Post-IPO stock performance for FinTech firms listed on US market between 2008 and 2016

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Post-IPO stock performance for FinTech firms listed

on US market between 2008 and 2016

Abstract

The 2008 financial crisis triggered FinTech revolution. Many FinTechs complete their IPOs recently and their stock performances differ widely. In this paper, I will analyze the post-IPO stock performance for in total 79 FinTech IPOs who are listed on NYSE or NASDAQ from 2008 to 2016. The results suggests that in the short run, FinTech IPOs are more underpriced than non-FinTechs and have a significant average initial return of 21.78%. In the long run, positive aftermarket returns are observed in both average adjusted returns(AR) and cumulative average benchmark-adjusted returns(CAR).

Institution: University of Amsterdam

Program: BSc Economics and Business, Economics and Finance track Type: Bachelor Thesis

Title: Post-IPO stock performance for FinTech firms listed on US market between 2008 and 2016 Author: Manqin He(10621245)

Date: 29 June 2016

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2 Statement of Originality

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

I declare that the text and the work presented in this document is 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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obligations and regulatory capital requirements of banks had increased after crisis and this made banks unable to or unwilling to issue low value loans (Arner, Barberis and Buckley, 2015). In addition, many financial professionals were unemployed due to crisis and educated-graduates were facing difficulties to find a job(Arner, Barberis and Buckley, 2015). The changing of public attitudes, the regulatory weakness of banking systems and the availability of financial workforce, all factors described above gave rise to FinTech industry.

Investments in FinTech industry are growing fast. According to a recent quarterly report from KPMG(2016), the amount of funding flows to venture capital-backed FinTech firms during the first quarter of 2016 has been substantially increased to 4.9 billion dollars, compared to the 0.6 billion dollars at the beginning of 2011. Also, it is considered to be a rebound of approximately 158% from Q4 2015.

Many FinTech firms went public recently and their post-IPO stock performances differ widely. For example, the stock price of LendingClub(NYSE: LC), who went public in 2014 and is offering peer to peer lending services, has been continuously decreased since its initial public offerings and the drop in 2016 reached to 64% (Bloomberg, 2016). According to a recent article from American

Banker(2015), people started to wonder if the FinTech industry has been overheated and whether it is similar to the dot-com bubble in late 1990s. On the other hand, Black Knight Financial

Services(NYSE: BKFS), who is providing integrated technology, data and analytics to the mortgage and real estate industries and has completed its IPO in 2015, had a solid stock performance over months and there was a 19.64% increase in the stock price for the last quarter(Engelwood Daily, 2016).

These have brought me the idea to construct a research to analyze the post-IPO stock performance in the FinTech industry. In this paper, I will take the period from 2008 to 2016 and will focus on US market because it is more mature in FinTech compared to the rest of the world.

Previous studies over US IPOs are mainly focusing on the phenomena of short run underpricing and long run underperformance and some papers are analyzing specially for the internet bubble period. Ritter and Welch(2002) reported the average first day return was 18.8% from 1980 to 2001 and

Ritter(1991) found statistically significant evidence to support the long run underformance during the period of 1975-84. As a result, he found a three-year cumulative benchmark-adjusted return equaled to -29.13%. For dot-com IPOs, Johnston and Madura (2002) reported an average initial return of 78.5% for 366 internet firms from 1996 to 2000.

The following paper is structured as follows. In section two I will provide some background information about financial technology. After that I will review the previous studies about short run

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5 and long run IPO stock performance and provide relevant theories and explanations. In section four I will construct the main hypotheses and after it, data and methodology used for this paper will be explained. Empirical results can be found in section six and conclusion and limitations will be discussed in the last section.

2. Financial Technology

FinTech is the abbreviation of financial technology and is referring to the application of information technology to financial services (KPMG, 2015). Alternatively, Deutsche Bank(2014) defined FinTech firms as “non-bank, technology-driven providers of financial services” which are built on digitalized, web and data based processes. In terms of FinTech industry, it describes the phenomenon where a non-financial business uses innovative technology to provide financial services (Kim, Park, Choi and Yeon, 2015).

According to the FinTech Database of FT Partners, a San Francisco based investment banking firm that is focused solely on the financial technology sector, FinTech industry can be categorized into seven sectors, namely payments, lending(primarily peer-to-peer lending platforms), financial business process outsourcing, financial management solutions, securities/capital markets/wealth management, financial healthcare internet technologies and insurance. Among them, payments is the most mature segment and lending is regarded as a high growth segment which attracts the most of the investments from 2010 to 2015(Accenture, 2016).

From another perspective, FinTech companies can be separated into competitive and collaborative firms. Competitive FinTechs are mainly operating in less profitable segments such as payments and lending to capture revenues by offering better customer experience, while collaborative FinTechs are focusing on sectors like back office operations in order to provide supports and solutions to existing financial market players(Accenture, 2016). From 2010 to 2015 in US, there was a dramatic shift to collaboration. Statistics showed that the percentage of investments in collaborative FinTechs has been increased from 37% in 2010 to 83% in 2015(Accenture, 2016).

Another remarkable thing is that big traditional banks intend to work with FinTechs and this can be preceded by FinTech incubators and accelerators, who can help to build partnerships between big banks and FinTechs(WSJ, 2016). There are many FinTech accelerators available in US, for example FinTech Innovation Lab, Barclays Accelerator, Wells Fargo Startup Accelerator and FinTech Sandbox(efinancialcareers, 2016). Through them, banks have the access to new technology and are able to test new business models without risking their current business too much(WSJ, 2016).

3. Literature Review

In this section, I will first provide separate explanations for short run underpricing and long run underperformance, and then analyze factors which have effects on both short run and long run performance.

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6 Previous studies generally agreed that IPOs are significantly underpriced in the short run. Several explanations are available for this. Information asymmetry leads to underpricing and diversified business is assumed to have a higher level of information asymmetry due complexity. Moreover, younger firms and IPOs with smaller offerings are related to higher initial returns. As for long run performance, IPOs will be more underperformed during the high volume periods in which many firms went public. This is due to the overheating effect caused by fads or by investor overoptimism. Also, there is a mysterious tendency that the more underpricing in the short run will lead to worse

aftermarket performance. Additionally, the low interest rate after financial crisis is beneficial for long run performance.

Besides above discussions, two more factors play key roles in both short run and long run IPO performance. First, VC backed IPOs are generally more underpriced, but high-quality VC may lead to lower underpricing and better long run performance. Second, prestigious underwriters help to improve the long run performance. But in the period when issuers change their focus from IPO proceeds to research coverage, highly ranked underwriters will however lead to larger short run underpricing.

3.1 Initial public offerings

An initial public offering(IPO) means private firms successfully raise capital in the public market for the first time(Carter and Manaster, 1990). According to Deeds, Decarolis and Coombs(1997), obtaining a large amount of investment can be the main reason for IPOs with the purpose to support the research and development projects for the firms and to overcome heavy debt-based financing. Other reasons for IPOs are to increase firms’ legitimacy and to provide an exit method for major shareholder(Deeds, Decarolis and Coombs(1997).

