The impact of bargaining power of the underwriting firm and underpricing
of IPOs
Etan Wijnberg 6279457 / 10000652 Economics and Business Finance and Organization University of Amsterdam, FEB
July 2013
Bachelor Thesis
Thesis Supervisor: Dr. J.E. Ligterink
1. Introduction
One way for a company to get external financing is to do a public equity offering. Companies do this in order to lower the costs of funding of the operations and investments and to enable the current shareholders partial exit and the possibility to diversify their portfolio (Ljungqvist, 2004). In order to diversify the portfolio of the owners, the firm can sell the shares on the market and the owners can then diversify their personal portfolio. In an initial public offering a company goes to the equity market for the first time and tries to get as much money as possible from the public for its stocks. In this process the firm usually is accompanied by an underwriting bank who creates a public market for the shares. After an IPO the firm is not required to pay back the capital to the investors like with debt. When a firm goes back to the market to retain more money after the IPO it is called a seasoned equity offering. SEOs will not be discussed in this research.
Historically, initial investors in an IPO gain more than other investors. In 2011 there was a 10.8 percent underpricing of IPOs in the USA; the prices of shares went up with this amount on average on the first day according to the data used in this research. In this year the standard deviation was 24 percent. This amount of underpricing has not always been the case, IPO pricing has changed radically over the years. In the 1980s the underpricing on the first day was 7 percent for American IPOs. In the 1990s until 1998 the average underpricing was 15 percent, in 1999 and 2000 the average was 65 percent and after that the underpricing decreased strongly (Ritter and Loughran, 2004). Underpricing is defined as the pricing of a stock below its market value.
The purpose of this paper is to combine the leading theories on bargaining power and use those with the empirical data collected in this research. In this paper the relationship between underpricing of a stock and the influence of bargaining power of the issuing firm in comparison the underwriter will be researched. This will be done by looking at previous researches, introducing a new variable called the size of an IPO and by looking at the relevance of the corruption hypothesis by Ritter and Loughran.
The first hypothesis that will be examined is: bigger sized IPOs have relatively less underpricing than small IPOs. This is based on economic theory about bargaining power of a issuing company versus the underwriter. Bargaining power can be explained by the principal-agent theory (Baron, 1982) where the principal is the issuing firm and the principal-agent is the
underwriter. The principal-agent theory is relevant because the size of the IPO influences the bargaining power and therefor the relationship between the principal and the agent. The principal needs to align the goal of the agent with his goal. Economic theory about
asymmetric information will be taken into account and there will be taken a closer look at the corruption hypothesis of Ritter (2004). The corruption hypothesis states that managers of the issuing firm search for underpricing because it benefits them. Managers can own all kinds of constructions in their private portfolio causing them to gain more when underpricing is the case, an example can be a construction with call options. In the 1980s for instance there was no such thing as looking for underpricing and it became a trend in the 1990s. According to Ritter and Loughran (2004) underwriters in the 1990s started to win over venture capitalists and executives in the company and giving them private broker accounts and then the
underwriters used to allocate hot IPOs to these accounts. Because of all the attention on underpricing after the bubble of 1999-2000 (Ritter and Loughran, 2004), in this paper is assumed at first that there is no corruption.
On bargaining power, the assumption is that small IPOs are more vulnerable. This means that the smaller companies with smaller IPOs are an easy target for the underwriters to manage significant underpricing (Ritter and Loughran, 2004). If the size of the IPO is bigger, the bargaining position of the offering company will be stronger versus the underwriting
bank, so the underpricing is expected to be smaller (Karlis, 2000). Assumed is also that an issuing firm wants to have underpricing as low as possible, because it wants to sell its shares at the right price and not too low (Ljungqvist, 2004). Therefor the second hypothesis is introduced: Managers of an issuing firm try to achieve as little underpricing as possible. The agent, underwriting bank, can choose to maximize the benefit of the principal, keep a good reputation and not underprice too much. Or it can try to maximize its own profit and increase underpricing at the cost of the offering company and its pre-IPO shareholders. The
underwriter in the second case will earn money by using options and “bet” that the stock price will go up after the IPO. The underwriter in this case has special rights that allows him to buy the stock or options before the others have the opportunity to buy them. Beatty and Welch (1996) find that there was a difference between the decennia 1980s and 1990s concerning the managerial behavior. In the 1980s managers were not looking for underpricing while in the 1990s this became the trend.
Underpricing also reduces the banks risk of an unsuccessful IPO due to insufficient demand for the new shares (Karlis, 2000). I consider such research to possibly provide new insights into the pricing mechanism of shares in an IPO as there is a clear conflict of interest between the owners of the company and the underwriters.
In the past, underpricing has gone up and down severely because of many different reasons. One of these reasons is the changing interest of the issuing party’s management. During the internet bubble of 2000, the underpricing was 64 percent and was driven partially up by managers of the offering company who were looking for underpricing at the cost of the existing shareholders. This is called “corruption” and this is extra relevant when venture capital plays a role (Ritter and Loughran, 2004). According to Ritter and Loughran (2004) when there is venture capital present in a firm, the chances for corruption go up since venture capital firms are more sensitive for corruption.
Another important factor is the chancing risk of the IPOs. Security risk is always expressed in the price of a stock, when there is high default risk the price goes down. With high risk, caused by higher chance for IPO failure, there is high underpricing. When there is lower risk for IPO failure, caused by for instance more stability and by being an older firm, the price goes up. Security risk does not fully explain the behavior of stocks during an IPO (Knopf and Teall, 1999). In the past the internet firms have shown to be high risk, so being an internet firm increases the underpricing. For instance, risk can be reflected by uncertain technology or by being an internet firm (Ritter and Loughran, 2004). Aggarwal, Krigman, and Womack (2002) show that internet firms are higher risk because there are many
unsophisticated bidders in the market. These unsophisticated bidders increase the asymmetry and therefor the underpricing.
