Working Paper

Series _______________________________________________________________________________________________________________________

National Centre of Competence in Research Financial Valuation and Risk Management

Working Paper No. 91

Performance of Initial Public Offerings: The Evidence for Switzerland

Wolfgang Drobetz Matthias Kammermann

Urs Waelchli

First version: June 2002

Current version: June 2003

This research has been carried out within the NCCR FINRISK projects on “Asset Pricing and Portfolio Management”.

___________________________________________________________________________________________________________

Performance of Initial Public Offerings: The Evidence for Switzerland

Wolfgang Drobetz1, Matthias Kammermann2, and Urs Wälchli3,4

Abstract

We examine the underpricing and long-term performance of a broad set of Swiss IPOs from 1983 to 2000. The average market adjusted initial return is 34.97%. Our results support the ex ante uncertainty hypothesis, the signal-ling hypothesis and, to some extent, the market cyclicality hypothesis as possible explanations for the underpricing phenomenon on the Swiss IPO market. We also find evidence for lower initial returns under increased competition among investment banks, and more accurate pricing when book-building is used. To accurately measure the long-term performance of Swiss IPOs, we use a variety of different methods and adjust for possible biases. In contrast to previous findings for the U.S., we do not find a signifi-cant drop or strong continuous underperformance of Swiss IPO stock prices in the aftermarket. If there was any evidence for underperformance at all, Swiss IPOs show poor returns only in the very long-run after 48 months of trading.

Keywords: initial public offerings, underpricing, long-run stock perform-ance, market efficiency, Swiss stock market.

JEL classification codes: G14, G12, G24.

1 Wolfgang Drobetz, Department of Finance, University of Basel and WHU Otto Beisheim

Graduate School of Management, Holbeinstrasse 12, 4051 Basel, Switzerland, Phone: +41-79-2611982, Mail: wolfgang.drobetz@unibas.ch

2 Matthias Kammermann, Greifenstrasse 13, 9000 St. Gallen, Switzerland, Phone: +41-79-407 53 55, Mail: matthias.kammermann@alumni.unisg.ch

3 Urs Wälchli, Institute of Financial Management, University of Bern, Engehaldenstrasse 4, 3012 Bern, Switzerland, Phone: +41-31-631 34 78, Mail: urs.waelchli@ifm.unibe.ch

4 We thank Stefan Beiner, Dusan Isakov, Claudio Loderer, and Heinz Zimmermann for valu-able comments. Financial support by the National Center of Competence in Research “Finan-cial Valuation and Risk Management” (NCCR FINRISK) is gratefully acknowledged..

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1 Introduction

A large number of studies have examined the performance of initial public offer-ings (IPOs). While there has been a growing empirical literature for countries out-side the U.S. in recent years, most studies have still used U.S. data. The typical pat-terns documented in these studies are underpricing and long-run underperformance. For example, using U.S. data from 1980 to 2001, Welch and Ritter (2002) report that, at the end of the first day of trading, IPOs traded at 18.6% (on average) above the price at which the company sold them. Nevertheless, over three years the aver-age IPO underperformed the CRSP value-weighted index by 23.4%. This paper examines these effects for the Swiss IPO market. Similar to previous studies, we also document the existence of significant short-run underpricing. In contrast to the U.S. evidence, however, this initial underpricing persists for quite a long period of time, i.e., issuing firms do not significantly underperform in the medium- and long-term. If there was any evidence for underperformance at all, Swiss IPOs show poor returns only in the very long-run after 48 months of trading.

Empirical evidence for the Swiss IPO market is interesting for several reasons. First, the Swiss equity market is one of the largest in Europe, with a share of ap-proximately 8 percent of total European market capitalization. More important, apart from the United Kingdom, Switzerland is the only developed stock market in Europe outside the Euro-zone. As in most other major market places, the Swiss IPO market was quite active during the technology boom starting in the mid 1990s. Ad-vantages of a listing in Switzerland included not only the well-known international-ity of the market place, but also a large number of investment companies and specialized funds as well as relatively low listing charges. Second, there are only a few studies specifically examining the performance of Swiss IPOs. In fact, we are aware of only two thorough studies by Bill (1991) and Kunz and Aggarwal (1994). Recently, Schuster (2001) includes a limited sample of Swiss IPOs in an extensive study of pan-European IPOs. Third, the Swiss capital market has undergone many institutional changes. In 1993 the seven Swiss stock exchanges have been consoli-dated into the SWX Swiss Exchange. Trading of shares and derivative instruments from foreign issuers started in 1995. A fully computerized trading system for stocks, bonds, and derivatives has been introduced in 1996. However, by far the most important institutional change was the establishment of the SWX New Market

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in January 1999, which ought to enable access to the capital market for innovative firms. This market segment is reserved for growth companies with good ”invest-ment stories”. The official purpose is to ”[...] serve as a means for listing equity securities of companies which are characterized by an orientation towards opening up new markets for their products, utilizing innovative technologies, or developing new products or services. This market segment has the specific purpose of making it easier for young companies to gain entrance to the capital marketplace.”5 Accord-ingly, most companies are from sectors such as life science, information technol-ogy, and microtechnology. To enable capital market access for growth companies, there are much less stringent listing requirements in place with respect to the amount of required equity capital, the length of the firm history, or the record of profitability. On the other hand, the SWX New Market has stricter rules regarding information disclosure and transparency. Similar market segments have been estab-lished in other major European stock markets, such as the Neuer Markt in Ger-many, the Nouveau Marché in France, or the Nuovo Mercato in Italy. All these markets boosted extraordinary performance from inception until mid-2000, when the new economy bubble bursted. By the end of 2000, the number of companies listed on the SWX New Market has grown to 17. Our sample covers all companies from the growth segment and hence allows an interesting comparison between these firms and the behavior of IPOs on the main segment of the Swiss stock ex-change. A final interesting observation is that investment banks changed their methods for conducting going publics. Swiss firms in general used the fixed price offer method, in which case potential investors specify the number of shares to which they wish to subscribe at a preannounced price. Tender offers, where the ap-plicants specify a price (at or above a minimum price) and a quantity of shares, were rarely used in Switzerland. More recently, however, Swiss firms have fol-lowed an international trend and switched to the book-building mechanism. Book-building refers to the collection of bids from investors, which is based on an indica-tive price range, the issuing price being fixed after the bid closing date. The princi-pal intermediaries involved in a book building process are the IPO candidate, the lead manager and syndicate members who are eligible to act as underwriters. The syndicate members decide the indicative price range and the investors decide the

5 For more details see http://www.swx.ch/admission.

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price of the issue through a tender method. Long-term investors usually benefit from preferential treatment in the allocation of shares in order to guarantee a stable aftermarket performance. This recent trend on the Swiss IPO market is interesting in light of the findings by Loughran, Ritter and Rydquist (1994). They report an average 37% underpricing for fixed price offers, 27% for tender offers, and only 12% for book-building. We examine whether book-building also leads to more ac-curate pricing for Swiss IPOs.

Apart from examining the peculiarities of the Swiss IPO market, we also shed light on the difficulties of measuring long-term abnormal returns from an asset pricing standpoint. We measure secondary market returns up to 120 months of trading after going public. This is a much longer sample period than usual in the literature, but at the same time makes statistical inferences even more challenging. In the light of extreme skewness and severe bad-model problems, we apply a variety of different methods and make several necessary adjustments: buy-and-hold abnormal returns (BHARs), wealth relatives (WRs), cumulative abnormal returns (CARs), and inter-cept tests using time series regressions. This is interesting for itself, because all techniques have very distinct economic implications and statistical properties. Therefore, we devote an entire section to discuss the pros and cons of each method. The comparison of the empirical results from different methods should be inter-preted as an important check for robustness and consistency, as forcefully demon-strated by Gompers and Lerner (2001).

The remainder of this paper is organized as follows. Section 2 contains a data de-scription. Section 3 examines the short-run underpricing of Swiss IPOs and tests a variety of possible explanations for this phenomenon. Section 4 contains an exten-sive analysis of the long-term performance of Swiss IPOs up to 120 months in the aftermarket. Section 5 concludes.

2 Data description

Figure 1 shows the number of Swiss IPOs on all different segments of the Swiss Stock Exchanges during the 1962 to 2000 period. Including the 34 investment companies, there were 225 firms going public during this period of time. The most apparent observation is that the number of IPOs varies substantially from year to

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year. Yet, a clear trend towards going public can only be observed since the mid-1980s. Specifically, there were two notable IPO waves, one from 1985 to 1988, and the other from 1996 to 2000. In contrast, when the investment companies are ex-cluded, there were no IPOs that took place during the 1991/92 recession period. Since 1991 the number of investment companies that has gone public increases steadily. These companies concentrate on specific sectors and themes (e.g.,venture capital and hedge funds). Because of their special purpose as investment vehicles, we exclude them from our analysis.

[Figure 1: Swiss initial public offerings (IPOs): 1962-2000]

For reasons of data availability, our analysis also excludes all IPOs before 1983. In addition, many of the early IPOs do not fulfill all data requirements to be included in our sample. Accordingly, our sample contains the 150 Swiss IPOs from 1983 to 2000. For the 1983 to 1989 period we combine the data from Mettler (1990), Bill (1991), and Kunz (1991). The data for 1990 to 1993 are from Thommen (1996), and the data for 1994 to 2000 are from various publications of Bank Vontobel. From this sample of 150 IPOs we had to exclude another 31 firms due to insuffi-cient data or other firm-specific reasons. One firm, Kardex AG, issued both bearer shares and participation notes in their IPO. Hence, it shows up twice in our sample. This leaves us with a final data set of 120 new issues for our analysis of short-term performance. To examine the long-term performance of Swiss IPOs, we had to ex-clude another 11 firms due to data limitations, leaving a reduced sample of 109 firms. Our sample is unique for three reasons. First, it contains the 17 firms listed on the SWX New Market, which has been established in 1999 as the growth seg-ment of the Swiss stock exchange. This enables us to examine whether the per-formance of Swiss IPOs depends on the segment on which a firm is being listed. Second, and perhaps more important, our extensive data set allows us to examine long-term performance on secondary markets up to 10 years.6 We are not aware of any other work related to the Swiss stock market with this length of aftermarket performance. Since our sample starts as early as 1983, we examine long-term per-

6 Data collection has not always been easy. Specifically, the aftermarket performance of

several IPOs from the early sample years is not readily available from standard electronic data sources. Accordingly, information has been collected from various other sources, such as annual financial statements and local newspapers.

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formance over both IPO waves since then. Finally, we have been very careful to avoid potential biases in measuring long-term returns. To avoid any survivorship bias, we include in our sample the 5 firms that went bankrupt during the 120 months aftermarket period. In all cases we assume investors suffered a complete loss of their claims. Furthermore, 11 firms were delisted due to mergers or acquisi-tions during the sample period. In the case of a cash offer, we assume shareholders invest their proceeds in the respective benchmark index. If, however, an exchange of shares was offered, we assume that shareholders accept the offer and become shareholders in the new venture.7 A description of potential biases concerning the choice of the benchmark and how we deal with them is postponed until section 4.

[Table 1: Composition of Swiss IPOs (1983-2000)]

To provide some general information about our sample, Table 1 shows the compo-sition according to sectors. Evidently, the sample is evenly split between industrials and services. Finally, Table 2 shows that, taken together, the 119 sample firms is-sued shares worth 32bn Swiss francs. In contrast, the combined value at the the end of the first trading day was 36.1bn Swiss francs. Accordingly, initial shareholders left roughly 4.1bn Swiss francs on the table. The issuing size varies strongly across the sample. The smallest IPO was Intersport Holding AG, with an issuing volume of mere 5.66m Swiss francs. In contrast, Swisscom boosted an issuing volume of 8.6bn Swiss francs, accounting for almost 25% of the total sample IPO volume. Taken together, the four largest IPOs had an issuing volume of 17.2bn Swiss francs, accounting for almost 50% of all IPOs. A final observation from Table 2 is that small firms suffer from larger underpricing than large firms. We will further examine this observation below.