3.2 Short run underpricing

Rock (1986) argued that information asymmetry among traders will lead to underpricing. He explained that some investors have superior information and when good shares are issued they can crowd out others. In order to attract the rest of the traders and to compensate the risk of uninformed investors, the offering firms need to underpriced their new issues for initial public offerings(Rock, 1986). Therefore, investors who purchase new shares in the short run will gain substantial excess return. Moreover, Baron(1982) illustrated that information asymmetry also exists between

underwriters and issuers since underwriters such as investment banks are better informed about the demand of capital market than issuers. He explained that when issuers are uncertain about how the market is going to react towards their new securities and when they are unable to well observe the distribution efforts of investment banks, bankers have an incentive to underprice the shares more in order to reduce their efforts.

Additionally, diversification can increase the level of information asymmetry. Cohen and Dean (2005) argued that organizations with complex leadership, culture, technology, products and strategy

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7 are likely to create information asymmetry to potential investors. Firms can diversify their business either by differentiating produces and services or by expanding geographically(Mercieca, Schaeck and Wolfe, 2007). Specifically, a financial company is considered to be diversified when it combines at least two financial activities, banking, securities or insurance (Baele, Jonghe and Vennet, 2007). In the paper of Ibbotson(1975), some other explanations about underpricing are available. First, regulations may implicitly prevent underwriters to set the offer price higher than the expected value. Second, underwriters may collude with issuers to favor investors due the competition between underwriters. Third, when the compensation to underwriter does not cover all the risks, underwriters need to underprice to eliminate the cost these risks. Lastly underpricing can be used as an insurance to avoid legal suits.

Loughran and Ritter(2004) stated that younger firms are usually more underpriced because they are more risky and should have larger initial returns. Beatty and Ritter(1986) used the inverse of the gross proceeds as proxy to ex ante uncertainty and they found that smaller offerings are more speculative and therefore are more underpriced.

3.3 Long run underperformance

In the long run, Ritter(1991) found firms were substantially underperformed for the three-year period after they went public and it is worse for younger firms and for companies went public in large-volume years. He explained this by fads and by the fact that investors are overoptimistic about the earning potential of firms, which is especially true for young growth companies. Consistently, he found higher market-to-book ratios are linked to the poor long-run performance of the younger IPOs. From issuer’s perspective, Jain and Kini (1994) explained the decline in the post-issue

performance by the fact that firms attempt to window-dress their accounting numbers before they went public to make them look like a better firm and this will lead to overestimation of their pre-IPO performance.

Moreover, Ritter(1991) found the tendency that firms with higher initial returns will have worse long run performance and this tendency is stronger for smaller issues in period of 1975-84. However, this relationship is not reliable enough. In another paper of him (together with Welch) in 2002, he stated that whether or not this tendency is observable mainly depends on if the internet bubble period has been included. Most of the IPOs in that period are characterized by large initial returns and very bad aftermarket performance.

Additionally, financial crisis caused a prolonged period of abnormally low interest rates and the drop of interest rate is beneficial for the long-run performance for financial firms(Bordo, 2008; Ritter, 1991).

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8 FinTech funding mainly come from activities such as angels, PE firms, mutual funds and hedge funds(KPMG, 2016). According to the recent report from KPMG, total FinTech funding hit 5.7 billion dollars during the first quarter of 2016, among which 4.9 billion dollars are findings to VC-backed FinTech companies. Hence, venture capital can be regarded as the main source of funding for FinTech companies.

Black and Gilson(1998) defined venture capital as the investment made by specialized venture capital organizations in high-growth, high-risk, often high-technology firms. They constructed an IPO exit model and found that successful entrepreneurs often prefer exit VC industry by IPO. Moreover, their model predicted that IPO exits will be more profitable than other exit strategies and this is confirmed empirically by Gompers’(1995) study.

As for post-IPO stock performance, Lee and Wahal (2004) showed that VC-backed IPOs are generally more underpriced than non VC-backed IPOs. Due to the fact that the main returns of venture capital firms are from making companies went public, venture capitalists are willing to bear the cost of underpricing to capture the benefits from both grandstanding and spinning(Lee and Wahal, 2004). Gompers(1996) proposed grandstanding hypothesis stating that less well-known VCs may have the inventive to “grandstand” by taking private firms to public sooner than others and by that, they can establish a good reputation in the marketplace for future capital raising. And spinning is referring to the collusion opportunities for underwriters and decision makers of IPO firms to benefit from underpricing at the expense of other equity holders of the firms(Loughran and Ritter, 2002). On the other hand, Barry, Muscarella, Peavy and Vetsuypens (1990) found that IPOs with better quality VCs can be less underpriced since those venture capitalists intend to effectively monitor their portfolio firms by focusing on a few sectors where they have expertise in it. Moreover, Krishnan, Ivanov, Masulis and Singh (2011) argued that reputable VCs are more careful in selecting firms to invest and will provide stronger support for them after they went public and therefore will lead to better long run IPO stock performance.

3.5 Underwriter reputation

Underwriters are the intermediaries in IPO process who advise the issuers on the offer price(Loughran and Ritter, 2003).

Good IPO firms intend to select prestigious underwriters to be less underpriced by proving their quality and guarantying their performance to investors since underpricing is costly to issuers and is a sign of risk(Carter and Manaster, 1990). Similarly, Loughran and Ritter(2003) found that IPOs with prestigious underwriters are indeed less underpriced and the reason behind is that if the underwriters collude with issuers by underpricing more or less than needed, they will lose customers and hence harm their reputation. However, during the dot-com bubble period, there was evidence to show that highly ranked underwriters are associated with larger underpricing (Loughran and Ritter, 2003). Loughran and Ritter(2003) explained this by the changing issuer objective function hypothesis. The

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9 hypothesis stated that IPO proceeds are not anymore the only considering factor of issuers and there is an increased emphasis on research coverage. They mentioned that offering research coverage service is costly for investment banks and issuers provide compensation to cover the costs either by gross spread or by underpricing. As a result, issuers who seek for a lead underwriter with highly ranked analyst to cover the firm will be more willing to accept underpricing.

As for long run, Dong, Michel and Pandes (2011) analyzed 7407 IPOs during the period from 1980 to 2006 and found the number of underwriters and underwriter reputation are positively related to aftermarket performance, especially for firms with high uncertainty. These findings are mainly driven by the marketing role of the underwriters which helps issuers to promote and to increase the demand of the stock. Moreover, they also reported that underwriter’s marketing role can lift the long run price more than short run. Another remarkable thing is that, for the internet bubble period from 1999 to 2000, the relation between underwriter quality and the long run return was inverted(Dong, Michel and Pandes, 2011).

