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Factors that impact customer loyalty in the investment banking industry.

Executive Summary

The main goal of this article is to provide empirical evidence about some factors that impact customer loyalty in the investment banking industry sector. We find that only 45.11% of our sample repeated dealings with the same lead investment bank during the first seasoned equity offering (SEO). Our results suggest that customer loyalty has been declining in the securities firm industry during last decade. Also, we find that the number of days between offerings has a negative relationship with the probability of a firm to remain loyal to the same underwriter. We also find that firms with IPOs offered during 1999 and 2000 have a higher probability of underwriting loyalty during their first SEO; however, this relationship disappears during the second and third SEO. Similarly, we find that underwriter reputation has explanatory power over the probability of a firm's loyalty to the same underwriter only during the first SEO.

Introduction

The main goal of this article is to provide empirical evidence about some factors that impact customer loyalty in the investment banking industry sector. We define customer loyalty as those firms that repeat dealings with the same lead investment bank or underwriter. We study a sample of 552 initial public offerings (IPOs) of firms that engaged in one or more seasoned equity offerings (SEOs). We find that firms that remain loyal during their first SEO experience an average underpricing of 16.59% while disloyal ones have an average underpricing of 22.32%. Our results contradict those of Krigman, Shaw, and Womack (2001), who find that loyal IPOs are significantly less underpriced than disloyal IPOs. However, our results are consistent with those of James (1992) who finds significantly lower levels of underpricing when firms remain loyal to the same underwriter than when firms switch underwriters.

Since all IPOs in our sample have one or more seasoned equity offerings (SEOs), our analysis goes beyond the first SEO. Specifically, our study tries to analyze whether the factors that influence a firm's loyalty to the same lead underwriter during its first SEO also explain such loyalty for its second and third SEO. This constitutes the main differentiating factor of our study compared to previous academic works. Indeed, Krigman, Shaw, and Womack (2001) study firms that conducted an IPO from 1993-1995, which then offered just one SEO within three years following their IPOs. James (1992) studies a sample of 520 IPOs, of which 121 (23%) made at least one security offer after their IPO, but only 24 have a first 5E0,18 a second SEO, and just five have a third SEO. James did not discriminate between debt and equity issues in his analyses, and this constitutes a significant difference with our sample of firms where we only considered equity offerings after the IPO. Also, the small number of first, second, and third SEOs did not allow James to perform meaningful statistical analysis considering each offering individually. Our sample is large enough to derive meaningful conclusions of each offering considered individually.

Our results suggest that the customer loyalty during the first seasoned equity offering is unique, and that not all the same factors can explain why firms switch underwriters in their second and third SEOs. Our results may prove to be extremely valuable for the securities firm industry, since those factors that explain customer loyalty in several offerings can be considered by investment banks to improve the services they provide. The value of our proposed research work is also reflected by our summary statistics: just 45.1% of our sample remained loyal to the same underwriter during their first SEO. This constitutes a significant reduction compared to the 70% reported by Krigman, Shaw, and Womack (2001) and the 65% reported by James (1992). Our results, compared to previous academic works, suggest that customer loyalty has declined over the last couple of decades.

The rest of this paper is organized as follows: Section 1 summarizes the relevant literature review on this topic; Section 2 explains our proposed hypotheses; Section 3 describes our methodology and proposed models; Section 4 explains our sample and provides some summary statistics; Section 5 provides details of our empirical results; Section 6 summarizes major conclusions about our research work; and the last section lists the bibliographic references cited in this article.

1. Literature Review

A few academic authors have studied the factors that impact a firm's decision to switch its lead IPO underwriter during its SEOs. Krigman, Shaw, and Womack (2001) study a sample of 572 IPOs between January 1993 and December 1995 that returned to the market for a SEO within three years after their IPOs. They find underwriter switchers are significantly less underpriced than non-switcher IPOs, which is exactly the opposite result that we find in this article. They also find that the main two reasons for underwriter switching is to improve underwriter reputation and analyst coverage. They support these results with a survey they applied, and with some logistic regression models.

James (1992) finds a positive and significant relationship between the probability of switching underwriters and the time between the IPO and the SEOs. He also finds significantly lower levels of underpricing when firms remain loyal to the same underwriter than when firms switch underwriters.

Vikram and Warther (1998) study the issuer-investment bank relationship from 1970-1996. They find that loyal firms pay higher fees when issuing securities compared to disloyal ones. They also find that fees are not important in the switching decision. They conclude that small firms that issue securities less often and with low credit ratings tend to be more loyal to their underwriters.

Our proposed research work constitutes an original contribution, compared to previous research works, in the sense that our analysis goes beyond evaluating customer loyalty between the IPO and the first SEO. Our sample is large enough to allow us to analyze factors that explain loyalty not only during the first SEO, but also during the second and third SEO. Our results suggest that independent variables of customer loyalty have different explanatory power for each SEO, since some of them are significant during the first SEO, but such significance disappears during the second and third SEO.

2. Proposed Hypotheses

Dunbar (2000) finds a negative relationship between the level of underpricing and the market share over time. In other words, he finds that the higher the level of underpricing, the lower the subsequent market share of the investment bank. Similarly, Beatty and Ritter (1986) find that investment banks that misprice offerings in one period lose market share in the subsequent period. These findings suggest that the level of underpricing should have a negative impact in the issuer's decision to remain loyal to the same investment bank. Therefore we hypothesize that: (H1) the level of underpricing is negatively related to the probability that the issuer will repeat dealings with the same lead underwriter. The rationale for this hypothesis is that issuers that experience low levels of IPO underpricing will maximize the IPO proceeds and will be more satisfied than those issuers who experience high levels of underpricing and that are forced to leave more money on the table.

Megginson and Weiss (1991) compare market share and the ranking of Carter and Manaster (1990) for underwriter reputation and find a high degree of positive correlation between the two. In other words, those investment banks with the highest reputation also have the largest market share. From an issuer's loyalty perspective, it seems reasonable to assume that the higher the lead underwriter's reputation, the greater the probability that the issuer will remain loyal to the same lead underwriter. Therefore we hypothesize that: (H2) the underwriter reputation is positively related to the probability that the issuer will repeat dealings with the same lead underwriter. The rationale for this hypothesis is that issuers that hire investment banks with high reputation will be more satisfied with the services they receive and will probably hire them again in subsequent SEOs.

