IPO underpricing of Fintech firms : a closer look at Europe and the United States

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IPO Underpricing of Fintech Firms

A closer look at Europe and the United States

Author: N.P.F. Siersema Student number: 10439730 Thesis supervisor: Dr J.J.G. Lemmen

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ABSTRACT

This thesis aims to investigate the level of Initial Public Offering underpricing at Fintech firms. A sample of 238 firms is collected of Fintech and non-Fintech firms from both Europe and the United States. The regression results showed that there is a significant difference in underpricing in the European and the US market. Evidence was found for a higher level of Fintech underpricing in the United States, while in Europe Fintech firms seem to be less underpriced than non-Fintech firms. However, the overall level of Fintech firm underpricing was not significant in this sample.

Keywords: Fintech, Initial Public Offerings, underpricing, Europe, United States JEL Classification: G24, G32

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Statement of Originality

This document is written by Student Nick Siersema who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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List of Tables

Table 1: Sample Data ... 16

Table 2: Correlations ... 21

Table 3: Descriptive Statistics ... 22

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1. Introduction

The Fintech industry is booming, over the last decade a relatively new industry developed itself to a

dominant player in the financial service sector. According to the PWC report on Fintech, the financial services industry will be unrecognizable within five years. As new companies disrupt the market with innovative products, they gain a market share. This concerns the existing financial companies and to maintain their current position, a majority of these companies are seeking partnerships with innovative Fintech start-ups (PWC, 2017). Looking at the figures from this industry, it is clear the Fintech sector is booming. According to Reuters (2017) the level of investments in Fintech has increased with 250 percent in the second quarter of 2017 to 667 million euros in Europe alone. The leading markets of the Fintech industry are currently Europe and the United States. Therefore, this thesis examines the data from the European and the United States market. The Fintech companies are often young companies that grow rapidly. This means that these companies need to have enough money to finance their growth. There are several ways to finance this, however the most common way to finance is by going public. The first time a company offers shares to the public is called an Initial Public Offering (IPO). Last year the biggest Initial Public Offering from the Fintech sector was that of Quadian, which raised 900 million dollars. Quadian is an innovative player in the financial services market, as they provide micro loans to people via a mobile phone application. Like many Fintech companies, Quadian took one product that is usually offered by a bank and made that very accessible and user-friendly (CNBC, 2017). In the case of Quadian’s Initial Public Offering, the phenomenon of underpricing can be seen very well. At the end of the first trading day the share price increased by almost 40 percent, this shows that the shares were underpriced significantly. The issuer ‘left money on the table’ so Quadian raised less funds than they could, which is costly for the firm but favourable for investors. This is a well-known phenomenon which on average happens at all Initial Public Offerings. It seems that technological firms have a higher level of underpricing than other firms. Hence, this suggests that Fintech companies are more

underpriced than other companies as well. However, until now there is no literature that compares the Fintech IPO market with the traditional financial services IPO market. That makes it interesting to research these two markets and see if Fintech companies have indeed higher degree of underpricing than other financial companies. Therefore, the research question that I want to answer in this thesis is: are Fintech firms subject to a higher level of underpricing than non-Fintech firms? In order to answer this, I will take a sample of European and United States Fintech firms that went public and compare them with non-Fintech firms. The non-Fintech firms are firms that operate in the traditional financial services industry. I also investigate the influence of the firm’s age, the underwriters rank, and the region were the IPO takes place. The analysis will be made with an Ordinary Least Squares (OLS) regression.

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In the second chapter, the Fintech landscape is first described. The definition of the Fintech industry will be described according to previous literature on the topic. Then, the most important Fintech sub-sectors are discussed to get a deeper understanding of the Fintech industry. Once the Fintech industry is described, I will discuss several theories on IPO underpricing. As there is a vast amount of literature on the topic of IPO underpricing, I will only discuss the main theories.

In the third chapter, the methodology, I will first describe how the sample data was collected. The different variables I collected will be described and I will show how some variables were calculated from the collected data. After that, the hypotheses are explained and then tested with an OLS regression. Furthermore, I will provide the descriptive statistics and the correlations in this chapter.

In chapter four I will discuss the main results of the performed OLS regression. All hypotheses will be tested for significance with the student t-test. The hypotheses will then be rejected or accepted, and an

interpretation of the findings will be provided.

In chapter five I will summarise the main findings of this thesis based on the literature review and the regression results. Finally, I will give a few suggestions for further research on the underpricing of Fintech firms.

2. Literature review

2.1 Fintech

In the late 1960s, the first Automatic Teller Machines (ATMs) were installed by the banks, now seen as the first big innovation of technology in the financial services industry. After that, the debit cards were

introduced in the 1970s, which made it possible to pay without cash or cheque. After this innovation, it took until the late 1990s for the next big step forwards in the use of technology in the financial services industry, namely the invention of internet banking. The rise of the internet changed the way different industries did their business, for example online shopping instead of going to a store or booking a holiday on the internet instead of at a travel agency. However, it was not until the financial crisis that the financial services industry started to innovate their business model. Even though the industry did introduce us to internet banking before the crisis, major changes did not come until after. When the financial crisis happened in 2008, a considerable number of financial companies bankrupted. The most famous bankruptcy was probably that of

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Leeman Brothers, while other big financial companies like Marril Lynch had to be rescued by the government (Guardian 2008). Moreover, traditional banks had to make sure that they did not collapse because of the crisis, which meant that their priorities were different from innovating in their products or services. This resulted in the start-up of a new type of company to fill that gap between ongoing technological

developments and financial services, the Fintech companies. These companies combined the two industries and brought new financial products (Earner, Barberis, & Buckley, 2015). At the time of the crisis, Shatoshi Nakamoto suggested a new payment system and a new currency, a digital currency called the Bitcoin. In addition, he developed the blockchain technology behind this cryptocurrency. Even though these technologies are completely different from lending firms, they both belong to the Fintech industry.

Before elaborating on the theory of Fintech companies, it is important to define the term. There are several definitions of Fintech used in financial literature and newspapers. Lee and Kim (2015) state that Fintech is an IT based financial services companies. The services include remittance, payments, asset management, digital lending and so on. A report from Dapp, Slomka, and Hoffman (2014) describes the industry as the

digitalization of the financial sector. This includes everything that is an advanced internet-based technology that is used for the financial sector (Dapp et al., 2014). The paper by Zavolokina, Dolata, and Schwabe (2016) describes most of the different definitions of Fintech from existing literature. From the definitions in that paper it is possible to divide the Fintech industry into seven sectors, namely blockchain, digital payments technology, digital asset management/personal finance, digital lending and peer-to-peer lending (Zavolokina et al., 2016). In the following sections I will elaborate on each sector.