4. Hypotheses

Based on the literature review, I construct two hypotheses. First, due to the general trend of short run underpricing and the fact that most of the FinTechs are VC-backed and are with highly ranked underwriters (Appendix 1: average rank equals 8.54 with the scale up to 9.001), I would assume FinTech IPOs will be more underpriced in the short run:

H1: FinTech IPOs are more underpriced in the short run

Second, besides the positive effect of reputable venture capitalists and underwriters on the long run performance, there are not enough arguments available to assume FinTechs will deviate from the general results of the long run underperformance. I would propose that FinTech IPOs will still underperform in three years:

H2: FinTech IPOs will be underperformed in the long run

5. Data and methodology

Before analysis, I first use the FinTech Database of FT partners to list all the FinTech firms who went public from 2008 to 2016 were listed on NYSE or NASDAQ. Then I check the IPO completion date with other databases such as IPOScoop.com, Zephyr and NASDAQ website for each FinTech firm to make sure all of them are still relevant. There are in total 79 FinTech IPOs in my

sample(Appendix 1).

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10 I will use initial returns to test underpricing. Initial return is calculated as,

where Pi is the closing price of the first trading day after IPO of stock iand Ei is the offer price. Offer price and the first day trading price and are downloaded mainly from IPOScoop.com and few of them are added manually based on the NASDAQ website.

First I will calculate the initial returns for all US FinTech IPOs. Then for each event month which has at least one FinTech went public, I will calculate the monthly average initial returns and compare the difference between the underpricing level of FinTech IPOs and non-FinTech IPOs.

There are in total 705 US IPOs in the selected event months, of which 79 of them are FinTechs and 626 are non-FinTechs.

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Afterwards, I will run the OLS regression to test the relevance of each explanatory variable that has been discussed in the literature review and is assumed to have effects on underpricing. The regression model I will use is,

where UPi is the underpricing level (mearsured as initial return) for stock i.

fintech is a dummy variable which has value equal to 1 if it is FinTech and 0 if it is non-FinTech. underwriterrank is the underwriter reputation ranking and I will calculate the ranking by using the file IPO Underwriter Reputation Rankings (1980 – 2014)1, which can be downloaded from Jay Ritter’s website. The scale of Ritter’s ranking is from 1.001 to 9.001. The higher the ranking, the more prestigious is the underwriter. For each underwriter, I will select the most recent ranking. And for each IPO, I will take the average ranking of all stated underwriters. Names of underwriters for each IPO can be found directly in IPOScoop.com and few of them I search on NASDAQ website and add them manually. For IPOs who have underwriters but with no rankings (possible reason is the ranking file is updated till 2014), I will delete those firms.

dealsize is the amount of IPO proceeds in dollars and this information can be found in the Zephyr database. Firms without deal value will be taken out.

firmage is the difference between the IPO date and company’s founding date. This information is collected from Ritter’s website 2

. Few of them which miss the information of founding years were gathered directly from company website or yahoo.finance. Firm age will be calculated in years. First I will calculate the number of days between founding date and IPO date (for the firms only with years I assume the date is 1 Jan), then I divided the number of days by 365 to get the firm age.

1 https://site.warrington.ufl.edu/ritter/ipo-data/ 2 https://site.warrington.ufl.edu/ritter/files/2015/08/Founding-dates-for-10266-firms-going-public-in-the-US-during-1975-2015-2015-07.pdf

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11 After taking out firms with incomplete information, the sample size is now 594 IPOs with 78 of them are FinTechs.

5.2 Mearsurement of long run performance

For the long run performance I will select the FinTechs who have been publically traded for at least three years(sample size is 40) and I will use Ritter’s(1991) method to analyze.

First I will calculate the weekly benchmark adjusted return for stock i,

where ri is the raw weekly return for FinTech firm and rm is the market index return(NASDAQ and

NYSE). Adjusted closing price for all firms are downloaded from datastream.

The average weekly benchmark-adjusted return (AR) is the equally-weighted arithmetic average of the benchmark-adjusted returns,

Cumulative benchmark-adjusted return is calculated as, t statistic of CAR is calculated as,

where t is the event month, n is the number of firms, var is the average (over three years) cross-sectional variance and cov is the first-order autocovariance of the ARt series.

I will interpret the results of AR and CAR and afterwards, I will calculate AR for each FinTech sectors to analyze the segment’s aftermarket performance.

6. Empirical result

The main results of my analysis will be provided in the section. 6.1 Short run underpricing

6.1.1 Underpricing for all FinTech IPOs

Detailed information about FinTech IPOs is provided in Table 1. Average initial return for all 79 FinTechs is 21.78% and t-statistic (=5.4561) shows that initial return for FinTech is significantly higher than 0. Therefore underpricing exists for FinTech. The big range between the maximum initial return of222.47% in year 2008 and the minimum initial return of -26.08% in year 2010 shows that FinTech’s initial returns differ widely. And this is also in line with the high level of standard error 3.99%.

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12 US initial public offerings have been extensively examined by previous studies. Johnston and Madura (2002) studied the post IPO performance of internet and non-internet IPOs from 1996 to 2000 and found the average initial return for 366 internet IPOs was 78.5%. Ritter and Welch(2002) found the average first day return of 6249 IPOs from 1980 to 2001was 18.8%. Ibbotson(1975) reported an average initial performance of 11.4% during 1960s. Compared to my results, FinTech’s underpricing is higher than average US IPOs but lower than internet firms.

The average deal size of FinTech IPOs is 653 million dollars including one big offer value of 19650 million dollars for Visa, whose firm age is 39.24 years and underwriter ranking is 9.00. Average firm age till IPO date is approximately 20.15 years. Underwriter reputation ranking for all FinTechs is on average 8.54 with the scale up to 9.001. This means that most of the FinTechs have prestigious underwriters.

Table 1 Summary characteristic of FinTech IPOs from 2008 to 2016

This table provides information about FinTech’s initial public offerings in each year from 2008 to 2016. Initial return (r) measures the level of underpricing. Maximum and minimum initial return are selected on yearly basis. Deal size is in million dollars. Underwriter reputation rank scales from 1.001 to 9.001.Firm age is the years between IPO date and firm’s year of founding. Full list of information for each FinTech firm can be found in Appendix 1.

No. of FinTech IPOs

Initial Return (r)

Max r Min r Deal size ($m) Underwriter rank Firm age +1 (years) 2016 1 -9.95% - - 238 9.00 38.42 2015 14 18.20% 87.50% -17.43% 434 8.60 14.02 2014 19 23.82% 148.75% -18.75% 558 8.22 19.09 2013 10 22.39% 102.08% 0.47% 215 8.63 23.23 2012 9 19.60% 73.89% -7.11% 359 8.53 28.92 2011 8 17.52% 46.69% 2.27% 682 8.51 19.24 2010 13 13.83% 43.75% -26.08% 157 8.76 16.55 2009 3 17.30% 23.73% 6.75% 712 8.71 27.42 2008 2 125.44% 222.47% 28.41% 9844 9.00* 25.43 All 79 21.78% 222.47% -26.08% 653 8.54 20.15

*9.00 is not the average but equals to the rank of one of the firms Visa since the other firm LendingTree, Inc. (NYSE: TREE) is “Self-underwritten” according to NASDAQ website3

. It has initial return of 222.47%, deal size of 37.81million dollars and firm age of 11.62 years.