Ellis, Michaely, and O'Hara (2000) find, in their sample of NASDAQ IPOs, that the lead underwriter performs the role of dominant market maker by handling about 60% of the trading volume in the first few days after the IPO, and about 50% of the volume over the first few months after the IPO. Indeed, in addition to being involved in developing the prospectus, organizing road shows, performing bookbuilding-related activities, determining the stock price, and selling stock shares, the lead underwriter must also perform the role of market maker when the IPO is listed in NASDAQ. In our sample, 65.40% of the firms are listed in NASDAQ. Ellis, Michaely, and O'Hara (2000) also find that the lead investment bank takes significant inventory positions in stock that ranges between 4% and 22% of the issue. This in turn creates a significant positive relationship between the lead underwriter's trading profits and the level of IPO underpricing. However, they report that aftermarket trading constitutes an insignificant source of profits for the lead underwriter compared to the fees collected from underwriting activities. Regardless of the relative significance of profits generated by aftermarket trading, the issuers' perceptions about such profits may have an impact in their loyalty towards the lead underwriter. We consider that the potential conflicts of interest associated with the inventory position of the lead underwriter may have a negative influence in the issuer's future decision about repeating dealings with that same underwriter. Therefore we hypothesize that: (H3) there is a negative relationship between stocks listed in NASDAQ and the probability that the issuer will repeat dealings with the same lead underwriter. The rationale for this hypothesis is that issuers may perceive aftermarket trading profits as a result of opportunistic behavior on the part of the lead underwriter, and therefore issuers will be less likely to remain loyal to such investment banks.

DuCharme, Rajgopal, and Sefcik (2001) find that the long-run return performance of hot Internet IPOs during 1998 and 1999 was worse for those firms that received media attention before the IPO date. They also find a significant level of underpricing in these IPOs. Chan and Meidan (2005) also find poor long-run performance for IPOs issued during 1999-2000. This poor long-run performance of IPOs offered in 1999 and 2000 could have had an impact in the issuer perception about the responsibility of the lead underwriter in such performance. We consider that IPOs offered during this period were probably more likely to associate their poor long-run stock performance with the lead underwriter than with the overall market irrationality characteristic of that period of time. As a result, we hypothesize that: (H4) there is a negative relationship between stocks offered during 1999-2000 and the probability that the issuer will repeat dealings with the same lead underwriter. The rationale for this hypothesis is that issuers may associate poor long-run stock performance with their lead underwriter and may decide to switch in the event of a subsequent SEO.

Krigman, Shaw, and Womack (2001) hypothesize that when the offer price is higher than the middle point of the filing range, issuers can obtain higher IPO proceeds than originally anticipated, and they might interpret these large proceeds as the result of the lead underwriter's successful marketing efforts. Under such circumstances, the issuer will be more likely to repeat dealings with the same lead underwriter. They find that shares of loyal firms were offered at prices 3.2% higher than the middle point of the filing range while shares of disloyal firms were offered at prices 4.6% below the middle point of the filing range. In this article we define the level of price adjustment as the percentage deviation of the IPO offer price from the middle point of its filing range. Following the same rationale of Krigman, Shaw, and Womack (2001) we hypothesize that (H5) there is a positive relationship between the price adjustment and the probability that the issuer will repeat dealings with the same lead underwriter. The rationale for this hypothesis is that issuers will associate positive price adjustments with the lead underwriter's successful marketing efforts and will privilege dealings with this same underwriter again in the event of a successive SEO. We want to test the same hypothesis proposed by Krigman, Shaw, and Womack (2001), evaluated not only during the first SEO, but also in the second and third SEO.

James (1992) finds that firms are more likely to be disloyal to the same underwriter when the time between the IPO and the subsequent SEO increases. We consider that the lead underwriter has a better chance to be selected again in subsequent equity offerings if they occur close to each other over time. Consequently, we hypothesize that: (H6) there is a positive relationship between the number of days among equity offerings and the probability that the issuer will repeat dealings with the same lead underwriter. The rationale for this hypothesis is that the lead underwriter will have a better chance of being selected again if the satisfaction for the services rendered is still fresh in the issuer's memory. Also, the sooner the SEO occurs, the higher the probability that the issuer's representatives involved in the lead underwriter selection process will be involved again during a successive SEO, so the selection outcome will probably be the same. We want to test the same hypothesis tested by James (1992), but evaluated in the first, second and third SEO individually considered.

Vikram and Warther (1998) find that loyal firms tend to be smaller in size, have lower credit ratings, and issue securities less often than loyal firms. Conversely, they also find that disloyal firms tend to be large in size, often utility companies, with significant internal resources that create disincentive for them to establish a close relationship with any particular lead underwriter, so they tend to switch opportunistically among underwriters. We consider that the probability of repeating business with the same lead underwriter is influenced by the size of the offering measured by the natural logarithm of the offering's proceeds. Indeed, the size of the firm is related with the probability of survival and the chances Factors that Impact Customer Loyalty in the Investment Banking Industry of issuing securities again. Also, some large national investment banks have a competitive advantage when underwriting a large issue. Accordingly, we hypothesize that: (H7) there is a negative relationship between the size of the offering and the probability that the issuer will repeat dealings with the same lead underwriter. We want to test this hypothesis as it applies to the first, second and third SEO individually considered.

Krigman, Shaw, and Womack (2001) find that there are two main reasons for being disloyal to one particular underwriter: to deal with a lead underwriter with superior reputation, and to obtain superior analyst coverage. They surveyed chief financial officers (CFO) and chief executive officers (CEO) of firms that completed their IPOs. They find that 44% of respondents mentioned more or improved research coverage as the top reason to switch underwriter, while 88% mention research as one of the top three reasons for being disloyal to the same underwriter. Although this might have been true in 2001, we decided not to include any variable to control for research coverage. The rationale for this decision follows: during 2001-2002 the analysts' process of rating stocks was severely criticized. Many issuers associated the demand for their stock with the nature of research coverage their stock might have received. In order to minimize the obvious conflict of interest, the Securities and Exchange Commission (SEC) implemented the Regulation AC in 2002. In section I. Introduction and Summary of Regulation Analyst Certification, the SEC states:
  "... We were particularly concerned that many investors who rely
  on analysts' recommendations may not know, among other things,
  that favorable research coverage could be used to market the
  investment banking services provided by an analyst's firm, and
  that an analyst's compensation may be based significantly on
  generating investment banking business ..."
  (SEC, 2002)


Therefore, since research analysis has been heavily regulated since 2002, we decided to ignore this factor as a possible explanatory variable for underwriting loyalty. Similarly, we ignore underwriting fees as a factor affecting issuer loyalty. Indeed, Vikram and Warther (1998) find that when highly loyal firms decide to switch underwriters, they are charged higher fees than those charged to firms with lower measures of past loyalty. Also, Krigman, Shaw, and Womack (2001) find that underwriting fees have the lowest ranking among the surveyed reasons to switch lead underwriter. These findings are consistent with those of Chen and Ritter (2000) who find that from 1995 to 1998, 90% of their sample of 1,111 IPOs paid their corresponding investment bank spreads of exactly seven percent, suggesting that no competition among investment banks exist in terms of fees. Based on these previous findings we decided to exclude underwriting fees from our analysis.