[Table 2: Issuing volume of Swiss IPOs (1983-2000)]

7 In addition, four firms abolished the duality between their bearer and registered shares

and introduced unitary shares. In these cases we link the series of historical share prices with that of the new unitary share.

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3 Underpricing of Swiss IPOs

3.1 Empirical methodology

To examine the amount of underpricing of Swiss IPOs, we calculate initial returns and market-adjusted initial returns. Following previous research, the initial return period covers one day; it is defined as the offering price to the first closing price. Benchmark-adjusted returns are calculated as the raw return on a stock minus the benchmark return over the corresponding initial period. The raw return for IPO i, denoted as IRi, is defined as:

(1) 0i

0i1ii P

PPIR

−=

where Pi1 denotes the closing price at the first trading day, and Pi0 is the offering price. The benchmark-adjusted initial return on IPO i, denoted as AIRi, is computed as the difference between the initial return on IPO i and the return on the market portfolio over the same period:

(2) 0m

0m1m

0i

0i1ii P

PPP

PPAIR

−−

−=

where Pm1 denotes the closing price of the benchmark index on the first trading day, and Pm0 is the previous day’s closing price.8 We use daily returns on the Swiss total market index provided by Datastream as the benchmark.9 Theoretically, the under-lying trading strategy is ex ante implementable and follows a simple rule: Each IPO is bought at the offering price and sold at the closing price of the first trading day. Because there is no ex ante information about a specific weighting scheme, initial returns are equally-weighted, i.e., the same amount of money is invested in every IPO.

[Figure 2: Distribution of initial returns (1983-2000)]

8 Given that initial returns are measured only over a single day, we do not correct for dif-

ferences in beta as required by the CAPM. There is no way in the IPO literature to esti-mate betas for new issues. In addition, assuming that all betas are equal to one should not significantly affect the results for daily returns.

9 Alternatively, we also use the Swiss Performance Index as the benchmark. The results are qualitatively the same.

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Figure 2 shows the empirical distribution of benchmark-adjusted initial returns. They range from -7.81% to +287.85%, which implies strong positive skewness. It is well known that skewness contributes to a misspecification of standard test statistics. To measure whether abnormal initial returns are significant, it is therefore not appropriate to apply a simple t-statistic. Barber and Lyon (1997) document that positive skewness leads to negatively biased t-statistics.10 To conduct significance tests for initial returns, we therefore apply the skewness-adjusted t-statistic, as sug-gested by Johnson (1978). Lyon, Barber, and Tsai (1999) argue that only the boot-strapped application of this skewness-adjusted test statistic yields well-specified test statistics. We follow their approach and report the adjusted t-statistics on the basis of the distribution of bootstrapped resamples.11 Alternatively, we also report the results of a nonparametric sign test. The null hypothesis is that the number of observed positive initial returns equals the number of negative returns.

3.2 Distribution of initial returns

Table 3 shows the mean initial returns for Swiss IPOs. The mean market-adjusted initial return over the whole sample period from 1983 to 2000 is 34.97%. Both the skew-adjusted t-test and the non-parametric sign test indicate that it is highly sig-nificant. Accordingly, a trading strategy that invested a fixed amount in each IPO to be sold at the end of the first day of trading earned a significant positive return. Since we have no data on the actual allocations, we cannot determine whether the average investor could actually capture this return. However, recent evidence from Amihud, Hauser, and Kirsh (2001) using allocation-weighted returns from the Is-raeli IPO market sheds doubt on this proposition. The table also reveals that only 13 Swiss IPOs were overpriced, all other 107 IPOs in our sample were underpriced. This is also reflected in the high significance of the sign test statistic.

[Table 3: Distribution of initial returns (1983-2000)]

10 Positive skewness in the distribution from which observations arise results in the sam-

pling distribution of t being negatively skewed. This leads to an inflated significance level for lower-tailed tests (i.e., reported p-value will be smaller than they should be) and a loss of power for upper tailed tests (i.e., reported p-values will be too large).

11 See Barber, Lyon, and Tsai (1999), p. 174, for an exact description of the procedure.

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Observing that there were two major IPO waves on the Swiss market, we look at two subsamples, the first one from 1983 to 1990 and the other one from 1994 to 2000. Table 3 shows that the market-adjusted mean returns are 53.61% and 18.66%, respectively. Again, both are highly significant. Interestingly, the amount of underpricing is much larger during the early sample years. A Wilcoxon-Mann-Whitney test delivers a p-value of 0.000 for the null hypothesis that the median ini-tial return is equal in both subperiods, indicating that the amount of underpricing of Swiss IPOs has become smaller over time. We further explore this observation in our regression analysis below. The magnitude of underpricing in the 1983 to 1990 period differs from previous results in Kunz (1991). He reports an average under-pricing of 35.8% for almost the same time interval. The difference to our results stems from his definition of the sample in that he neglects all IPOs with issuing volumes smaller than CHF 19.5m. In contrast, Bill (1991) reports an average mar-ket-adjusted initial return of 57.56%, which is close to our results.

[Table 4: Distribution of initial returns (1994-2000)]

Another interesting aspect is to look at the differences between the SWX New Market and the main and local segments of the Swiss stock exchange. We split the sample from 1994 to 2000 into two subsamples: (i) the 17 IPOs listed on the SWX New Market, and (ii) the 45 IPOs on the main segment and the 2 IPOs on the local segment. Table 4 shows that the amount of underpricing is much more pronounced on the SWX New Market. The average market-adjusted initial return for IPOs on the growth segment is 38.98%, compared to 11.32% for the issues on the main and local segments. Both numbers are significantly different from zero. A Wilcoxon-Mann-Whitney test further shows that the median initial returns (both unadjusted and adjusted) are significantly different from each other. This is what could be ex-pected intuitively, given that the SWX New Market contains growth firms with a lot of uncertainty in their valuations. This notion is also supported by the large 68.02% standard deviation of adjusted initial returns across firms listed on the SWX New Market.

A final observation from Table 4 is that the tendency for the underpricing to de-crease over time accentuates when the sample is split into the growth and the main (and local) segments. The average initial return for IPOs on the main and local

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segments reduces to roughly 11% for the 1994 to 2000 period, compared to almost 19% for the total sample over the same sample period (see table 3).

3.3 Explaining initial abnormal returns

3.3.1 Methodology

The analysis so far has demonstrated significant underpricing of Swiss IPOs over the period from 1983 to 2000. In this section we examine possible explanations for this phenomenon on the Swiss stock market. The method used to examine the ex-planatory power of different hypothesis is ordinary least squares regression. The initial return for each IPO is regressed on variables that proxy for different hy-pothesis suggested in the literature. Specifically, we estimate cross-sectional re-gressions of the following type:

(3) i

n

1k

kiki XcAIR ε+⋅β+= ∑=

where AIRi denotes the market-adjusted underpricing for IPO i, α is a constant, Xki the explanatory variable k for the underpricing of IPO i, and εi an error term. Typi-cally, looking at a cross-section of firms, heteroscedasticity may be a problem. We suspect that the error terms associated with small going publics will have greater variances than those associated with larger firms. Ordinary least squares estimates are consistent in the presence of heteroscedasticity, but conventional standard er-rors are no longer valid and, hence, the estimators are no longer efficient. We test for heteroscedasticity using the White covariance test. If the null hypothesis of ho-moscedasticity in error terms can be rejected at the 5% level, we correct the t-values using the White covariance matrix estimator. In this case, a regression is marked with ”H”.

3.3.2 Single hypothesis tests

We analyze five possible explanations for underpricing, each of which is repre-sented by specific firm proxies, which are collected in the X vector of explanatory variables in equation (3). Table 5 contains a summary of expected relationships be-

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tween our proxy variables and the magnitude of benchmark-adjusted underpric-ing.12

[Table 5: Proxy variables for tests of underpricing]

[Figure 3: Initial returns and issuing volumes]

Before we lay down and formally test our hypothesis, we start by quantifying two observations already discussed above (they are referred to as ”Hypothesis 0” in Ta-ble 5): (i) smaller issues have more pronounced underpricing and (ii) the magnitude of underpricing has declined over time. Figure 3 exhibits the relationship between issuing volume (LEVOL) and the magnitude of underpricing, Figure 4 shows the underpricing over time (TIME). It becomes clear from the mere visual inspection that larger and more recent IPOs experienced lower underpricing. The IPO with the highest underpricing was Think Tools on the SWX New Market.

[Figure 4: Underpricing over time]

Table 6 contains the cross-sectional regression results.13 The regression coefficient on LEVOL (defined as the natural logarithm of the issuing volume) is negative and significant. The coefficient on TIME (defined as the corresponding sample day an IPO takes place) is also negative and significant. The two regressors, LEVOL and TIME, are highly correlated, with a correlation coefficient of 0.62. This is not sur-prising, given that most large IPOs were conducted only recently. In a multivariate regression with both proxies, only TIME remains significant, but LEVOL looses its explanatory power. Accordingly, LEVOL is not only a measure for size, but also contains a strong component related to time.

[Table 6: Issuing volume, time, and underpricing]

A natural explanation for decreasing underpricing is learning. As more IPOs have taken place, all market participants learn and the average amount of underpricing reduces. Therefore, we use NR, defined as the number of each IPO in the sample,

12 Of course, this is not a complete summary of possible explanation hypothesis suggested

in the literature. For an overview see Ritter (2002). 13 In tables 6-12 underpricing is measured in percent, i.e., all coefficients are scaled by a

factor 100.

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as a simple proxy for learning. For Autophon, the first IPO in our sample, NR is 1, and for TeleMedicine, the last IPO in our sample, NR is 119. When NR is intro-duced into the regression, both LEVOL and TIME loose their significance.14 We interpret this result as evidence for decreasing underpricing through learning over time. We now proceed by formally stating and testing our main hypothesis for un-derpricing.

Hypothesis H1: Ex ante uncertainty An important rationale suggested in previous research for the underpricing phenomenon of IPOs is the winner’s curse explana-tion, originally proposed by Rock (1986). Underpricing is the result of asymmetric information between potential investors. Informed investors will only submit orders for the most desirable firms. Because they are well-informed, they are more likely to buy shares when an issue is underpriced. This leads to a positive relationship be-tween the amount of excess demand and the amount of underpricing. In contrast, non-informed investors will be allocated only a small fraction of the most desirable new issues, while they are allocated most of the least desirable new issues. In other words, if non-informed investors get all of the shares which they ask for, it is be-cause the informed investors do not want the shares, i.e., the non-informed face a winner‘s curse. In order to induce non-informed investors to submit purchase or-ders, on average, IPOs are underpriced sufficiently to compensate them for the bias in the allocation of new shares. If there was no underpricing, they would suffer negative average initial returns and stay away from the market for IPOs in the fu-ture. Welch (1992) proposes a similar argument, where pricing too high might in-duce investors to fear a negative cascade. Investors attempt to judge the interest of other investors and only request shares when they believe the offering is hot. Pric-ing just a little too high leaves investors with a too high probability of failure, and they abstain from the issue because other investors also abstain.15

Rock (1986) posits that the amount of underpricing increases with the ex ante un-certainty about the firm’s true value. Unfortunately, we cannot observe ex ante un-certainty. We therefore follow the procedure in Ritter (1984), McGuiness (1992),

14 Clearly, NR and TIME are almost perfectly correlated. 15 In support of this hypothesis, Amihud, Hauser, and Kirsh (2001) find that Israeli IPOs

tend to be undersubscribed or hugely oversubscribed, but very few issues are moderately oversubscribed.