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13 T-test for 79 FinTech initial returns

Mean =21.78%; Standard error =3.99%; t-statistic = 5.4561

6.1.2 Fintech IPOs underpricing compared to all US non-FinTech IPOs in each event month

According to Table 2, both FinTech and non-FinTech IPOs are found to have significantly positive initial returns. On average, FinTechs have a larger monthly mean value of 22.62% compared to non-FinTechs of 9.90%. The difference of the average monthly initial returns between FinTech and non-FinTech indicates that FinTechs are significantly more underpriced than non-FinTechs with the mean difference value of 12.72% and t-statistic of 2.0686.

Table 2 Underpricing of Fintech IPOs compared to all US non-FinTech IPOs in each event month This table compares the underpricing level for all FinTech IPO to non-FinTech IPOs in equivalent event months. Event Month (beg.) No. FinTech Average monthly initial return (r_fintech) No. Non FinTech (US IPOs) Average monthly initial return (r_ nonfintech) FinTech minus US non-FinTechs 1 2016/5/1 1 -9.95% 14 7.33% -17.28% 2 2015/12/1 1 -9.00% 1 32.29% -41.29% 3 2015/11/1 1 45.22% 11 12.14% 33.08% 4 2015/10/1 2 10.07% 14 2.28% 7.79% 5 2015/6/1 5 6.50% 23 26.32% -19.83% 6 2015/5/1 2 30.86% 19 1.30% 29.55% 7 2015/4/1 2 52.12% 12 10.73% 41.39% 8 2015/2/1 1 0.00% 12 -0.34% 0.34% 9 2014/12/1 4 26.08% 11 19.12% 6.96% 10 2014/11/1 1 -18.75% 25 10.06% -28.81% 11 2014/7/1 3 3.77% 33 10.83% -7.06% 12 2014/6/1 2 9.79% 29 12.60% -2.81% 13 2014/4/1 4 5.60% 24 9.30% -3.70% 14 2014/3/1 5 62.75% 25 18.53% 44.21% 15 2013/10/1 3 13.06% 29 20.79% -7.73% 16 2013/9/1 1 102.08% 21 21.04% 81.04% 17 2013/7/1 1 31.90% 18 8.92% 22.98% 18 2013/5/1 2 3.29% 26 10.49% -7.20% 19 2013/4/1 2 7.64% 12 10.95% -3.31% 20 2013/3/1 1 28.90% 13 8.89% 20.02% 21 2012/10/1 3 31.15% 21 7.46% 23.69% 22 2012/8/1 1 17.78% 6 7.99% 9.79% 23 2012/5/1 2 8.50% 10 5.12% 3.38% 24 2012/3/1 2 8.25% 20 18.63% -10.39% 25 2012/1/1 1 31.69% 2 5.95% 25.74% 26 2011/11/1 1 30.55% 16 3.86% 26.69% 27 2011/7/1 1 16.50% 15 18.90% -2.40% 28 2011/6/1 1 2.27% 12 5.10% -2.84%

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14 29 2011/4/1 1 12.83% 20 8.51% 4.32% 30 2011/3/1 3 23.96% 5 26.02% -2.05% 31 2011/1/1 1 6.15% 6 10.01% -3.86% 32 2010/12/1 3 8.35% 18 14.18% -5.83% 33 2010/11/1 2 20.21% 19 6.14% 14.07% 34 2010/8/1 1 -26.08% 11 8.60% -34.68% 35 2010/7/1 3 21.29% 8 3.04% 18.25% 36 2010/6/1 1 18.92% 10 5.64% 13.28% 37 2010/4/1 1 13.33% 13 2.18% 11.16% 38 2010/3/1 2 22.14% 11 9.91% 12.24% 39 2009/12/1 1 6.75% 6 -2.45% 9.20% 40 2009/10/1 1 23.73% 13 0.82% 22.90% 41 2009/6/1 1 21.43% 5 7.23% 14.20% 42 2008/8/1 1 222.47% 3 -7.50% 229.97% 43 2008/3/1 1 28.41% 4 6.57% 21.84% Sum 79 972.51% 626 425.51% 547.00%

T test results for initial returns for 43 event months

Mean Standard error t-statistic Observation

FinTech monthly 22.62% 5.77% 3.9210 43

Non-FinTech monthly 9.90% 1.22% 8.1300 43

FinTech minus non-FinTech monthly 12.72% 6.15% 2.0686 43 6.1.3 Regression results

Before run the regression, I first analyze each variable. From the correlation table, we can see the underpricing is negatively related with deal size and firm age. This is in line with previous researches. Older firms are less risky and lead to less underpricing(Ritter, 1991)andIPOs with smaller deal value are more risky and therefore have larger initial returns (Beatty and Ritter, 1986). However, log(deal size) and log(firm age +1) are found to be positively related to initial returns. This may due to the high correlation between log value and its raw value. Log(deal size) (6) and deal size (4) have a correlation of 0.4629 while log(firm age plus one) (7) and firm age plus one (year) (5) have a correlation of 0.8325. These large correlations will offset their small negative relations with initial returns(-0.0068 and -0.0271 respectively).

Table 3 Descriptive statistics and correlation table for all explanatory variables

Table 3 provides information about descriptive statistics for each variable and the correlation between them. The following results are based on all FinTech and non-FinTech IPOs selected for short run analysis.

Descriptive statistics for each variable

Mean Standard Error t-statistic Number of observations

Initial returns 0.1298 0.0103 12.5726 594

FinTech(dummy) 0.1313 0.0139 9.4678 594

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Deal size($) 324000000 55800000 5.8049 594

Firm age plus one (year) 16.2744 0.7735 21.0407 594

Log(deal size) 18.6720 0.0489 381.4795 594

Log(firm age plus one) 2.3120 0.0402 57.4896 594

Correlation (1) (2) (3) (4) (5) (6) (7) Initial returns (1) 1 FinTech(dummy) (2) 0.0963 1 Underwriter Ranking (3) 0.1864 0.153 1 Deal size($) (4) -0.0068 0.0965 0.107 1

Firm age plus one (year) (5) -0.0271 0.0823 0.1576 0.1965 1

Log(deal size) (6) 0.1042 0.1588 0.4722 0.4629 0.2025 1 Log(firm age plus one) (7) 0.0508 0.1725 0.1657 0.1231 0.8325 0.0813 1

Regression results can be found in Table 4. The coefficient of the dummy variable FinTech (0.0496; t=1.61) shows that being a FinTech is positively related to underpricing and with 95% confidence interval this result is barely significant. When regress initial returns only on the dummy variable FinTech, we can see that it is significantly positively related to underpricing. Underwriter’s reputation has a positive relation with underpricing (with significant t-statistic of 3.62) and this is not in line with previous studies. This can be partially explained by the changing issuer objective function hypothesis which argued that issuers are willing underprice more to seek for a lead underwriter with highly ranked analyst to cover the firm(Loughran and Ritter, 2004).