2.1.1 Blockchain

A well-known sector of the Fintech is the blockchain sector, which has been reported on in the news frequently due to the fact that the Bitcoin uses the blockchain technology. Companies that are active in this sector provide blockchain software or provide services for the trade in cryptocurrencies. An example of this is KeepKey, a company that offers ‘wallets’ for cryptocurrencies. Nowadays there are already 1500 different cryptocurrencies available at Coinmarketcap, the lead database for cryptocurrencies. A lot of companies provide wallets or other services for people who trade in these currencies. A key difference with regular stock exchanges is that the online cryptocurrency exchange never closes. Furthermore, the transactions that are made with cryptocurrencies are processed with the blockchain technology. The blockchain basically registers every transaction that is made automatically, without human intervention. Thus, making the blockchain technology incredibly efficient, compared to the traditional way of processing transactions by hand. The efficiency of the blockchain makes the process quicker as well. In addition, the costs are lower because less people are needed in the process when everything is done by computer algorithms. The blockchain

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technology can be used for all kinds of transactions and administrative work. Hence, it is of great interest of financial firms (S&P Global, 2016).

2.1.2 Digital payments

The payment and billing industry has seen a big transformation over the last decades. Since the introduction of the debit card, consumers were able to acquire goods and services without cash. This shift towards digital payments has evolved over the years with nowadays possibilities to pay with your mobile phone for example. There are four major factors that influenced the shift towards cashless payments. First, the development of the technological industry and the prominent role of the smartphone in our daily lives. Second, there are, like in the rest of the Fintech industry, non-banking firms who entered the payment market with innovative ideas. Third, consumers are aware of the technological possibilities and demand instant digital payment methods. Finally, the regulations in the digital payment sector have been softened, which made these developments possible (BCG, 2016).

The payment and billing industry can be classified into three sub-categories that are all part of the payment process. First, there are the person-to-person payments, these are transactions done from and to personal accounts. Companies that operate in this are for example the payment company IDEAL or more recent TIKKIE, that allows people to complete payments by using WhatsApp. Second, there are in-store payments, which used to be done manual and paid in cash, but with the technology of today all companies have barcodes or QR codes to quickly calculate the price and payments can often be done using applications on a mobile phone. The best-known companies for this service are Apple Pay and Android Pay, the two largest mobile software providers worldwide. Finally, there are business-to-business payments, which are the payments done between companies that supply and buy. Innovations such as electronic invoicing and cross-border payments made this industry more efficient (S&P Global, 2016). These changes will increase the consumption as it gets easier for consumers to buy things. Simultaneously, companies are able to match the customer’s needs using big data that is made available (BCG, 2016).

2.1.3 Digital Lending

According to the S&P global Fintech report from 2016, the alternative lending sector is a “technology-driven nonbank lending, with access to expansive data, sophisticated algorithms and considerable computing power” (p. 3). There are three key factors for companies in this sector that give the companies a competitive

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advantage according to the 2016 EY report. The first point the EY report makes, is that the new lending companies are better able to serve consumer demands. Alternative lending companies are run more efficient, hence consumers are able to get their product quicker than with a traditional bank. The second advantage is that alternative lending companies are able to collect and process big data from their consumers. By doing so, the alternative lending companies are able to offer personalized products and advertisements to different groups of consumers. The third and last advantage of alternative lending

companies is that their products are priced lower than the loans of traditional banks. This is possible because of the efficiency of the first two advantages that are mentioned (EY, 2016). An example of an alternative lending company is The Lending. It is a peer-to-peer lending company that provides loans to small businesses. The Lending Club is not offering funds to its users, but instead provides their users with a platform to get funds from others. The Lending Club went public in 2014 and raised 900 million dollars with their Initial Public Offering, this made it the biggest IPO in the United States that year. The IPO of Lending Club was interesting in particular because of the high level of underpricing, which was 56.2 percent (Appendix IV).

2.1.4 Wealth Management

According to Financial Technology Partners, wealth tech refers to improving investments and wealth management with technology. Everything that supports financial advisors with technology belongs to the wealth tech market. Registered Investment Advisors (RIA) at the traditional firms are not always using these new technologies, that is why Fintech companies started to enter the wealth management market. This market is growing at an annual growth rate of six percent for the last 15 years. The global wealth tech industry is expected to reach 101.7 trillion dollars in 2020, with Europe and the United States as the biggest markets (FT Partners, 2017). One of the developments that is responsible for that growth are programmed Robo-advisors. These Robo-advisors use algorithms to give wealth management advice to the clients of these wealth tech companies. The Robo-advisors are designed in a way that they are able to estimate returns of a portfolio, while taking the risk of an individual portfolio into account. This makes it possible to massage a portfolio from a computer, which is uncomplicated for investors. The S&P Global report from 2016 says that the biggest advantage of these wealth tech companies is that they make wealth advice more accessible for people. The main reason for this is that Robo-advisors are less expensive than human advisors.

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2.2 Initial Public Offerings

An Initial Public Offering (IPO) is a way for companies to raise funds by selling shares to the public for the first time. The firm, an investment bank, that supervises the IPO is called the underwriter. This underwriter sets the offer price and allocates the shares to investors. There are different reasons for companies to raise funds with an Initial Public Offering. According to a study by Brau, Ryan and DeGraw (2006), Chief Financial Officers use IPOs as a way of creating liquidity for the firm in order to finance growth. Another reason they give is that by financing through an Initial Public Offering, firms create the optimal capital structure. Furthermore, they argue that firms should always go for the cheapest way to finance their activities and that is financing with an IPO. Only when the IPO option is not available anymore, firms should look for other financing options (Brau et al., 2006). More recent theories focus more on the timing of an Initial Public Offering. Firms carefully time the date of their IPO in order to maximize the funds they raise. When firms time an Initial Public Offering they take into consideration the current market characteristics, as well as the firms current reputation with investors (Brau et al., 2006).

2.2.1 Underpricing

The underpricing of a firm’s share means that the offer price to investors at the IPO is lower than the share price at the end of the first trading day, as investors value the share higher. In addition, the underpricing of shares is an indirect cost for the issuing firm since they could have earned more money with their shares, this is called ‘leaving money on the table’. The underpricing of Initial Public Offerings has been studied frequently over the years and there is no doubt that underpricing takes place at IPOs. In 2002, Ritter and Welch

conducted a research on this topic and found that the US firms in their sample, during the period of 1980-2001, were on average 18.8 percent underpriced (Ritter & Welch, 2002). Furthermore, Ibbotson (1975) found an average underpricing of 11.4 percent. Even though these studies were done for IPOs in the United States, Hopp and Dreher (2012) showed in their cross-country sample that the average underpricing in 24 countries, amongst which Sweden, China and Australia, was 20 percent. All existing literature shows that IPOs are, on average, underpriced and in the financial literature there are several reasons given for that. In the following sections I will discuss the main explanations for underpricing.

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2.2.2 Asymmetric Information

One explanation for underpricing is information asymmetry between investors, the issuing firm, and the underwriter. Rock (1986) describes one cause of underpricing as asymmetric information. Rock argues that there are two types of investors, an informed investor and an uninformed investor. The informed investor knows what the value of the issued IPO is, while the uninformed investor obviously does not know. The informed investors will buy the share if the price is fair, but on the other hand there are the uninformed investors that will buy the shares anyway, underpriced or overpriced. Therefore, to make sure that both investors are satisfied the firm has to discount the shares (Rock, 1986).