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and Aussenegg (1997) and use the ex post standard deviation as a proxy for ex ante uncertainty in our regression analysis. High volatility of returns is assumed to be an indicator for asymmetric information; higher uncertainty and more pronounced in-formation asymmetries lead to higher volatility. We use as our proxy for uncer-tainty the standard deviation (VOLA) of excess returns from trading day +2 to +21 after the day of the issue.16 According to theory, we expect a positive relationship between the amount of underpricing and the ex post volatility (VOLA).

[Table 7: Ex-ante risk hypothesis]

Table 7 shows the empirical evidence for the ex ante risk hypothesis. In a univari-ate regression of market-adjusted underpricing on VOLA, the estimated coefficient is positive, but insignificant. However, in a multivariate regression that also in-cludes NR, the coefficient on VOLA is highly significant. We interpret this result as evidence for the ex ante risk hypothesis. The magnitude of underpricing in-creases with the uncertainty about the value of an issue in order to keep uninformed investors in the market for IPOs.

Hypothesis 2: Market cyclicality Many empirical studies, e.g., Ritter (1984) and Lerner (1994) for the U.S. and Uhlir (1989) for Germany, document a positive rela-tionship between the amount of underpricing and the market performance shortly before an IPO. This is closely related to the observation that high initial returns tend to be followed by rising IPO volume. Periods of high average initial returns and rising issuing volume are referred to as hot markets. The market cyclicality hy-pothesis posits that the volume of IPOs shows a strong tendency to be high follow-ing periods of high stock market returns, i.e., when stocks are selling at a premium relative to their fundamental values. The academic literature has tended to view increases in the valuation of comparable firms as reflecting improved growth op-portunity.17 In addition, Baker and Wurgler (2000) and Lowry (2002) find that when investors are overoptimistic, firms respond by issuing equity in a window of

16 Due to data availability, we use the data from Bill (1991) to cover the time period from

1983 to 1988. He uses the SBC Swiss Market Index to adjust for market movements. Be-cause this index is no longer constructed, for the companies not covered in his study we compute excess returns using the Datastream Total Market Index for Switzerland.

17 See Lucas and McDonald (1990) and Choe, Masulis, and Nanda (1993) for theoretical models.

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opportunity. Welch and Ritter (2002) emphasize that the most important unan-swered question is why issuing volume drops so dramatically following stock mar-ket drops, i.e., why we observe quantity adjustment instead of price adjustment.

We use two proxy variables to characterize the influence of the pre-issue market environment on the magnitude of underpricing: (i) aggregate market performance (MARKET) and (ii) the number of IPOs (NUMBER). MARKET measures the per-formance of the market index over the period from day -20 to -1, where day 0 de-notes the day of the issue. We choose the preceding 20 days because this roughly reflects the period from the start of the book-building process to the first day of trading. We expect a positive relationship between MARKET and the magnitude of underpricing. In contrast, NUMBER measures the number of other IPOs being is-sued during the period from day -20 to -1 before the day of the issue. Intuitively, a high number of IPOs indicates a favorable market environment. Therefore, we also expect a positive relationship between NUMBER and the amount of underpricing. A negative relationship could possibly indicate the market’s saturation for IPOs.

[Table 8: Market cyclicality hypothesis]

The empirical evidence for the market cyclicality hypothesis is shown in Table 8. Regressing the market-adjusted initial return on either MARKET or NUMBER alone, neither explanatory variable is statistically significant. However, including NR in the regression, the number of each IPO in the sample, MARKET becomes positive and significant. Unfortunately, NUMBER remains insignificant in the mul-tivariate regression. This result can be interpreted as evidence in favor of the mar-ket cyclicality hypothesis in the sense that there is a positive, albeit weak, relation-ship between the amount of underpricing and Swiss market performance shortly before an IPO.

Hypothesis 3: Signaling Underpriced issues leave a good taste with investors, al-lowing firms and insiders to sell future offerings at a higher price than would oth-erwise be the case. This reputational argument has been formalized in different sig-nalling models.18 Models of this type are ultimately based on the classical analysis by Akerlof (1970). Issuing firms possess private information about whether they

18 See Allen and Faulhaber (1989), Welch (1989), and Grinblatt and Hwang (1989).

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have high or low values. Managers will follow a dynamic issue strategy, in which the IPO is followed by a seasoned equity offering. On the other hand, investors will only submit orders for IPOs when the issuing price does not exceed an average of expected issuing prices of future issues. Akerlof’s (1970) analysis implies that only lemons, i.e., low value firms, tend to find their way to the market. In contrast, high quality IPOs will issue only a small fraction of their total equity capital, withhold-ing the rest for a future seasoned equity offering.

Following Aussenegg (1997), we collect information from the stock guide of Fi-nanz und Wirtschaft on whether a firm conducts a secondary offering over the fol-lowing 24 months after going public. We construct a dummy variable, SEO, which is one if a firm issued new equity in a later stage, and zero if it has not. According to the signalling hypothesis, we expect a positive relationship between SEO and the amount of underpricing. High quality firms accept higher underpricing, ”leaving a good taste in investors’ mouth” to achieve higher prices in a later secondary offer-ing. Because of data unavailable at the time of our analysis, we eliminate the latest 19 IPOs from our analysis. Therefore, all our regressions that contain SEO as an explanatory variable use only the 100 observations from 1983 to 1998.

We also construct a variable measuring the percentage of total equity capital issued in an IPO, denoted as SHARE. To construct SHARE, we use the data from Kunz (1991) and various publications from Bank Vontobel. Unfortunately, we could not gather data for 16 firms. According to the signalling hypothesis, we expect a nega-tive relationship between SHARE and the amount of underpricing; a small value of SHARE is interpreted as a positive signal, because a large amount of stock remains with insiders or for a secondary offering.

[Table 9: Signaling hypothesis]

Single regression analysis in Table 9 shows that both variables, SEO and SHARE, are statistically significant, with the sign of the coefficients as suggested by the sig-nalling hypothesis. Controlling for NR, the number of each IPO in the sample, SEO remains significant, but SHARE is only marginally significant.

Hypothesis 4: Underwriter reputation In a recent article, Carter, Dark, and Singh (1998) show that IPOs managed by more reputable underwriters are associated with

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less short-run underpricing.19 The underwriter reputation hypothesis states that by choosing a highly reputated investment bank as the lead manager, firms could send a credible signal to be a high value IPO. Previous research suggests that under-writer reputation signals the underlying risks of the offering that are impounded in the immediate aftermarket return.

[Table 10: Reputation hypothesis]

To investigate this hypothesis, we construct two variables: (i) the logarithm of the cumulative issuing volume of all IPOs conducted by an issuing house as the lead manager before the IPO under consideration has taken place (REP), and (ii) the to-tal number of IPOs where an underwriter has been part of the syndicate of issuing banks (IPOBANK). Following Megginson and Weis (1991), REP gives the lead manager of each IPO the full credit for the total amount underwritten. In contrast, IPOBANK also accounts for the less prestigious positions on the bottom of a tomb-stone advertisement.20 Both variables are constructed on the basis of our Swiss data set and may thus be biased with regards to the reputation outside Switzerland. Given that several smaller, regional banks have been active on the Swiss IPO mar-ket, this is particularly important for the big investment banks.

Given earlier results in the literature, we expect a negative relationship between both REP and IPOBANK and the magnitude of underpricing. Univariate regression analysis in Table 10 shows that both REP and IPOBANK are significant at conven-tional levels, with the sign of the relationship as expected. However, in the pres-ence of NR, i.e., the number of an IPO in the sample, both variables loose signifi-cance. This could suggest that the reputation hypothesis has lost explanatory power for the magnitude of underpricing on the Swiss IPO market over time, as issuing houses have gained more and more experience.

Hypothesis 5: Changes in market environment The number of an IPO in the sam-ple, denoted as NR above, has been used as a proxy for unspecified changes in the

19 Earlier work is by Beatty and Ritter (1986) and Titman and Trueman (1986). 20 Carter, Dark, and Singh (1998) develop a new version of the reputation measure origi-

nally developed by Carter and Manaster (1990). By comparing the relative placement of investment banks in the tombstone advertisements over time, they place underwriters into 10 categories.

17

market environment. However, NR can capture such changes only in a very sim-plistic way. Therefore, we attempt to find more elaborate proxies and suggest five additional variables:

• NBUNDERW denotes a proxy for the increasing competition among in-vestment banks for equity issues. It is defined as the cumulated amount of investment banks being active on the Swiss IPO market. The intuition fol-lows the banking hypothesis proposed in Beatty and Ritter (1986). On the one hand, investment banks must underprice IPOs to maintain good relation-ships with their buy-side clientele, i.e., potential investors who purchase an issue. On the other hand, if investment banks underprice too heavily, they will loose reputation with potential issuers. This conflict of interest would result in some equilibrium underpricing. We suppose that there is a negative relationship between NBUNDERW and the amount of underpricing, i.e., with increasing competition issuing houses must reduce underpricing to maintain credibility in the market.

• FIX denotes a dummy variable that takes a value of 1 for fixed price offers, and 0 for tender offers and for IPOs using a book-building mechanism. Av-eraging across a set of countries, Loughran, Ritter and Rydqvist (1994) re-port an average 37% underpricing for fixed price offers, 27% for tender of-fers, and 12% for book-building. In fact, partly because it results in more ac-curate pricing compared to fixed price offers (where the offer price is set too early) many countries, including Switzerland, have moved to book-building in recent years. Therefore, we expect a positive relationship between FIX and the amount of underpricing.

• BOOK is a dummy variable that takes a value of 1 for IPOs using book-building, and 0 for tender offers and fixed price offers. The intuition behind this proxy is the same as for FIX. Due to the more accurate pricing of book-building, we expect a negative sign for the relationship between BOOK and the amount of underpricing.

• SWX denotes a dummy variable that takes a value of 1 for the 17 IPOs listed on the SWX New Market since its inception in 1999, and 0 for all other IPOs

18

on the main and local segments. The analysis above has demonstrated that underpricing has been more pronounced on the growth segment than on the established segments. Consequently, we expect a positive relationship.

• OWN is a dummy variable that takes a value of 1 if an underwriter goes pub-lic itself, and 0 for all other IPOs.21 The use of this proxy is motivated by the investment banker’s monopsony power hypothesis proposed by Baron and Holmström (1980) and Baron (1982). Investment banks will take advantage of their superior knowledge of market conditions to underprice offerings, which permits them to expend less effort and ingratiate themselves with buy-side clients. This implies that investment banks would underprice themselves by less than other IPOs of similar size, i.e., a negative sign in our regression analysis can be expected. However, earlier work by Muscarella and Vet-suypens (1989) shows that this hypothesis cannot be verified for U.S. data. Of course, the monopsony power hypothesis is closely related to the compe-tition between investment banks, as captured by NBUNDERW. Underpric-ing is determined by the desire to establish new or strengthen existing busi-ness relationships with investors who purchase an issue.22

The empirical results for these proxies are shown in Table 11. Except SWX, all our variables are significant in univariate regressions, i.e., they help to explain the un-derpricing phenomenon on the Swiss IPO market. The observation that the SWX dummy turns out insignificant should not come as a surprise. The underpricing of new market IPOs is higher than that of IPOs on the main and local segments. Con-trolling for NR, the number of each IPO in the sample, leads to a significant regres-sion coefficient on SWX.

[Table 11: Change in market environment hypothesis]

21 Only three banks conducted their own IPO over the sample period. 22 We also tried to construct a proxy for the public attention an IPO receives during the

issuing process. A possible variable is the number of entries in the Reuters Business Briefing database over the month preceding an issue. We suppose that firms with more publicity experience less underpricing. Unfortunately, this variable never showed up significantly in our analysis.