Moreover, deal size (t=0.29) and firm age(t=0.25) are found to be positively related to initial returns but are not statistically significant, therefore this is the line with our expectations.

Table 4 Regression result

Regress initial returns on the following variables.

(2) (3) (6) (7) All 4 variables Intercept .1203728 (10.91) -.123144 (-2.21) -.2804274 (-1.74) .0996101 (3.77) -.1635033 (-1.00) FinTech(dummy) (2) .0716896 (2.35) .0496324 (1.61) Underwriter Rank (3) .0317956 (4.62) .0286907 (3.62) Log(deal size) (6) .0219694 (2.55) .002805 (0.29)

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16 Log(firm age) (7) .013052 (1.24) .0026669 (0.25) Number of obs 594 594 594 594 594 Adjusted R2 0.0076 0.0331 0.0092 0.0009 0.0332 Root MSE .25063 .24739 .25044 .25148 .24738

*t-statistics in parentheses (95% confidence level)

6.2 Long run performance 6.2.1 Summary statistics

A brief summary of FinTech IPOs per year is given in Table 5. We can see that most of the firms (30 out of 40) went public from 2010 to 2012 and many of them (25 out of 40) chose to list on New York Stock Exchange. The average deal size of all firms is around 2001 million dollars and the average firm age is approximately 21.88 years. It is also remarkable that in 2010, when the highest amount of firms(13) went public, had the lowest average deal size of 157 million dollars and a relatively younger group of firms (16.55 years).

Table 5 Summary characteristic of FinTech IPOs from 2008 to 2013

This table summarizes the key statistics of FinTech IPOs from 2008 to 2013. The first three columns count for the number of IPOs in each year and for how many of them are listed on NYSE and on NASDAQ. Deal size is the offer value or the proceeds of initial public offerings and is calculated on average for each year in million dollars. Firm age + 1 is calculated in years and measures the difference between the year of founding and the year of IPO. Moreover, the average ranking of underwriters for 39 FinTechs(excluding LendingTree) is 8.66.

Number of FinTech IPOs Listed on NYSE Listed on NASDAQ

Deal size ($m) Firm age +1 (year)

2008 2 1 1 9844 25.43 2009 3 1 2 712 27.42 2010 13 8 5 157 16.55 2011 8 4 4 682 19.24 2012 9 8 1 359 28.92 2013 5 3 2 251 13.70

Total 40 25 15 2001 (average) 21.88 (average)

6.2.2 Benchmark adjusted returns

Table 6 summarizes the mean, standard error and t-statistics for benchmark-adjusted returns and counts for the number of positive or negative returns in the AR series. In panel A,83 weeks after IPO

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17 have positive weekly ARs while only 9 weeks (out of 83) have t-statistic larger than 1.65(with 95% confidence level). On the other hand, 74 weeks have negative average returns and 9 of them are significant. In panel B, 11(out of 87) weeks have significantly positive returns and 6 (out of 70) weeks have significantly negative returns.

When using NASDAQ as benchmark, the mean of all weekly AR is 0.07%. With the t-statistic value of 0.8511, there is not enough evidence to show that the mean AR is significantly positive. However, when adjusting against NYSE, the mean increases to 0.20% with the same level of standard error and so a higher t-statistic value of 2.3652. This indicates that FinTech IPOs are significantly outperforming NYSE market at 99% confindence level over the three year period.

To find out the possible reason behind this, I create a small table as follows.

count count count count count Count Sum

2016 5 5 2015 9 5 14 2014 8 9 5 22 2013 13 8 9 5 35 2012 3 13 8 9 33 2011 2 3 13 8 26 2010 2 3 13 18 2009 2 3 5 2008 2 2

I roughly counted for each involving year the number of times it has been reused. For instance, IPOs in 2008 need to include year 2008,2009, 2010 and 2011 when analyzing the three year

aftermarket performance. I have found that years from 2011 to 2014 are used for more than 20 times. I select this period and compare the performance of NYSE to NASDAQ. As shown in Table 11, during that period, NYSE was significantly underperformed than NASDAQ. This could explain why AR againt NYSE is larger than AR against NASDAQ.

Table 6 Descriptive statistics of average benchmark adjusted returns(AR) for all FinTechs

Panel A NASDAQ Panel B NYSE

r(NYSE)-r(NASDAQ) 2011 to 2014

Mean 0.07% 0.20% -0.13%

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18

t-statistics 0.8511 2.3652 -2.1592

Number of postive returns 83 (9 significant) 87 (11 significant) - Number of negative returns 74 (9 significant) 70 (6 significant) -

Number of observations 157 157 209

Table 7 provides the cumulative benchmark-adjusted return (CRR) in each event week based on AR. For both panel A and B, CAR is in general increasing over the three year period, from 0.20% to 11.19% in A and from 0.49% to 30.92% in B. The difference is that in A, only week 11, 21and 41have significant results but in B, all weeks expect for week 1 have significantly positive returns. This means that when analyze against NASDAQ, FinTech will generally ourperform within the first year after IPO and will at least not significantly underperformed for the following weeks.

The above findings are in contrast to Ritter’s (1991) results. He studied US IPOs in the 1975-84 period and found a list contains majorly significant and negative CARs and CAR was getting more negative over three years. This could be explained by the underwriter marketing hypothesis that highly ranked underwriters(average rank equals to 8.66) will boost the long run IPO returns(Dong, Michel and Pandes, 2011). Additionally, due to that fact that big banks are seeking to collaborate with FinTechs for its new technology, they probably will offer help after they went public in

return(referring back to section 2).

Moreover, the time-series one-day-lag autocorrelation(=cov/var) for AR series is -0.001928 (=-0.000008/0.00415) in A and is -0.001453(=-0.000006 / 0.00413) in B, the negative autocorrelation indicating that the AR series is not increasing steadily week by week but fluctuates a little bit(not much since the autocorrelation is very close to zero)4.

Table 7 Benchmark adjusted returns of all FinTechs

This table shows the three-year aftermarket performance for all FinTech IPOs against different benchmarks. The measurement methods includes average benchmark-adjusted return(AR) on weekly basis and cumulative benchmark-adjusted return(CAR). According to datastream, there are in total 157 event weeks(t) in three years and event week 1 means the first week after IPO. Number of FinTech IPOs is 40(n=40) for all event weeks because all selected firms start trading one week after IPO and there was no firm delisted afterwards. Panel A uses NASDAQ

COMPOSITE as benchmark and panel B uses NYSE COMPOSITE. While calculating the t-statistics for CAR, the average (over 157 weeks) cross-sectional variance are 0.00415

(NASDAQ-based) and 0.00413 (NYSE-based) respectively. And the first-order autocovariance (cov) has value of -0.000008 for panel A and of -0.000006 for panel B. This table presents results for every 10 weeks and full list of findings can be found in Appendix 2 and 3.