Also Carter and Manaster (1990) prove empirically that when a firm has an
underwriter with a high reputation, the underpricing goes down compared to firms that have low reputation underwriters. Carter and Manaster wrote an extension on Rock’s theory where they say that if the risk increases, the informed demand increases causing the gap between people who lack information and people who have information to increase and so the information asymmetry will go up causing more underpricing.
The market anomaly of underpricing will be looked upon closely in this empirical paper. To do this the paper is split up into five parts. The first section, the introduction, describes the background and the objectives. The second part, literature and hypothesis, discusses the literature on this topic and the hypothesis here is looked at different empirical works of other researchers. Then there is looked at bargaining power and the new variable that is added. The third section, data and methodology, is about the empirical model. The fourth discusses the results from the empirical model. Here the analysis of the results of the research take place. The fifth section provides the conclusions.
2. Literature and hypotheses 2.1 Underpricing
Share prices are the outcome of supply and demand on the stock market. For creating an IPO price, not supply and demand is used. Rather contracts and negotiations between the underwriter and the issuer determine the IPO price. When creating a price for the IPO many more things play a role than only the security risk. When a new company wants to enter this market; an underwriter, in many cases supported by a consortium of banks, helps the firm issue its new shares. The underwriter is responsible for selling all or part of the shares of the company (Martani, Sinaga and Syahroza, 2012). The underwriter is usually a bank. The bank sells to its own clients and other mostly professional investors. These investors, usually institutional investors (Corwin and Schult, 2005), wish to maximize the gain on their
investments as either in the short or long run. It is in the underwriters interest to underprice in order to minimize the risk of not being able to sell the shares (Martani, Sinaga and Syahroza, 2012). Also it is in the best interest of the underwriter to please its clients and business relations and to create underpricing of the shares.
The owners of the company on the other hand want to sell their company for the maximum price they can get for it, the owners are represented by the managers. The managers of the offering company have to negotiate what is best for the company with the underwriting bank. As we have seen in previous papers, there is a conflict of interest between the managers and investment banks, caused by asymmetry between managers and investment banks and moral hazard (Ritter and Loughran, 2004).
Underpricing is the difference between the pricing of an IPO and the market value of the IPO (Ritter and Loughran, 2004). Baron (1982) says in his research that underpricing is the result of asymmetric information when the issuer has less information than the
underwriter. Since underpricing is a hot topic many researches have been done about underpricing. The most important once are discussed in section 2.2.
2.2 Leading theories
The first theory discussed is by Leland and Pyle (1977) who showed in their research that a firm can signal value to the outside world by looking at the retention rates of the stock under employees. If many employees have stock of the firm and do not want to sell it during the IPO, it means that they want to sell it later at an SEO (Leland and Pyle, 1977). This
indicator would then be expressed in the IPO price. This research suggests market asymmetry. A common market anomaly associated with IPOs is the winner’s curse by Rock
(1986). In the 1980’s it was one of the main reasons for explaining underpricing together with Baron’s theory (1982) discussed in 2.4. The winner’s curse occurs at an auction with
incomplete information. There are basically two groups. There are informed bidders, like banks, and there are uninformed bidders who bid without discriminating between good priced stock and badly priced stock. Winners tend to overpay for the product they are buying. This happens because when the uninformed bidders bid on a badly priced stock, they win all the stocks. When they bid on a well-priced stock, they get crowded out by the market and
someone else wins the stock. Because of this situation, the uninformed bidders are reluctant to bid, if this would be the situation. That causes a problem for the IPO because the primary market needs the unsophisticated bidders as well, so they introduce underpricing to keep them in the market (Rock, 1986). According to Ritter and Loughran (2004) the underwriter can eliminate the winner’s curse when underwriters allocate shares in hot IPOs only to investors that are willing to buy other IPOs. Underwriters have the power to almost eliminate all problems caused by asymmetric information.
An extension to Rock’s theory is the theory by Beatty (1989). Information asymmetry is the cause for underpricing according to Beatty (1989). The information asymmetry occurs between the issuing party, the underwriter and investors. Usually the underwriter knows the capital market, and the issuer does not. Financial firms are taken out of Beatty’s sample, because they have the same information about the capital markets as the underwriter. In this case there is no information asymmetry. Beatty and Welch (1996) and Cooney, Singh, Carter, and Dark (2001) show that the higher the ranking of the underwriting party the higher the underpricing was in the 1990s. This is contrary with the current hypothesis, where we say that managers of the issuing firm try to achieve as little underpricing as possible, but that is
because the hypothesis is assumed to change over time. The 1990s was a period when managers of the offering companies would seek for underpricing of the shares of the
companies they represented, before this period seeking for underpricing was not the case. The companies seeking for underpricing in the 1990s were characterized by having venture capital.
Moral hazard is one of the two results of information asymmetry that are discussed in this paper. The moral hazard that results in the agency problem caused Eisenbeis and
McEnally to come up with the Hazard-model in 1995. The issuing firm does not know the true value of itself, but the investment bank does. This causes changing behavior by the party who does not bear the risk, predicted by the model. The other result of information asymmetry is adverse selection where undesired results occur when buying or selling the IPO.
Knopf and Teall (1999) say that security risk does not solely explain the behavior of the stock during an IPO. Knopf and Teall acknowledge the fact asymmetric information explains at least a part of the IPO pricing phenomenon. Ritter and Loughran (2002) continue on this and state that when looking at bargaining power a behavioral approach is used. In Ritters and Loughran’s research in 2002 they show how issuers do not get upset while leaving money on the table at an IPO. The underwriting bank uses this knowledge to its own benefit. The issuer sees it as a gain since he usually still holds shares. In this theory we say that it is a loss since he could have sold the shares for an higher price. Because the perceived gain is bigger than the loss caused by underpricing, the issuer does not see the underpricing as a loss (Ritter and Loughran, 2002).