19

As expected, there is a negative relationship between our proxy for competition among investment banks, NBUNDERW, and the magnitude of underpricing. The proxies for the type of issuing mechanism, FIX and BOOK, have positive and negative regression coefficient, respectively. This is consistent with the observation by Loughran, Ritter, and Rydqvist (1994) that book-building leads to more accurate pricing. In multivariate regressions in the presence of NR, however, both FIX and BOOK are no longer significant. This is also not surprising, given that the IPOs in the first half of the sample were fixed price offers, while book-building was domi-nant in the second half of the sample. Unfortunately, OWN is the exception in the sense that its coefficient is significant, but with he wrong sign. However, as dis-cussed above, only three banks in our sample conducted their own IPO. The nega-tive sign on OWN may indicate a lack of experience at the time when they went public. In fact, for all three banks their own IPO was the first issue they ever man-aged. Finally, it should be noted that the adjusted R-squares of our regressions in-crease up to 0.29, explaining 29% of the cross-sectional variation in the amount of underpricing.

3.3.3 Joint-test of hypothesis

In this section we conduct some robustness tests for the results from the univariate analysis. All explanatory variables introduced above are now used in multivariate regressions. The results are summarized in Table 12. The table indicates the sample size for each regression, the F-statistic for the joint significance of our variables, and the corresponding p-values. The null hypothesis of homoscedasticity had to be rejected for all regressions. Hence, we report heteroscedasticity consistent estimates using the White covariance matrix (indicated by ”H”).

The analysis for the entire sample of Swiss IPOs over the sample period from 1983 to 2000 in regression RA 1 reveals strong evidence for the ex ante uncertainty hy-pothesis (or information hypothesis) and the signalling hypothesis. We infer this from the significant coefficient estimates on VOLA and SEO, respectively. There is no evidence for the market cyclicality and the reputation hypothesis. Both proxies, MARKET and REP, are insignificant in multivariate regressions. Increasing com-petition, measured by NBUNDERW, leads to decreasing adjusted initial returns. FIX and BOOK also maintain the correct sign. The simple regression model with

20

all proxies can explain 44% of the cross-sectional differences in initial returns. The regressions RA 2 to RA 5 show the results for different combinations of our proxy variables. The only important change is that MARKET becomes significant once SEO is excluded from the analysis. We interpret this as weak evidence for the mar-ket cyclicality hypothesis. Finally, we ran separate regressions for the two different market segments, the main and local segments and the SWX New Market. The re-sults for these more homogenous subsamples are qualitatively similar to those re-ported in Table 12 for the total sample.

[Table 12: Test of multiple hypothesis]

To summarize, our analysis strongly supports the ex ante uncertainty hypothesis (or information hypothesis) and the signalling hypothesis as possible explanations for the underpricing phenomenon on the Swiss IPO market. There is also some evi-dence for the market cyclicality hypothesis. However, the reputation hypothesis must be rejected. Finally, we report evidence for lower initial returns under in-creased competition among investment banks, and more accurate pricing when book-building is used.

4 Secondary market performance

4.1 Methodological issues

Using a large data set, Ritter (1991) presents impressive evidence for the poor long-run performance of U.S. initial public offerings. Loughran and Ritter (1995), using an even larger sample of U.S. IPOs and applying different benchmark portfolios to measure abnormal returns, come to the same result. They conclude raising the warning flag that ”investing in firms issuing stock is hazardous to your wealth”.23 Brav and Gompers (1997) report that the underperformance comes primarily from small, non-venture backed IPOs. In a recent study for the long-run performance of German IPOs Stehle, Erhardt, and Przyborowsky (2000) show that size portfolios and matching stocks are better benchmarks than market portfolios. Interestingly, using buy-and-hold abnormal returns and accounting for the size effect, they report a long-run underperformance for German IPOs of roughly -6% over three years.

23 See Loughran and Ritter (1995), p. 46.

21

This is substantially below the corresponding numbers documented for U.S. data. For example, also using buy-and-hold abnormal returns, Loughran and Ritter (1995) calculate a -26.9% underperformance against matched firms in three years.

We use a variety of different methods to measure the long-term performance of Swiss IPOs. In all cases we apply standard event-study methodologies. Our event windows cover up to 10 years of monthly data, which is much longer than in virtu-ally all previous studies. This extensive data set allows us a thorough assessment of the aftermarket performance of Swiss IPOs in the very long-run, much beyond the usual 5 year holding period. Previous studies by Brav and Gompers (1997) and Schuster (2001), among others, show that long-term IPO performance is sensitive to the benchmark employed. Throughout the empirical analysis, we therefore apply two different benchmarks. On the one hand, we use the Swiss Performance Index (SPI), a value weighted index which contains all quoted stocks of domestic compa-nies.24 On the other hand, we use the Vontobel Small Companies Index (VSCI), a widely recognized benchmark index for Swiss small caps throughout the industry. The VSCI comprises listed companies whose market capitalization is no more than 0.2% of total market capitalization.”25 An alternative approach is to assign to each IPO a portfolio of non-IPO matching firms.26 The matching can be based on crite-rions such as market capitalization, the industry, or other firm specifics (e.g., the book-to-market ratio). However, this method is difficult to apply because there are only a few comparable firms on the Swiss stock market. Our methodology of using broad stock market indexes as the benchmark has one important shortcoming. The index includes all the IPOs under examination as well. Lyon, Barber, and Tsai (1999) refer to this as the ‘new listing bias’. However, the size of the Swiss stock market seems sufficient to ensure that a broad market index contains many more non-IPO firms than issuers. The new listing bias should therefore not significantly affect our results.

Before we present our empirical results, we shall briefly discuss some important general issues associated with determining long-term stock performance. In a re-

24 The SPI was first calculated on June 1, 1987. For the observation period prior to this

date, we approximated the market index with the SBC General Total Return Index. 25 See http://www.cyberhaven.com/globalinvesting/swisssmallcaps.html 26 For example, see Brav and Gompers (1997).

22

cent paper, Fama (1998) forcefully argues that it is notoriously hard to measure long-term abnormal returns and that long-term return anomalies are sensitive to methodology. The poor long-term performance of IPOs is usually interpreted as evidence against market efficiency. Following the terminology in the influential work by De Bondt and Thaler (1985) and the more recent theoretical models by Barberis, Shleifer, and Vishny (1998) and Daniel, Hirshleifer, and Subramanyam (1998), among others in the growing behavioral literature, Fama (1998) classifies the IPO ‘anomaly’ in the overreaction camp. He argues that it is safe to presume that IPOs have strong past earnings to display when they go public. If the market does not understand that earnings growth tends to mean revert, stock prices at the time of the IPO are too high. If the market only gradually recognizes its mistakes, the overreaction to past earnings growth is corrected only slowly in the future.27 However, a general problem with behavioral models it that they lack a specific al-ternative to the null hypothesis of market efficiency. This problem is particularly severe because investors tend to overreact to some events (e.g., IPOs), but underre-act to others (e.g., dividend initiations).28 Fama (1998) argues that existing models do well on the anomalies they are designed to explain, but they do not explain the ‘big picture’. Reviewing the results of empirical studies testing long-term returns following a variety of corporate events, he argues that the literature is “more con-sistent with the market efficiency prediction that long-term return continuation and long-term return reversal are equally likely chance results”.29

Fama’s (1998) proposition is strengthened by his convincing evidence that, even viewed individually, most anomalies are at best weak. Most important, long-term performance studies are always contaminated by a bad-model problem, which re-sults from the empirical observation that all models for expected returns are only incomplete descriptions of the underlying cross-sectional risk-return relationship. This is a crucial problem, because market efficiency can only be tested jointly with a model for expected returns. The bad-model problem is negligible for event stud-ies with short observation periods, but grows with the measurement horizon. Look-

27 See Fama (1998), p. 296. 28 For a summary of pre-event, announcement, and post-event returns reported by various

long-term return studies see Fama (1998), p. 290. 29 See Fama (1998), p. 289.

23

ing at cumulative monthly abnormal returns (CARs), this is easy to see. A spurious abnormal average return in a single month becomes statistically significant in CARs measured over multiple peridos. This is because the average of the CAR grows linear in the measurement horizon, while the standard error only grows with the square root of the measurement horizon. The problem is even more severe with long-term buy-and-hold abnormal returns (BHARs). In this case, an asset pricing model’s problem in explaining short-term returns grows exponentially, because short-term returns are multiplied (compounded).

Interestingly, average monthly abnormal returns (CARs) and buy-and-hold abnor-mal returns (BHARs) can produce different inferences on the same set of data. As a starting point, recall that all asset pricing models commonly assume normally dis-tributed returns. Normality is a better approximation for short horizons (e.g., one month) than for longer horizons, where skewness becomes a problem.30 One prob-lem with CARs, however, is that they do not represent an ex ante applicable in-vestment (trading) strategy, i.e., they do not accurately measure the return to an in-vestor who holds a security for a long post-event period. Since investor experience is important, most empirical studies report long-term buy-and-hold returns (BHARs). The problem with BHARs, however, is that by compounding (multiply-ing) monthly returns long-term BHARs are extremely skewed. As described above, Barber, Lyon, and Tsai (1999) suggest a skewness-adjusted test statistic, but even then one fails to correct fully for the correlation of returns across events not ab-sorbed by the model used to adjust for expected returns. Clearly, this problem also grows with the measurement horizon.31 In contrast, using CARs, the mean and the variance of the time series of average abnormal returns can be used to test the aver-age monthly response of the prices of event stocks in the usual way even for a long measurement period. A final methodological issue to be considered is that CARs require monthly rebalancing, which may lead to an inflated long-horizon return on the reference portfolio. This is likely to be attributed to bid-ask bounce and non-

30 It must be noted, however, that standard asset pricing models do not specify the relevant

interval for expected returns. 31 See Brav and Gompers (1997) for a more detailed discussion.

24

synchronous trading.32 Blume and Stambaugh (1983) refer to this as the ‘rebalanc-ing bias’.

On the basis of the theoretical and statistical considerations just described, Fama (1998) argues that CARs are a more appropriate way to measure long-term abnor-mal returns than BHARs. To give a complete picture of the long-term performance of Swiss IPOs, we test a variety of different methodologies and compare their re-sults. We start with buy-and-hold abnormal returns and wealth relatives. Given that wealth relatives tend to be strongly upward biased, we compute volatility-adjusted wealth relatives using the technique proposed by Jakobson and Voetman (2001). Loughran and Ritter (1995) and Brav and Gompers (1997) do not present direct statistical inferences from long-term buy-and-hold returns. In contrast, using the bootstrap procedure proposed by Barber, Lyon , and Tsai (1999), we again compute skewness-adjusted test statistics for the null hypothesis of zero abnormal returns. To check the consistency of our results, we proceed by computing cumulative monthly abnormal returns (CARs). Finally, we use the market model and an ex-tended market model to measure the long-term performance of Swiss IPOs in an asset pricing context. This approach also has the advantage that long-term infer-ences are based on monthly returns. To avoid any biases, we exclude the initial IPO returns from the first day of trading in the long-term analysis.

4.2 Buy-and-hold abnormal returns

In this section we examine the impact on investors’ wealth if the same amount of money is invested passively in each IPO after the first day of trading, i.e., we calcu-late buy-and-hold returns for the sample of IPOs (excluding the initial return) and compare them with the buy-and-hold returns achieved by investing in two different benchmarks.33 Specifically, we calculate the T period buy-and-hold abnormal re-

32 See Blume and Stambaugh (1983), Roll (1983), and Conrad and Kaul (1993). 33 The reasons for using equally-weighted portfolios are twofold. On the one hand, severe

data limitation in the 1980s and the early 1990s makes it virtually impossible to calcu-late value-weighted portfolios for Swiss IPOs. On the other hand, when investing ac-cording to the market capitalization after the first day of trading, the sample is domi-nated by very few big issuers. Specifically, the ten biggest issuers account for more than two thirds of the issued capital.

25

turns (BHAR) as the difference between the holding-period return of IPO i and the benchmark return:

(4) ( ) ( )∏∏==

+−+=T

1t

Bt

T

1t

itT,i R1R1BHAR .