4

“A negative autocorrelation implies that if a particular value is above average the next value (or for that matter the previous value) is more likely to be below average. If a particular value is below average, the next value is likely to be above average.” http://www.pmean.com/09/NegativeAutocorrelation.html

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19 Panel A NASDAQ Event week Number of FinTech IPOs

AR(%) t-statistic (AR) CAR(%) t-statistic (CAR)

1 40 0.20% 0.1995 0.20% 0.1952 11 40 -0.29% -0.3146 7.74% 2.2969 21 40 2.05% 1.0426 10.46% 2.2461 31 40 0.39% 0.4575 7.49% 1.3230 41 40 -0.90% -0.6952 11.01% 1.6926 51 40 -0.52% -0.4572 10.41% 1.4346 61 40 0.64% 0.9234 10.75% 1.3546 71 40 -1.06% -0.9273 11.75% 1.3722 81 40 1.10% 1.1936 12.26% 1.3401 91 40 -0.76% -1.1251 7.23% 0.7455 101 40 0.72% 1.3515 8.08% 0.7907 111 40 0.95% 1.09 9.65% 0.9012 121 40 0.58% 0.5393 11.13% 0.9957 131 40 0.26% 0.2602 11.30% 0.9717 141 40 0.25% 0.2386 10.08% 0.8353 151 40 1.09% 1.0836 10.07% 0.8063 157 40 -0.55% -0.6981 11.19% 0.8787 Panel B NYSE Event week Number of FinTech IPOs

AR(%) t-statistic (AR) CAR(%) t-statistic (CAR)

1 40 0.49% 0.4873 0.49% 0.4809 11 40 -0.08% -0.0820 8.83% 2.6254 21 40 2.32% 1.1747 12.76% 2.7449 31 40 0.44% 0.5141 11.12% 1.9694 41 40 -0.78% -0.5850 15.79% 2.4310 51 40 -0.70% -0.6223 16.16% 2.2317 61 40 1.07% 1.4676 18.34% 2.3150 71 40 -0.85% -0.7352 21.45% 2.5098 81 40 1.34% 1.4508 22.80% 2.4979 91 40 -0.55% -0.7743 18.96% 1.9598 101 40 0.77% 1.4354 20.76% 2.0362 111 40 1.16% 1.3069 23.26% 2.1770 121 40 0.80% 0.7620 26.09% 2.3387 131 40 0.36% 0.3609 27.58% 2.3761 141 40 0.40% 0.3801 28.17% 2.3393 151 40 1.30% 1.3093 29.30% 2.3510 157 40 -0.69% -0.8625 30.92% 2.4331

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20 Table 8 provides insights of AR per FinTech sectors. In panel A, Financial BPO and Payments / Loyalty / eCommerce have negative mean of AR while the rest are positive. However, none of them has significant result. In panel B, FinHCIT is the only sector which has significant positive return. Moreover, Insurance (t= 1.6007), Banking/Lending (t=1.318) and Securities/Capital Markets/Wealth (t=1.2334) are barely significant.

Table 8 AR for each FinTech sectors

This table analyzes the average benchmark-adjusted return (AR) for each FinTech sectors. FT Partners’ FinTech Database has grouped FinTechs into 7 categories based on the products or services provided by the firms.

* BPO is Business Process Outsourcing * HCIT is Healthcare Internet Technologies No.

FinTech

Panel A NASDAQ Panel B NYSE

Mean Std. Err. t-statistic Mean Std. Err. t-statistic Banking/Lending 8 0.13% 0.20% 0.6519 0.263% 0.199% 1.318 Financial BPO 4 -0.12% 0.27% -0.4390 0.004% 0.268% 0.0167 Financial Management Solutions 3 0.16% 0.34% 0.4800 0.269% 0.339% 0.7923 FinHCIT 6 0.21% 0.17% 1.2246 0.343% 0.165% 2.0776 Insurance 2 0.29% 0.26% 1.1059 0.424% 0.265% 1.6007 Securities/Capital Markets/Wealth Management 7 0.15% 0.20% 0.7522 0.245% 0.198% 1.2334 Payments / Loyalty / eCommerce 10 -0.10% 0.16% -0.6583 0.033% 0.160% 0.2092

7. Conclusion and limitations

In this paper, I analyzed the short run and long run post-IPO performance for 79 FinTech IPOs from 2008 to 2016.

In the short run, FinTech IPOs are more underpriced and this is line with my hypothesis. Average initial return for all 79 FinTechs is 21.78% is significantly higher than zero. Compared to other US IPOs in the equivalent months, FinTech’s underpricing is higher than non-FinTechs. On average, FinTechs have a larger monthly mean value of 22.62% compared to non-FinTechs of 9.90%. The difference between them is 12.72% and is significantly larger than zero. The regression results suggest that being a FinTech is positively related to underpricing(barely significant). The variables of

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21 deal size and firm age are also found to be positively related to initial returns but are not statistically significant. In contrast to previous studies, underwriter’s reputation has a significantly positive relation with underpricing and this deviation can be explained by the changing issuer objective function hypothesis proposed by Loughran and Ritter(2004).

As for long run, I found positive aftermarket performance and hence my second hypothesis is rejected. The mean weekly average benchmark-adjusted return against NASDAQ is not significantly different from zero but when using NYSE as benchmark, the mean weekly AR increased to 0.2% and is significantly positive. In terms of cumulative average benchmark-adjusted return, both NASDAQ and NYSE panels suggesting continuous increase during the three years period after IPO(end value of CAR are 11.19% and 30.92% respectively). These findings are different than Ritter’s (1991) results. He found mainly negative CARs and they were getting more negative over years. This derivation could be explained by the underwriter marketing hypothesis which argued that highly ranked

underwriters(the average rank for FinTechs equals to 8.66) will boost the long run IPO returns(Dong, Michel and Pandes, 2011).

There are limitations in my research. In general, the sample size for FinTech IPOs is relatively small compared to other studies. Especially for the long run analysis, many recent IPOs need to be taken out since they are not gone public for at least 3 years. For future studies, I would suggest including more explanatory variables such as the number of underwriters and the amount of shares offered, and maybe also include the reputation ranking for venture capitalists when the information is available. The partnerships with big banks could also be analyzed more for the long run stock

performance.

