Ansgar Wohlschlegel (669049,663791,659997,666756,608418).pdf

Ansgar Wohlschlegel

Auctions

U20454

Auctioned IPOs: The Impact of Firm and Market Characteristics on Unconditional Prices

Chloe Kincer (669049) Cristina Dumitru (663791) Adam Lixenfield (659997) Leanne OLeary (666756) Mike Smith (608418)

Abstract Between 1999 and 2011, WR Hambrecht offered auction IPO services to 53 firms, 15 of which withdrew from the IPO. We analyse firm and market characteristics made known before the auction date and its influence in determining investor valuation. We therefore test auctions and their ability to provide the issuing firm with certainty measures regarding investor participation. We base our analysis with both completed and withdrawn auction IPO data to determine whether previous literature on auctioned IPOs has omitted data that would suggest to influence their findings. We find firm characteristics to significantly influence the investors and their valuation for the IPO. Further findings reveal the exclusion of withdrawn data impacts the results found with the total observations. Our results suggest the findings on investor valuation in auctioned IPOs in previous literature may be misleading due to the omission of withdrawn IPO data.

1. Introduction In 2014, the United States initial public offering (IPO) market raised $249 billion in total proceeds (1.4% of GDP) reflecting policy implications and the ability for the three main mechanisms (American) bookbuilding, fixed price and auctions in providing efficient outcomes in the IPO market (Lorenzetti, 2015). In 1999, WR Hambrecht developed an auction mechanism to compete with the otherwise dominant procedure, bookbuilding.1 They provided auction services for 53 companies, including completed and withdrawn issues, 1 The competitive environment in IPO mechanisms is essentially a monopoly with bookbuilding driving auctions out of the

IPO mechanism market in virtually all countries where it has been introduced (Kaneko & Pettway 2003, Kutsuna & Smith 2004, Ljungqvist 2005, Cornelli & Goldreich 2001, Sherman 2004).


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during the period 1999 2011. This paper uses these auction observations to provide an analysis on the firm and market characteristics known before the auction to test whether investor valuation may be predicted. Previous literature analyses auctions and their ability to predict investor participation, however, they fail to include withdrawn observations from their sample suggesting their results may be biased. We aim to fill this gap in the literature by providing an empirical analysis, using withdrawn data, to suggest withdrawn observations and their inclusion in future auction IPO research be used. The literature suggests auctions are underutilized because they are prone to undersubscription i.e low investor demand (Sherman, 2005; Sherman & Jagannathan, 2006; Ljungqvist, 2005). In this theory, it is suggested institutional investors (informed) do not receive enough incentive to obtain accurate valuation information - needed for the success of an IPO - due to the allocation of shares not being guaranteed.2 This suggests firms choose bookbuilding because they believe investor participation is more certain and therefore, providing their IPO with preferred outcomes. DeGeorge, Derrien and Womack (2010) analyse the US auction IPO market using detailed bidding data to conclude investor participation can be explained by the size of the issue and market conditions. They further support auctions and their ability to extract valuable information from investors due to the high percentages of participation from informed investors. A wide range also finds market conditions to be significant in determining investor participation (Amihud et al., 2003; Degeorge et al., 2010, Neupane & Poshakwale, 2012; Neupane & Thapa, 2013). They also determine retail investors (uninformed) positively base their decision to participate in an IPO if informed investors are also participating. Given these findings, our report will use market conditions with additional firm characteristic data known before the auction to analyse their significance in determining the price of an IPO through the auction mechanism. Our results provide insight on auctioned IPOs and investor valuation due to our sample containing withdrawn IPO observations. We use offer price as an indication for investor valuation to determine the explanatory power of market and firm characteristics in determining the IPO after auction price and therefore, the firms IPO proceeds. We find - when using both completed and withdrawn observations - the revenue per share and the percent of ownership the firm is willing to sell to be significant in determining the price of the IPO through the auction mechanism. When we exclude the withdrawn observations from the regression, we find these variables and all others are not significant. Therefore, we find the withdrawn observations in our analysis impact our findings suggesting the future literature on auction IPOs use these observations to provide more accurate results. The report is structured as follows. In Section 2 a description of the US IPO auction mechanism, otherwise known as Open-IPO, is provided. Section 3 provides the data and summary statistics for the sample under our analysis. Section 4 tests our dependent variable, offer price, and its ability to accurately reflect investor valuation. In Section 5 we provide an analysis using our whole sample on the determinants for offer price. Section 6 provides a comparison of our results from the total sample to the modified sample excluding withdrawn observations. In Section 7 we test the explanatory power of the variables in our analysis on the firms decision to withdraw. Finally, Section 8 provides a conclusion of our findings. 2 Underwriters using bookbuilding possess quid pro quo relationships with their investors allocating shares to their preferred investors (Cornelli & Goldreich 2001; DeGeorge et al., 2010)