A positive buy-and-hold abnormal return is interpreted as a better performance of the respective IPO compared to the benchmark. The mean buy-and-hold abnormal return is computed as the arithmetic average of abnormal returns on all IPOs in the sample of size N:34

(5) ∑=

=N

1i

T,iT,IPO BHARN1

BHAR .

In addition, because the distribution of long-run abnormal stock returns is posi-tively skewed, we report the median buy-and-hold abnormal return. In this vein, the simulation results in Kothari and Warner (1997) and Barber, Lyon, and Tsai (1999) show that standard tests of long-run performance are often misspecified. Therefore, we again test for the significance of the BHARs using the bootsrapped application of the skewness-adjusted t-test as described above. In another line of critique, Shumway (1997) notes that the U.S. IPO data from the Center for Research in Se-curity Prices (CRSP) exhibit a survivorship-bias due to delistings. Since we explic-itly account for non-surviving firms, we do not expect this problem in our sample.

[Table 13: Buy-and-hold abnormal returns]

Panel A in table 13 shows the buy-and-hold abnormal returns for up to 120 months of secondary market trading. Relative to the Swiss Performance Index (SPI), a broad value-weighted market index, we find significant underperformance during the first month of trading. After two and three years of trading, on average, IPOs show almost exactly the same performance as the broad market index. In this re-spect, our results are even more pronounced than those in Stehle, Erhard, and Przy-borowsky (2000), who find that German IPOs exhibit only a moderate degree of

34 To the degree that the IPO betas are higher than the betas of control portfolios, comput-

ing abnormal returns without explicitly adjusting for beta differences results in conser-vative estimates of IPO underperformance when the market risk premium is positive.

26

underperformance after 36 months of trading. Furthermore, their results are only on the margin of being statistically significant at best. However, increasing the obser-vation period, the performance of IPO stocks sharply deteriorates. For example, after five years the average BHAR is -26.17%. This is similar in magnitude to the results in Ritter (1991), Loughran and Ritter (1995), and Brav and Gompers (1997) for U.S. data, among others. After 120 months of secondary market trading, the average underperformance even increases to -173.46%.

While economically large, the statistical significance of this underperformance is extremely sensitive with respect to the sample constituents. Table 13 shows that BHARs are severely right-skewed, as indicated by the fact that the median BHARs are much lower than the respective means. Hence, to test for the statistical signifi-cance of long-run performance, we again apply the skewness-adjusted test statistic and combine it with the bootstrap procedure as suggested by Lyon, Barber and Tsai (1999). Column 6 in panel A shows that the underperformance for the first month of trading is significant at the 10% level. Beyond the first month, however, boot-straps of the whole sample do not reveal any statistically significant underperfor-mance for Swiss IPOs, as compared to the broad SPI index. However, these results are heavily driven by some few extreme performers. When the best (Phoenix Me-cano, +752%) and the worst (Omni Holding, -571%) performers are excluded from the sample, we document highly significant underperformance for a holding period of 96 months and beyond. Similarly, when we exclude the three best and worst per-formers, the underperformance is significant for holding periods of 60 months and thereafter. Our results, therefore, fall between those previously reported for other countries. Similar to Stehle, Erhard, and Przyborowsky (2000) for German IPOs, but in contrast to studies using U.S. data, we document only modest and mostly insignificant underperformance of Swiss IPOs up to 4 years after the first day of trading. However, our extensive data set allows us to examine buy-and-hold returns for up to 120 months of aftermarket trading. Indeed, we find evidence that Swiss IPOs severely underpeform in the very long-run. In this case, however, the bad-model problem discussed above can be expected to be an issue.

Previous studies for the U.S. have indicated that the long-run underperformance is not an IPO-specific phenomenon, but rather affects small companies with a low book-to-market ratio (e.g., Brav and Gompers, 1997). Therefore, in a second step

27

we use an alternative benchmark to measure abnormal returns. Specifically, given that IPOs tend to be smaller firms, we use the Vontobel Small Companies Index (VSCI) to account for the differences in market capitalization. Surprisingly, we find that hardly any evidence for underperformance remains. Panel B in table 13 show that, after 60 months, the average BHAR is actually a positive 11.56%. Again, it becomes negative only in the very long-run. The median BHAR is much lower than the mean, indicating that the distribution is skewed to the right. But in this case, with the VCSI as the benchmark, the bootstrap procedure for the skewness-adjusted test-statistic reveals no longer any evidence for either significant under- or outper-formance even for the very long-run. Only after excluding the two best and worst performers, we find some evidence for underperformance in the very long-run. Overall, these results provide little support for a distinct IPO effect. Specifically, the poor performance of IPOs is not due to sample firms being initial public offer-ing firms, but rather results from the fact that they tend to be small firms. This re-sult is also consistent with the observation that there was a reverse size-effect on the Swiss stock market.

Due to severe data limitations for the IPO stocks in the early sample period, we are unable to cluster the sample stocks by their book-to-market ratios. Still, we can analyze whether there are differences in the performance of Swiss IPOs on the ba-sis of the size of an issue. In figure 5 we group all sample stocks into size quartiles; depending on the issuing volume, figure 5a shows the corresponding mean BHARs and figure 5b the median BHARs relative to the Swiss Performance Index (SPI). Confirming the results by Schuster (2001) for a sample of pan-European IPOs, but in contrast to the findings by Brav and Gompers (1997) for U.S. data, we cannot detect a size-effect across our sample of IPOs. We conclude that underperformance of Swiss IPOs is not driven by the smallest quartiles of firms. In fact, small issues exhibit a slightly better performance than big ones.35

[Figure 5: BHARs of size quartiles]

35 This is consistent with the findings of Kammermann (1997), who reports that there was

no no significant size premium on the Swiss stock market for the 1985 to 1996 period. If anything, there was even a negative size premium in up-markets, where large firms outperformed small firms.

28

4.3 Wealth relatives

Following Ritter (1991) and Loughran and Ritter (1995), we also measure the sec-ondary market performance using wealth relatives. In a first step, we explore how the sample of IPOs performed relative to the two benchmarks applied above. Spe-cifically, the security-to-market wealth relative, denoted as WRi-M,T, is computed as follows:

(6)

( )

( )∏∏

=

=−

+

+= T

1t

t,B

T

1t

t,i

T,Mi

R1

R1WR .

A wealth ratio greater than 1 is generally interpreted as the specific IPO outper-forming the market, whereas a wealth ratio of less than 1 indicates underperfor-mance.36 In a second step, we explore how the benchmark performed relative to the sample of IPOs by computing the market-to-security wealth relative, denoted by WRM-i,T. This wealth ratio is the reciprocal of the expression in equation (6). A simple example, taken from Fama (1998), shows why wealth relatives are more appropriate for measuring the long-run performance than BHARs.37 Suppose the returns for the first year are 10 percent for a specific IPO firm and zero for the benchmark, i.e., the first-year average abnormal return is 10 percent. Over the next four years, both the event firm and the benchmark have a 100 percent buy-and-hold return. Although there is no abnormal return after the first year, the BHAR after five years grows to 20 percent (=(1.1×2) – (1.0×2)). In contrast, the security-to-market wealth relative is

( ) ( )( ) ( ) 1.1

0.22.2

0.110.010.111.01

WR Mi ==+×++×+

=− ,

36 Note that this is an accumulated wealth ratio, i.e., the wealth relative between two ac-

cumulated values is the ratio between the wealth Wi,T of investing in a security and the wealth WM,T of investing in the benchmark after T periods. In contrast, Ritter (1991) and Loughran and Ritter (1995) use averages in both the numerator and the denomina-tor. Jakobsen and Voetmann (2001) argue that this is not appropriate, because the wealth relative is log-normal distributed.

37 See Fama (1998), p. 294.

29

and, hence, accurately reflects the 10 percent outperformance of the IPO firm rela-tive to the benchmark.

Table 14 displays the development of both wealth ratios for holding periods up to 120 months. Again, we use two different benchmarks, the Swiss performance index (SPI) and the Vontobel small-cap index (VSCI). Interestingly, the results are quite different, depending on whether we use the security-to-market wealth relative or the market-to-security wealth relative to assess the secondary market performance of IPOs. For example, after 60 months the average security-to-market wealth rela-tive of our sample stocks is 0.7763, indicating that, on average, IPO stocks under-performed the SPI by 22.37 percent (see panel A). Instead of asking how much IPOs underperformed the market, an equivalent approach is to investigate how much the market outperformed IPOs. A first guess might be 28.8 percent, i.e., one minus the reciprocal of the average security-to-market wealth relative. Our results in table 14 (panel A), however, suggest that after five years of trading the SPI, on average, outperformed IPOs by almost 100 percent. The huge difference between the security-to-market wealth relative and the market-to-security wealth relative is attributable to Jensen’s inequality. As the transformation between the two wealth relative implies a convex function, the average market-to-security wealth relative will always exceed the reciprocal of the average security-to-market wealth relative. Specifically, higher cross-sectional volatility of the wealth relatives will lead to more dispersion between the two values. Accordingly, Jakobsen and Voetmann (2001) suggest to adjust the wealth relatives for their cross-sectional volatility. They assume that the security-to-market wealth relative follows a log-normal dis-tribution. Accordingly, the logarithm of the wealth relative is normally distributed with transformed mean TT ⋅α and variance T2

T ⋅σ . The expected mean of the wealth relative is then given as:

(7) ( ) ( ) ( )4342143421

t

2TT

2TTTT

componenolatilityVcomponentmean

ransformedT

T21

expTexpT21

TexpTexpWRE

σ⋅α=

σ+α=µ= ,

with .5.0 2TTT σ+α≡µ The expression in equation (7) shows that the volatility

component implies an upward bias on the average wealth ratio, which is always positive and identical, irrespective of the wealth-relative measure (security-to-

30

market or market-to-security). To assess the unbiased long-run security perform-ance, Jakobsen and Voetmann (2001) suggest to adjust the expected average cross-sectional wealth relative for the volatility component. Since the volatility compo-nent imposes an upward bias on the expected average buy-and-hold return, the cross-sectional mean of the wealth ratio can be decomposed into its transformed mean component, ( )Texp T ⋅α , and its volatility component, ( )T5.0exp 2

T ⋅σ⋅ . Clearly, this is just another way of adjusting for skewness. Given that the logarithm of the wealth relative is normally distributed, the mean and variance parameters can be estimated from the cross-section of wealth relatives using maximum likelihood:

(8) ( )∑=

−⋅=α

N

1i

T,MiT WRlogNT

1ˆ and

(9) ( ) ( )( )∑=

− ⋅α−−⋅

=σN

1i

2

TT,Mi2T TˆWRlog

1NT1ˆ .

The parameter estimates Tα̂ and 2Tσ̂ are marginal parameter estimates at any point

in time, i.e., they are only based on the expected cross-sectional wealth relative at time T. At time T, the transformed mean and standard deviation of log(Wi-M,T) are

Tˆ T ⋅α and Tˆ T ⋅σ , respectively. The marginal confidence interval of the marginal estimate Tˆ T ⋅α is t-distributed at time T and the marginal confidence interval of the marginal estimate Tˆ T ⋅σ is χ2-distributed at time T (both with N-1 degrees of freedom). The marginal estimates can be transformed back to levels through the exponential function and the resulting expected mean buy-and-hold returns can be compared to the wealth relative. For example, the transformed mean component is

( ) 1Tˆexp T −⋅α . A negative sign of this expression indicates negative buy-and-hold abnormal returns.38

[Table 14: Wealth relatives]

Figure 6 shows the decomposition of the wealth relatives. In panel A we compare the actual security-to-market relative with its expected value, where the SPI is used

38 A problem with this volatility-adjusted approach is that the marginal parameters at time

T are treated independently of marginal parameters estimated in any other periods. However, with event windows of several years the marginal parameter estimates will be influenced by serial correlation and other time-series dependencies.