Another theory on asymmetric information comes from Baron (1982), he argues that the underwriter has more knowledge on setting the fair price but also has a different incentive. On the one hand the underwriter wants to minimize his risk of not selling all the issued shares, this would mean that the underwriter has to buy those shares and that is costly for the underwriter. But on the other hand, the issuing firm wants to maximize the amount of capital raised (Baron, 1982). Thus, asymmetric information between the issuer, the underwriter, and investors all affect the level of underpricing.

2.2.3 Underwriters reputation

Another explanation for underpricing is the reputation of the underwriter. An underwriter with a good reputation is usually better in predicting the ex-ante uncertainty, thus by choosing a high ranked underwriter the firm gives a good signal to investors. This will result in a lower underpricing of the IPO (Jones &

Swaleheen, 2010). From the underwriter’s point of view, the pricing of the IPO has to be fair because they want to maintain their reputation as a reliable underwriter, otherwise the underwriter will lose investors (Beatty & Ritter, 1986). Early literature suggests that a high-rated underwriter will have less instances of underpricing than low-rated underwriters. As the high-rated underwriter is seen as more reliable, they could set the price higher (Johnson & Miller, 1988). Carter and Manaster (1990) found a significant relation between a high-rated underwriter and low underpricing. However, more recent research by Loughran and Ritter (2004) suggest that from 1990 on, this relation would have been the other way around. They found that high ranked underwriters had higher underpricing than low ranked underwriters. Loughran and Ritter argue that the high ranked firm stopped charging high fees and instead underpriced their IPOs more for their investors in order to get a larger market share. At the same time, high ranked underwriters started to underwrite riskier IPOs of younger firms. This made the investors demand a higher return to compensate for the higher risk and thus a higher level of underpricing (Loughran & Ritter, 2004).

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There have been several studies on how to rank the reputation of an underwriter. According to Megginson and Weiss (1991), underwriters can be ranked by market share. They suggested that the quality of the underwriter correlated with the total value of the underwriter’s IPOs. Johnson and Miller (1988) rank underwriters by the role they have in high-quality issues. Another way to determine the reputation of the underwriter is discussed by Carter and Manaster (1998). They rank underwriters from zero to nine based on their tombstone announcements positions in the newspapers. There are other ways to rank underwriters, however a study conducted by Cooney, Carter and Dark (2001) suggested that the reputation measurement of Carter and Manaster was the most reliable.

2.2.4 Technological firms

Initial Public Offerings by technological firms are underpriced more often than other firms. According to Brau and Fawcett (2006) a reason for that could be that investors demand a higher return because of the higher risk. Technological firms are typically riskier because their future returns are more uncertain (Brau & Fawcett, 2006). A study by Loughran and Ritter in 2004 showed that, indeed technological companies were more underpriced. They argued that the technological market is more uncertain than traditional markers and therefore a higher underpricing is demanded. Demers and Joos (2007) give another reason why technological companies are riskier is that they are younger than non-technological firms. They found that on average a technological firm is 10 years old when filling for an IPO while a non-technology company is on average 17 years old. For technological companies there is a larger amount of uncertainty because a record of past performances is smaller than that of non-technological companies. This causes a negative relation between the age of a firm and the performance of an Initial Public Offering (Demers and Joos, 2007). Thus, the uncertainty about past performances and the uncertainty about future returns affects the level of underpricing for technological firms.

2.2.5 European versus United States IPO market

The European IPO market and the IPO market in the United States (US) are similar on a lot of points. However, secondary literature describes some differences. The European IPO market used to be much smaller than the US IPO market. This changed in 2000 when the European market had a bigger volume than that of the United States. This development was stimulated by regulatory changes in Europe which made it easier for firms to go public. One of those laws was that before 2000, a company had to deliver a net profit

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for three years before allowed to go public. After 2000 this was not necessary anymore, so it became easier for companies to go public (Ritter, 2003). According to Ritter, the regulations on this are less strict in the United States. Another difference that Ritter (2003) describes is that in Europe there is a larger variety of underwriters and the fees paid by issuing firms is lower in Europe than in the United States. Furthermore, firms that decide to go public cannot communicate any financial information for the first 40 days in order to get all the information in the prospectus only and not in order publications. This is done by the analyst which makes his role more important and firms are accepting to have more ‘money on the table’ for a good analyst, which obviously leaves ‘money on the table’ of the issuing firm (Ritter, 2003).

3. Methodology

In this chapter, I will first describe how the data was collected. Then, I will explain my hypothesis, and after this will be tested with a regression which I will discuss as well. All variables used in my model are interpreted and the relation to the dependent variable is discussed.

3.1 Sample data

The data sample is put together using the database of ThomsonONE. A sample of European and US firms was selected and divided into two groups of Fintech and non-Fintech. For the non-Fintech firms, firms that operate in the financial market were selected, this gave some double data with the non-Fintech, so they had to be removed manually. The financial market firms were easily retrieved from ThomsonONE under macro-industry code financials. However, the Fintech firms did not have a macro-macro-industry code, therefore this list had to be put together manually by looking at Fintech markets reports and databases from Financial

Technology Partners. These companies were manually selected in ThomsonONE and the variables were then retrieved from the database. These variables are market value of the firm, founding date, IPO issue date, IPO offer price, end of day 1 closing price, net proceeds, and the name of the lead underwriter.

Not all variables were available for every company, so some companies had to be removed from the sample. A total of 12 firms did not have a closing price at day 1 available, therefore it was not possible to calculate the underpricing. Therefore, these companies were removed from the sample. Furthermore, a total of 18 firms had no information on deal size so these were filtered out as well, another 51 firms had no information about

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their market value, so these were removed too. The final sample consisted of 238 firms divided into the Fintech and non-Fintech industry, and divided into the European region and the United States region. For the European Fintech firms, the data of 39 firms was used and for the European non-Fintech data from 39 firms was used as well. For the United States, the data of 84 Fintech firms was collected and from the non-Fintech industry in the United States 76 firms were collected. Table 1, as seen below, shows how the sample was divided into four groups, in the appendix a detailed table of all firms used in this sample can be found.