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2. Open-IPO Mechanism In 1999, WR Hambrecht developed the Open-IPO auction mechanism in order to compete with the only IPO method used before that time in the US, bookbuilding. The Open-IPO mechanism resembles that of a Dutch style second price sealed bid auction predominantly known as a Vickrey auction. The steps of the process are as follows: Firstly, WR Hambrecht publicises on their website the number of shares made available and an indicative predetermined price range based on the issuers own valuation and the expected proceeds. An information prospectus containing the annual financial information of the firm is also made available, as required by the Securities Exchange Commission (SEC). WR Hambrecht proceeds to organise a road show presenting the deal to institutional investors, otherwise known as informed investors. This is similar to the approach adopted by bookbuilding. The roadshow is also made electronically available to retail investors, otherwise known as uninformed investors. After collecting the information from investors, the auction begins approximately 2-3 weeks prior to the expected IPO date. During this time investors privately submit price and quantity bids with the permission to retract or modify their bids until the auction closing date. They may also submit bids outside of the predetermined price range. When the auction closes, WR Hambrecht determines the market-clearing price from the demand received during the auction. The market clearing price is the share price at which all shares offered can be successfully allocated to investors. However, after the auction closes, issuers can use the market information generated by the auction to adjust the amount of shares offered and even choose to sell below (but not above) the market-clearing price. In this case, the offer price will be lower than the market clearing price. The SEC requires the firm to refile the IPO if the total proceeds differs from the expected proceeds by more than 20% as a form of regulation for price and quantity adjustments. Finally, after the offer price is set, shares are allocated to investors who bid a price at least equal this. If the demand exceeds the number of shares offered at the offer price, the shares will be distributed on a pro-rata basis rounded to a multiple of 100 or 1000 depending on the size of the offering. 3. Data & Summary Statistics Our data set contains information on the characteristics of the firm and their IPO and on market conditions for each of the firms in our sample. Characteristics of the firm are collected from the NASDAQ stock exchange website. For each firm we collected figures for net income, revenue and stockholder equity. We obtained data on the characteristics of the IPOs from final prospectuses also made available on NASDAQ. We collected figures for expected price range, offered price, number of shares offered and outstanding, industry and the age of the firm at IPO. Market conditions data is collected from the S&P 500 Index. Table 1 presents in Panel A annual numbers and percentages that reflect auctioned IPOs and total IPOs completed in the US over the period 1999-2011, introducing in Panel B the number of withdrawn IPOs. Panel A presents the number of completed auctioned IPOs in our sample relative to the total number of IPOs in the US over the analysed period. It is apparent that throughout the analysed period, the highest number of auctioned IPOs has been achieved in 2004. However, relative figures reveal that in fact, in the year 2008 auctions had the highest percentage of auctioned IPOs relative to total IPOs. Following 2004 however, auctioned IPOs experienced a decrease in popularity. This may suggest the higher number of auctions in that period may have failed to capture the demand for future auction IPOs. When considering the total sample figures, we find the


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whole IPO market experienced the same decrease over this period. This is most likely attributed to the financial crisis with less investors willing to invest in new equity markets. The market for IPO auctions in the US is valued at 1.8% further reflecting the unpopularity of auctions. In Table 1, Panel B we provide the number of both withdrawn and completed issues performed by WR Hambrecht. This panel reflects the total number of auctioned IPOs that represents the sample used in our analysis. The distribution between withdrawn and completed issues reveals the success and withdrawal rate for WR Hambrecht. They are shown to have a total withdrawal rate of 28% over the period analysed. In Degeorge, Derrien and Womacks (2010) US auction sample, they find a withdrawal rate of 24%. Their smaller number can be attributed to their analysis only extending to 2007 and consequently not including the 2 additional withdrawn issues in 2008 and 2011. Dunbar and Foerster (2008) find 20% of total US IPOs were withdrawn during the period 1985-2000. With our analysis extending on a similar period of time, we can conclude that over the two decades, the withdrawal rate of auctioned IPOs has increased. The withdrawal rates reflect the percent of observations that have been omitted in recent literature. Our study aims to fill this gap and determine whether the omitted observations influence empirical findings and conclusions for auctioned IPOs and their ability to predict investor willingness to pay.


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Table 1 Panel A: IPO characteristics

IPO year

Number of auctioned IPOs in

sample Total number of IPOs in the US

Percentage of total

1999 5 477 1.05% 2000 3 382 0.79% 2001 1 80 1.25% 2002 0 66 0.00% 2003 2 63 3.17% 2004 10 174 5.75% 2005 7 161 4.35% Subtotal 27 1,403 1.92% 2006 5 157 3.18% 2007 2 159 1.26% 2008 2 31 6.45% 2009 2 63 3.17% 2010 0 154 0.00% 2011 1 125 0.80% Subtotal 11 689 1.60% Total 38 2,092 1.82% Panel B: Withdrawn and successful auctioned IPOs Year

filed Withdrawn Issues Percentage Withdrawn Completed Issues

Percentage Completed Total issues filed

1999 1 20.00% 4 80.00% 5 2000 3 42.86% 4 57.14 7 2001 1 33.33% 2 66.67 3 2002 0 - 0% - 0 2003 1 33.33% 2 66.67 3 2004 3 30% 7 70 10 2005 2 20% 8 80 10 Subtotal 11 28.90% 27 71% 38 2006 0 0.00% 5 100.00% 5 2007 2 50% 2 50% 4 2008 1 50% 1 50% 2 2009 0 0% 2 100% 2 2010 0 - 0 - 0 2011 1 50% 1 50% 2 Subtotal 4 27% 11 73% 15 Total 15 28.30% 38 71.40% 53