3. Methodology

In this research work we applied several logistic regression models (logit models) to determine the factors that impact the probability of customer loyalty defined as those firms who repeat dealings with the same lead investment bank or underwriter. The following logic model is considered:

ln[[p.sub.i]/[1-[p.sub.i]]=[[alpha].sub.1]+[[beta].sub.1]DB[O.sub.i]+[[beta].sub.2]U[P.sub.i]+[[beta].sub.3]P[A.sub.i]+[[beta].sub.4]NASDA[Q.sub.i]+[[beta].sub.5]LnP[R.sub.i]+[[beta].sub.6]U[R.sub.i]+[[beta].sub.7]Bibbl[e.sub.i]

where [p.sub.i] is the probability that the firm will remain loyal and will repeat dealings with the same securities firm or underwriter. [[alpha].sub.1] is the intercept term. DB[O.sub.i] is the number of days between equity offerings. U[P.sub.i] is the level of IPO underpricing measured by the 1-day holding period return as ([p.sub.1] -- [p.sub.0])/[p.sub.0], where [p.sub.0] is the offer price and [p.sub.1] is the first-tradingday closing price. The level of underpricing is determined for each firm in our sample of IPOs. PAi is the level of price adjustment measured as the percentage deviation of the IPO offer price from the middle point of its filing range. NASDA[Q.sub.i], is a dummy variable that takes the value of one if the firm is listed in the NASDAQ stock exchange and zero otherwise. LnP[R.sub.i], is the natural logarithm of the total dollar amount resulting from the proceeds of the equity offering measured as the total shares offered times the offer price. U[R.sub.i], is the underwriter reputation measured by the adjusted CarterManaster ranking taken from Jay Ritter's website at http://bearwarrington.ufl.edu/ritter/ipodata.htm (see Carter and Manaster, 1990). Bubbl[e.sub.i] is a dummy variable that takes the value of one if IPO i was offered in 1999 or 2000, and zero otherwise. Our analysis also includes some difference-in-mean tests to analyze differences in several independent variables of our sample.

We applied the same logistic regression model twice, but excluding shelf-registered SEOs the second time. The rationale for this exclusion follows: the SEC adopted Rule 415 in November 1983 to allow shelf registration offerings. This regulation allows public firms to offer securities without additional filing notice with the SEC during a three-year period since 2005 and a two-year period before that year. Before 1992 only two types of shelf-registration existed: equity and debt shelves. Since October 1992 the SEC allowed universal shelves to offer both debt and equity securities. The mechanism allows firms to offer securities when capital market conditions are favorable, with minimum filing and administrative preparation expenses. Denis (1991) finds evidence that firms avoid shelf-registrations due to the lack of underwriter certification associated with the shelf procedure. He also finds that firms who announce an SEO using a shelf-registration experience an average stock price decline of 0.7%-0.8%. Denis explains that Rule 415 does not require any disclosure of the participant underwriters until the offering date, if at all. Under Rule 415 once the shelf-registration has been filed, the lead underwriter is usually selected based on a competitive bid for the issue. As a result, the selected underwriter has no time to perform any reasonable investigation of the issuer, and therefore no credible certification of the offering is provided. For this reason, our logistic regression model may produce different results by considering only non-shelf SEOs.

4. Sample and Summary Statistics

Our sample consists of 552 IPOs offered between 1997 and 2008 that issued at least one SEO; 245 of these have at least two SEOs; 82 of these have at least three SEOs; 43 of these have at least four SEOs; and 18 of these have five SEOs. The IPO-related data is obtained from Securities Data Corporation's (SDC) Global New Issues database. The firms in our sample have stock price data available at the Center for Research in Security Prices (CRSP). The first day closing prices for the IPOs are obtained from CRSP. Those IPOs without price data at CRSP were eliminated. We excluded IPOs of American Depositary Receipts (ADRs), enhanced income securities, stocks with warrants, income depositary shares, and unit IPOs. Data for our control variables are retrieved from the Securities and Exchange Commission (SEC) filings at the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) database; specifically from the IPO's initial prospectus and proxy statements. Accounting data are obtained from Standard and Poor's Research Insight database.
Table 1: Loyal versus Disloyal Firms for each Offering

Loyalty     Loyal     Disloyal     Total
During        (%)          (%)

1st SEO       249  303(54.89%)    552
         (45.11%)

2nd SEO        70          175    245
         (28.57%)     (71.43%)

1st &          41          204    245
2nd SEO  (16.73%)     (83.27%)

3rd SEO        35  47 (57.32%)     82
         (42.68%)

1st,           12  70 (85.37%)     82
2nd, &   (14.63%)
3rd SEO


Table 1 shows summary statistics of the number of loyal firms in this sample. Remember that we define loyal firms as those who repeat dealings with the same lead investment bank or underwriter between one offering and the next one. Only 249 firms (45.11%) out of 552 of the sample repeated dealings with the same lead investment bank or underwriter for the first SEO. This constitutes a significant reduction compared to the 70% reported by Krigman, Shaw, and Womack (2001) and the 65% reported by James (1992). These results suggest that customer loyalty has been declining in the securities firm industry. Similarly, 70 firms (28.57%) out of 245 in the sample that had a second SEO remained loyal to the same underwriter from their first SEO. From this group of 245 firms with two SEOs, just 41 (16.73%) remained loyal to the same underwriter two times in a row since their IPOs. Finally, 35 firms (42.68%) out of the 82 in the sample that offered a third SEO remained loyal to the same underwriter from their second SEO. From this group of 82 firms with three SEOs, just 12 (14.63%) remained loyal to the same underwriter three times in a row since their IPOs.
Table 2: Summary Statistics about Days between Offerings

Variable         N      Mean       Std.
                              Deviation

Days Between   552  1,152.81     965.14
IPO and 1st
SEO
(DB[O.sub.i])

Days Between   245    672.84     534.66
1st and 2nd
SEO
(DB[O.sub.i])

Days Between    82    503.06     420.02
2nd and 3rd
SEO
(DB[O.sub.i])

Days Between   245  1,766.40   1,055.73
IPO and 2nd
SEO
(DB[O.sub.i])

Days Between    82  2,028.20     997.99
IPO and 3rd
SEO
(DB[O.sub.i])


Table 2: provides summary statistics about the number of days between equity offerings starting at the IPO offer date of each firm in this sample. The average number of days between the IPO and the first SEO in the sample is about 1,153 days. The average number of days between the first and second SEO is about 673 days, and between the second and third SEO is about 503 days. Similarly, the average number of days from the IPO to the second SEO of those 245 firms that offered two SEOs is about 1,766 days. Finally the average number of days between the IPO and the third SEO of 82 firms in this sample that offered three SEOs is about 2,028 days.
Table 3: Summary Statistics of Underpricing  per Offering.