31

as the benchmark. The small differences indicate that the volatility-adjustment model is sufficiently accurate. Specifically, after 120 months the expected under-performance is 52.4 percent, while the actual underperformance of IPOs on the ba-sis of the security-to-market ratio is 55.5 (=0.4448−1) percent.39 More important, the positive volatility component is large in size and implies that the unadjusted security-to-market relative severely underestimates the true underperformance of IPOs. In the very long-run, for example, after 120 months of secondary market trading the transformed component of the mean indicates an adjusted underperfor-mance of 70.14 percent.

[Figure 6: Volatility-adjusted performance]

Panel A in figure 7 explores the statistical significance of the transformed mean component in equation (7). The solid line depicts the transformed mean component, computed as ( ) 1Tˆexp T −⋅α , for the security-to-market wealth relative, the dotted lines indicate the 95 percent confidence intervals.40 The interpretation is interesting. Recall, there is no underperformance of IPOs on the basis of security-to-market wealth relatives up to 30 months of trading, and underperformance is only present after 60 months of trading (and beyond) on the basis of buy-and-hold abnormal re-turns. In contrast, using the volatility-adjusted performance measure reveals that IPOs underperform dramatically. For holding periods of 8 months and longer, the underperformance of Swiss IPOs is statistically significant at the 5 percent level.

[Figure 7: Test of the volatility-adjusted performance]

Panel B in table 6 shows the same volatility decomposition analysis for the market-to-security wealth relative. By construction, the volatility component is the same as in panel A for the security-to-market wealth relative.41 In this case, the (positive) volatility component implies that the market-to-security wealth relative overesti-mates the true outperformance of the market. Using the SPI index as the bench-mark, after 120 months of secondary market trading the expected value indicates an outperformance of 433.1 percent, while adjustment for the cross-sectional volatility

39 See the last line in table 14. 40 The confidence intervals are computed using the volatility estimates from equation (9)

and assuming that the marginal estimate of Tˆ ⋅α is t-distributed at time T. 41 Note that the vertical axis are differently scaled.

32

reduces the outperformance to 234.9 percent. Again, the actual market-to-security wealth relative closely matches its expected value. The statistical significance of the outperformance of the market relative to the sample of IPOs is analysed in panel B of figure 7. The results correspond with those in panel A for the security-to-market wealth relative. Using a 95 percent confidence interval, IPOs signifi-cantly outperform the market in the first month of secondary market trading, but after 8 months and beyond the market significantly outperforms IPO firms. The results can easily be checked for consistency. The underperformance of 70.14 per-cent after 120 months implies that the security-to-market wealth relative decreased from 1 to 0.2986. The reciprocal of this value is 3.349, which is exactly 1 plus the 234.9 (=1+2.349) percent volatility-adjusted outperformance of the benchmark against our sample of IPOs.

Finally, panels c and d in figure 6 show the decomposition of the security-to-market wealth relative and the market-to-security wealth relatives, respectively, using the VSCI index as the benchmark. Again, the volatility adjustment reveals that the usual wealth relatives exaggerate the performance of IPOs. The statistical significance is assessed in panels c and d in figure 7. Using the small-cap index as the benchmark, the underperformance of IPOs is only significant for the very long-run. Specifically, at a 5 percent level, the underperformance is significant only after holding periods of 90 months and beyond.

4.4 Cumulative abnormal returns (CAR)

To evaluate the long-run performance of IPOs, we also use cumulative average ab-normal returns (CARs) calculated with monthly portfolio rebalancing. They can be understood as consistency checks for the buy-and-hold abnormal returns reported above. Monthly benchmark-adjusted returns are calculated as the monthly raw re-turns on an IPO stock minus the benchmark returns. Following Ritter (1991), the benchmark-adjusted returns for stock i in event month t is defined as:

(10) Btitit RRar −= .

The average benchmark-adjusted return on a portfolio of N stocks for event month t is the equally-weighted arithmetic average of the benchmark-adjusted returns:

33

(11) ∑=

=N

1i

itt arN1

AR .

The cumulative benchmark-adjusted aftermarket performance from event month 1 to event month T is the summation of the average benchmark-adjusted returns:

(12) ∑=

=T

1t

tiT ARN1

CAR .

Again, we use both the Swiss Performance Index (SPI) and the Vontobel small-cap index (VSCI) as alternative benchmarks. Ikenberry, Lakonishok, and Vermaelen (1995) argue that CARs do not represent realistic strategies and, hence, should only be viewed as indicative. However, at least in theory, the statistical properties of CARs are more favourable than those of BHARs.

[Table 15: Abnormal returns]

Panel A in table 15 shows the cumulative abnormal returns for up to 120 months of aftermarket performance. After 36 months the CAR is –7.45 percent, which is much smaller in magnitude than the –29.13 percent for U.S. data reported in Ritter (1991). Overall, however, underperformance of IPOs is pronounced. For example, after 60 months of secondary market trading the cumulated average abnormal re-turn is –31.15 percent, after 120 months it is even –101.33 percent. Using a stan-dard t-test, the underperformance is significant only after 48 months of aftermarket trading. However, using Jarque-Bera test for normality, we find that the null hy-pothesis is strongly rejected for all holding periods. Therefore, we also compute the skewness-adjusted t-statistics and combine it with the bootstrap procedure proposed by Barber, Lyon, and Tsai (1999). We find that long-term CARs are negatively skewed and, hence, a standard t-test underestimates the performance of IPOs. Therefore, the skewness-adjusted t-statistics are larger in absolute values and indi-cate an even more pronounced underperformance from a statistical point of view, especially in the very long-run.

Panel B in table 15 presents cumulative abnormal returns for up to 120 months us-ing the VSCI index at the benchmark. The underperformance is small in absolute numbers and never statistically significant. Even after 120 months of trading, the

34

underperformance is a mere -9.51%. Overall, we interpret these results as quantita-tively similar to those for buy-and-hold returns. The poor performance of IPOs is not due to sample firms being initial public offering firms, but rather results from the fact that they tend to be small firms. The robustness of our results is in contrast to the result by Brav and Gompers (2001) for U.S. data, who report that the relative performance of IPOs depends heavily on the methodology applied.

4.5 Pricing the IPOs

If IPOs underperform on a risk-adjusted basis, they should consistently underper-form relative to an explicit asset pricing model. To examine this proposition, we use the market model and an extended version of the market model. This standard procedure allows us to adjust for systematic risk. This is important for two reasons. First, Eckbo and Norli (2000) argue that IPO firms have lower leverage and, hence, should exhibit lower expected returns. Second, from a methodological point of view, Fama (1998) argues that this approach has the advantages that long-term in-ferences are based on time series regressions using monthly returns. On the other hand, a shortcoming is that factor models clearly cannot provide a full explanation of average returns.42 Hence, factor models also cannot avoid the bad model prob-lem. Nevertheless, from a more pragmatic point of view, we regard them as valu-able robustness tests for our previous results for the long-term performance of Swiss IPOs.

First, for each IPO i we test the simple market model by running the following re-gression in event time:43

(13) ( ) itftBtiiftit RRRR ε+−β+α=− ,

where Rf denotes the risk-free rate of return, which we proxy using the 3-month interest rate for Swiss francs on the Eurocurrency market. As before, RBt denotes the return on the SPI benchmark index. The estimated coefficients for each stock,

42 Even the three-factor model proposed by Fama and French (1993) cannot fully explain

the variation in expected returns on portfolios sorted on market capitalization and the book-to-market ratio, the dimensions of average returns that the model’s risk factors are designed to capture.

43 We excluded the five firms that were delisted during the sample period. Therefore, the sample reduces to a total of 104 firms.

35

iα̂ and iβ̂ , are averaged and tested for significance using a standard t-test. We use the intercept from time series regressions as an indicator of risk-adjusted perform-ance, analogous to Jensen’s alpha in the Capital Asset Pricing Model (CAPM) framework. Specifically, an average value for α̂ different from zero indicates ab-normal returns for our sample of Swiss IPOs.44 A value for β̂ different from one shows whether the differences in returns can be attributed (a least partly) to differ-ences in systematic risk. This approach has the added benefit that one can make statistical inferences given the assumption of multivariate normality of the residu-als. This was not possible before due to the skewness of long-horizon returns.

[Table 16: CAPM and multi-index models]

Panel A in table 16 shows that the beta of IPO stocks is indeed lower than one, on average.45 This suggests that the cumulated abnormal returns computed above un-derestimate the performance of IPOs and, therefore, the absolute values of the CAR’s are too large in magnitude. However, the estimates for the intercept reveal that even after controlling for market risk, the performance of IPO stocks is signifi-cantly negative for the very long-run. For example, using the full 120 months of aftermarket trading, the alpha is -0.74 percent per month, with a t-value of -4.8. In contrast, up to 48 months the risk-adjusted performance of IPOs is neutral. Again, these results are similar to those for buy-and-hold returns.

In the spirit of Fama and French (1993), we add a second factor to the regression in equation (13) to control for a possible size effect. Specifically, we extend the mar-ket model and add the monthly return difference between the Vontobel small-cap index (VSCI) and the Swiss Performance Index (SPI), MtVt RR − . For each stock i we run the following time series regression:

(14) ( ) ( ) itBtVtifBtiifit RRRRRR ε+−δ+−β+α=− .

Although there is an ongoing debate about whether this additional size-related fac-tor is a proxy for risk, we take no position on this issue and simply view the three-factor model as a method of performance attribution. Thus, we interpret the esti-

44 Note, of course, that the attribute ‘abnormal’ must always be understood as relative to a

specific asset pricing model. 45 The t-values are for the null hypothesis that beta equals 1.

36

mated average intercept coefficient, α̂ , as the abnormal return from an investment in IPOs in excess of what could have been achieved by passive investments in the factors. Panel B in table 16 shows the results. The estimates for δ̂ are positive, which can be expected, given that IPOs tend to be smaller firms.46 However, they are insignificant for all event windows except the first month of trading. Looking at the estimates for the intercept (and consistent with our previous results), the under-performance of Swiss IPO vanishes in this extended market model. For example, after 120 months the alpha reduces to –0.12 percent per month. For the event win-dows between 36 and 108 months in length it is virtually negligible. In addition, the α̂ estimates are insignificant, except for the 18 months event window.

5 Conclusion

We present fresh evidence for underpricing and long-term performance of a broad set of Swiss IPOs from 1983 to 2000. The Swiss case is interesting because of many institutional changes that have taken place over the last few years. Firstly, Swiss firms changed from fixed price offers to book-building. Secondly, following the new economy euphoria starting the late 1990s, a designated growth segment has been established on the Swiss stock market. We report that the average market ad-justed initial return is 34.97%. Looking at the different market segments, the aver-age market adjusted initial return is 11.32% on the main segment (over the 1994-2000 period) and 38.98% on the SWX New Market (over the 1999-2000 period). The differences are statistically significant. Our results support the ex ante uncer-tainty hypothesis, the signalling hypothesis and, to some extent, the market cycli-cality hypothesis as possible explanations for the underpricing phenomenon on the Swiss IPO market. Compared to fixed price offers, we find more accurate pricing when book-building is used. We also report evidence for lower initial returns under increased competition among investment banks. Previous findings for U.S. data indicate dramatically poor long-term performance of IPOs. This has been interpreted as IPO investments being hazardous to inves-tors’ wealth. Our results suggest that this warning may be exaggerated, at least for the Swiss case. In fact, we report significant underperformance using a broad mar-

46 See also table 4 in Brav and Gompers (1997).

37

ket index as the benchmark. However, underperformance tends to be significant (both in absolute values and in statistical terms) only in the very long run, i.e., after 4 years (or 48 months) of aftermarket trading. Even more important, underperfor-mance tends to vanish when a small capitalization index is used as an alternative benchmark. We interpret this result as indicating that underperformance is not an IPO effect. IPO firms tend to be small, and firms of similar size that have not is-sued equity perform equally poorly as IPOs. We measure the performance of Swiss IPOs up to 120 months after going public and use different measurement tech-niques with very distinct economic implications and statistical properties. In con-trast to Brav and Gompers (2001), our results are qualitatively similar for all meth-odologies and robust for a variety of possible biases.