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In Table 2, we provide the summary statistics for our sample with their values characterised according to the variables that are used in our empirical analysis. We define our variables as follows: OPS is the offer price determined after Open-IPO auction for completed issues. Assuming that firms withdraw their IPO because they expect their price range not to be met, we define OPW as the reserve price (lowest price in the prospectus price range) for the withdrawn issues. These variables are used to form our main dependent variable, offer price, which includes values from the whole sample allocating OPS and OPW to the firms they adhere to. The variables defining the firms characteristics include: the firms age at IPO, revenue per share, net income per share, stock equity per share and the percent of ownership offered. Age at IPO is defined as the number of years since the firms incorporation to the date of the IPO. Net income per share is the profit for the firm in the year prior to the IPO filing divided by the firms number of outstanding shares. Revenue per share is defined as the revenue for the firm in the year prior to the IPO filing divided by the firms number of outstanding shares. Stock equity per share is the book value for the firm in year prior to the IPO filing divided by the firms number of outstanding shares. This figure represents how much the company would have left over in assets if it were to go out of business immediately. The percent of ownership offered is calculated as the total number of shares offered in the IPO divided by the firms number of outstanding shares. For the variable that illustrates market characteristics, MKTC, two definitions are required in order to include both completed and withdrawn IPOs in our sample. MKTC_S illustrates the market conditions for completed IPOs defined as the weighted average of market returns in the three months preceding the auction date with weights of 1 for the most recent month, 2 for the second most recent month and 3 for the third most recent month. MKTC_W illustrates market conditions for withdrawn IPOs defined as the weighted average of market returns in the three months preceding the withdrawal date with weights of 1 for the most recent month, 2 for the second most recent month and 3 for the third most recent month. MKTC illustrates weighted average of market returns for the whole sample, incorporating MKTC_S for completed observations and MKTC_W for withdrawn observations

Table 2 shows the mean values for the variables indicating the firms performance (revenue/ share, net income/ share, and stock equity/ share) to be lower for the firms that decided to withdraw their IPO when compared to the mean values of the completed IPOs. Assuming the withdrawal decision is directly related to the firms expected investors valuation, this suggests a firms performance may be significant in determining the investors willingness to pay. On the other hand, it may suggest these performance indicators are the reasons a firm chooses to withdraw from the auction. The mean value for our dependent variables, also present smaller values for withdrawn IPOs. However, this is not a surprising result given our definition of offer price for withdrawn observations (smallest price in the price range provided by the IPO prospectus). These values may suggest firms that completed their IPO achieve offer prices higher than the lowest value in their pre-auction price range. Completed issues are shown to have a 3-year higher mean average in age when compared to the mean value in withdrawn observations suggesting completed IPO firms are relatively older. We find the OPS summary statistics are skewed due to the presence of an outlier. The offered price for Google has been set at $85, 6 standard deviations away from the mean. Therefore, when removing it from the sample, we find the mean value of OPS to decrease from $14.2 to $11.7. These values still reflect completed issues to have a higher mean average relative to the withdrawn observations.


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Furthermore, the table shows the mean percent of ownership offered is relatively similar for both completed and withdrawn issues with withdrawn having a slightly higher mean and a lower maximum suggesting these firms offer higher percentages of ownership to the public. This is also shown in their minimum values. The withdrawn data shows market conditions to have a negative mean where completed have a positive value. This may also suggest, in support of existing literature, that market conditions are significant in determining investor valuation.


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Table 2

Panel A: Full sample

Variables Mean Median Std Dev Min Max

Offer price ($) 12.9 10.5 11.3 5.5 85.0 Age at IPO (years) 8.9 7.0 7.4 0 35 Revenue ($/ share) 2.3 1.4 3.02 1.1 14.2 Net Income ($/ share) -0.02 -0.1 1.1 -1.5 5.9 Stockholder Equity ($/ share) -0.4 0.04 3.4 -10.05 10.05 Percent of ownership offered 32.7% 25.9% 21.8% 7.2% 100.0% MKTC 3.3% 3.1% 7.4% -9.6% 20.08%

Panel B: Completed IPOs

Variables Mean Median Std Dev Min Max OPS ($) 14.2 10.5 13.05 5.5 85.0 Age at IPO (years) 9.9 7.0 7.9 1 35 Revenue ($/ share) 2.7 1.5 3.4 0.03 14.2 Net Income ($/ share) 0.09 -0.05 1.2 -1.5 5.9 Stockholder Equity ($/ share) -0.4 0.02 3.6 -8.8 10.05 Percent of ownership offered 31.6% 25.9% 22.04% 7.2% 100.0% MKTC_S 5.3% 4.8% 6.6% -7.6% 20.08%