Variable        N    Mean       Std.
                           Deviation

IPO           551  19.74%     41.97%
Underpricing
(U[P.sub.i])

1st SEO's     476   3.95%     10.72%
Underpricing
(U[P.sub.i])

2nd SEO's     137   2.70%      9.22%
Underpricing
(U[P.sub.i])

3rd SEO's      45  -3.18%      4.48%
Underpricing
(U[P.sub.i])


Table 3 shows summary statistics about the level of underpricing for each equity offering. The average level of IPO underpricing in this sample is about 19.74%. This average is significantly lower than that reported by Dempere (2009), who finds an average level of underpricing of 34.61% for the same period. The level of SEO underpricing for the first, second, and third SEO are 3.95, 2.7, and -3.18% respectively.
Table 4: Summary Statistics of Price Adjustment during IPO

Variable      N  Min.  Max.     Mean       Std.
                                      Deviation

Price       546  -50%   40%  -0.947%   13.0537%
Adjustment


Table 4 provides summary statistics about the IPO price adjustment of the sample. The average percentage of deviation of the IPO offer price from the middle point of its filing range is -0.947%. This result suggest that for most firms in this sample the underwriter decided to adjust the offer price below that estimated in the initial prospectus submitted to the SEC.
Table 5: Firms per Stock Exchange

Firms    Firms  Total  Percentage

Firms      361    552      65.40%
in
NASDAQ

Loyal      149    552      27.00%
Firms
During
1st SEO
in
NASDAQ

Loyal       43    245      17.55%
Firms
During
2nd SEO
in
NASDAQ

Loyal       22    245       8.98%
Firms
During
1st and
2nd SEO
in
NASDAQ

Loyal       17     82      20.73%
Firms
During
3rd SEO
in
NASDAQ

Loyal        3     82       3.66%
Firms
During
1st,
2nd,
3rd SEO
in
NASDAQ


Table 5 shows descriptive statistics of those firms in the sample listed in the NASDAQ stock exchanges versus those listed in other exchanges. Most firms in the sample (65.40%) are listed in the NASDAQ stock exchange. However, that percentage declines to 27% for those firms in the sample that remained loyal during the first SEO. Only 17.55% of those firms in the sample that remained loyal during the second SEO are listed in NASDAQ, while 20.73% of those loyal firms during their third SEO are listed in NASDAQ. Similarly, only 8.98% of firms that remained loyal during their first and second SEOs are listed in NASDAQ and only 3.66% of those loyal during their first, second, and third SEOs are also listed in NASDAQ.
Table 6: Summary Statistics of Underwriter Reputation per Offering

Variables      N   Min.   Max.      Mean      Std.
                                              Dev.

Underwriter  552  1.001  9.001  8.062594  1.420555
Reputation
(U[R.sub.i])
for
IPO

Underwriter  552  1.001  9.001  7.539043  2.334621
Reputation
(U[R.sub.i])
for
1st SEO

Underwriter  245  1.001  9.001  5.368347  3.652385
Reputation
(U[R.sub.i])
for
2nd SEO

Underwriter   84  1.001  9.001  5.709333  3.418772
Reputation
(U[R.sub.i]) for
3rd SEO


Table 6 provides summary statistics of the average underwriter reputation measured by the adjusted Carter-Manaster ranking. The average underwriter reputation during the IPO of this sample is 8.06, where the maximum possible value of the ranking is 9.001 and the minimum is 1.001. This average declines significantly to about 7.54 during the first SEO and declines again to 5.37 during the second SEO. However, the average increases slightly to 5.71 during the third SEO.

Table 7 shows descriptive statistics of the average dollar amount of the proceeds of each offering in this sample. The average dollar amount from the IPOs is about 201 million, while for the first SEO is about 182 million. Similarly, the mean dollar amounts of the proceeds from the second and third SEOs are 192 and 159 million respectively. Table 7 also includes the maximum and minimum dollar amount of each offering proceeds, plus the standard deviation for each variable.
Table 7: Summary Statistics about Offering Proceeds

Variable    N       Min.           Max.           Mean           Std.
                                                            Deviation

Proceeds  552  7,500,000  4,600,000,000  201,575,918.7  382,782,951.3
from IPO

Proceeds  496  3,780,000  4,176,983,175  182,225,848.4  315,397,107.2
from 1st
SEo

Proceeds  149    825,000  2,360,000,000  192,141,482.9  339,630,327.7
from 2nd
SEo

Proceeds   56    922,200  1,317,080,000  159,456,537.9  262,507,568.7
from 3rd
SEo


5. Empirical Results

Table 8: shows the average number of days between equity offerings starting with the IPO date of each firm in this sample. Remember that we define loyal firms as those who repeat dealings with the same lead investment bank or underwriter between one offering and the next one. The average number of days between the IPO and the first SE0 is 641.6 days for loyal firms and 1,572.9 days for disloyal ones. This difference is significant at a 0.1% confidence level. The difference between loyal and disloyal firms for the average number of days between the first and second SE0 is also significant at a 0.1% confidence level with 487.5 days and 751.9 days respectively. However, the average period of time between the second and third SE0 is not statistically different between loyal and disloyal firms. This result suggests that the time between offerings is only a relevant explanatory factor of customer loyalty at the early stages of the firm as a public entity.
Table 8: Independent Sample Tests of Average Days between
Equity Offerings

                        Mean   Levene's  t-test for
                               Test for    Equality
                               Equality    of Means
                                     of       (Sig.
                              Variances   2-tailed)

Average Days
Between IPO am 1st
SEO (N: 249 vs.
303)

Loyal                  641.6        F =       Equal       t =
                               106.4973   variances    -13.51
                                                not
                                            assumed

Disloyal             1,572.9   (0.0000)              (0.0000)
                                  ****                  ****

Average Days
Between 1st and 2nd
SEOs (N: 70 vs.
1174)

Loyal                  487.5        F =       Equal       t =
                                10.2845   variances    -4.046
                                                not
                                            assumed

Disloyal               751.9  (0.00152)               (0.000)
                                   ***                  ****

Avenge pays
Bettween 2nd and
3rd SEO is (N: 35
vs. 47)

Loyal                  537.2        F =       Equal       t =
                                0.72886   variances     0.535
                                                not
                                            assumed

Disloyal               486.4   (0.3958)               (0.593)

Average Days
Between IPO and 2nd
5E0 (N: 1 vs. 203)

Loyal                1,101.2        F =       Equal       t =
                                 15.256   variances     -6.53
                                                not
                                            assumed

Disloyal             1,903.0   (0.0000)               (0.000)
                                  ****                  ****

Average Days
Between IPO and 3rd
550 (N: 12 vs. 70)

Loyal                1,343.9  F = 4.013       Equal       t =
                                          variances    -3'737
                                                not
                                            assumed

Disloyal             2,151.1   (0.0485)               (0.001)
                                    **                   ***

Notes: The p-values are shown in parentheses. (*), (**), (***),
and (****) denote statistical significance at 10%, 5%, 1%, and 0.1%
significance levels, respectively.