The information asymmetry also caused Ritter and Loughran (2004) to come up with the corruption hypothesis. Here is described that managers seek underpricing because they benefit from it, even though it is against the best interests of the shareholders they represent. This was the case because decision makers used to be, and some are still, rewarded for hot IPOs (Ritter and Loughran, 2002). Rewarding can be done in the form of shares and options. Decision makers are in this context managers and agents. A hot IPO is an IPO that has great demand, causing the share price often to surge after it is offered. This asymmetry became less since the internet bubble in 1999 and 2000. In this research is assumed that the corruption by managers has changed, due to the attention devoted to underpricing and the changes in the market. How firms are brought to the markets, and the type of firms that are brought to the market changed (Ritter and Loughran, 2004). Ritter and Loughran (2004) mean with type of firm either technology firm, internet firm or not-technology firm. Thus the hypothesis changed from the 1990s period where companies were seeking underpricing to now when companies are not seeking underpricing.
Ljungqvist and Wilhelm in 2003 show take a behavioral approach and that pricing behavior followed from incentives created by certain characteristics companies have. The part that relates to bargaining power is discussed in 2.3. Karlis (2000) uses deal size of the IPO in his research instead of sales and assets making it interesting to discuss his theory on the variable. The discussion of the variable is done in 2.4.
2.3 Bargaining power
Bargaining power is in this research a situation when both parties try to get their own interests satisfied. With bargaining power is meant the relative amount of influence one party can exert in a situation over the other party. When there is a perfectly competitive market the bargaining power of both parties is equal or another special case where there is equal power. Inequality of bargaining power is the general case with IPO negotiations. With the exception of really special IPOs like Facebook or other giants, the bargaining power favors the
underwriting bank (Ljungqvist, 2004).
When issuers bargain hard with the underwriter, the IPO will be underpriced less than when the issuers bargain less. Bargaining power can be gained by being a respectable firm, being a respectable underwriter, having a lot of assets or sales and giving signals to the public and third parties. Bargaining power is influenced by all of the previously named variables because the risk of the IPO for the underwriter goes down, causing less underpricing for the firm. When there is less risk the firm can choose between underwriters and negotiate harder (Karlis, 2000). Aligning the interests of the principal and the agent is the solution to the principal-agent problem where everyone does not have the same information. Therefor this is one of the goals of bargaining power (Ljungqvist and Wilhelm, 2003). If the agent and the principal have the same information, there is no information asymmetry and therefor no underpricing according to Ljungqvist and Wilhelm (2003).
The bargaining power argument says that with increase in size of the IPO the
bargaining position of the firm strengthens and therefor can bargain a better price. There are two possible places to make your bargaining power count for a firm. The first is the price of the stock and the second is the contract with the bank. The first is self-explanatory when is assumed that a firm wants as little underpricing and as high as possible gain on the stock, they sell it at the highest price possible. In the contract with the bank the goal is to align the goal of the issuer and the underwriter Ljungqvist and Wilhelm (2003). Bargaining power becomes more relevant when a company is expected to do not so good during the IPO. There will be bargained harder when the IPO is expected to do badly. When the IPO is expected to do good, the bargaining will be less, and the bargaining power is less relevant. This is another market asymmetry, because bargaining correlates with how well the stock will do Ljungqvist (2004). 2.4 Size of the IPO
Not included in one of the most famous works about IPO underpricing of Ritter (2004) is the variable Size of the IPO. This is not included even though it seems logical to include. Considering that the bargaining argument is based on the size of IPO and not on the size of the firm. Size of the IPO is more relevant than for instance assets or sales (Karlis, 2000). This is because when a company does an IPO and asks the underwriter to work together with the firm, it is more important for the underwriter to know the size of the IPO and thus how much money he is going to make. If the bank does not make enough money he will want to use call options on the IPO and thus underprice more. For small and big IPOs a bank is being paid differently. This has to do with the size of the firm, but it has more to do with the size of the IPO. Reasons for not including this variable could be that the information is already included in Log(sales) and Log(asset). Therefor when adding it afterwards, it shows correlation and does not add information. If replacing sales or assets with size of the IPO, there might be a different outcome. Log(sales) and Log(asset) say something about the size of a firm and therefor the risk. If a firm is big it is less unpredictable and the IPO is less likely to fail. In the methodology the variable Size of the IPO will be further explained. There will be also
explained how the variable is included.
The economic theory behind this variable can be explained with the principal-agent model according to Eisenbeis and McEnally (1995). In the principal-agent model information
asymmetry is present that results in moral hazard. The situation that then exists during the IPO is that the issuing firm does not know the true value of the firm and has to rely on the underwriting bank to tell him (Eisenbeis and McEnally, 1995). The underwriting bank has to decide how much he wants to underprice based on many things like quality, risk, positioning, corruption et cetera. The agent, underwriting bank, has to decide whether he would like to maximize the benefit of the principal and to keep a good reputation and not underprice too much; or to maximize its own profit and increase the underpricing. The underwriting bank has this incentive because he gets rewarded for the underpricing in several ways. First of all, the bank gets his clients what they want, short term gain on stock. Secondly, the bank gets rewarded by getting call options on the stock at the IPO the so-called “Green shoe”, so that the bank generates a greater gain through the underpricing. Baron (1982) shows in his research by using the principal-agent model that if there is information asymmetry and the underwriting bank has more information than the issuing firm, the issue will be underpriced.
According to Karlis (2000), another reason for size of the IPO to be important for underpricing is the fact that smaller sized IPOs need more attention to sell their stocks because they are less known. Because the companies are less known the investors look for shares that will soar, causing insiders not to sell the stock but to wait for the SEO and causing outsiders to notice the stock. Looking for more attention is not free, there are costs involved with looking for buyers.