Table 1: Sample Data

Observations Average underpricing EU-Fintech 39 43% NonFintech 39 69% US-Fintech 84 19% US-NonFintech 76 6% Total 238 27%

3.2 Hypotheses

The aim of this thesis is to show that there is a difference in underpricing for Fintech firms and non-Fintech firms. Furthermore, this thesis examines which variables affect the level of IPO underpricing. In the literature review it was discussed that previous research on this topic suggested certain factors that influence the degree of underpricing. However, no research has yet been done on the ‘young’ Fintech market. Previous research suggests that technological companies have a higher degree of underpricing than non-technological companies, and since Fintech is a combination of technology and financial services, it is expected that the Fintech sector has a higher degree of underpricing than the non-Fintech sector. This leads to the first alternative hypothesis of this research:

H1: Fintech firms have a higher degree of underpricing than non-Fintech firms

Another aspect that is interesting to look at is if there is a regional difference between European IPOs and IPOs from the United States. There are some differences in the two markets as discussed, but it is interesting

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to see if that also leads to a different level of underpricing. By taking comparable observations from both regions, it is possible to contrast the data. Because of the differences in the European and United States market characteristics, it is expected that the level of underpricing is different as well. However, it is uncertain whether the underpricing is higher or lower in Europe compared to the United States. Therefore, the alternative hypothesis is as follows:

H2: There is a difference in underpricing in Europe and the United States

In the literature review, the age of a company is also mentioned as a possible explanation for higher levels of underpricing. Because of the higher risks that young companies would have, investors want to be

compensated more, and therefore this creates more underpricing. This thesis aims to examine whether this is the case for this sample as well, leading to the third alternative hypothesis:

H3: The age of a firm is negatively related with the level of underpricing

Finally, as discussed in the literature review, the underwriters play an important role in the IPO process as well as in the level of underpricing. The correlation of the underwriters’ reputation and the level of underpricing has changed over the years. It used to be a negative relationship before the 1990s, but according to secondary literature that changed in the 1990s to a positive relationship. This thesis aims to examine what the influence of this reputation is in this sample. Therefore, the final alternative hypothesis will be as following:

H4: The rank of the underwriter is positively related with the level of underpricing.

3.3 Variables

To test the hypotheses, several variables had to be created or collected. In this section I will introduce the variables and explain how the variables are used in the research method. To explain the dependent variable underpricing, I used seven variables: Age, Deal Size, Underwriters Rank, Market Value, Region, Fintech, and interaction variable Region x Fintech.

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Underpricing is the dependent variable in the regression and it is equal to the return of the first trading day. In order to obtain the first day return, the offer price and the closing price for all the firms had to be

collected. This variable is in percentages because it is the relative change at day 1. However, there were some large outliers with this variable and because these were positive and negative it is not possible to solve this by taking the logarithm of the variables. Instead of that, the collected data was Winsorized. By Winzorizing the data, the data was first ranked from zero to a hundred percent. Then, the top and bottom 2.5 percent were deleted and replaced with the first value after the 2.5 percent region. Thus, in the upper part there were 12 cases in the 2.5 percent region, and they therefore got the value of the 13th_{case. The same was}

repeated for the lower bound, which totalled the replaced cases to 25. This decreased the skewness, which has to be between -2 and 2 to assume a normal distribution (Kim, 2013). The following formula was then used to compute the first day return of the share.

𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔 = 𝐹𝑖𝑟𝑠𝑡 𝐷𝑎𝑦 𝐶𝑙𝑜𝑠𝑖𝑛𝑔 𝑃𝑟𝑖𝑐𝑒 − 𝑂𝑓𝑓𝑒𝑟 𝑃𝑟𝑖𝑐𝑒 𝑂𝑓𝑓𝑒𝑟 𝑃𝑟𝑖𝑐𝑒

Log_Age

The variable age is the number of years the firm was operating at time of the IPO. From the ThomsonONE database, the founding dates and issue dates were collected. The difference between those made the variable age. The variable age had some outliers in this sample, this caused the skewness to be higher than 2. In order to lower the skewness, it is possible to take the logarithm of age, as all companies obviously have a positive age.

𝐴𝑔𝑒 = 𝐹𝑜𝑢𝑛𝑑𝑖𝑛𝑔 𝐷𝑎𝑡𝑒 − 𝐼𝑃𝑂 𝐼𝑠𝑠𝑢𝑒 𝐷𝑎𝑡𝑒

Underwriters ranking

The literature review showed that the ranking of the underwriters is important. To collect this data, first the lead managers information from ThomsonONE was obtained. After that, only the lead manager was included in the ranking. There are several ways to rank underwriters, but as discussed in the literature review, the ranking from Carter and Manaster is the most accurate. However, in this sample there were some IPOs where the underwriter was not ranked in this list. These IPOs were given the rank zero if there was no other

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underwriter, otherwise I looked at the second underwriter and applied the ranking to that underwriter. The ranking of Carter and Manaster goes from zero to nine and has five decimals.

Log_Deal Size

This variable is also called net proceeds from IPO and was used as a control variable. This variable could directly be collected from ThomsonONE. The same issues came up with variable deal size as with age, there were several big outliers. Because some deals had values of billions, this caused the skewness to be too high and it is possible to convert the variable Deal Size to a logarithm which solved the issue.

Log_Market Value

The market value of the firms, before the Initial Public Offering, was used as a control variable in this regression. The data for this variable could directly be collected from ThomsonONE. However, the skewness of this variable was too high due to several large outliers, thus the variable Market Value is transformed to a logarithm in order to fix the high skewness.

Region Dummy

In order to differentiate between the European market and the United States market, a dummy variable was created. This dummy variable will have the value 1 for United States and 0 for Europe.

𝑅𝑒𝑔𝑖𝑜𝑛 = {1,_0, 𝑥 = 𝑈𝑛𝑖𝑡𝑒𝑑 𝑆𝑡𝑎𝑡𝑒𝑠_{𝑥 = 𝐸𝑢𝑟𝑜𝑝𝑒}

Fintech Dummy

Similar to the region dummy, a dummy variable was made for the Fintech and non-Fintech industry. This dummy variable will be 1 for Fintech firms and 0 for non-Fintech firms.

𝐹𝑖𝑛𝑡𝑒𝑐ℎ = {1, 𝑥 = 𝐹𝑖𝑛𝑡𝑒𝑐ℎ 0, 𝑥 = 𝑁𝑜𝑛 𝐹𝑖𝑛𝑡𝑒𝑐ℎ

Region x Fintech Interaction variable

In order to distinguish the difference between Fintech firms in Europe and the United States, an interaction variable was added to the regression. Both region and industry are dummy variables, and thus the

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interpretation of this coefficient is not the same as the others. For the level of underpricing of European Fintech firms we only look at the coefficient of Fintech and consider the interaction variable to be zero. For the Fintech firms in the United States we consider both dummy variables Region and Fintech to be one, so we have to add the two coefficients with each other to see the underpricing of Fintech firms in the United States.

𝑅𝑒𝑔𝑖𝑜𝑛 × 𝐹𝑖𝑛𝑡𝑒𝑐ℎ = {1,_0, 𝑥 = 𝑈𝑛𝑖𝑡𝑒𝑑 𝑆𝑡𝑎𝑡𝑒𝑠 𝐹𝑖𝑛𝑡𝑒𝑐ℎ_{𝑥 = 𝐸𝑢𝑟𝑜𝑝𝑒𝑎𝑛 𝐹𝑖𝑛𝑡𝑒𝑐ℎ}

3.4 Research Method

In the previous section, the manner in which the variables were constructed was discussed. In this chapter I will focus on the research method. In order to analyse the level of underpricing and the effects of all variables on underpricing, an OLS regression had to be performed. For this research, two models were used, the first model is without the interaction term Region x Fintech and the second model includes this interaction term. In this regression, underpricing is the dependent variable that is estimated by the model. As shown in the variables section, there are five explanatory variables and two control variables in the first model. The control variables are added to prevent that the estimation is subjected to an omitted variable bias. In the second model, another explanatory variable is added. For both regressions robust standard errors are used to make sure that the OLS assumption of no-heteroscedasticity holds. The two regression equations are shown below.