Panel C: Withdrawn IPOs

Variables Mean Median Std Dev Min Max OPW ($) 9.8 10.0 2.8 6.0 17.1 Age at IPO (years) 6.6 6.0 5.3 0 17 Revenue ($/ share) 1.5 0.6 1.6 0.0 4.5 Net Income ($/ share) -0.3 -0.2 0.5 -1.5 0.6 Stockholder Equity ($/ share) -0.4 0.2 2.8 -10.05 2.3 Percent of ownership offered 35.5% 30.9% 21.6% 9.7% 80.0% MKTC_W -1.9% -3.7% 6.4% -9.6% 16.1%

Offer price is the price for the whole sample with OPS for completed observations and OPW for withdrawn observations. Age at IPO is the number of years since incorporation at IPO date. Revenue per share is the revenue for the firm for the year prior to the IPO filing divided by the number of outstanding shares. Net income per share is the profit for the firm in the year prior to the IPO filing divided by the number of outstanding shares. Stockholder equity per share is the book value of the firm divided by the number of outstanding shares. Percent of ownership is number of offered shares divided by the number of outstanding shares. MKTC is the weighted average of market returns for the whole sample with MKTC_S for completed observations and MKTC_W for withdrawn observations. OPS is the offer price for completed issues determined after the auction. MKTC_S is the market conditions for completed IPOs defined as the weighted average of market returns in the three months preceding the auction date with weights of 1 for the most recent month, 2 for the second most recent month and 3 for the third most recent month. OPW is the price for withdrawn issues defined as the reserve price in the pre-auction price range. MKTC_W is the market conditions for withdrawn IPOs defined as the weighted average of market returns in the three months preceding the withdrawal date with weights of 1 for the most recent month, 2 for the second most recent month and 3 for the third most recent month. We report the p-values in parentheses. ***, **, and * denote significance at the 1%, 5%, and10% level, respectively. We report the p-values (calculated with clustering at the IPO level) in parentheses. ***, **, and * denote significance at the 1%, 5%, and10% level, respectively.


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4. Offer Price WR Hambrechts Open-IPO mechanism explicitly allows for some discretion in the setting of the IPO price. This suggests that our dependent variable, offer price, may not fully reflect the investors willingness to pay. Previous research incorporates detailed bidding data and demand information in their dependent variable, investor participation, suggesting our assumption in the offer price being directly related to investor valuation be tested (Degeorge et al.,2010; Cornelli & Goldreich, 2001). Therefore, we test our assumption by analysing the number of completed IPOs in our sample that have used this discretion in determining the offer price. Because we do not have access to the market-clearing prices for these observations, we base our analysis on the research provided by DeGeorge, Derrien & Womack (2010). DeGeorge, et al. (2010) also empirically examine the US IPO Auction market in a slightly shorter period between 1999 and 2007. They find 37% of auctions in their sample were underpriced. Because the auction mechanism does not allow for prices above the market-clearing price, this percentage reflects the number of auctions that exercised discretion in determining offer price. Table 3 provides the number of completed auctioned IPOs that were priced within and outside the pre-auction price range for our sample. We find 37% of the auctions were priced outside the price range, the same percentage of exercised discretion found in DeGeorge et al (2010). Even though our sample includes 3 more completed auctions from the period 2008 to 2011, shown in Table 1, we assume these auctions were priced at the clearing price. We also assume the firms adhering to the price within their pre-auction price range reflects their offer price to be at the market-clearing price. We base our assumptions on the fact our percentage of IPOs priced outside the price range remains identical to the percentage of price adjusted auctions found in DeGeorge et al. (2010). With these assumptions, we test whether the number of completed issues priced outside the price range can be predicted by our explanatory variables in a probit regression. Therefore, our model, in Table 5, will calculate a predicted probability of an IPO being priced outside the price range based on six predictors: revenue per share, net income per share, stockholder equity per share, percent of ownership offered, age at IPO and market returns.

Table 3 Panel A: IPOs range Variables IPOs % of total completed Priced within range 24 63% Priced outside range 14 37% Total Completed IPOs 38 Panel B: Outside range IPOs % outside the range Relative to total outside range Below range 8 21% 57% Above range 6 16% 43%


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The regression includes the 38 observations we have in our sample for completed issues. Our dependent variable is an indicator variable holding a value of 1 for completed issues falling outside the price range and 0 otherwise. Table 4 shows the dependent variable frequencies and Table 5 presents the results of our probit regression.