This test, referred to in Tables 8, 9, 10, 11 and 12, was proposed by H. Levene in his article "In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, I. Olkin et al." eds., Stanford University Press, pp. 278-292.

Table 8 also shows that those firms that remained loyal two offerings in a row (first and second SEO) have issued these offerings during the first 1,101.2 days after their IPO dates, while disloyal firms have done the same during the first 1,903 days. Finally, those firms that have been loyal three times in a row (first, second, and third SEO) issued their equity offerings during the first 1,343.9 days after their IPO dates, while disloyal firms did the same during the first 2,151.1 days. These last two results are also statistically significant at 0.1% and 1% confidence levels respectively. These results partially confirm hypothesis H6 about the positive relationship between the number of days between equity offerings and the probability that the issuer will repeat dealings with the same lead underwriter.

Table 9 shows the average underwriter reputation of each firm in our sample at different offering issues. The average underwriter reputation for loyal firms is higher than that of disloyal ones for each equity offering without exceptions, and all these results are statistically significant at conventional levels of confidence. Specifically, the average IPO underwriter reputation is 8.43 for loyal firms and 7.76 for disloyal ones. Similar results can be verified during the first SEO, where loyal firms have an average underwriter reputation of 8.39, while disloyal ones have an average of 6.84. These two results are both significant at a 0.1% confidence level. Similarly, during the third SEO the average reputation is 6.77 for loyal firms and 4.95 for disloyal ones. This is the only result that is significant at a 5% confidence level. Likewise, the average underwriter reputation for those firms that remained loyal during the first and second SEO is 8.20 compared to 4.80 of disloyal firms. Finally, those firms that have been loyal three times in a row have an average underwriter reputation of 8.58, while disloyal ones have an average of 5.24. These two last results are also significant at a 0.1% confidence level. These results support our hypothesis H2 about the positive relationship between underwriter reputation and the probability that the issuer will repeat dealings with the same lead underwriter.

Table 10 contains the level of underpricing for each firm in our sample during each offering starting with their IPOs. Loyal firms have an average underpricing level of 16.59% during their IPOs while disloyal ones have an average underpricing level of 22.32%. This is the only significant result at a 10% confidence level. This result is exactly the opposite result of Krigman, Shaw, and Womack (2001) who find that underwriter switchers are significantly less underpriced than non-switcher IPOs. This result partially supports hypothesis H1 about the negative relationship between the level of underpricing and the probability that the issuer will repeat dealings with the same lead underwriter. Nevertheless, the level of 5E0 underpricing seems to be irrelevant between loyal and disloyal firms for the second and third SEO, and for firms that have been loyal two and three times in a row. This result suggests that the level of IPO underpricing has explanatory power over customer loyalty only for the first SEO after the IPO. One reasonable explanation is that the pricing process during the IPO is completely different than that during a SEO. Indeed, during the IPO the lead underwriter looks for interested investors during road shows and keeps records in the bookbuilding process. This information helps the underwriter to determine the reasonable offer price for the IPO. The price of an SEO is based exclusively on historical price data, so no analysis is required. Therefore, the average underpricing level in most SEOs is extremely low, and the impact is therefore insignificant.
Table 9: Independent Sample Tests of Underwriter Reputation for
each Equity Offering

                       Mean   Levene's  t-test for
                              Test for    Equality
                              Equality    of Means
                                    of       (Sig.
                             Variances   2-tailed)

Average IP's
Underwriter
Reputation (N: 249
vs. 303)

Loyal                  8.43        F =       Equal   t=6.01
                                53.292   variances
                                               not
                                           assumed

Disloyal               7.76   (0.0000)              (0.000)
                                 ****                 ****

Average 1st SEO's
Underwriter
Reputation (N:249 vs.
303)

Loyal                  8.39        F =       Equal      t =
                                196.47   variances     8.89
                                               not
                                           assumed

Disloyal               6.84   (0.0000)              (0.000)
                                 ****                 ****

Average 2nd SEO's
Underwriter
Reputation (N: 70 vs.
175)

Loyal                  7.62        F =       Equal      t =
                               135.286   variances     7.87
                                               not
                                           assumed

Disloyal               4.47   (0.0000)              (0.000)
                                 ****                 ****

Average 3rd SEO's
Underwriter
Reputation (N: 35 vs.
47)

Loyal                  6.77        F =       Equal      t =
                                6.4542   variances     2.53
                                               not
                                           assumed

Disloyal               4.95   (0.0000)              (0.013)
                                   **                   **

Average 2nd SEO's
Underwriter
Reputation for Loyal
Firms during 1st and
2nd SEO (N: 41 vs.
204)

Loyal                  8.20        F =       Equal      t =
                               290.744   variances     9.77
                                               not
                                           assumed

Disloyal               4.80   (0.0000)              (0.000)
                                 ****                 ****

Average 3rd SEO's
Underwriter
Reputation for Loyal
Firms during 1st,
2nd, and 3rd SEO (N:
12 vs.70)

Loyal                  8.58        F =       Equal      t =
                                54.524   variances     7.37
                                               not
                                           assumed

Disloyal               5.24   (0.0000)              (0.000)
                                 ****                 ****

Notes: The p-values are shown in parentheses. (*), (**), (***), and
(****) denote statistical significance at 10%, 5%, 1%, and 0.1%
significance levels, respectively.

Table 10: Independent Sample Tests of IPO Underpricing

                           Mean   Levene's  t-test for
                                  Test for    Equality

Equality               of Means
                                        of       (Sig.
                                 Variances   2-tailed)

Average IPO
Underpricing of
Loyal/Disloyal Firms
During 1st SEO (N:
248 vs. 303)

Loyal                    16.59%        F =       Equal      t =
                                    21.278   variances   -1.715
                                                   not
                                               assumed

Disloyal                 22.32%   (0.0000)              (0.087)
                                     ****                    *

Average 1st SEO
Underpricing of
Loyal/Disloyal Firms
During 1st SEO (N:
210 vs. 266)

Loyal                     3.63%        F =       Equal        t
                                   0.02996   variances  =-0.582
                                                   not
                                               assumed

Disloyal                  4.20%   (0.8626)              (0.561)

Average 2nd SEO
Underpricing of
Loyal/Disloyal Firms
During 2nd SEO (N: 54
vs. 82)

Loyal                     2.96%        F =       Equal      t =
                                    1.2709   variances    0.262
                                                   not
                                               assumed

Disloyal                  2.51%   (0.2616)              (0.781)