(1)𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔𝑊 = 𝛼 + 𝛽1 𝐿𝑜𝑔𝐴𝑔𝑒+ 𝛽2 𝐿𝑜𝑔𝐷𝑒𝑎𝑙𝑆𝑖𝑧𝑒+ 𝛽3 𝐿𝑜𝑔𝑀𝑉+ 𝛽4 𝑅𝑎𝑛𝑘 + 𝛽5 𝑅𝑒𝑔𝑖𝑜𝑛 +

𝛽6 𝐹𝑖𝑛𝑡𝑒𝑐ℎ + 𝜀

(2)𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔𝑊 = 𝛼 + 𝛽1 𝐿𝑜𝑔𝐴𝑔𝑒+ 𝛽2 𝐿𝑜𝑔𝐷𝑒𝑎𝑙𝑆𝑖𝑧𝑒+ 𝛽3 𝐿𝑜𝑔𝑀𝑉+ 𝛽4 𝑅𝑎𝑛𝑘 + 𝛽5 𝑅𝑒𝑔𝑖𝑜𝑛 +

𝛽6 𝐹𝑖𝑛𝑡𝑒𝑐ℎ + 𝐵7 𝑅𝑒𝑔𝑖𝑜𝑛 × 𝐹𝑖𝑛𝑡𝑒𝑐ℎ + 𝜀

To be certain that the regression model is valid, previous literature on this topic was consulted and that showed that similar OLS regressions are used to analyse the effects on IPO underpricing (Ljunqvist, 1997). To check for perfect multicollinearity, the correlations from SPSS were examined as well. Table 2, see below, clearly shows how the variables are correlated with the dependent variable underpricing. From this table, it becomes clear that all explanatory and control variables are correlated with the dependent variable. Furthermore, it is also clear that there is no perfect multicollinearity since all the correlations are below 1.0.

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The highest correlation is 0.836, which is the market value with deal size which is still acceptable. A test to check for multicollinearity is the VIF-test, this test was performed with SPSS. The VIF-score does not show signs of multicollinearity, so the variables are proper to use. The VIF-test output can be found in Appendix V.

Table 2: Correlations

Underpricing_W Log_Age Log_DealSize Log_MV Rank Region FinTech INT_USxFin

Underpricing_W 1.000 Log_Age 0.027 1.000 Log_DealSize -0.168 0.280 1.000 Log_MV -0.192 0.316 0.836 1.000 Rank -0.069 0.181 0.521 0.566 1.000 Region -0.483 0.078 0.161 0.089 0.193 1.000 FinTech -0.010 0.194 0.226 0.389 0.397 0.023 1.000 INT_USxFin -0.143 0.221 0.293 0.375 0.479 0.516 0.714 1.000

To confirm that the results are valid, outliers were verified and the variables Age, DealSize and Market Value were transformed into logarithms. In addition, the dependent variable underpricing was Windsorized as described in the previous chapter. The descriptive statistics table below, Table 3, shows that the Skewness is between -0.743 and -1.336 for all variables, which is acceptable (Kim, 2013). Furthermore, the descriptive statistics also show that the average level of underpricing is 27.10 percent for this sample. In the next chapter the regression results will be discussed to determine whether that level of underpricing is significant or not.

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Table 3: Descriptive Statistics

Mean Minimum Maximum Skewness

Underpricing_W 0.271 -0.419 1.229 0.789 Log_Age 2.503 0.000 4.605 -0.139 Log_DealSize 3.867 _-5.810 _8.100 _-1.336 Log_MV 5.373 -1.609 12.299 -0.743 Rank 3.023 0.000 9.240 0.706 Region 0.672 ₀ ₁ _- FinTech 0.517 ₀ ₁ _- INT_USxFin 0.353 ₀ ₁ _-

4. Results

This chapter will discuss the results of the tested hypothesis. This study performed two regressions to determine whether the added interaction variable added something to the regression model. The second model showed a higher R2_{, which moved from 0.291 in model 1 to a value of 0.336 in model 2. This could}

indicate that the second model is better in explaining the variance of the dependent variable underpricing. However, this result does not express anything about the causality of the interaction variable and the dependent variable. To confirm whether or not the increase in R2_{is significant, the F-statistic is checked. This}

showed that model 2 has a significant higher R2_{, so model 2 will be used to test the hypotheses. All}

hypotheses were tested with the statistic that can be found in the regression output. The formula for the t-statistic is as follows.

𝑡 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 = 𝑥̅ − 𝜇

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H1: Fintech firms have a higher degree of underpricing than non-Fintech firms H2: There is a difference in underpricing for Europe and the United States

The first and second hypothesis that was tested was whether Fintech firms have a higher level of underpricing than non-Fintech firms. The results from the two models that were regressed are shown in Table 4 below. The first model shows that there is a higher level of underpricing for Fintech firms than for non-Fintech firms in general, hence for Europe and the United States together. To verify whether there is a difference between Fintech firms and non-Fintech firms underpricing in the two regions, the interaction variable was added in the second model. This shows that Fintech firms in Europe are significantly less underpriced than non-Fintech firms in Europe. However, in the United States the Fintech firms are significantly more underpriced than the non-Fintech firms. The first null hypotheses cannot be rejected because the difference in underpricing between Fintech and non-Fintech in both regions is not significant. The second null hypotheses can however be rejected as there is a significant difference in the level of underpricing for US and European Fintech firms at 1 percent significance.

H3: The age of a firm is negatively related with the level of underpricing

The third hypothesis that was tested was that the age of a firm has a negative relationship with the level of underpricing. The coefficient AGE has the value of 0.047, which means that age has a positive effect on the level of underpricing. The coefficient AGE is significant at 10 percent significance, which means that the third null hypotheses can be rejected. Therefore, in this sample, Age does have a significant positive influence on the level of underpricing.

H4: The rank of the underwriter is positively related with the level of underpricing.

The fourth hypothesis that was tested was that the rank of the underwriter has a negative relationship with the level of underpricing. The coefficient has a positive value of 0.010, this means that there is a positive effect of the underwriters rank on the level of underpricing. The coefficient is not significant at 10 percent, so the null hypotheses cannot be rejected. This means that there is no evidence for the positive relation of the underwriters rank and the level of underpricing in this sample.