Table 4

Dep. Value Count Percent Count Cumulative %

0 26 68.42 26 68.42 1 12 31.58 38 100

Table 5 Explanatory variables Dependent variable

P-OUT

Revenue/ share -0.03 (0.81) Net Income/ share -0.10 (0.77) Stockholder Equity/ share 0.15 (0.16) Percent of ownership offered -0.51 (0.68) Age at IPO -0.03 (0.31) Market Conditions 3.46 (0.33) Coefficient -0.09 (0.08)

LR statistic 4.69 Prob (LR statistic) 0.58 N 38

P-OUT is an indicator variable holding a value of 1 for completed issues falling outside the price range and 0 otherwise. Revenue per share is the revenue for the firm for the year prior to the IPO filing divided by the number of outstanding shares. Net income per share is the profit for the firm in the year prior to the IPO filing divided by the number of outstanding shares. Stockholder equity per share is the book value of the firm divided by the number of outstanding shares. Percent of ownership is number of offered shares divided by the number of outstanding shares. Age at IPO is the number of years since incorporation at IPO date. MKTC is the weighted average of market returns for the whole sample with MKTC_S for completed observations and MKTC_W for withdrawn observations. We report the p-values in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.


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We do not find any of our explanatory variables to influence significantly the underwriter and issuer decision to underprice. Therefore, underwriters and issuers base their decision on factors not made available to us. Underpricing is shown to be exhibited in virtually all IPO markets around the world (Loughran & Ritter, 1994). It is often assumed underpricing is an obligatory cost to the issuer (Derrien & Womack, 2003). This is inconsistent with the characteristics defining an efficient IPO mechanism, however, when compared to bookbuilding, auctions exhibit lower underpricing with mean values of 16.89% bookbuilding and 9.68% for auctions (Derrien & Womack, 2003; Sarigh & Wohl, 2001). DeGeorge et al. (2010) tests the determinants in WR Hambrechts underpricing decision finding the variable to be significantly explained by the bids made by informed investors. They suggest the decision to underprice may be related to the popular assumption in underwriters compensating informed investors for their risk. They also suggest underpricing may be a result of a large number of informed investor bids being made below the market-clearing price. Therefore, the issuer may price the issue lower than the market-clearing price in response to the possibility of retail investors submitting high bids suggesting inaccurate results for the value of the company. Our results provide further support in underpricing with interpretations that may also suggest underpricing to be efficient by recognising the influence of retail investor bids. Furthermore, our explanatory variables are not significant in determining the decision to adjust the market-clearing price in our sample suggesting any significance found in later regressions is an indication for investors willingness to pay. Therefore, we proceed with our analysis in using offer price as an indication of investor valuation.


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5. Investor Valuation If too few investors decide to acquire information and participate in the auction, the set IPO price might be far from the firms aftermarket price, and the firm could also suffer low aftermarket liquidity reflecting the issuers incentive to capture investor demand (DeGeorge et al., 2010). Therefore, in this section we empirically test whether the offer price can be explained from information known before the auction to provide insights on investor valuation and whether it can be predicted in auctioned IPOs. In order to be able to include withdrawn IPO observations in our analysis, we employ a censored normal regression. This is because the dependent variable, offer price, is only partially known due to withdrawn IPOs never reaching the auction stage and therefore, never determining a market clearing price. As defined in our Summary Statistics, the values for withdrawn issues in our dependent variable are provided by the lowest price in the price range submitted before the auction, also known as reserve price. It is sensible to assume the offering price for the withdrawn IPOs would have been set under the price range following an auction and therefore, we censor the data for withdrawn issues to the left. This is made by assuming the firms decision to withdraw is related to their expectation in not achieving the desired amount of proceeds (expected proceeds), and therefore, a low share price. This is the common assumption made on the issuer in other IPO literature (Bonini & Voloshyna, 2013; Draho, 2004).

We control for net income per share and market conditions throughout our first six specifications while adding an additional explanatory variable one at a time age at the IPO, dummy_tech, revenue per share and percent of ownership offered. We construct a dummy variable to reflect whether the firm in our sample is in information technology or biotechnology industries with values of 1 if they are in a hi-tech industry and 0 otherwise. This is also done in previous literature due to the dot-com bubble phenomenon when hi-tech industries exhibited high demand for their stock in 2000 (Derrien, 2005). The results of our regression are included in Table 7.

We expect investors to be influenced by the firms net income per share. This is because the profit of the company is commonly referred to in providing an indication on the firms performance. We control for market conditions preceding the auction due to previous literature finding this variable to be significant in determining investor participation (Amihud et al., 2003; Degeorge et al., 2010; Dorn, 2009; Neupane, & Poshakwale, 2012; Neupane, & Thapa, 2013).


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Table 7

Explanatory Variables Dependent variable: Offer price (1) (2) (3) (4) (5) (6) (7)

Net Income/ share 1.616 (0.21) 1.638 (0.35) 1.009 (0.56) 0.751 (0.75) 0.602 (0.78) 2.296 (0.19) Market Conditions 34.477 (0.35) 34.917 (0.21) 34.126 (0.21) 33.584 (0.23) 34.184 (0.21) 30.747 (0.26) 26.999 (0.31) Age at IPO 0.054 (0.83) Dummy_tech 5.776 (0.15) Revenue/ share 0.453 (0.60) 1.176* (0.08) Stockholder Equity/ share 0.527 (0.48) Percent of ownership offered -0.139 (0.14) -0.181* (0.07) Coefficient 8.651*** (0.00) 8.126** (0.02) 6.293** (0.02) 7.607*** (0.01) 8.923*** (0.00) 13.325*** (0.00) 12.080*** (0.00) AIC 6.261 6.298 6.260 6.293 6.289 6.258 6.234 BIC 6.409 6.484 6.446 6.479 6.475 6.444 6.420 N 53 53 53 53 53 53 53 Left censored 15 15 15 15 15 15 15 Uncensored 38 38 38 38 38 38 38