2.5 Hypotheses
The previously stated papers have in common that they explain underpricing of a stock. They use in models the explanatory variables sales, assets et cetera. For instance the bargaining power argument in the previous paragraph is based on the size of the IPO firstly and not on the size of the company. Then it is important to highlight the fact that incentives change especially for managers. This leads to the following two H1 hypotheses:
Hypothesis 1: Bigger sized IPOs have relatively less underpricing than small IPOs.
Hypothesis 2: Managers of an issuing firm try to achieve as little underpricing as possible. For the second hypothesis the corruption hypothesis of Ritter and Loughran is very important (2004). First is looked at the bargaining power of the issuing firm, measured by variable size of the IPO. Then is looked at the dependent variable underpricing. If there is a significant difference for the bigger IPOs in comparison to the smaller IPOs then there is an asymmetry. So the corruption hypothesis might be relevant again. This is because in this research is said that bargaining power can be used to get closer to the goals of the issuing firm. If this is the case then bigger sized IPOs have more bargaining power than smaller sized IPOs and therefor can get closer to their goals. If the bigger sized IPOs have more underpricing, then it might be likely that there is some kind of asymmetry.
There is a risk of endogeneity in this hypothesis since the independent variable size of the IPO is used to explain the dependent variable underpricing. There is no endogeneity of this variable expected though. In this case there is correlation measured. It is not likely that causality is measured instead of correlation, but this still has to be considered. The effect is not likely to happen before its cause.
3. Data and methodology 3.1 Data
The data used is from Thompson Financial Security Data also known as Thompson Reuters. There had to be corrections done in the output, because of missing values. There still are missing values for: Assets, Sales and the Pure Primary Dummy. The data for the missing values was found in the annual reports of the listed companies or from their sites. The data about which IPOs were done in the United States during 2011 are from Hoovers. Hoovers is a Dun & Bradstreet company that licenses information on businesses for the public. The data included information of 102 IPOs. With Thompson Financial I found: the IPO offer price in dollars, the stock price at closing of the first day, proceeds, total assets before offering, founding date, total revenues before offering, book runner(s), a descriptive variable showing whether the firm is a technology firm and/or an internet firm or not and primary shares as a percentage of shares offered. For the Top-Tier Underwriter Dummy I used the bookrunner(s) from Thompson Financial and marked them dummy 1 if they had a rating of 8 or higher in the list of underwriter ranking on Ritters' site. The underwriter ranking on Ritters' site is written in the spirit of the methodology of Carter and Manaster (1990).
Unlike Ritter, IPOs are not excluded of: banks, savings and loan associations, partnerships, small best offers, closed-end funds and real estate investment funds. Not
excluding these companies, even though the information asymmetry differs is done because of being unable to differ between companies who have more information than others. Neither are offerings excluded with an offer price of less than 5 dollars a share, because there were no IPOs with an offer price of less than 5 dollars. Another big difference with Ritters’ research is the sample size. Ritter’s sample size was much greater than the sample size used in this research.
3.2 Methodology
Underpricingi = β0+ β1ln(Size of IPO)i+ β2ln(Asset)i+ β3ln(1 + Age)i+
β4ln(Sales)i+ β5Top Tier Underwriter Dummyi+ β6Tech Dummyi+
β7Internet Dummyi+ β8Venture Capital Dummyi+ β9Pure Primary Dummyi+ εi
Dependent variable:
Underpricing is explained by all of the explanatory variables. It can be calculated with the following formula. The following variables are used in the formula:
1. Underpricing of the stock on the first day = Return on the first day = 𝑅𝑅𝐹𝐹𝐹𝐹 2. IPO offer price = OP
3. Closing price on the first day = 𝑃𝑃𝐹𝐹𝐹𝐹 𝑅𝑅𝐹𝐹𝐹𝐹 = 𝑅𝑅𝐹𝐹𝐹𝐹𝑂𝑂𝑂𝑂−𝑂𝑂𝑂𝑂
Motivation for variables:
All of the variables except for size of the IPO were used before by Ritter and Loughran in why has IPO underpricing changed over time, 2004. Most of them were proven to be significant over a large sample. Not all of Ritters variables are used, simply because his research had other goals than this research. Also it was used to compare between years, this is not done in this research. All dummies used in this paper have a value of either 0 or 1.
Size of the IPO (Sizeipo)
The bigger the size of the IPO, the smaller the underpricing relatively (Karlis, 2000) is the previously stated hypothesis and expectation. This is a result of the position of the firm against the underwriter. The reason for including size of the IPO is the bargaining power that is being gained when an IPO has a certain size. Previously the size of the firm was taken into account, which is correct, but it is more relevant to use size of the IPO instead. This is made a logarithmic function so that it is more explanatory.
Total assets of the firm before offering (Asset)
If a firm has more assets the risk goes down for the underwriter and the underpricing goes down. Even though β1 has a high correlation with β2 and β4, it is important to maintain all of them in at least in one of the model for comparison. This variable has to do with bargaining power. Ritter and Loughran (2004) included this variable. When a firm has more assets, the risk of failing the IPO is less. This is made a logarithmic function so that it is more
explanatory.
Age of the Firm (Age)
The older the firm, the lower the IPO underpricing (Carter and Manaster, 1990). This is because there is less risk, and the market knows better what to expect. The variable is based on behavioral theory, because when a bank knows the firm and the funds he sells it to know the firm, it is easier to sell so the risk goes down. Ritter and other IPO researchers used this variable in their work, and it is a fact that with age the underpricing went down in the recent past in the USA. This is made a logarithmic function so that it is more explanatory.