38

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43

Figure 1: Swiss initial public offerings (IPOs): 1962-2000

0

5

10

15

20

25

30

3519

62

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Num

ber

IPOs Investment companies

44

Figure 2: Distribution of initial returns (1983-2000)

0

10

20

30

40

50

0 40 80 120 160 200 240 280

Adjusted initial return

Num

ber o

f IPO

s

45

Figure 3: Initial returns and issuing volumes

-50

0

50

100

150

200

250

300

350

1 10 100 1000 10000

Issuing volume in CHF mill. (log scale)

Mar

ket a

djus

ted

initi

al re

turn

(%)

46

Figure 4: Underpricing over time

-50

0

50

100

150

200

250

300

350

10 20 30 40 50 60 70 80 90 100 110 120

IPO number

Adj

uste

d in

itial

retu

rn in

%

47

Figure 5: BHARs of size quartiles

Figure 5a: Mean BHARs

Figure 5b: Median BHARs

Note: The SPI index is used as the benchmark.

-250%

-200%

-150%

-100%

-50%

0%

50%

0 10 20 30 40 50 60 70 80 90 100 110 120

M onths since the IPO

BHA

R (A

vera

ge)

Sm allest Quartile

Q2Q3

Largest Quartile

-250%

-200%

-150%

-100%

-50%

0%

50%

0 10 20 30 40 50 60 70 80 90 100 110 120

M onths since the IPO

BHA

R (M

edia

n)

Smallest QuartileQ2Q3Largest Quartile

48Figure 6: Volatility-adjusted Performance: Graphical decomposition of the mean expected wealth relative

Panel a) Security-To-Market Wealth Relative (SPI) Panel c) Security-To-Market Wealth Relative (VSCI)

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

0 10 20 30 40 50 60 70 80 90 100 110 120

Months since the IPO

WR

-1

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

0 10 20 30 40 50 60 70 80 90 100 110 120

Month since the IPO

WR-

1

Panel b) Market-To-Security Wealth Relative (SPI) Panel d) Market-To-Security Wealth Relative (VSCI)

-100%

0%

100%

200%

300%

400%

500%

0 10 20 30 40 50 60 70 80 90 100 110 120

M onths since the IPO

WR

-1

-50%

0%

50%

100%

150%

200%

250%

0 10 20 30 40 50 60 70 80 90 100 110 120

Month since the IPO

WR

-1

-200%0%

200%

0 10 20 30 4 0 50 60 70 8 0 90 1 00 11 0 120

Mon th s since the IP O

Component of the Transformed Mean Volatility Component

Expected WR Observed WR

49Figure 7: Test of the volatility-adjusted performance

Panel a) Security-To-Market Wealth Relative (SPI) Panel c) Security-To-Market Wealth Relative (VSCI)

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

0 10 20 30 40 50 60 70 80 90 100 110 120

Months since the IPO

WR

- 1

Upper BoundTransformed MeanLower Bound

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

0 10 20 30 40 50 60 70 80 90 100 110 120

M onth since the IPO

WR-

1

Upper Bound

Transform ed M eanLower Bound

Panel b) Market-To-Security Wealth Relative (SPI) Panel d) Market-To-Security Wealth Relative (VSCI)

-50%

0%

50%

100%

150%

200%

250%

300%

350%

0 10 20 30 40 50 60 70 80 90 100 110 120

Month since the IPO

WR

-1

Upper BoundTransformed MeanLower Bound

-20%

0%

20%

40%

60%

80%

100%

120%

140%

0 10 20 30 40 50 60 70 80 90 100 110 120

M onth since the IPO

WR-

1

Upper BoundTransformed MeanLower Bound

50

Table 1: Composition of Swiss IPOs (1983-2000)

Sector Number Miscellaneous industrials 34 Miscellaneous services 30 Electrical engineering & electronics 17 Chemicals & pharmaceuticals 9 Retailers 8 Machinery 8 Banks 5 Food & luxury goods 4 Insurance 3 Transport 1 Building contractors & materials 1

51

Table 2: Issuing volume of Swiss IPOs (1983-2000)

Sector Issuing volume Market value Total CHF 32’037.87m CHF 36’132.47m Mean 266.98m 301.10m Median 67.20m 86.55m Minimum 5.66m 9.80m Maximum 8’627.42m 9’553.59m

52

Table 3: Distribution of initial returns (1983-2000)

1983-2000 1983-1990 1994-2000 Initial return Initial return Initial return unadjusted adjusted unadjusted adjusted unadjusted adjusted Mean 34.94% 34.97% 53.67% 53.61% 18.55% 18.66% Volatility 57.55% 57.65% 69.16% 69.14% 39.28% 39.05% tsa-value 10.19 10.23 8.33 8.32 6.98 7.12 p-value 0.000 0.000 0.000 0.000 0.000 0.000 Median 12.98% 13.24% 24.42% 24.57% 7.67% 7.11% z-value* 9.12 8.58 6.80 6.68 6.12 5.50 p-value 0.000 0.000 0.000 0.000 0.000 0.000 # positive 105 107 52 53 53 54 # zeros 7 0 2 0 5 0 # negative 8 13 2 3 6 10 Minimum -8.61% -7.81% -2.31% -2.22% -8.61% -7.81% Maximum 288.89% 287.85% 285.71% 285.36% 288.89% 287.85% (*) Test statistic of sign test.

53

Table 4: Distribution of initial returns (1994-2000)

1994-2000 1999-1990 Main and local segment SWX New Market unadjusted adjusted unadjusted adjusted Mean 11.16% 11.32% 38.99% 38.98% Volatility 16.63% 16.47% 68.39% 68.02% tsa-value 10.94 10.94 9.78 9.78 p-value 0.000 0.000 0.000 0.000 Median 4.89% 4.71.% 19.06% 19.19% z-value* 4.94 4.23 3.64 3.64 p-value 0.000 0.000 0.000 0.000 # positive 37 38 16 16 # zeros 5 0 0 0 # negative 5 9 1 1 Minimum -5.55% -5.15% -8.61% -7.81% Maximum 63.23% 63.56% 288.89% 287.85% (*) Test statistic of sign test.

54

Table 5: Proxy variables for tests of underpricing

Description Proxy Expected sign

Hypothesis H0 LEVOL TIME NR

− − −

Hypothesis H1 Ex-ante uncertainty VOLA +

Hypothesis H2 Market cyclicality MARKET NUMBER

+ +

Hypothesis H3 Signalling SEO SHARE

+ −

Hypothesis H4 Reputation REP IPOBANK

− −

Hypothesis H5 Market environment

NR NBUNDERW FIX BOOK SWX OWN

− − + − + −

55

Table 6: Issuing volume, time, and underpricing

C LEVOL TIME NR adj. R2

R 0.1 94.33 (5.10)

-13.54 (-3.33) 0.08

R 0.2 (H) 65.85 (5.45) -0.01

(-3.41) 0.24

R 0.3 85.85 (4.62)

-6.38 (-1.25)

-0.01 (-2.24) 0.11

R 0.4 (H) 72.39 (5.33) -0.62

(-3.43) 0.13

R 0.5 86.64 (4.77)

-4.72 (-0.94) -0.51

(-2.82) 0.13

R 0.6 73.55 (7.34) 0.01

(0.86) -1.03

(-2.06) 0.13

t-values are in parenthesis.

56

Table 7: Ex-ante risk hypothesis

C VOLA NR adj. R2

R 1.1 23.69 (2.38)

5.50 (1.35) 0.01

R 1.2 (H) 55.10 (3.89)

17.46 (2.97)

-0.93 (-5.36) 0.24

t-values are in parenthesis.

57

Table 8: Market cyclicality hypothesis

C NR MARKET NUMBER adj. R2

R 2.1 34.05 (6.35) 1.29

(1.14) 0.00

R 2.2 (H) 72.45 (5.41)

-0.64 (3.64)

1.78 (2.38) 0.14

R 2.3 35.44 (4.89) -0.33

(-0.07) 0.00

R 2.4 71.40 (6.67)

-0.62 (-4.33) 1.04

(0.25) 0.12

t-values are in parenthesis.

58

Table 9: Signaling hypothesis

C SEO SHARE NR adj. R2 Sample

R 3.1 (H) 26.38 (5.36)

43.13 (2.42) 0.09 100

R 3.2 (H) 73.20 (5.29)

29.33 (1.91) -0.86

(-4.43) 0.28 100

R 3.3 45.13 (4.85) -0.50

(-2.46) 0.05 103

R 3.4 56.17 (5.05) -0.37

(-1.72) 0.25

(-1.78) 0.07 103

R 3.5 (H) 59.74 (3.43)

0.25 (0.02)

-0.07 (-0.42)

-0.60 (-2.76) 0.16 84

t-values are in parenthesis.

59

Table 10: Reputation hypothesis

C REP IPOBANK NR adj. R2

R 4.1 66.65 (6.20)

-5.82 (-3.34) 0.08

R 4.2 79.79 (7.07)

-2.69 (-1.35) -0.50

(-2.99) 0.14

R 4.3 52.55 (6.94) -1.29

(-3.10) 0.07

R 4.4 73.57 (7.34) -0.27

(-0.52) -0.57

(-3.06) 0.13

R 4.5 76.08 (5.90)

-4.07 (-1.70)

-0.45 (-0.69) 0.07

t-values are in parenthesis.

60

Table 11: Change in market environment hypothesis

C NR NBUNDERW FIX BOOK SWX OWN adj. R2

R 5.1 112.76 (6.77) -6.26

(-4.87) 0.16

R 5.2 108.20 (5.50)

-0.12 (0.44)

-5.31 (-2.10) 0.16

R 5.3 16.11 (2.00) 31-79

(3.04) 0.07

R 5.4 74.85 (3.56)

-0.64 (-2.82) -1.99

(-0.19) 0.12

R 5.5 45.94 (7.12) -30.05

(-2.80) 0.05

R 5.6 73.59 (5.07)

-0.67 (-2.99) 4.93

(0.48) 0.12

R 5.7 34.43 (5.98) 4.55

(0.30) -0.01

R 5.8 82.03 (5.88)

-0.90 (-4.95) 52.58

(2.96) 0.20

R 5.9 32.74 (6.27) 92.80

(2.82) 0.06

R 5.10 67.56 (4.83)

-0.57 (-3.08) 74.45

(2.12) 0.16

R 5.11 112.48 (5.09)

0.11 (0.33)

-8.51 (-3.35) 57.16

(3.73) 94.17 (3.18) 0.28

R 5.12 112.93 (3.83) -7.82

(-3.67) 14.47 (2.43) 57.76

(3.10) 92.03 (2.83) 0.29

t-values are in parenthesis.