Average 3rd SEO
Underpricing of
Loyal/Disloyal Firms
During 3rd SEO (G\1:
22 vs. 22)

Loyal                    -2.79%        F =       Equal      t =
                                    1.2051   variances    0.687
                                                   not
                                               assumed

Disloyal                 -3.73%  (0.27855)              (0.496)

Average 2nd SEO
Underpricing of Firms
that Remained Loyal
from its IPO to its
2nd SEO (N: 33 vs.
103)

Loyal                     3.05%        F =       Equal      t =
                                   0.65917   variances    0.255
                                                   not
                                               assumed

Disloyal                  2.58%   (0.4183)              (0.799)

Average 3rd SEO
Underpricing of Firms
that Remained Loyal
from its IPO to its
3rd SEO (N: 7 vs.
37)

Loyal                    -1.74%        F =       Equal      t =
                                    1.0072   variances    0.971
                                                   not
                                               assumed

Disloyal                 -3.54%  (0.32133)              (0.337)

Notes: The p-values are shown in parentheses. (*), (**), (***), and
(****) denote statistical significance at 10%, 5%, 1%, and 0.1%
significance levels, respectively.


Table 11 shows the average price adjustment for the IPO of each firm in our sample. Remember that we define price adjustment as the percentage of deviation of the IPO offer price from the middle point of its filing range. The average price adjustment is 0.91% for loyal firms and -0.025% for disloyal ones. These results are significant at 1% level of confidence. This result partially supports our hypothesis H5 about the positive relationship between price adjustment and the probability that the issuer will repeat dealings with the same lead underwriter.

We argue that issuers will associate positive price adjustment with successful marketing efforts of the lead underwriter and will privilege dealings with the same underwriter again in the event of a successive seasoned equity offering. These results partially support this view.
Table 11: Independent Sample Tests of Price Adjustment.

            Mean      Levene's Test     t = test for
                     for Equality of    Equality of
                        Variances       Means (Sig
                                           2-tailed)

Average IPO Underpricing of Loyal/Dispoyal Firms During 1st SEO
(N: 246 vs. 300)

Loyal      0.91%     F = 0.2506                          t=3.026

Disloyal  -0.025%     (0.61687)     Equal variances      (0.003)
                                            assumed       ***

Notes: The p-values are shown in parentheses. (*), (**), (***), and
(****) denote statistical significance at 10%, 5%, 1%, and 0.1%
significance levels, respectively.

Table 12: Independent Sample Tests of Proceedings for each Offering

          Mean   Levene's Test   t-test for Equality
                  for Equality     of Means (Sig.
                   Variances          2-tailed)

IPO's Proceeds (N: 249 vs. 303) of Loyal and Disloyal Firms during
the 1st SEO

Loyal     $223M    F = 0.024       Equal variances      t = 1.190
                                       assumed

Disloyal  $184M    ( 0.8768)                            (0.234)

1st SEO's Proceeds (N: 222 vs. 274) of Loyal and Disloyal Firms
during the 1st SEO

Loyal     $230M    F = 5.1605    Equal variances not    t = -2.935
                                       assumed

Disloyal  $143M   (0.024) (**)                          (0.004)

2nd SEO's Proceeds (N: 60 vs. 87) of Loyal and Disloyal Firms during
the 2nd SEO

Loyal     $256M     F = 6.02     Equal variances not    t = 1.665
                                       assumed

Disloyal  $150M    (0.01533)                            (0.099)

3rd SEO's Proceeds (N: 27 vs. 28) of Loyal and Disloyal Firms during
the 3rd SEO

Loyal     $242M    F = 16.497    Equal variances not    t= 2.34
                                       assumed

Disloyal  $79M   (0.000) (****)                         (0.027)

2nd SEO's Proceeds (N: 37 vs. 110) of Loyal and Disloyal Firms during
the 1st and 2nd SEO

Loyal     $249M    F = 0.7426      Equal variances      t = 1.142
                                       assumed

Disloyal  $175M     (0.3902)                            (0.255)

3rd SEO's Proceeds (N: 10 vs. 45) of Loyal and Disloyal Firms during
the 1st, 2nd, and 3rd SEO

Loyal     $325M    F = 7.5516    Equal variances not    t = 1.53
                                       assumed

Disloyal  $122M  (0.008) (***)                          (0.149)

Notes: The p-values are shown in parentheses. (*), (**), (***), and
(****) denote statistical significance at 10%, 5%, 1%, and 0.1%
significance levels, respectively.


Table 12 provides the results of difference-in-mean tests of the proceeds of each equity offering. In all cases, loyal firms are able to collect higher proceeds from each equity offering than disloyal ones. However, not all results are statistically significant at conventional levels of confidence. Indeed, the average offering proceeds of loyal firms during the first ($230 million), second ($256 million), and third SEO ($242 million) are lower than those of disloyal firms during the same offerings ($143 million, $150 million, $79 million, respectively.) These results are significant at one, ten, and five percent confidence levels respectively. The remaining results are insignificant at conventional levels of confidence. These results partially contradict hypothesis H7 about the negative relationship between the size of the offering and the probability that the issuer will repeat dealings with the same lead underwriter. Indeed, according to these results loyal firms are bigger than disloyal ones in terms of offering size, but again, since not all results are statistically significant, we cannot derive meaningful conclusions.

Table 13 shows the results of two logit regression models that evaluate the impact of several independent variables on the probability of a firm to remain loyal to the same underwriter during their first SEO. The results confirm hypothesis H6; that the number of days between the IPO and the first SEO has a negative relationship with the probability of the firm to remain loyal to the same underwriter.
Table 13: Logit Regression Model of the Probability of Loyalty during
the First SEO

                       Model 1               Model 2
                   Coefficient          Z -  Coefficient         Z -
                                  Statistic                Statistic

Intercept           - 6.633577  ( - 2.4858)   - 7.833969   ( - 2.87)
                                        **                      ***

Days between        - 0.001507  ( - 6.7644)   - 0.001505   ( - 6.80)
Offerings                             ****                     ****
(DB[O.sub.i])

Underpricing           0.00651   (0.020333)   - 0.146074  ( - 0.422)
(U[P.sub.])

Price Adjustment      1.353768   (1.413463)     1.125908  (1.047715)
(P[A.sub.])

NASDAQ                0.294849   (1.170146)     0.351384  (1.327753)
(NASDA[Q.sub.i])

Natural Logarithm     0.234932   (1.682987)     0.294694     (2.056)
of Proceeds                              *                       **
(LnP[R.sub.i])

Underwriter           0.398649     (4.1274)     0.417631     (4.013)
Reputation                            ****                     ****
(U[R.sub.i])

Bubble              - 0.702901  ( - 1.6686)   - 0.725204  ( - 1.707)
(Bubbl[e.sub.i])                         *                        *

McFadden              0.278317                  0.295884
[R.sup.2]

LR statistic          208.3965                  209.3477

N                          544                       511

Notes: (*), (**), (***), and (****) denote statistical significance
at 10%, 5%, 1%, and 0.1% significance levels, respectively. Model 1
includes shelf registrations while Model 2 excludes them. We found no
multicollinearity problems with Allison's (1999) methodology by
estimating the equivalent linear regression model and evaluating the
tolerance and the variance inflation factor for each independent
variable. The standard error was estimated by using the generalized
linear model method.