Total sales before offering (Sales)
If a firm has more sales the risk goes down for the underwriter and the underpricing goes down. The danger of correlation and thus multicollinearity exists between Sales, Assets and Size of the IPO. This will be considered when making different models for testing Size of the IPO with its control variables. Also here bargaining power and risk reduction are the main arguments for including this variable. Ritter and Loughran (2004) included this variable in their research. This is made a logarithmic function so that it is more explanatory.
Top-tier underwriter dummy (Underwriter)
In the 1980’s the higher the ranking of the lead underwriter, the lower the underpricing was the rule (Beatty, 1989). This changed in the 1990’s when the higher the ranking of the lead underwriter, the higher it’s underpricing became the rule. Beatty and Welch (1996), Cooney, Singh, Carter, and Dark (2001) did their researches about this phenomenon. The higher underpricing in the 1990s existed because high ranked underwriters usually do big companies who in their turn demand a smaller underpricing. An even more important reason for high ranked underwriters to offer less underpricing is because of the prestige capital in the companies they offer. Prestige capital causes investors to demand less underpricing. Lead underwriter can be found through Thompsons Financial and the ranking can be found with Carter and Manaster (1990). These days an underwriter with prestige will underwrite less than an underwriter without prestige, because big and important underwriters have less risk selling the firms shares on the market according to Ritter.
Set the dummy to 0 if the ranking is of 0 until 7 and set the dummy to 1 when the ranking is 8 or higher on the underwriter ranking list.
Technology dummy (Tech)
Technology firms will behave in the same way as internet firms, there is a lot of speculation on new technology stock. What causes the risk of the given stock to go up. When the risk goes up, the underpricing goes up. Thus technology firms will be underpriced more than not
technology firms according to Ritter and Loughran (2004). This is why there is differentiated between industries in this research. There is differentiated between non-technology firms and technology firms and also between non-internet firms and internet firms.
Internet Dummy (Internet)
Internet stocks are underpriced more, because there are a lot of unsophisticated bidders on that market who look at future returns more than actual value. These traders cause noise on the market by bidding high. Because underwriters do not determine market price based on these traders, the internet stocks have higher underpricing. Aggarwal, Krigman, and Womack (2002). Also internet stock is usually characterized by low age, low assets or intangible assets. This may cause correlation.
Venture Capital Dummy (Venture)
When a firm is backed up with venture capital underpricing is higher because of “corruption”. With corruption is an interest of the managers to seek underpricing meant. The chance for corruption is higher when there is venture capital involved according to Ritter and Loughran (2004). Ritter and Loughran show the difference in interests between the managers and the underwriters. Managers want the underpricing as low as possible and underwriters as high as possible. When there is venture capital involved the venture capitalists are more willing to make complex constructions with the bank so that they will benefit from underpricing. Pure Primary Dummy (Pureprimary)
100 Percent of the shares offered are primary. No secondary shares were offered. Pure
primary causes underpricing to go up. This is the case because there are different parties in the primary and secondary market. This causes affiliation between different parties according to Ljungqvist and Wilhelm (2003).
4. Results 4.1 Tables
After using the data from Thompson Reuters and the program Stata12, seven
regressions can explain the results. The results concern 102 firms in the United States that did their IPO in the year 2011. The underpricing was 10,8 percent and that for not all the firms all the data is complete. Due to correlations found between variables, the results must be
considered accordingly. Table 1 - Descriptive statistics
Table 1 is descriptive statistics of the variables Underpricing, Sizeipo, Asset, Age, Sales, Top-Tier Underwriter Dummy, Tech Dummy, Internet Dummy, Venture Capital Dummy and Pure Primary Dummy.
When looking at the descriptive statistics, in most cases the sample size is 102. This is not high compared with other researches in this field, who usually use samples of multiple years. In Ritter’s and Loughran’s research (2002) the sample size is 6169 IPOs. Another research done by Loughran and Ritter in 2004 had a sample size of 3025 IPOs. In Ritters database can be seen that he used 81 IPOs for the year 2011 (Ritter and Loughran, 2004). These IPOs had an underpricing of 13.3 percent when equally weighted. In this research an underpricing of 11 percent was found. This complies with the following theory. Since Ritter excluded companies who had more information about capital markets, causing them to have a stronger bargaining position, the remaining have more underpricing than the sample in this research. Although the gap between 11 and 13.3 percent is not big, it shows that there is more asymmetry of
information in Ritters sample. Variables
Observa-tions
Mean Standard deviation
Minimum Maximum Expected effect on underpricing Underpricing 102 0,11 0,24 -0,22 1,34 Logsizeipo 102 5,10 0,99 2,80 8,24 - Logasset 89 5,50 1,68 1,82 10,19 - Logsales 75 4,77 1,93 -1,61 10,33 - Logage 102 2,00 1,06 0 4,49 - Underwriter 102 0,75 0,43 0 1 - Tech 102 0,54 0,50 0 1 + Internet 102 0,21 0,41 0 1 + Venture 102 0,43 0,50 0 1 + Pureprimary 96 0,61 0,49 0 1 + 11
Table 2 – Comparison between the 51 biggest sized IPOs and the 51 smallest sized IPOs Variables Smaller sized IPOs Biggger sized IPOs Totals (N=102) D = 1 or 0 Underpricing 0,08 0,13 Size of IPO 86,08 509,54 Asset 125,41 2231,75 Sales 9,43 13,18 Age 106,15 1212,66 Underwriter (D=1) 32 45 77 Tech (D=1) 35 20 55 Internet (D=1) 11 10 21 Venture (D=1) 29 15 44 Pureprimary (D=1) 30 29 59
In table 2; D=1 means that the dummy is 1 and not 0. After sorting the sample according to size of the IPO the sample can be split into two halves, the half with the bigger IPOs and the half with the smaller IPOs. There can be found a huge difference in underpricing between these two halves. The smaller sized IPOs had on average an underpricing of 8.3 percent. The bigger sized IPOs had on average an underpricing of 13.2 percent. The smaller sized half consisted out of 69 percent technology firms, but only 11 out of 51 were internet firms. The bigger sized half had 39 percent technology firms and 10 out of 51 were internet firms. Other variables who differed were the underwriter and the venture capital. The smaller sized half had more venture capital and lower rated underwriters. Also the bigger sized half of the IPOs were on average four years older.