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Our results find neither the firms net income per share nor the market conditions to be significant in determining investors valuation. Throughout the first six specifications neither one becomes significant nor the added explanatory variables. We find market conditions are not significant which is inconsistent with the previous literature. However, we interpret this finding in relation to DeGeorge et al. (2010). They find, in their similar sample, informed investors account for around 84% of demand in dollar value. They also find they receive around 87% of the shares offered in the IPO on average. This suggests our sample possesses similar results. Furthermore, Chiang (2011) finds empirical results revealing informed investors base their valuation on the information they have, unrelated to market returns. Subsequently, our insignificant values in market conditions may be related to these findings.

Furthermore, if the net income per share is not accounted for in determining an IPO offer price, we believe this may be related to a large amount of firms undergoing an IPO are usually young in age (Draho, 2004). A young firm most likely possesses low profit figures given the higher costs assumed in young firms. We construct Table 6 to determine verify our assumption.

Table 6

Positive income Negative income

% of total IPOs 32% 68% Withdrawn IPOs 1 13 Withdrawn % of total IPOs 1.89% 24.53% Successful IPOs 16 23 Successful % of total IPOs 30% 43% Average age 10 9 Average withdrawn IPO age 7.46 2.00 Average successful IPO age 9.81 9.09

Table 6 shows only one withdrawn IPO in our sample possesses a positive income value. We also see 43% of the successful IPOs possess a negative income. Most of our sample, 68%, possess negative income figures. This may reflect the reason investors did not find net income to influence their valuation. This suggests investors may be more concerned with expected future profit figures. Table 6 shows that negative profits are more prevalent for younger firms than for relatively older ones, with the average age of the withdrawn IPOs being only 2 for the negative-profit-firms while for the positive-profit-firms this is only 7.46. Therefore, the analysis reveals our assumption to be true and perhaps investors choosing to participate in IPO auction recognise the financial characteristics for a young firm, higher costs, does not necessarily indicate their long term performance. Bhabra & Pettway (2003) further support this hypothesis by showing the firms net income known before an IPO does not reflect its future performance. Because we found in specification (7) in Table 7 revenue per share to be significant, our analysis turns to testing the robustness of this variable and its explanatory power on the offer price. We also believe the percent of ownership offered may influence the investors willingness to pay for the IPO due to the reflection on whether the firm is looking for an exit strategy by offering too many of its shares to the public and subsequently inducing investors to believe the firm owners are not confident in their future performance. Therefore, our dependent variable remains the offering price and our two core variables are revenue per share and percentage of ownership offered. Similar to our previous regression, we censor the 15 withdrawn observations to the left based on the assumption that firms did not expect their price range would be met.


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We regress six specifications in which we control for one additional explanatory variable one at a time age at the IPO, dummy_tech, market conditions, shareholder equity per share and net income per share. The results of our regression are presented in Table 8. The values of the coefficients are interpreted in a similar manner to OLS regression coefficients. Therefore, the expected offer price changes by the value of the coefficient estimate for each unit increase in the corresponding predictor. Specification (1) includes only the two core variables. We find that revenue per share affects positively the offer price while the percent of ownership is negatively related to the dependent variable. The negative coefficient exhibited in the percent of ownership supports our hypothesis in investors finding higher amounts of equity offered to the public as a negative quality in investment decisions. The two core variables remain significant and with relatively constant coefficients during the first five robustness checks, indicating our model is well specified. Market conditions does not become significant in this regression either. When further controlling for age, dummy_tech, stockholder equity per share and net income per share, neither one of them if found to have a significant effect on the dependent variable, at any conventional level of significance. In specification (6), when regressed along with net income per share, revenue per share becomes insignificant. Therefore, we conclude that the revenue per share and the percent of ownership are significant in determining the investors willingness to pay.


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Table 8

Explanatory variables Dependent variable: Offer price

(1) (2) (3) (4) (5) (6)

Revenue per share 1.196* (0.07) 1.272* (0.06) 1.098* (0.10) 1.176* (0.08) 0.972** (0.18) 1.123 (0.21) Percent of ownership offered 0.189** (0.05) 0.199** (0.04) -0.168* (0.08) 0.181* (0.07) 0.190** (0.05) - 0.189** (0.05) Age at IPO -0.127 (0.62) Dummy_tech 4.854 (0.20) MKTC 26.999 (0.31) Stockholder Equity per share 0.482 (0.42) Net Income per share 0.286 (0.90)

Coefficient 13.388*** (0.00) 14.691*** (0.00) 10.923*** (0.00) 12.080*** (0.00) 14.185*** (0.00) 13.545*** (0.00)