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Table 4: Regression Output

Model Variable 1 2 (Constant) 0.643*** 0.794*** (0.082) (0.088) Log_Age 0.055** 0.047* (0.027) (0.026) Log_DealSize 0.021 0.022 (0.020) (0.020) Log_MV -0.066*** -0.067*** (0.020) (0.019) Rank 0.016** 0.010 (0.008) (0.008) Region -0.451*** -0.639*** (0.051) (0.069) FinTech 0.035 -0.204** (0.053) (0.080) INT_USxFin 0.389*** (0.099) R Square 0.291 0.336 Adjusted R Square 0.273 0.316 F-Statistic 15.825 16.626

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5. Conclusion

The aim of this thesis was to investigate whether Fintech firms have a higher level of underpricing than non-Fintech firms. In addition, the European and the US markets were analysed to examine whether there is a underpricing difference between the two markets. Furthermore, the effects of the underwriters rank and the age of a firm were analysed as well. In addition, two control variables were added to make the model more reliable.

In order to answer the research questions, a sample of 238 firms was collected. The sample consisted of 39 Fintech and 39 non-Fintech firms from Europe. In addition, the US market sample consisted of 84 Fintech and 74 non-Fintech firms. The sample Fintech and non-Fintech firm data was collected from a period of 1990 until 2017 with 78 firms in Europe and 160 firms in the United States. In the overall sample the average level of underpricing was 27.10 percent. In order to determine whether that was significant or different in both markets, further analyses were performed.

To analyse the level of underpricing and the influence of region, age, and the underwriter’s reputation, an Ordinary Least Squares regression was performed to see if the values were significant. The regression results showed no evidence of a higher level of underpricing when looking at both European and US markets. However, the results did show that in the United States there was a significant higher level of underpricing at Fintech firms than for European Fintech firms. While on the other hand, European Fintech firms seem significantly less underpriced than non-Fintech firms. The first null hypotheses cannot be rejected because for both markets there is no evidence for different underpricing levels for Fintech and non-Fintech firms. The first explanatory variable age was added to the regression as previous research suggested that age had a positive relation to underpricing. The variable age was tested with the OLS regression and did indeed show a significant effect on the level of underpricing. Therefore, evidence is provided for the positive effect of age on the level of underpricing. The second explanatory variable, the underwriters rank, was also added because previous literature suggested its influence on underpricing. However, the regression results did not provide evidence for the relation of the underwriters rank with the level of underpricing. Therefore, the null hypotheses that there is no effect of rank on underpricing cannot be rejected.

Thus, the research performed in this thesis provided some more insights in the level of underpricing at Fintech firms. It turns out that there are some significant regional differences in the level of underpricing, which I did not expect based on previous research on this topic. Another interesting thing that can be concluded from this thesis is that the underwriters rank does not affect the level of underpricing, while early research suggested that this was the case.

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5.1 Limitations

In this research there were some limitations that will be addressed in this section. Unfortunately, not all company data was available for the first day closing price. Furthermore, it was not possible to retrieve all ranks of the underwriters and some information on the market value was missing as well. The observations with missing information on closing price, underwriters rank, and market value had to be deleted from the sample. This might have influenced the results because of the smaller sample. In addition, it was not possible to match all companies from the different industries on criteria such as market value and age, as the sample would decrease even more. By not doing this there were several large outliers in age and market value which could have influenced the outcome of this research.

5.2 Suggestions

There is still room for more research on the IPO underpricing of Fintech firms. The Fintech market is still very young compared to other IPO markets, and the market is developing rapidly. In this study, a difference in the level of underpricing for European and US IPOs was shown. It might be interesting to see wat causes this difference, therefore further research could focus on the differences in these IPO markets. Another aspect that has not been investigated in this research is the financial crisis that started in 2008. It might be interesting to see what the effect of the crisis was on the level of IPO underpricing. Thus, further research could include a variable for the financial crisis to see whether this affects underpricing.

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APPENDICES

Appendix I: European Fintech Firm Characteristics

Issuer Offer Price Closing Price IPO Deal Size Market Value Age Lead Underwriter Rank Alfa Finl Software Hldg PLC 3.250 5.499 324.353 1247.500 28 BARCLAY-BK 0.012 Avrasya Petrol ve Turistik 1.360 2.473 30.570 32.800 11 Not Available 0.000 Bango PLC 0.335 0.573 0.563 159.830 18 PANMURE 0.150 Bolsas y Mercados Espanoles 42.900 60.625 167.968 4940.200 15 CREDIT-SUISSE 7.650 Brightside Group PLC 0.220 0.393 33.268 153.300 11 EVOLUTION-SEC 0.000 CMC Markets PLC 2.400 3.480 315.409 1002.000 13 Goldman Sachs 9.240 Collector AB 55.000 12.697 58.204 640.300 19 SEB 0.000 Digital Magics SpA 4.035 4.502 1.790 28.600 14 Not Available 0.000 eCard SA 2.000 1.162 4.397 41.685 17 UNICREDIT 0.006 eFront SA 6.400 8.025 8.840 66.160 19 INVESTSEC-FR 0.000 Equiniti Group PLC 1.650 2.311 484.890 702.800 8 Goldman Sachs 9.240 EuroInvestor.com A/S 10.500 2.951 3.185 205.610 21 Not Available 0.000 Euronext NV 45.000 50.915 221.747 3526.800 18 BNP-PARIBAS 0.550 Experian Group Ltd 5.600 10.755 1496.000 1432.520 11 Merrill Lynch 7.820 FAIRFX Group PLC 0.580 0.928 34.686 114.200 3 CENKOS 0.000 Financial Payment Systems Ltd 0.120 0.241 9.261 20.400 13 DANSTE 0.000 Fireone Group PLC 2.410 5.291 43.744 218.700 2 NUMIS-SECS 0.000 FreeAgent Holdings PLC 0.840 1.088 13.287 42.400 11 SINGER 0.000 Leonteq AG 140.250 179.980 0.754 1230.800 11 Credit Suisse 7.650 Luxoft Holding Inc 17.000 20.380 64.696 554.900 17 UBS 0.910 Markit Ltd 24.000 26.700 1232.003 4250.600 4 Merrill Lynch 7.820 Meilleurtaux SA 13.700 17.564 58.740 58.700 18 NOT-AVAILABLE 0.000 Mobile Credit Baltic Plc 1.150 2.476 7.539 39.400 10 LIBERTAS 0.000 Monitise PLC 0.220 0.432 42.713 111.700 14 INVBK-UK 0.000 MyBucks SA 13.500 16.423 15.194 154.620 7 HACF 0.000 Nektan plc 2.360 3.770 5.817 80.200 4 PANMURE-GOR 0.150 QIWI 30.500 37.260 240.659 1589.000 10 Credit Suisse 7.650 QIWI PLC 17.000 17.080 199.750 892.500 10 JP Morgan 6.613 Rentabiliweb Group SA 7.050 9.997 26.114 161.100 16 ARKEON-FINANCE 0.000 Rosslyn Data Tech PLC 0.330 0.570 17.244 41.900 13 CENKOS 0.000 SafeCharge Intl Group Ltd 1.620 2.818 126.056 403.700 7 SHORE-CAP 0.000 Spark Ventures PLC 9.000 13.171 15.567 117.247 18 Liberum Capital 0.000 Travelport Worldwide Ltd 16.000 16.400 451.200 1921.700 12 Morgan Stanley 9.160 Tungsten Corp PLC 3.400 6.119 19.750 579.300 18 Canaccord 0.000 West International AB 2.200 0.226 2.318 5.400 30 Not Available 0.000 Woogroup SA 12.730 17.462 0.003 20.200 3 Europe Finance 0.000 Worldline SA 16.400 22.389 782.288 3029.300 27 Societe Generale 0.550 Worldpay Group PLC 2.400 4.091 3294.000 7320.000 25 Merrill Lynch 7.820 Xchanging PLC 2.800 5.901 106.228 1197.900 7 Citigroup 7.730