The biggest difference between the two sides was obviously in the sales and assets. Assets were almost 18 times as big and sales were more than 11 times as big. Size of the IPO was only six times bigger than the smaller half.
Table 3 – Underpricing explained by the size of an IPO and control variables
Table 3, consisting out of 2 tables shows the 7 regressions. This table shows the results of an OSL regression-estimation on underpricing in the USA in 2011. The standard errors are in parentheses. Table 3.1 1 2 3 4 Logsizeipo 0,09 0,09 0,08 [0,11] [0,04] [0,04] Logasset -0,08 0,04 0,07 0,05 [0,05] [0,21] [0,02] [0,02] Logsales 0,02 0,02 [0,42] [0,42] Logage 0,01 0,00 0,03 [0,75] [0,92] [0,35] Underwriter 0,15 0,20 0,12 0,11 [0,08] [0,01] [0,10] [0,08] Tech 0,03 0,04 0,02 0,04 [0,70] [0,64] [0,72] [0,44] Internet 0,18 0,19 0,21 0,19 [0,02] [0,01] [0,00] [0,00] Venture 0,03 0,02 0,04 [0,68] [0,79] [0,47] Pureprimary 0,00 0,01 0,01 [0,97] [0,84] [0,93] Constant 0,15 0,01 0,18 0,17 [0,38] [0,95] [0,27] [0,19] N 71 71 85 89 F-value 3,12 3,11 4,29 7,13 Prob > F 0 0,01 0 0 R2 0,32 0,29 0,31 0,30 Adj R2 0,21 0,19 0,24 0,26 13
Table 3.2 5 6 7 Logsizeipo 0,08 0,03 [0,04] [0,24] Logasset 0,06 0,04 [0,01] [0,14] Logsales 0,04 [0,14] Logage Underwriter 0,11 [0,07] Tech Internet 0,21 0,22 [0] [0] Venture Pureprimary Constant 0,13 0,12 0,36 [0,26] [0,21] [0,77] N 89 74 102 F-value 8,80 5,58 1,38 Prob > F 0 0 0,24 R2 0,30 0,19 0,01 Adj R2 0,26 0,16 0
Table 3 shows that of the 102 firms used in the sample, only 71 are left, due to data that is not supplied by Thompson Reuter. This is only the case when all the variables are used.
Firstly the new variable ln(sizeipo) will be discussed. As can be seen in the regressions 1, 3, 4, 5 and 8 the variable is always positive. This means that whenever the size of the IPO goes up, the underpricing goes up. In the most relevant regressions it is either 8 or 9 percent. The variable is not significant when all other variables are included. The reason for this is probably the small sample size and the correlation between ln(size ipo), ln(asset) and
ln(sales). In the other regressions it is significant for an alpha of 0,05 except for regression 8. In the first hypothesis is assumed that the relationship would be negative. In other words there is assumed that the bigger the IPO, the lower the underpricing. The opposite is true: the bigger the IPO, the bigger the underpricing. Before the 1990s managers where not searching for underpricing. Now it seems, that when size of the IPO and thus bargaining power goes up, the underpricing goes up. Managers are still looking for underpricing like in the 1990s. The Corruption hypothesis presented by Ritter is still relevant. So the second hypothesis is not true either. Even though is shown that the result is contrary to expectation, the variable still adds to the regression in R², but only 2,9 percent. In other words, there can be
seen in the regressions that when the size of the IPO goes up, the IPO becomes more overpriced. This is contrary to the bargaining hypothesis that is tested with ln(sizeipo).
Regression 1 is a regression of underpricing on ln(sizeipo), ln(Asset), ln(Age), ln(Sales), Top-Tier Underwriter Dummy, Tech Dummy, Internet Dummy, Venture Capital Dummy and Pure Primary Dummy. These are all the variables and when looking at the results, the R² is 32 percent. This is not high, but approximately the same as Ritter had. Also interesting is that the regression is insignificant for most of the variables for 2011. This might be due to different factors, but the most likely reason is the small sample (n). Eventhough in the correlation table is shown that logasset and logsizeipo have a high correlation, they both have more significant results than logsales. Ritter decided to include logsales and logassets in his work eventhough they have a high correlation. Also in Ritters’ work is logsales
insignificant in almost every year or period. Even when the sample size is 5990. Regression 2 has all variables, except for logsales. Logsizeipo is positive, this means that the bigger the IPO, the bigger the underpricing. This is contrary to the first hypothesis. 9 Percent of the underpricing is explained by the logarithmic size of the IPO. Regression 3 shows a Regression on only a few variables in order to more significant results.
Regression 4 and 5 are two regressions where all but one of the regressors is significant for the lowest table an alpha of 0.1. This model excludes four and five of the variables. There is a possibility for omitted variable bias in this model.
For underpricing as a whole it is hard to draw conclusions based on the 10.3 percent, but compared with previous years based on Ritter’s research (2004), you could say that either asymmetry has gone down over the years or that it has become less attractive to underprice. Comparing it to the internet bubble years 1999-2000 when underpricing was 65 percent, it is obvious that it has gone down ever since, in a consequent way. This was explained in
Loughan and Ritters work, where they state that the corruption caused the underpricing to go up by a lot, because IPO managers were looking for underwriters who underprice relatively a lot. 1999-2000 Was extraordinary because due to high valuations in the previous decade the choice of underwriter became more important. Also venture capitalists and managers were working together to make money for each other. The managers would look for underpricing and so would the venture capitalist. They would allocate hot IPOs to each other’s personal accounts and therefor the manager wouldn’t mind if the company lost value as long as he could gain value on the hot IPO. This was noticed by financial markets quickly. The response could be called successful, looking at the decrease of money left on the table.