Appendix

Appendix 1 List of 79 FinTech IPOs

IPO Date Issuer Symbol r(UP) Deal Size

(m$)

Underwriter reputation ranking(Ave)

Firm age +1 (in years) 2016/5/26 Cotiviti Holdings COTV -9.95% 238 9.00 38.42 2015/12/18 Yirendai Ltd. YRD -9.00% 75 8.17 4.96 2015/11/19 Square SQ 45.22% 243 9.00 7.88 2015/10/14 First Data FDC -1.56% 2560 8.30 27.80

2015/10/9 CPI Card Group PMTS 21.70% 173 7.75 9.78

2015/6/26 Xactly XTLY 8.75% 65 8.67 11.49

2015/6/26 AppFolio APPF 17.33% 86 8.75 10.49

2015/6/25 TransUnion TRU 12.89% 665 8.75 48.51

2015/6/19 MINDBODY MB -17.43% 100 8.67 18.47

2015/6/5 Evolent Health EVH 10.94% 225 9.00 5.43

2015/5/21 Shopify SHOP 51.06% 151 8.50 12.39

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22 Financial

Services

2015/4/16 Virtu Financial VIRT 16.74% 361 8.50 8.29

2015/4/16 Etsy ETSY 87.50% 267 9.00 10.29 2015/2/12 Inovalon Holdings INOV 0.00% 600 8.80 11.12 2014/12/17 On Deck Capital ONDK 39.90% 230 8.60 8.96 2014/12/12 Workiva WK -1.79% 101 8.75 7.95 2014/12/12 Connecture CNXR 10.00% 53 9.00 16.96 2014/12/11 LendingClub LC 56.20% 1001 8.88 9.95 2014/11/6 Upland Software UPLD -18.75% 46 7.00 18.86 2014/7/31 HealthEquity HQY 25.71% 147 8.50 13.59 2014/7/31 Synchrony Financial SYF 0.00% 2955 8.69 12.59 2014/7/23 Medical Transcription Billing MTBC -14.40% 20 3.00 16.57 2014/6/25 Imprivata IMPR 8.33% 86 8.25 14.49 2014/6/19 Markit Ltd. MRKT 11.25% 1283 8.60 12.47 2014/4/15 Paycom Software PAYC 2.33% 100 8.50 17.30

2014/4/10 Ally Financial ALLY -4.08% 2375 8.70 91.33

2014/4/4 Five9 FIVN 9.14% 70 8.50 14.26

2014/4/4 IMS Health

Holdings

IMS 15.00% 1300 9.00 27.27

2014/3/27 TriNet Group TNET 19.38% 240 8.83 27.25

2014/3/20 Q2 Holdings QTWO 16.69% 116 8.00 10.22 2014/3/19 Paylocity Holding PCTY 41.41% 138 8.00 18.22 2014/3/13 Castlight Health CSLT 148.75% 178 9.00 7.20 2014/3/7 Quotient Technology QUOT 87.50% 168 8.38 17.19 2013/10/31 Marcus & Millichap MMI 11.83% 83 9.00 43.86 2013/10/16 Springleaf Holdings LEAF 13.29% 358 8.50 87.85 2013/10/10 Stonegate Mortgage SGM 14.06% 114 7.50 9.78 2013/9/18 Benefitfocus BNFY 102.08% 150 8.50 14.72 2013/7/19 RetailMeNot SALE 31.90% 191 8.83 7.55 2013/5/9 PennyMac Financial Services PFSI 6.11% 189 8.75 6.36 2013/5/3 QIWI plc QIWI 0.47% 213 8.75 7.34 2013/4/19 Blackhawk Network Holdings HAWK 13.09% 230 8.75 13.30 2013/4/12 EVERTEC EVTC 2.20% 505 9.00 26.30 2013/3/20 Model N MODN 28.90% 120 8.75 15.22 2012/10/12 Workday WDAY 73.89% 637 9.00 8.78 2012/10/11 Realogy Holdings RLGY 26.67% 1242 8.63 107.85 2012/10/3 LifeLock LOCK -7.11% 140 8.67 8.76

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23 2012/8/10 Performant Financial PFMT 17.78% 81 8.63 37.63 2012/5/10 WageWorks WAGE 11.00% 59 7.00 13.36 2012/5/3 EverBank Financial EVER 6.00% 221 8.67 9.34 2012/3/22 Vantiv VNTV 14.71% 500 8.80 43.25 2012/3/8 Nationstar Mortgage Holdings NSM 1.79% 233 8.50 19.19 2012/1/25 Guidewire Software GWRE 31.69% 115 8.83 12.07 2011/11/4 Groupon GRPN 30.55% 700 8.83 4.84 2011/7/27 Tangoe TNGO 16.50% 101 8.00 12.58 2011/6/17 Bankrate RATE 2.27% 300 8.88 36.48

2011/4/15 Ellie Mae ELLI 12.83% 45 8.00 15.29

2011/3/25 ServiceSource International SREV 21.80% 119 8.75 13.24 2011/3/17 Cornerstone OnDemand CSOD 46.69% 137 8.50 13.21

2011/3/10 HCA Holdings HCA 3.40% 3786 8.61 44.22

2011/1/28 InterXion Holding N.V. INXN 6.15% 265 8.50 14.08 2010/12/15 GAIN Capital Holdings GCAP -1.67% 4 9.00 12.96 2010/12/15 FleetCor Technologies FLT 20.65% 335 9.00 8.96 2010/12/2 FXCM FXCM 6.07% 211 8.83 6.92 2010/11/18 LPL investmens LPLA 7.17% 468 9.00 43.91 2010/11/10 Noah Holdings Limited NOAH 33.25% 101 8.75 6.86 2010/8/6 Intralink Holdings IL -26.08% 143 8.83 15.61 2010/7/29 Envestnet ENV 13.67% 63 8.50 12.58

2010/7/22 Green Dot GDOT 22.19% 164 9.00 12.56

2010/7/16 Qlink Technologies QLIK 28.00% 129 9.00 18.55 2010/6/17 Higher One Holdings ONE 18.92% 108 9.00 11.47 2010/4/22 SPS Commerce SPSC 13.33% 23 7.00 24.32 2010/3/31 SS&C Technologies Holdings SSNC 0.53% 161 9.00 25.26 2010/3/16 Financial Engines FNGN 43.75% 127 9.00 15.21 2009/12/16 Team Health Holdings TMH 6.75% 160 8.63 31.98 2009/10/7 Verisk Analytics VRSK 23.73% 1876 8.75 39.79 2009/6/25 Medidata Solutions MDSO 21.43% 101 8.75 10.49

2008/8/12 LendingTree TREE 222.47% 38

Self-underwritten

11.62

2008/3/18 Visa V 28.41% 19650 9.00 39.24

Average 21.78% 653 8.54 20.15

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24 *remark: LendingTree, Inc. (NYSE: TREE) has been taken out since it is “self-underwritten”

according to NASDAQ website.