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Appendix II: European non-Fintech Firm Characteristics

Issuer Offer Price Closing Price IPO Deal Size Market Value Age Lead Underwriter Rank

Aisi Realty Public 0.660 0.802 33.140 991.700 13 Libertas Capital Group 0.000

Allied Minds 1.900 3.232 199.390 534.100 14 Jefferies International 0.390

Aquilo 0.023 0.041 0.060 0.200 5 Brewin Dolphin Securities 0.000

Arbor 1.000 1.825 116.200 528.170 14 Collins Stewart Tullett 0.000

Argentaria Spain 16.070 17.630 125.600 6180.000 10 Morgan Stanley & Co 9.160

Auctus Growth 0.500 0.986 1.610 0.400 3 Not Applicable 0.000

Aurora Russia 1.000 1.830 130.700 436.000 12 Investec Investment Banking 0.000

Capital For Colleagues 0.500 0.904 0.500 1.800 5 Not Applicable 0.000

Ceres Media International 0.180 0.278 1.000 7.600 11 Not Applicable 0.000

Cleantech Bldg Materials 0.010 0.020 1.120 5.200 3 Not Applicable 0.000

Cleeve Capital 0.030 0.091 4.930 1.000 4 Not Applicable 0.000

Direct Line Group 1.750 2.808 1241.960 4212.100 32 Morgan Stanley & Co 9.160

Esure Group 2.900 4.675 920.470 1764.800 18 Deutsche Bank 1.645

Fair Oaks Income Fund 1.000 1.010 80.200 432.100 4 Numis Securities Ltd 0.000

Gate Ventures 0.100 0.226 4.880 10.340 3 Beaumont Cornish 0.000

General Industries 0.250 0.560 6.420 15.740 34 KBC Peel Hunt Ltd 0.000

Golden Rock Global 0.100 0.208 0.440 1.500 2 Not Applicable 0.000

Great Leisure Group 13.730 19.522 0.030 16.400 10 Financieres D'Uzes 0.000

Harwood Wealth Management 0.810 1.309 13.500 50.400 14 Singer Capital Markets Ltd 0.000

Honeycomb Investment Trust 10.000 14.868 148.680 1130.000 3 Liberum Capital 0.000

Inspired Energy 0.030 0.062 3.400 11.600 6 Shore Capital Group 0.000

Intl Biotechnology Trust 1.000 1.870 61.000 172.900 61 Merrill Lynch International Ltd 7.820

John Laing Group 1.950 2.984 375.080 899.700 100 Barclays Bank PLC 0.012

Mithril Capital 0.030 0.090 4.990 1.000 10 Not Applicable 0.000

Nimrod Sea Assets 1.000 1.020 130.000 832.400 6 Nimrod Capital LLP 0.000

Noble Bank 10.500 5.670 111.500 744.000 14 Not Available 0.000

ORA Capital Partners 1.200 2.675 80.300 168.600 12 Kaupthing HF 0.000

Orchard Funding Group 0.960 1.514 15.610 16.400 3 Panmure Gordon (UK) Ltd 0.150

Renta-4 9.250 12.599 133.100 491.500 32 ING Bank NV 0.360

River & Mercantile Asset Mn. 1.830 3.278 70.670 210.700 12 Canaccord Genuity Ltd 0.000

Sanditon Investment Trust 1.000 1.704 84.910 4050.000 4 JP Morgan Cazenove 6.610

Schroder UK Growth Fund 0.500 1.897 30.970 91.000 12 De Zoete and Bevan 0.000

Shawbrook Group 2.900 4.536 322.440 945.700 7 Merrill Lynch 7.820

Silver Falcon 0.030 0.053 0.800 2.900 4 Not Applicable 0.000

Starwood European RE. 1.000 1.636 368.750 405.000 27 Dexion Capital 0.000

THB Group 1.200 1.952 7.600 29.480 50 Numis Securities Ltd 0.000

Toro Assicurazioni 11.250 14.688 231.680 2497.200 100 Lehman Brothers 4.080

TSB Banking Group 2.600 4.933 760.410 2211.300 5 Citigroup Global Markets 7.730

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Appendix III: US Fintech Firm Characteristics

Issuer Offer Price Closing Price IPO Deal Size Market Value Age Lead Underwriter Rank

Amber Road Inc 13.000 17.000 89.364 336.700 34 Stifel Nicolaus & Co Inc 0.000

Apigee Corp 17.000 16.700 80.868 494.700 13 Morgan Stanley & Co 9.160

AppFolio Inc 12.000 14.080 69.192 324.600 12 Morgan Stanley & Co 9.160

Bankrate Inc 48.250 50.930 106.682 868.200 22 Credit Suisse 7.650

BATS Global Markets Inc 19.000 23.000 138.564 1666.300 12 Morgan Stanley & Co 9.160

Black Knight Inc 45.500 45.950 315.455 6982.900 4 Goldman Sachs & Co 9.240

Blackhawk Network Holdings In 23.000 26.010 215.050 1194.500 17 Goldman Sachs & Co 9.240

BofI Holding Inc 13.000 14.410 13.276 120.300 18 B Riley & Company 0.000

BTL Group Ltd 0.600 0.486 0.116 6.900 6 Not Applicable 0.000

Cachet Financial Solutions In 1.500 1.500 6.210 16.800 7 Northland Capital Markets 0.000

Cardtronics Inc 12.000 12.570 79.590 499.900 29 UBS Investment Bank 1.610

Carolina National Corp 16.000 18.070 14.960 38.800 18 Scott & Stringfellow Investment 0.070

CBOT Holdings Inc 54.000 80.300 160.707 2825.700 100 Credit Suisse First Boston Corp 7.650

ChannelAdvisor Corp 14.000 18.440 74.865 298.700 16 Goldman Sachs & Co 9.240

Cotiviti Holdings Inc 19.000 17.110 222.063 1704.900 5 Goldman Sachs & Co 9.240

Cowen Group Inc 5.000 5.350 72.220 362.200 100 Cowen & Co 1.750

CPI Card Group Inc 10.000 12.170 142.500 587.300 36 BMO Capital Markets 0.103

Currency Exchange Intl Corp 6.650 7.320 7.548 24.800 19 Jones, Gable & Company Limited 0.000

Demandware Inc 16.000 23.590 81.840 461.200 14 Goldman Sachs & Co 9.240

Dollar Financial Corp 16.650 17.300 80.750 386.900 39 Piper Jaffray Cos Jefferies & Co Inc 1.610