In other words, our results support the notion that the longer the period of time between the IPO and the first SEO, the lower the probability that the firm will repeat dealings with the same underwriter. This result is significant at a 0.1% confidence level in both models: with and without shelf registrations. This result is consistent with that of James (1992), who finds a positive and significant relationship between the probability of switching underwriters (disloyal firms) and the time between the IPO and the seasoned offering. The natural logarithm of the dollar amount from the offering proceeds is positively related to the probability of the firm to remain loyal to the same underwriter. This result suggests that the higher the dollar amount of the proceeds, the greater the probability of the firm remaining loyal to the same underwriter. This result contradicts hypothesis H7 and is also opposite to that of James (1992). This result is significant at a 10% confidence level in the model with shelf registrations, and at a 5% level in the model without shelf registrations. Underwriter reputation is another independent variable that has explanatory power over the probability of loyalty in our sample. This result supports hypothesis H2 about the notion that the higher the underwriter reputation, the greater the probability that the firm will repeat dealings with the same underwriter. This result is significant at a 0.1% confidence level in both models.

Finally, table 13 provides evidence that contradicts hypothesis H4: those IPOs offered during 1999 and 2000 have a higher probability of remaining loyal to the same underwriter than those offered at any other year. This result is significant at a 10% confidence level in both models. The remaining independent variables considered in our analysis have no explanatory power on the probability of underwriting loyalty in our sample.
Table 14: Logit Model Results for the Loyalty between 1st and 2nd SEO

                   Model 1                 Model 2
                   Coefficient  Z -        Coefficient  Z -
                                Statistic               Statistic

Intercept           - 0.642982  -0.949235   - 1.456142   - 0.3508

Days between         - 0.00018    - 3.604   - 0.001343    - 3.038
Offerings                           ****                     ***
(DB[O.sub.i])

Underpricing        - 0.454852  -0.951385   - 3.480435    - 1.001
(U[P.sub.i])

NASDAQ                0.026167   0.426612     0.100563     0.2338
(NASDA[Q.sub.i])

Natural Logarithm     0.052945   1.465122     0.016728     0.0763
of Proceeds
(LnP[R.sub.i])

Underwriter           0.005448   0.454364     0.204245     2.3373
Reputation                                                   **
(U[R.sub.i])

Bubble                0.069003      0.976     0.540079   1.132643
(Bubbl[e.sub.i])

McFadden              0 069909   0.117012
[R.sup.2]

LR statistic          2.818650   22.96392

N                          232        144

Notes: (*), (**), (***), and (****) denote statistical significance
at 10%, 5%, 1%, and 0.1% significance levels, respectively. Model 1
includes shelf registrations while Model 2 excludes them. We found no
multicollinearity problems with Allison's (1999) methodology by
estimating the equivalent linear regression model and evaluating the
tolerance and the variance inflation factor for each independent
variable. The standard error was estimated by using the generalized
linear model method.


Table 14 shows the results of two logit regression models that evaluate the impact of the same independent variables on the probability of a firm remaining loyal during its second SEO to the same underwriter that helped the firm in its first SEO. The results show again that the number of days between the first SEO and the second one has a negative relationship with the probability of the firm remaining loyal to the same underwriter between the first and second SEO. This result is significant at a 0.1% confidence level in both models: with and without shelf registrations.

Underwriter reputation is another significant result, but only in the model that excludes shelf registrations. This result supports our hypothesis H2 about the positive relationship between the underwriter reputation and the probability that the firm will repeat dealings in the second SEO with the same underwriter of the first SEO. This result is significant at a 5% confidence level. The lack of significant results in the model that includes shelf-registrations is consistent with the lack of underwriter certification hypothesis proposed by Denis (1991.) Indeed, in the model that includes shelf-registration, underwriter reputation has no explanatory power since the underwriter certification is irrelevant for shelves.

Table 15 provides the results of two logit regression models about the probability of a firm remaining loyal two times in a row: during the first and second SEO. These results confirm hypothesis H6 again; that there is a negative relationship between the number of days from the IPO to the second SEO, and the probability of the firm to stay loyal to the same underwriter. This result is significant at a 0.1% confidence level in both models: with and without shelf registrations. The second significant independent variable is the underwriter reputation, but only in the model that excludes shelf registrations. This result also supports hypothesis H2 about the positive relationship between underwriter reputation and the probability that the firm will repeat dealings during the second SEO with the same IPO underwriter. This result is significant at a 10% confidence level.
Table 15: Logit Model Results for the Loyalty between IPO and 2nd SEO

                       Model 1                 Model 2
                   Coefficient        Z -  Coefficient        Z -
                                Statistic               Statistic

Intercept           - 8.102009  - 1.76833   - 4.782199   -0.964672
                                       *

Days between        - 0.001494  - 3.46728   - 0.001489    - 3.325
Offerings                            ***                     ***
(DB[O.sub.i])

Underpricing          1.155192   0.282049     0.804246   0.186204
(U[P.sub.i])

NASDAQ                 0.29098   0.661071     0.478304   0.940079
(NASDA[Q.sub.i])

Natural Logarithm     0.356212   1.507309     0.172987   0.669946
of Proceeds
(LnP[R.sub.i])

Underwriter           0.182919   1.400378     0.269792    1.91798
Reputation                                                     *
(U[R.sub.i])

Bubble                0.929049   1.324009     0.690555   0.928472
(Bubbl[e.sub.i])

McFadden              0.175805                0.213812
[R.sup.2]

LR statistic          35.79534                35.089240

N                          232                     144

Notes: (*), (**), (***), and (****) denote statistical significance
at 10%, 5%, 1%, and 0.1% significance levels, respectively. Model 1
includes shelf registrations while Model 2 excludes them. We found no
multicollinearity problems with Allison's (1999) methodology by
estimating the equivalent linear regression model and evaluating the
tolerance and the variance inflation factor for each independent
variable. The standard error was estimated by using the generalized
linear model method.