61

Table 12: Test of multiple hypothesis

C VOLA MAR-KET

SEO REP NBUNDERW

OWN FIX BOOK SWX F-test (p-value) adj. R2 Sample

RA 1 (H)

109.10 (3.42)

7.47 (2.54)

0.66 (1.33)

31.56 (2.32)

-0.40 (-0.22)

-9.28 (-3.32)

93.71 (3.25)

14.03 (2.30)

13.40 (0.86)

10.82 (0.00) 0.44 100

RA 2 (H)

110.63 (3.29)

7.74 (2.83)

1.00 (1.88)

-0.66 (-0.37)

-9.25 (-3.22)

94.87 (2.80)

21.52 (3.82)

21.29 (1.64)

10.29 (0.00) 0.40 100

RA 3 (H)

92.21 (3.15)

12.85 (2.46)

1.52 (2.38)

-1.16 (-0.78)

-7.76 (-3.41)

93.45 (2.97)

17.47 (2.99)

33.67 (2.57)

9.93 (0.00) 0.35 119

RA 4 (H)

110.76 (4.03)

12.76 (2.47)

1.50 (2.33)

-1.14 (-0.76)

-7.91 (-3.50)

94.24 (2.98)

-17.34 (-3.21)

34.36 (2.64)

9.93 (0.00) 0.35 119

RA 5 (H)

110.70 (4.06)

12.31 (2.45)

1.44 (2.34)

-8.33 (-4.05)

97.07 (2.94)

-18.79 (-3.46)

36.97 (3.02)

11.59 (0.00) 0.35 119

t-values are in parenthesis.

62

Table 13: Buy-and-hold abnormal returns

Panel A:

BHAR compared to SPI Month N Average Median tsa-value p-value

(1) (2) (3) (4) (5) (6) 1 109 -1.79% -1.77% -1.682 0.097 °° 2 109 0.98% -1.60% 0.553 0.594 3 109 3.96% -2.80% 1.596 0.138 6 109 2.38% -3.41% 0.804 0.443 12 109 -2.12% -7.95% -0.413 0.714 18 106 -4.80% -12.79% -0.740 0.531 24 97 0.89% -15.62% 0.148 0.887 36 87 -1.69% -20.49% -0.125 0.902 48 75 -12.20% -35.28% -0.993 0.346 60 66 -26.17% -52.36% -1.819 0.135aa 72 60 -36.15% -71.02% -1.079 0.486aa 84 57 -59.01% -92.73% -1.606 0.340aaa 96 54 -85.62% -115.67% -1.074 0.375*** 108 54 -127.80% -145.28% -0.262 0.859*** 120 53 -173.46% -199.92% -1.235 0.303***

*/**/*** denotes the significance at the 10%/5%/1% level, excluding the best and the worst perform-ing IPOs. Similarly, °/°°/°°° and a/aa/aaa denote the statistical significance at the 10%/5%/1% level, ex-cluding the two and three extreme performers from the sample, respectively.

63

Table 13: Continued

Panel B:

BHAR compared to VSCI Month N Average Median tsa-value p-value

(1) (2) (3) (4) (5) (6) 1 109 -1.14% -1.73% -1.0820 0.332 2 109 1.91% 0.16% 1.1710 0.285 3 109 4.33% -2.14% 1.9609 0.082 6 109 2.28% -3.29% 0.8237 0.425 12 109 -1.51% -4.12% -0.2947 0.781 18 106 -2.23% -12.66% -0.3248 0.797 24 97 2.33% -13.27% 0.3348 0.768 36 87 5.12% -7.31% 0.5565 0.632 48 75 6.31% -1.65% 0.6070 0.559 60 66 11.56% -2.81% 1.0466 0.378 72 60 17.74% -10.65% 1.0440 0.561 84 57 9.83% -14.93% 0.5369 0.714 96 54 -5.23% -20.18% -0.1601 0.917 108 54 -13.40% -32.42% -0.4191 0.766°° 120 53 -17.30% -34.97% -0.4993 0.735°

*/**/*** denotes the significance at the 10%/5%/1% level, excluding the best and the worst perform-ing IPOs. Similarly, °/°°/°°° and a/aa/aaa denote the statistical significance at the 10%/5%/1% level, excluding the two and three extreme performers from the sample, respectively.

64

Table 14: Wealth relatives

Panel A:

Relative to the SPI

Security-to-market wealth relative

Market-to-security wealth relative

Month Mean tsa-value p-value Mean tsa-value p-value 1 0.9817 -1.7358 0.086 1.0307 2.7548 0.013*** 2 0.9981 0.1103 0.902 1.0264 1.5631 0.131 3 0.9718 1.1362 0.305 1.0238 1.0656 0.309 6 1.0263 0.8352 0.394 1.0628 1.8416 0.071 12 0.9772 -0.5208 0.628 1.4766 2.5602 0.170aaa 18 0.9608 -0.7343 0.532 2.1982 2.2046 0.233°° 24 1.0325 0.4449 0.645 2.1797 1.6717 0.432°°° 36 0.9808 -0.2430 0.890a 1.4824 3.9701 0.001 48 0.8701 -1.7952 0.150°° 1.7916 5.1408 0.000 60 0.7763 -3.2606 0.024°°° 1.9988 5.3348 0.000 72 0.6956 -3.2846 0.350aaa 2.8705 4.2965 0.000 84 0.6643 -3.3323 0.250aaa 3.7421 3.9709 0.000 96 0.5983 -4.8558 0.235*** 3.7814 3.6381 0.000 108 0.4915 -7.5529 0.469*** 5.5442 2.4545 0.000 120 0.4448 -8.8340 0.312°°° 5.7623 3.6689 0.000

*/**/*** denotes the significance at the 10%/5%/1% level, excluding the best and the worst perform-ing IPOs. Similarly, °/°°/°°° and a/aa/aaa denote the statistical significance at the 10%/5%/1% level, excluding the two and three extreme performers from the sample, respectively.

65

Table 14: Continued

Panel B:

Relative to the VSCI

Security-to-market wealth relative

Market-to-security wealth relative

Month Mean tsa-value p-value Mean tsa-value p-value 1 0.9886 -1.0807 0.325 1.0235 2.1166 0.049 2 1.0115 0.7272 0.457 1.0129 0.8311 0.391 3 1.0314 1.4113 0.148 1.0115 0.5699 0.595 6 1.0238 0.8703 0.389 1.0499 1.5959 0.093 12 0.9874 -0.2853 0.803 1.4544 2.4727 0.185°° 18 0.9853 -0.2513 0.868 2.0357 2.2623 0.222aaa 24 1.0441 0.6045 0.540 2.1391 1.6625 0.435°°° 36 1.0660 0.7600 0.489 1.4434 3.0220 0.004 48 1.0700 0.6601 0.567 1.5614 3.3545 0.002 60 1.1219 1.1497 0.244 1.4686 2.9089 0.016 72 1.1620 1.0563 0.386 1.8312 3.1261 0.019 84 1.2348 1.1144 0.347 2.4044 3.3077 0.015 96 1.1912 0.8939 0.365 2.3794 3.1174 0.008 108 1.0982 0.5199 0.617 3.0307 2.5996 0.018 120 1.1114 0.5694 0.595 3.1661 3.4426 0.017

*/**/*** denotes the significance at the 10%/5%/1% level, excluding the best and the worst perform-ing IPOs. Similarly, °/°°/°°° and a/aa/aaa denote the statistical significance at the 10%/5%/1% level, excluding the two and three extreme performers from the sample, respectively.

66

Table 15: Cumulative abnormal returns (CARs)

Panel A:

SPI as the benchmark Month Sample size Average S.D. t-value tsa-value p-value

1 109 -1.79% 0.1084 -1.7247 -1.6821 0.088°° 2 109 0.78% 0.1959 0.4148 0.4440 0.678 3 109 2.80% 0.2630 1.1107 1.1878 0.257 4 109 3.31% 0.3136 1.1029 1.1580 0.282 5 109 3.15% 0.3071 1.0691 1.1258 0.293 6 109 1.64% 0.3033 0.5644 0.5829 0.593 7 109 -0.65% 0.3423 -0.1975 -0.1867 0.863 8 109 -3.52% 0.3690 -0.9972 -0.9761 0.357 9 109 -4.80% 0.3722 -1.3463 -1.3366 0.216

10 109 -5.37% 0.4037 -1.3876 -1.3915 0.186 11 109 -6.64% 0.4492 -1.5423 -1.5818 0.133 12 109 -8.50% 0.5212 -1.7019 -1.8159 0.097 18 106 -13.72% 0.6712 -2.1038 -2.3549 0.049 24 97 -8.26% 0.6985 -1.1653 -1.2286 0.278 36 87 -7.45% 0.6386 -1.0887 -1.1021 0.304 48 75 -21.71% 0.7787 -2.3983 -2.7723 0.017 60 66 -31.15% 0.8126 -3.1136 -3.8605 0.002 72 60 -52.30% 0.8952 -4.5257 -5.6103 0.000 84 57 -62.56% 1.0804 -4.3720 -5.3171 0.000 96 54 -62.50% 1.0669 -4.3048 -5.6284 0.000 108 54 -84.42% 1.1411 -5.3348 -7.3025 0.000 120 53 -101.33% 1.3802 -5.1916 -7.2329 0.000

67

Table 15: Continued

Panel B:

VSCI as the benchmark Month Sample size Average Median tsa-value p-value

1 109 -1.09% -1.73% -1.0363 0.341 2 109 1.76% -0.25% 1.0818 0.318 3 109 3.35% -1.81% 1.5894 0.150 6 109 2.23% -1.11% 0.8644 0.417

12 109 -7.61% -1.62% -1.6478 0.154 18 106 -11.65% -7.89% -2.0030 0.098 24 97 -7.68% -7.38% -1.1494 0.298 36 87 -1.02% 4.75% -0.1493 0.876 48 75 -1.99% 4.85% -0.2528 0.830 60 66 7.58% 13.20% 0.7101 0.518 72 60 3.71% 8.45% 0.2914 0.808 84 57 1.88% 9.61% 0.1081 0.928 96 54 7.28% 14.42% 0.4543 0.704 108 54 -3.47% 9.96% -0.2489 0.819 120 53 -9.51% -4.19% -0.5411 0.652

68

Table 16: CAPM regression

Panel A:

( ) itfBtiifit RRRR ε+−β+α=−

Months N Alpha t-value Beta t-value* 12 109 0.0010 0.2628 0.6562 -2.7492 18 106 -0.0036 -0.9721 0.7985 -2.7216 24 97 -0.0008 -0.2832 0.8224 -3.3029 30 90 -0.0009 -0.4373 0.8744 -2.8811 36 87 -0.0002 -0.0853 0.8339 -3.6942 48 75 -0.0027 -1.3350 0.8040 -4.6270 60 66 -0.0042 -2.4996 0.8723 -3.2585 72 60 -0.0064 -3.8837 0.8805 -3.0127 84 57 -0.0068 -3.9772 0.8883 -2.6864 96 54 -0.0057 -3.8960 0.8901 -2.7220 108 54 -0.0068 -4.9035 0.8721 -3.3606 120 53 -0.0074 -4.8336 0.8701 -3.7679

* The t-values are for the null hypothesis that beta equals 1.

69

Table 16: CAPM regression

Panel B:

( ) ( ) itBtVtifBtiifit RRRRRR ε+−δ+−β+α=−

Months N Alpha t-value Beta t-value* Delta t-value* 12 109 −0.0054 −1.1998 1.1487 1.4095 1.2554 1.8291 18 106 −0.0085 −2.0766 1.0681 0.9744 1.1106 0.8965 24 97 −0.0044 −1.5328 1.0490 0.8854 1.1025 1.0623 30 90 −0.0029 −1.4693 1.0903 1.8014 1.0151 0.1922 36 87 −0.0013 −0.6792 1.0741 1.4640 1.0004 0.0058 48 75 −0.0014 −0.7518 1.0793 1.6914 1.0151 0.1937 60 66 0.0003 0.2075 1.1443 3.0003 0.9939 -0.0777 72 60 0.0003 0.2159 1.1431 2.9813 0.9439 -0.8461 84 57 −0.0001 −0.0479 1.1533 3.0719 0.9899 -0.1568 96 54 0.0005 0.3648 1.1521 3.2155 0.9800 -0.3165 108 54 −0.0007 −0.4719 1.1462 3.1679 1.0032 0.0503 120 53 −0.0012 −0.8338 1.1573 3.8473 1.0311 0.5068

* The t-values are for the null hypothesis that the coefficients equal 1.