Elevate Credit Inc 6.500 7.760 75.361 268.800 3 UBS Securities Inc 0.910

Ellie Mae Inc 90.000 98.500 236.363 3002.500 20 JP Morgan & Co Inc 6.610

Envestnet Inc 9.000 10.230 58.590 277.200 19 Morgan Stanley 9.160

Equity Bancshares Inc 22.500 23.890 40.813 154.700 15 Keefe Bruyette & Woods Inc 0.120

ESI Ent Sys Inc 3.000 2.630 8.286 39.100 27 Desjardins Securities Inc. 0.000

EverBank Financial Corp 19.330 19.280 134.004 2424.700 13 UBS Investment Bank (USA) 0.910

Evertec Inc 20.000 20.440 477.474 1581.900 30 Goldman Sachs & Co 9.240

Fifth St Asset Mgmt Inc 17.000 13.370 95.880 830.600 3 Morgan Stanley & Co 9.160

Financial Engines Inc 12.000 17.250 118.296 474.100 21 Goldman Sachs & Co 9.240

First Data Corp 17.750 17.810 486.572 16609.200 29 KKR & Co LP 0.000

FleetCor Technologies Inc 36.500 38.590 216.120 3018.800 17 Deutsche Bank Securities Inc 1.645

FX Alliance Inc 12.000 13.740 58.032 339.800 11 Merrill Lynch 7.820

GFI Group Inc 21.000 26.440 114.392 561.800 31 Citigroup 7.730

Global Cash Access Hldg Inc 14.000 14.960 209.155 1127.000 20 Goldman Sachs & Co 9.240

Green Dot Corp 61.000 54.610 249.996 2549.500 18 JP Morgan & Co 6.610

Groupon Inc 11.800 11.780 159.406 8014.000 9 Morgan Stanley & Co 9.160

Guidewire Software Inc 28.250 28.650 216.960 1515.700 17 JP Morgan & Co Inc 6.610

HealthEquity Inc 14.000 17.600 118.482 713.500 15 JP Morgan & Co 6.610

Heartland Payment Systems Inc 18.000 24.510 112.995 583.300 20 Citigroup 7.730

Intercontinental Exchange Inc 241.000 244.580 1366.229 26453.300 4 Credit Suisse 7.650

International Sec Exchange In 18.000 30.400 168.225 661.300 21 Bear Stearns & Co 3.830

JGWPT Holdings Inc 14.000 12.820 127.969 550.800 4 Barclays PLC 0.012

LendingClub Corp 15.000 23.430 819.975 5539.100 11 Goldman Sachs & Co 9.240

Liquid Holdings Group Inc 1.250 1.270 37.200 71.100 7 JMP Securities LLC 0.000

Liquidity Services Inc 10.000 12.290 71.492 273.300 19 Friedman Billings Ramsey Group 0.660

MasterCard Inc 39.000 46.000 2287.747 5443.800 52 Goldman Sachs & Co 9.240

MINDBODY Inc 14.000 11.560 93.093 547.600 20 Morgan Stanley & Co 9.160

Mogo Finance Technology Inc 10.000 7.689 38.458 103.700 14 BMO Nesbitt Burns Inc 0.103

Morningstar Inc 18.500 20.050 138.015 711.300 34 WR Hambrecht & Co LLC 3.240

MSCI Inc 18.000 24.970 234.360 289.800 20 Morgan Stanley 9.160

NantHealth Inc 14.000 18.590 84.630 1690.300 8 Jefferies LLC 0.390

National Commerce Corp 19.500 21.120 30.912 184.100 11 Keefe Bruyette & Woods Inc 0.120

Nationstar Mortgage Hldg Inc 28.950 25.990 506.625 3142.200 24 Citigroup Global Markets Inc 7.730

NewStar Financial Inc 17.000 17.710 189.720 616.500 13 Goldman Sachs & Co 9.240

Northern Aspect Resources Ltd 0.150 0.268 0.229 0.700 6 Not Applicable 0.000

NYMEX Holdings Inc 59.000 132.990 358.540 5189.900 100 JP Morgan & Co Inc 6.610

On Deck Capital Inc 20.000 27.980 186.000 1353.200 11 Morgan Stanley & Co 9.160

optionsXpress Holdings Inc 16.500 20.300 184.140 1014.700 17 Goldman Sachs & Co 9.240

Paycom Software Inc 15.000 15.350 92.698 807.700 4 Barclays 3.060

Paylocity Holding Corp 17.000 24.040 111.381 837.900 21 Deutsche Bank Securities 1.645

Penson Worldwide Inc 17.000 19.500 118.034 406.700 23 JP Morgan & Co Inc 6.610

Planet Group Inc 1.250 2.764 12.286 2.570 19 Canaccord Adams 0.000

Q2 Holdings Inc 13.000 15.170 93.829 415.300 12 JP Morgan & Co Inc 6.610

Real Matters Inc 13.000 9.410 107.162 888.290 14 BMO Nesbitt Burns Inc 0.103

Redfin Corp 15.000 21.700 128.772 1213.800 14 Goldman Sachs & Co 9.240

Refco Inc 22.000 27.480 548.020 2805.000 25 Credit Suisse 7.650

RiskMetrics Group Inc 17.500 23.750 229.075 1011.500 24 Credit Suisse 7.650

SciQuest.com Inc 16.000 30.000 111.600 394.600 22 Donaldson Lufkin & Jenrette 0.000

Shopify Inc 17.000 25.680 121.737 1285.100 13 Morgan Stanley & Co 9.160

Springleaf Holdings Inc 17.000 19.260 334.513 1897.700 4 Merrill Lynch 7.820

SS&C Technologies Hold 15.000 15.080 149.614 1037.900 22 JP Morgan & Co Inc 6.610

Synchrony Financial 23.000 23.000 2788.750 19176.600 14 Goldman Sachs & Co 9.240

TransUnion 22.500 25.400 626.548 4099.900 5 Goldman Sachs & Co 9.240

TriNet Group Inc 16.000 19.100 223.200 1129.200 30 JP Morgan & Co Inc 6.610

Trulia Inc 17.000 24.000 94.860 463.700 12 JP Morgan & Co Inc 6.610

Vantiv Inc 17.000 19.500 472.504 2240.700 48 JP Morgan & Co Inc 6.610

VeriFone Holdings Inc 10.000 10.750 144.375 650.500 37 JP Morgan & Co Inc 6.610

Verisk Analytics Inc 22.000 27.220 1800.480 2487.700 47 Merrill Lynch 7.820

Virtu Financial Inc 19.000 22.180 292.125 719.100 4 Goldman Sachs & Co 9.240

Virtusa Corp 34.500 34.120 74.986 1006.700 21 JP Morgan & Co Inc 6.610

Visa Inc 95.180 95.230 209.276 219564.400 48 Merrill Lynch 7.820

Workday Inc 28.000 48.690 598.780 4488.100 12 Morgan Stanley 9.160

Xoom Corp 16.000 25.490 94.116 509.400 17 Barclays 0.012

IPO underpricing of Fintech firms : a closer look at Europe and the United States