AIC 6.216 6.249 6.224 6.234 6.242 6.254

BIC 6.365 6.435 6.409 6.420 6.428 6.439

N 53 53 53 53 53 53

Left censored 15 15 15 15 15 15

Uncensored 38 38 38 38 38 38

Offer price is the price for the whole sample with OPS for completed observations and OPW for withdrawn observations. Revenue per share is the revenue for the firm for the year prior to the IPO filing divided by the number of outstanding shares. Percent of ownership is number of offered shares divided by the number of outstanding shares. Age at IPO is the number of years since incorporation at IPO date. Dummy_tech is a dichotomous variable that takes the value of 1 if the firm operates in an information technology or biotechnology industry, or zero otherwise. MKTC is the weighted average of market returns for the whole sample with MKTC_S for completed observations and MKTC_W for withdrawn observations. Stockholder equity per share is the book value of the firm divided by the number of outstanding shares. Net income per share is the profit for the firm in the year prior to the IPO filing divided by the number of outstanding shares. We report the p-values in parentheses. ***, **, and * denote significance at the 1%, 5%, and10% level, respectively. Net income per share is the profit for the firm in the year prior to the IPO filing divided by the number of outstanding shares. Stockholder equity per share is the book value of the firm divided by the number of outstanding shares. Percent of ownership is number of offered shares divided by the number of outstanding shares. MKTC is the weighted average of market returns for the whole sample with MKTC_S for completed observations and MKTC_W for withdrawn observations. OPS is the offer price for completed issues determined after the auction. MKTC_S is the market conditions for completed IPOs defined as the weighted average of market returns in the three months preceding the auction date with weights of 1 for the most recent month, 2 for the second most recent month and 3 for the third most recent month. OPW is the price for withdrawn issues defined as the reserve price in the pre-auction price range. MKTC_W is the market conditions for withdrawn IPOs defined as the weighted average of market returns in the three months preceding the withdrawal date with weights of 1 for the most recent month, 2 for the second most recent month and 3 for the third most recent month. We report the p-values (calculated with clustering at the IPO level) in parentheses. ***, **, and * denote significance at the 1%, 5%, and10% level, respectively.


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6. Unconditional Pricing Impact The next step of our analysis is to determine whether the exclusion of withdrawn issues reflects any significant changes to our specified model when only completed IPOs are tested. To do so, we employ an OLS regression to examine the factors that influence the offer IPO price. Our dependent variable is OPS, the offer price for completed IPOs and, for comparison purposes we use as core variables revenue per share and percent of ownership offered. Consequently, the sample for the regression makes use of the 38 observations that were successfully priced following an Open-IPO auction. Table 9 reports the ordinary least squares regressions of OPS on the following explanatory variables: revenue per share, percent of ownership offered, stockholder equity per share, net income per share, market conditions, dummy_tech and age. We construct seven specifications, following the structure used in the previous regression. Specification (1) regresses OPS on all explanatory variables and is the only one to yield significant results. With a coefficient of 1.728, revenue per share affects positively OPS. Similarly, stockholder per share has a significant and positive effect on the dependent variable. In conclusion, we find the model including only successful observations does not provide the same results or any significance. This may suggest withdrawn data influences the results in determining investor participation. It may also suggest that the variables found to be significant in the total sample are affected by the characteristics of withdrawn IPOs. Therefore, the final step of our empirical analysis is testing what influences issuers and their decision to withdraw from the auction to see whether the revenue per share and percent of ownership is significant in influencing their decision. We expect to see significant results if the censored regression is influenced by characteristics of withdrawn IPOs.


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Table 9 Dependent variable: OPS Explanatory variables (1) (2) (3) (4) (5) (6) (7)

Revenue per share 1.73* (0.09) 0.72 (0.31) 0.33 (0.64) 0.75 (0.43) 0.70 (0.33) 0.65 (0.18) 0.94 (0.20) Percent of ownership offered -0.23** (0.06) -0.16 (0.14) -0.16 (0.14) -0.16 (0.15) -0.17 (0.13) -0.15 (0.18) -0.19 (0.10) Stockholder Equity per share 1.90** (0.04) 0.80 (0.23) Net Income per share -6.06* (0.04) -0.11 (0.96) MKTC -40.71 (0.21) -23.28 (0.47) Dummy_tech 0.1 (0.98) 4.63 (0.28) Age at IPO -0.65* (0.06) -0.32 (0.25)

Coefficient 26.84*** (0.00) 17.39*** (0.00) 18.85*** (0.00) 17.33*** (0.00) 18.90*** (0.00) 15.05 (0.00) 20.77 (0.00)

R-squared 0.25 0.06 0.10 0.06 0.07 0.09 0.09 Adjusted R-squared 0.07 0.01 0.02 -0.02 -0.05 0.01 0.02

N 38 38 38 38 38 38 38

OPS is the offer price for completed issues determined after the auction OPW is the price for withdrawn issues defined as the reserve price in the pre-auction price range. Revenue per share is the revenue for the firm for the year prior to the IPO filing divided by the number of outstanding shares. Percent of ownership is number of offered shares divided by the number of outstanding shares. Stockholder equity per share is the book value of the firm divided by the number of outstanding shares. Net income per share is the profit for the firm in the year prior to the IPO filing divided by the number of outstanding shares. MKTC is the weighted average of market returns for the whole sample with MKTC_S for completed observations and MKTC_W for withdrawn observations. Age at IPO is the number of years since incorporation at IPO date. We report the p-values in parentheses. ***, **, and * denote significance at the 1%, 5%, and10% level, respectively. We report the p-values (calculated with clustering at the IPO level) in parentheses. ***, **, and * denote significance at the 1%, 5%, and10% level, respectively.


