

|All Size Terciles | 0.006** |0.005 | 0.006** |

| |(2.32) |(1.27) |(2.14) |

|Smallest Size Tercile | 0.008** |0.006 | 0.008* |

| |(2.10) |(0.80) |(1.94) |

|Middle Size Tercile |0.005 |0.007 |0.002 |

| |(1.33) |(1.22) |(0.44) |

|Largest Size Tercile |0.007 |0.004 | 0.009** |

| |(1.62) |(0.50) |(2.09) |

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

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. For the one quarter horizon, we regress monthly returns net of the risk-free rate on the high minus low institutional holdings quintiles for firms that went public within the prior 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 table. For the one year and three year horizons, regression samples include firms that have gone public within the past one and three years, respectively.

| |Quintile 1 (lowest) |Quintile 2 |Quintile 3 |Quintile 4 |Quintile 5 (highest) |Difference |

|Returns Measured Over: | | | | | |(High – Low) |

|One Quarter: | | | | | | |

|Full Sample | -0.012** |0.0001 |-0.002 |0.004 |0.003 | 0.015** |

| |(-2.49) |(0.022) |(-0.53) |(0.92) |(0.61) |(2.55) |

|Venture Backed Firms | -0.016* |-0.0010 |-0.003 |0.005 | 0.014** | 0.033*** |

| |(-1.91) |(-0.17) |(-0.39) |(0.73) |(2.07) |(2.95) |

|Non-Venture Backed Firms |-0.008 |0.0002 |-0.004 |-0.0001 |-0.003 |0.004 |

| |(-1.60) |(0.05) |(-0.90) |(-0.02) |(-0.65) |(0.66) |

|One Year: | | | | | | |

|Full Sample | -0.017*** | -0.006** | -0.005* | -0.005* | -0.006** | 0.010*** |

| |(-5.04) |(-2.36) |(-1.84) |(-1.87) |(-2.34) |(3.18) |

|Venture Backed Firms | -0.013** |-0.006 |-0.003 |-0.002 |-0.002 |0.009 |

| |(-2.34) |(-1.61) |(-0.76) |(-0.55) |(-0.43) |(1.46) |

|Non-Venture Backed Firms | -0.017*** | -0.005* | -0.006** | -0.007*** | -0.009*** | 0.009** |

| |(-5.21) |(-1.87) |(-2.36) |(-2.83) |(-3.16) |(2.50) |

|Three Years: | | | | | | |

|Full Sample | -0.009*** | -0.004** | -0.005*** | -0.004** |-0.002 | 0.006** |

| |(-2.79) |(-2.40) |(-2.78) |(-2.22) |(-1.09) |(2.32) |

|Venture Backed Firms |-0.006 |-0.001 |-0.002 |-0.001 |-0.001 |0.005 |

| |(-1.42) |(-0.48) |(-0.94) |(-0.51) |(-0.22) |(1.27) |

|Non-Venture Backed Firms | -0.009*** | -0.006*** | -0.006*** | -0.006*** |-0.003 | 0.006** |

| |(-2.89) |(-3.00) |(-3.24) |(-3.22) |(-1.40) |(2.14) |

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

Table 6. What Factors Attract Institutional Investment in IPOs?

The sample consists of 5907 IPOs between 1980 and 2000. Percent institutional holdings are regressed on various firm and offer characteristics. Percent 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 size of the over-allotment option, to obtain percent institutional holdings. These holdings are measured at the first institutional holdings reporting date that occurs between one and four months after the IPO. Explanatory variables in these regressions represent information known at the time of the offering. Firm age is the average age of IPO firms as they go public; 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, zero otherwise; Proceeds are the proceeds raised in the IPO and given in 1983 million dollars; Price Run-Up is the percent difference between the midpoint of the filing range and the offer price; the positive EBIT dummy equals one if EBIT in year t-1 is positive, zero otherwise; sales/assets, the log(assets), liabilities/assets, and working capital/assets are all measured at the end of the fiscal year ending prior to the IPO. Yearly dummies are included as additional explanatory variables, but are not reported.

| |All Firms |Venture Backed |Non-Venture Backed |

|Intercept |-25.84*** |-24.38*** |-22.65*** |

| |(-9.75) |(-6.04) |(-6.24) |

|Log Proceeds |7.64*** |7.51*** |7.69*** |

| |(19.71) |(10.98) |(16.04) |

|Firm Age |0.01 |0.06* |0.001 |

| |(0.94) |(1.93) |(-0.21) |

|Underwriter Rank |1.33*** |0.75*** |1.60*** |

| |(10.50) |(3.31) |(10.47) |

|VC Dummy |1.28*** |  |  |

| |(2.79) | | |

|Positive EBIT Dummyt-1 |4.69*** |4.35*** |3.98*** |

| |(8.22) |(5.28) |(4.84) |

|Sales/Assetst=-1 |0.11 |0.50* |-0.16 |

| |(0.64) |(1.80) |(-0.76) |

|Log(Assetst=-1) |-0.24 |0.08 |-0.46* |

| |(-1.13) |(0.24) |(-1.68) |

|Liabilities / Assetst=-1 |0.35 |0.76 |-0.06 |

| |(0.80) |(1.08) |(-0.11) |

|Working Capital / Assetst=-1 |1.12** |1.03 |1.33** |

| |(2.08) |(1.14) |(1.99) |

|Price Run-Up |0.94 |2.33* |-1.12 |

| |(0.95) |(1.72) |(-0.72) |

|Adjusted R2 |0.40 |0.27 |0.48 |

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

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, working capital (scaled by total assets), and liabilities (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. For underwriter rank, we form two portfolios: returns on firms that have gone public within the past 36 months with an above-median underwriter, and returns on firms that have gone public within the past 36 months with a below-median underwriter. We regress the monthly returns 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 |-0.002 |-0.007*** |0.005*** |