There is still high correlation, but now between logsales and logasset. Regression 6 and 7 are two small regressions showing insignificant results. Reasons for why the regression did not yield significant results in table 1 might be because the model is not complete. I left out one variable in comparison to Ritter called Overhang, because Thompson Reuters did not have these data. Another difference from the paper of Ritter is that I did not fill all the empty spots in the datasets. This might have caused a bias in the variables.
Another difference between the work of Ritter and mine is that Ritter excluded banks, savings and loan associations, partnerships, small best offers, closed-end funds and real estate investment funds. This paper does not differ between companies with different amount of knowledge about the capital market, even though acknowledging its importance. It was impossible in this research to distinguish between the companies who had the same amount of information about the capital market as the underwriters and the companies that did not.
Because these firms know the capital market like their underwriters; part of the economic theory about information asymmetry does not add up here. Asymmetry in this context means that the underwriter knows the capital market and the issuers does not, this causes an imbalance in bargaining power due to the lack of information by the issuing party.
4.2 Alternative explanation for the underpricing of internet stock
For the internet dummy there is an alternative theory by Aggarwal, Krigman, and Womack (2002) who say that bidders were not looking at the market prices of the stock but rather at the lockup expiration of the stock. This can also explain the higher prices for stocks of internet IPOs. Aggarwal, Krigman, and Womack call this the “information momentum”. This is when high underpricing results in higher prices at the end of the lockup period. Ritter and Loughran (2004) agree with this but doubt whether the issuing firm benefits from this or not.
4.3 Control variables
It is interesting to look at the control variables in the different regressions. For instance log assets does what is expected. It is negative and therefore says that when Assets increase the underpricing goes down. This makes the bargaining position stronger and is therefor conform with the theory. In some of the regressions it is significant and in some it is not. The data for assets was not complete and therefor there is a sample size of 89 instead of 102.
Logsales varies between 4 and 8 percent. The second control variable, logsales, is positive like logsizeipo and is strongly correlated with logsizeipo and especially logasset. In the regressions logsales is always insignificant. Because of the insignificance and the high correlation, the variable is left out of most regressions. The positivity means that if the sales are relatively high, the underpricing will be higher. This is not conform with theory and is not expected when looking at previous research done with this variable on this subject. The dataset for logsales was not complete and therefor the sample size was 75 and not 102.
Then we can look at the variable Age, and see that it is very insignificant in the regression. The variable is positive and small. This is also counter intuitive because it says that when age goes up, underpricing goes up. This is strange because theory and previous research show that with an older company the risk goes down for the underwriting bank. It can be the case that there are more than usual companies who registered at the SEC only in 2011, so have an age of 0, but have an established mother company making the theory irrelevant.
The variable Underwriter is as said before a difficult variable for theory to explain because over the years the theories about this variable have changed. There is an explanation for both positivity and negativity for this dummy. In the 1980s issuing managers were looking for as little underpricing as possible (Beatty and Welch, 1996). The underwriter was less important and the issuers chose the underwriter based on their ability to underprice less. In the year 2011 in the USA the variable is strongly positive. This means that the current situation is that if the underwriter of the firm has a high ranking in the index of Carter and Manaster (1990), the underpricing is higher. This is strange because the expectation was that we would derive from the trend of rewarding for hot IPOs (Ritter and Loughran, 2004). Underwriter is always significant for an alpha of 0,1 and in some regressions for a smaller alpha.
Tech does what is expected. The variable is positive meaning that when a firm is a technology firm the risk goes up and this is expressed in a higher underpricing of the stock. The variable is insignificant, the lowest P>|t| is 0,44. This is much higher than the allowed 0,1 critical region. Even though this variable is not significant, it is in this research a very
important variable. The insignificance could be caused by a combination between a low sample size and the correlation of 0,49 with the internet variable. The internet dummy is on the other hand is significant for even an alpha of 0,025. The internet dummy is positive as expected and shows that theory explains that internet firms are riskier. Another explanation was given by Aggarwal, Krigman, and Womack (2002). They claim that for internet firms there are more unsophisticated bidders, that value the company higher than they should because of the fact that they look at future cash flows rather than actual value. Even though
future cash flows is the way to look at an investment, the bank is always very careful with a firm that has not got a stable history of revenues or assets like other companies do that value their IPO price the same.
The venture capital dummy is in all regressions negative, meaning that when venture capital is present the underpricing goes down. The corruption hypothesis of Ritter and Loughran is irrelevant in the year 2011. Even though the variable was always insignificant with a P>|t| always higher than 0,47. When there was venture capital in the firm, in the year 2011, the underpricing was less than without venture capital. This means that the venture capitalists got a better representation or position in the firm. Loughran and Ritter (2004) proved that in the 1990s firms were looking for underpricing and chose their underwriter based on underpricing. They used data until 2000, and since their research this has come to the attention of the financial markets and the players in that market.
The pure primary dummy is very small and in some regressions positive and in some regressions negative. Also it is insignificant with a P>|t| always higher than 0,84. The variable is for that reason not present in half of the regressions, since it does not add anything.