Table 16 has the results of two logit regression models about the probability of remaining loyal to the same underwriter from the second to the third SEO. The lack of explanatory power of the number of days between the second and third SEO is consistent with the results of table 8 where the only insignificant results are those of the difference-in-mean tests for the number of days between the second and third SEO. Also, as in the previous two models, the underwriter reputation has explanatory power over the probability of underwriting loyalty, but only in the model that excludes shelf registrations. This result also supports hypothesis H2 about the positive relationship between the underwriter reputation and the probability that the firm will repeat dealings during the second SEO with the same IPO underwriter. This result is significant at 5% level of confidence.
Table 16: Logit Model Results for the Loyalty between 2nd SEO and 3rd
SEO

                       Model 1                 Model 2
                   Coefficient        Z -   Coefficient        Z -
                                Statistic                Statistic

Intercept           - 5.587157   -0.989021   - 3.033652  -0.499976

Days between          0.000622    1.040846   - 0.000219  -0.268337
Offerings
(DB[O.sub.i])

Underpricing        - 5.237643    -1.33248   - 5.095246  -1.325258
(U[P.sub.i])

NASDAQ              - 0.911158   -1.365228   - 0.044785  -0.04851
(NASDA[Q.sub.i])

Natural Logarithm      0.30561    1.018832     0.066056   0.212196
of Proceeds
(LnP[R.sub.i])

Underwriter         - 0.046756   -0.507358    0.267991   2.481847
Reputation                                               (**)
(U[R.sub.i])

Bubble                1.294509    1.78986      0.38295   0.518037
(Bubbl[e.sub.i])

McFadden              0.135205                0.140577
[R.sup.2]

LR statistic          13.07642                9.744040

N                           71                      50

Notes: (*), (**), (***), and (****) denote statistical significance
at 10%, 5%, 1%, and 0.1% significance levels, respectively. Model 1
includes shelf registrations while Model 2 excludes them. We found no
multicollinearity problems with Allison's (1999) methodology by
estimating the equivalent linear regression model and evaluating the
tolerance and the variance inflation factor for each independent
variable. The standard error was estimated by using the generalized
linear model method.


Table 17 shows the results of two logit regression models about the probability of a firm remaining loyal three times in a row: during the first, second, and third SEO. As in all previous logit models, the number of days between the IPO and the third SEO has a negative relationship with the probability of the firm to stay loyal to the same underwriter three consecutive times. These results confirm hypothesis H6 again that there is a negative relationship between the number of days between the IPO and the third SEO, and the probability of the firm to stay loyal to the same underwriter. This is the only independent variable that is significant at a 10% confidence level in both models.

Table 17: Logit Model Results for the Loyalty between IPO and 3rd SEO
Table 17: Logit Model Results for the Loyalty between IPO and 3rd SEO

                       Model 1                 Model 2
                   Coefficient        Z -  Coefficient        Z -
                                Statistic               Statistic

Intercept           - 17.75291  -1.699606   - 12.98683  -1.398784
                                       *

Days between        - 0.001026  -1.683178   - 0.001435  -1.742177
Offerings                              *                       *
(DB[O.sub.i])

Underpricing        - 0.941419  -0.274398     0.292701   0.074535
(U[P.sub.i])

NASDAQ                0.213673    0.16803      0.52487   0.389688
(NASDA[Q.sub.i])

Natural Logarithm     0.643133   1.230466     0.458138   0.907346
of Proceeds
(LnP[R.sub.i])

Underwriter           0.641074   0.910322      0.60119   0.836864
Reputation
(U[R.sub.i])

Bubble                0.656846   0.529161     0.546938   0.437459
(Bubbl[e.sub.i])

McFadden              0.321708                0.328828
[R.sup.2]

LR statistic          17.36767                15.50074

N                           71                      50

Notes: (*), (**), (***), and (****) denote statistical significance
at 10%, 5%, 1%, and 0.1% significance levels, respectively. Model 1
includes shelf registrations while Model 2 excludes them. We found no
multicollinearity problems with Allison's (1999) methodology by
estimating the equivalent linear regression model and evaluating the
tolerance and the variance inflation factor for each independent
variable. The standard error was estimated by using the generalized
linear model method.


6. Conclusion

We find that only 45.11% of our sample repeated dealings with the same lead investment bank or underwriter during the first seasoned equity offering. This constitutes a significant reduction compared to the 70% reported by Krigman, Shaw, and Womack (2001) and the 65% reported by James (1992). Our results suggest that customer loyalty has been declining in the securities firm industry during the last two decades. Also, our results suggest that some factors considered in previous academic research works have explanatory power over customer loyalty at the early stages of the firm as a public entity. However, once the firm becomes a public corporation with shares of stock traded in the secondary market, customer loyalty is no longer explained by such variables. In particular, we find that the number of days between offerings has a negative relationship with the probability of a firm remaining loyal to the same underwriter once, during their first SEO and their second SEO, twice during their first and second SEO consecutively, and three times in a row during their first, second, and third SEO consecutively. However, the number of days between offerings has no explanatory power over the probability of underwriting loyalty between the second and third SEO. The lead underwriter seems to have a better chance of being selected again if the satisfaction for the services rendered is still fresh in the issuer's memory. Also, the sooner the SEO occurs, the higher the probability that the issuer's representatives involved in the lead underwriter selection process will be involved again during that SEO, so the selection outcome will probably be the same.

We also find that firms with IPOs offered in 1999 and 2000 have a higher probability remaining loyal to the same underwriter during their first SEO than other firms in our sample. However, this relationship disappears during the second and third SEO. Similarly we find that the higher the dollar amount of the proceeds, the greater the probability of the firm remaining loyal to the same underwriter during its first SEO. This relationship also disappears during the second and third SEO.

Finally, we find that underwriter reputation has explanatory power over the probability of a firm's loyalty to the same underwriter during the first SEO. This relationship remains when loyalty occurs between first and second SEO, between the second and third SEO, and twice during their first and second SEO consecutively, but only when the logistic models excludes shelf registrations. This relationship is insignificant when loyalty occurs three times in a row during their first, second, and third SEO consecutively. Our results suggest that issuers who hire investment banks with high reputation will be more satisfied with the services they provide and will probably hire them again in subsequent SEOs.

References

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Beatty, Randolph P. and J. R. Ritter. 1986. "Investment banking, reputation, and the underpricing of initial public offerings." Journal of Financial Economics, Vol. 15, Issue 1-2, pp. 213-232.

Carter, Richard, and S. Manaster. 1990. "Initial public offerings and underwriter reputation," Journal of Finance, Vol. 45, pp. 1045-1067.

Chan, Yue-Cheong and D. Meidan. 2005. "Individual Investor Sentiment and Long-Run Performance of IPOs." Working Paper, Hong Kong Polytechnic University and Northwestern University.

Chen, Hsuan-Chi and J. R. Ritter. 2000. "The seven percent solution." Journal of Finance, Vol. 55, No. 3, pp. 1105-1131.

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Dr. Juan M. Dempere, Metropolitan State College of Denver

jdempere@mscd.edu
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Author:Dempere, Juan M.
Publication:Review of Business
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Date:Jun 22, 2011
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