|(high quality = high rank) |(-1.60) |(-3.40) |(3.11) |

|VC Backing |-0.002 |-0.006*** |0.004** |

|(high quality = VC backed) |(-0.90) |(-3.85) |(2.38) |

|Positive EBITyr -1 |-0.002* |-0.007** |0.005* |

|(high quality = positive EBIT) |(-1.73) |(-2.33) |(1.67) |

|WC / TA |-0.001 |-0.007*** |0.006*** |

|(high quality = high WC/TA) |(-0.73) |(-3.86) |(4.02) |

|Leverage |-0.001 |-0.007*** |-0.006*** |

|(high quality = low leverage) |(-0.67) |(-4.05) |(-4.69) |

|Proceeds |-0.005*** |-0.005** |-0.0005 |

|(high quality = high proceeds) |(-3.38) |(-2.05) |(-0.27) |

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

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.

[pic]

Panel B. Average and Median Institutional Ownership 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.

[pic]

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: [pic]. 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 and three years following the institutional holdings report date.

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

[pic]

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

[pic]

Figure 3: Evolution of Institutional Holdings After the IPO

For this figure, we follow the original 13F institutions invested in each IPO at the first institutional holdings date that is at least four weeks after the IPO (quarter 0). We track the holdings of these institutions from quarter 1 through quarter 11. The solid line shows the fraction of original 13F institutional shareholders who are still invested in the IPO. For those institutions that are still invested, the two dotted lines depict the percent of institutions that have increased their holdings and the percent of institutions that have either kept their holdings the same or decreased them.

[pic]

Figure 4. Accounting Performance for Firms

With and Without Institutional Ownership at Quarter 1

The sample consists of IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured within one quarter of the IPO. In each figure, the black bars show median accounting ratios 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 at Quarter 1. The accounting ratios shown are EBITDA/Total Assets (EBIT/TA), Retained Earnings/Total Assets (RE/TA), Total Liabilities/Total Assets (TL/TA), and 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). Panel A also shows the fraction of firms with positive EBIT/TA for each group in each year. *** indicates a significant difference at the 1% level of the variable in question between the groups with and without institutional shareholders at Quarter 1.

Panel A: EBIT

[pic] [pic]

Panel B: Median RE/TA

[pic]

Figure 4. Accounting Performance for Firms

With and Without Institutional Ownership at Quarter 1 (continued)

The sample consists of IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured within one quarter of the IPO. In each figure, the black bars show accounting ratios 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 at Quarter 1. The accounting ratios shown are EBITDA/Total Assets (EBIT/TA), Retained Earnings/Total Assets (RE/TA), Total Liabilities/Total Assets (TL/TA), and 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 at Quarter 1.

Panel C: Median TL/TA

[pic]

Panel D: Median WC/TA

[pic]

Figure 5. Three Year Buy-and-Hold Abnormal Returns for Newly Public Firms

With and Without Institutional Ownership at Quarter 1

The sample consists of IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured within one quarter of the IPO. The broken line shows cumulative abnormal returns for the 651 firms with no initial institutional shareholders at Quarter 1, while the solid line shows cumulative 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.

[pic]

Figure 6. Firm Status Five Years After the IPO for Newly Public Firms

With and Without Institutional Ownership at Quarter 1

The sample consists of IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured within one quarter of the IPO. The black bars show firm status for the 651 firms with no initial institutional shareholders, while the grey bars show firm status for the 5,256 firms with institutional shareholders as of Quarter 1. *** indicates a significant difference at the 1% level of the variable in question between the groups with and without institutional shareholders at Quarter 1.

[pic]

Figure 7. Institutional Ownership Over Time for Newly Public Firms

With and Without Institutional Ownership at Quarter 1

The sample consists of IPOs from 1980-2000 and is broken into groups based on the presence of institutional ownership measured within one quarter of the IPO. 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.

[pic][pic]

Figure 8. 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, working capital (scaled by total assets), and liabilities (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

[pic] [pic]

[pic]

Figure 8. 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

[pic] [pic]

[pic]

-----------------------

[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.

[3] 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.

[4] 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.

[5] For example, shares subject to lock-up provisions and Rule 144A restrictions are not part of the float (see Field and Hanka, 2001).

[6] 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.

[7] 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 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, all results are qualitatively similar using this alternative measure.

[8] Specifically, a firm is included in the regression sample for the first three years after its first institutional reporting date. 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 is interpreted as evidence that the recent IPO firms earned positive (negative) abnormal returns over our 1980-2000 sample period.

[9] 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.

[10] We also test the significance of these relations using calendar time abnormal returns (not shown) and obtain similar results.

[11] 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 the significantly positive abnormal returns of the venture-backed firms with the highest institutional ownership (Q5). 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 by the significant negative performance of those firms with the least institutional interest (Q1), particularly among non-venture backed firms.

[12] 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/assets, either in addition to or instead of the positive EBIT dummy, we find that EBIT/assets is not a significant determinant of institutional investment.

[13] We also esti/—˜¿ÀÁÓÔúûN O r s t ‚ ƒ ¤ ¦ ­ ® EGmate 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.

[14] While leverage was not a significant variable in explaining institutional investment in Table 6, we include it here because Figure 4 showed significant differences in this variable between the Individuals Only and Institutions groups.

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