Table 4 - Correlations Variables 1 2 3 4 5 6 7 8 9 10 1 Underpricing - 2 Logsizeipo 0,14 - 3 Logasset -0,08 0,81 - 4 Logsales 0,08 0,66 0,77 - 5 Logage 0,14 0,06 0,09 - 6 underwriter 0,34 0,45 0,23 0,29 0,17 - 7 Tech 0,31 -0,08 -0,19 0,10 0,45 0,12 - 8 Internet 0,41 0,04 -0,08 0,06 0,14 0,10 0,49 - 9 Venture 0,27 -0,17 -0,38 -0,14 0,18 0,19 0,45 0,42 - 10 pureprimary -0,20 -0,04 0 -0,20 -0,27 -0,27 -0,42 -0,24 0,27 -
Table 4 shows that the correlation between logasset and logsizeipo is 0.81. The correlation between logasset and logsales is 0.66. The correlation of logsales and logasset is 0.77. All of the previously stated correlations are high. The height of these correlations causes
insignificance because of multicollinearity. This is a danger, but in order to avoid this problem, the research is split in different regressions so that the multicollinearity stays to a minimum. In most of the regressions in table 3 can be seen that one of the correlating variables is dropped, in order to create more significant variables.
Expected was that internet and tech would have a high correlation. The correlation between the two is 0,4878 which is not low, but neither a big concern. The two variables can exist in the same model.
Table 5 - Correlations with a significance of 0.05 Variables 1 2 3 4 5 6 7 1 Underpricing - - 2 Logsizeipo 0,12 - 0,24 - 3 Logasset -0,11 0,80* - 0,31 0 - 4 Logsales 0,06 0,70* 0,79* - 0,61 0 0 - 5 Logage 0,12 0,02 0,12 0,25* - 0,22 0,77 0,25 0,03 - 6 Underwriter 0,30* 0,43* 0,22* 0,27* 0,17 - 0 0 0,04 0,02 0,09 - 7 Tech 0,28* -0,19 *-0,27 0,01 0,30* 0,07 - 0 0,06 0,01 0,93 0 0,50 - 8 Internet 0,41* -0,01 -0,12 0,02 0,11 0,12 0,47* 0 0,91 0,25 0,88 0,28 0,23 0 9 Venture 0,24* *-0,20 *-0,40 -0,19 0,05 0,22* 0,49* 0,01 0,04 0 0,10 0,63 0,03 0 10 Pureprimary *-0,27 -0,07 0,04 -0,20 *-0,21 *-0,29 *-0,42 0,01 0,50 0,74 0,10 0,04 0 0 Variables 8 9 10 8 Internet - - 9 Venture 0,44* - 0 - 10 Pureprimary *-0,31 *-0,30 - 0 0 -
In table 5 the pairwise correlation is tested between all of the variables. The correlations with stars are the once that are significant for an alpha of 0,05 or better. The upper variable is the pairwise correlation and the lower variable is the significance.
5. Conclusion
Why is it interesting to look at the size of an IPO when measuring underpricing of a stock and what is the role of bargaining power in this story? There are many theories that explain underpricing. In this paper economic theory on bargaining power was put together. Also was a new variable introduced to Ritter’s and Loughran’s formula called size of the IPO. First was looked at research done by Leland and Pyle in 1977. They suggest market
asymmetry as an explanation for underpricing. Baron (1982) also shows that this is the cause of underpricing. Another explanation for underpricing, is the winners curse of Rock (1986). This research shows that if one wants to keep the unsophisticated bidders in the market, underpricing has to exist. Since the financial markets are partially relying on unsophisticated bidders, they need to create underpricing. Beatty in 1989 extends Rock’s theory and shows that there is difference in knowledge between the underwriter and issuer about the capital markets and shows information asymmetries.
There is economic theory on bargaining power, which says that when a company has a bigger sized IPO the position of the underwriting bank is less strong against the firm (Karlis, 2000). The company with the bigger sized IPO can bargain harder with the underwriter than the company with the smaller IPO. This causes the company with the bigger IPO to have less underpricing (Ritter and Loughran, 2004). This is also partially explained by the principal-agent theory (Eisenbeis and McEnally, 1995). The underwriting bank wants to maximize the underpricing, because of the gains it gets from it. And the firm wants to minimize the
underpricing, because this is a loss for owners who sell the shares (Ljungqvist, 2004). There can lure danger from information asymmetry between the managers and the underwriters, what can cause the firm not to behave as expected. Ritter and Loughran (2004) showed that managers in some cases look for underpricing instead of avoiding it. They call this the corruption hypothesis. The managers behave in their own interest because they get rewarded for hot IPOs.
In the empirical part of this research it has become evident that for the year 2011 in the United States the underpricing was 10,8 percent. This is explained by the significant
variables: logasset, underwriter and internet. These variables behaved as expected. The variable that behaved as expected, but is not significant, because of sample size is tech. Logsizeipo, logasset, logsales, venture and pureprimary are behaving not as expected and are insignificant. Therefor we can say that the second hypothesis is not true, taking bargaining power into consideration. If the size of the firm and IPO go up, measured by sales, assets and size of the IPO, the underpricing goes up as well. Therefor can be concluded that there is an asymmetry, and that the corruption hypothesis is still likely to be true.
Logsizeipo is always positive in the results meaning that when the deal size of the IPO of a firm goes up, the underpricing goes up. This does not comply with theory on bargaining power and the principal-agent theory. When looking at the corruption hypothesis, it says that bigger sized IPOs are looking for underpricing, this is only logical if managers get rewarded for hot IPOs and underpricing has been a topic for the last few years. This variable leaves room for other researches who can do the same test with a higher sample size, making the variable maybe significant. Another reason for why the variable may not be significant is that in the research there is not excluded for: banks, savings and loan associations, partnerships, small best offers, closed-end funds and real estate investment funds. Also there can be looked closer into this variable when one can access the payment structure of underwriting banks. When this is done, there might be found another explanation for the insignificant variable and more importantly the information asymmetry.
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Attachments
Graph 1 - The relationship between Underpricing and Logsizeipo
Graph 2 - The relationship between Underpricing and Sizeipo
-.5 0 .5 1 1. 5 un de rpr ic in g 3 4 5 6 7 8 logsizeipo -.5 0 .5 1 1. 5 un de rpr ic in g 0 1000 2000 3000 4000 sizeipo 23