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7. Withdrawal Decision In our final section, we employ another probit regression in order to examine the factors influencing a firms decision to withdraw its IPO. Therefore, the model makes use of all 53 observations in our sample. The dependent variable takes a value of 1 if the firm is withdrawn and 0 otherwise. Our regression contains three specifications and calculates a predicted probability of withdrawal based on the five predictors corresponding to our previous regression: revenue per share, net income per share, stockholder equity per share, percent of ownership offered and market returns. The dependent variable is an indicator variable that takes a value of 1 if the firm is withdrawn and 0 otherwise. Table 10 shows the frequency of the dependent variable.

Table 10 Cumulative

Dep. Value Count Percent Count Percent

0 38 71.7 38 71.7 1 15 28.3 53 100

Table 11 illustrates results from the probit regression. We find revenue per share to be significant only in the first specification and the percent of ownership is shown to be insignificant throughout all three specifications. However, we find market conditions to be highly significant in influencing the firms decision to withdraw with negative coefficients indicating a decrease in market conditions will increase the probability of the firms decision to withdraw from the auctioned IPO at any conventional level of significant. This result suggests adverse market conditions influence the decision of the firm to withdraw. Though we did not find this variable to be significant in determining investor valuation, other literature finds this variable to be significant and therefore suggests the firm is basing their decision on variables that will influence the demand for their IPO. However, the firm fails to consider the values we found to be significant in determining investor valuation. This may suggest the impact of market conditions may weigh more than the revenue per share and the percent of ownership offered in investor valuation. Further studies on the weight of market conditions in relation to other variables explaining investor valuation would be useful in order to determine which variables investors find more important.


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Table 11

Explanatory Variables (1) (2) (3)

Revenue per share -0.2* (0.10) -0.1 (0.16) -0.08 (-0.4) Net Income per share -0.7 (0.2) Stockholder Equity per share 0.09 (0.3) Percent of ownership offered 0.01 (0.19) 0.01 (0.34) 0.01 (0.3) MKTC -9.41*** (0.00) -10.0*** (0.0) Coefficient -0.7** (0.05) -0.4 (0.24) -0.6 (0.2) LR statistic 3.7 14.3 17.04 Prob(LR) 0.2 0.00 0.09 N 53 53 53

Revenue per share is the revenue for the firm for the year prior to the IPO filing divided by the number of outstanding shares. Net income per share is the profit for the firm in the year prior to the IPO filing divided by the number of outstanding shares. Stockholder equity per share is the book value of the firm divided by the number of outstanding shares. Percent of ownership is number of offered shares divided by the number of outstanding shares. MKTC is the weighted average of market returns for the whole sample with MKTC_S for completed observations and MKTC_W for withdrawn observations. We report the p-values (calculated with clustering at the IPO level) in parentheses. ***, **, and * denote significance at the 1%, 5%, and10% level, respectively.


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8. Conclusion Our study analysed the 53 observations including completed and withdrawn IPOs under WR Hambrecht to represent findings that reflect the entire US auction IPO market. We included withdrawn observations in order to provide an empirical analysis of whether the exclusion of these observations in the previous literatures empirical findings may be biased or misrepresented. We found, in our analysis, withdrawn data to impact the results. Therefore, we suggest previous literature may have failed to include a form of robust measures in their empirical analysis. Furthermore, future research may benefit from including these observations in order to further support any empirical findings on auction IPOs and their predictability power in determining investor valuation. We tested our entire sample on offer price, a direct indicator for investor valuation, to find revenue per share and the percent of ownership offered to significantly explain investor valuation in auctioned IPOs. We recognise our justification for this variable in being related to investor valuation may not be the most accurate measure. However, given the data made publicly available, we were unable to use similar definitions in previous literature. We also found market conditions to not influence investor valuation inconsistent with other literature. We offered a suggestion that this finding may be influenced by high volumes of informed investor demand where their values of the firm are determined through their own analysis, unrelated to market conditions. We are not able to test whether this variable is related to informed investors, therefore, we recognise the definition of our dependent variable may also be the reason for this insignificance. In addition to our research, the sample size is small and might also be influencing our findings. Despite these limitations, our contribution in determining the impact of withdrawn auction IPOs reflect future research may benefit from their inclusion in order to provide more accurate findings for auctioned IPOs.


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