Appendix 2 Benchmark adjusted return of all FinTechs against NASDAQ (long run performance)

Event week

Number of FinTech IPOs

AR(%) t-statistic (AR) CAR(%) t-statistic (CAR) 1 40 0.20% 0.1995 0.20% 0.1952 2 40 0.63% 0.6168 0.82% 0.5727 3 40 0.07% 0.0628 0.89% 0.5058 4 40 1.78% 1.7102 2.67% 1.3130 5 40 1.43% 1.3072 4.10% 1.8041 6 40 2.51% 2.3773 6.61% 2.6538 7 40 1.87% 1.2031 8.48% 3.1538 8 40 -1.28% -1.0529 7.20% 2.5047 9 40 -1.47% -1.054 5.73% 1.8792 10 40 2.30% 1.9436 8.03% 2.4998 11 40 -0.29% -0.3146 7.74% 2.2969 12 40 -0.72% -0.8852 7.02% 1.9936 13 40 1.31% 1.0102 8.33% 2.2737 14 40 1.24% 1.0646 9.57% 2.5165 15 40 -1.91% -1.9939 7.66% 1.9448 16 40 0.91% 0.8074 8.57% 2.1075 17 40 -0.99% -1.1471 7.57% 1.8072 18 40 -0.15% -0.1492 7.43% 1.7219 19 40 1.41% 1.5388 8.84% 1.9944 20 40 -0.42% -0.3796 8.41% 1.8513 21 40 2.05% 1.0426 10.46% 2.2461 22 40 0.83% 0.6429 11.29% 2.3680 23 40 0.33% 0.3649 11.62% 2.3845 24 40 -2.76% -2.4845 8.86% 1.7802 25 40 -1.74% -1.3713 7.13% 1.4021 26 40 -1.20% -1.1337 5.92% 1.1432 27 40 0.16% 0.2267 6.09% 1.1528 28 40 1.43% 1.2819 7.52% 1.3974 29 40 0.16% 0.1793 7.67% 1.4021 30 40 -0.58% -0.5786 7.09% 1.2745 31 40 0.39% 0.4575 7.49% 1.3230 32 40 -0.04% -0.036 7.45% 1.2951 33 40 0.06% 0.0717 7.51% 1.2864 34 40 1.01% 1.0914 8.52% 1.4371 35 40 -0.94% -0.7762 7.57% 1.2598 36 40 2.09% 2.0509 9.66% 1.5849 37 40 -0.55% -0.5278 9.12% 1.4747

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25 38 40 2.82% 1.3319 11.94% 1.9054 39 40 -0.12% -0.1076 11.82% 1.8616 40 40 0.10% 0.09 11.92% 1.8539 41 40 -0.90% -0.6952 11.01% 1.6926 42 40 -0.27% -0.2292 10.74% 1.6309 43 40 0.92% 1.2254 11.66% 1.7503 44 40 -1.41% -0.9907 10.25% 1.5212 45 40 0.77% 0.5223 11.02% 1.6169 46 40 -0.49% -0.4487 10.53% 1.5283 47 40 -0.62% -0.6246 9.91% 1.4228 48 40 2.15% 2.6118 12.06% 1.7133 49 40 0.01% 0.0116 12.07% 1.6972 50 40 -1.14% -1.3816 10.93% 1.5210 51 40 -0.52% -0.4572 10.41% 1.4346 52 40 -0.25% -0.2286 10.17% 1.3872 53 40 -1.68% -1.2453 8.49% 1.1473 54 40 0.55% 0.5532 9.04% 1.2103 55 40 1.45% 0.851 10.49% 1.3917 56 40 -0.26% -0.2797 10.23% 1.3454 57 40 1.45% 1.2101 11.68% 1.5228 58 40 -0.32% -0.3171 11.36% 1.4679 59 40 -0.69% -0.7541 10.67% 1.3669 60 40 -0.55% -0.5637 10.12% 1.2851 61 40 0.64% 0.9234 10.75% 1.3546 62 40 0.23% 0.2743 10.98% 1.3722 63 40 -1.45% -1.6563 9.53% 1.1815 64 40 0.63% 0.8377 10.16% 1.2497 65 40 0.80% 0.8068 10.96% 1.3376 66 40 0.00% 0 10.96% 1.3274 67 40 1.46% 1.1847 12.41% 1.4924 68 40 0.57% 0.5758 12.98% 1.5491 69 40 -1.23% -1.5624 11.75% 1.3922 70 40 1.06% 0.9052 12.81% 1.5071 71 40 -1.06% -0.9273 11.75% 1.3722 72 40 0.31% 0.351 12.06% 1.3984 73 40 -0.13% -0.1825 11.93% 1.3735 74 40 -0.23% -0.3069 11.69% 1.3373 75 40 0.29% 0.2843 11.98% 1.3609 76 40 0.02% 0.0236 12.00% 1.3541 77 40 -0.56% -0.6617 11.44% 1.2823 78 40 0.64% 0.7725 12.08% 1.3457 79 40 -0.31% -0.3243 11.77% 1.3030 80 40 -0.61% -0.6777 11.16% 1.2279 81 40 1.10% 1.1936 12.26% 1.3401

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26 82 40 -1.57% -1.7164 10.69% 1.1611 83 40 -1.66% -1.926 9.02% 0.9743 84 40 1.14% 1.4694 10.16% 1.0904 85 40 -1.92% -1.3946 8.23% 0.8789 86 40 -0.22% -0.2818 8.01% 0.8500 87 40 -0.59% -0.6146 7.42% 0.7824 88 40 1.39% 1.6645 8.80% 0.9235 89 40 -0.64% -0.8444 8.16% 0.8511 90 40 -0.17% -0.249 7.99% 0.8287 91 40 -0.76% -1.1251 7.23% 0.7455 92 40 -0.07% -0.0793 7.16% 0.7341 93 40 -0.68% -0.6639 6.48% 0.6608 94 40 -0.75% -0.938 5.72% 0.5809 95 40 0.94% 1.2876 6.67% 0.6730 96 40 1.30% 1.7362 7.97% 0.8000 97 40 -1.01% -1.4821 6.95% 0.6946 98 40 0.15% 0.1336 7.10% 0.7055 99 40 0.46% 0.6684 7.56% 0.7472 100 40 -0.20% -0.2481 7.36% 0.7240 101 40 0.72% 1.3515 8.08% 0.7907 102 40 0.24% 0.2412 8.32% 0.8102 103 40 -1.88% -2.0541 6.43% 0.6235 104 40 0.01% 0.0184 6.45% 0.6219 105 40 1.18% 1.2541 7.63% 0.7327 106 40 1.17% 0.6525 8.80% 0.8409 107 40 -0.37% -0.4943 8.43% 0.8019 108 40 -0.92% -1.1348 7.51% 0.7112 109 40 1.14% 1.4806 8.66% 0.8158 110 40 0.04% 0.0515 8.70% 0.8163 111 40 0.95% 1.09 9.65% 0.9012 112 40 0.34% 0.444 9.99% 0.9291 113 40 -0.07% -0.0869 9.92% 0.9187 114 40 0.90% 1.2413 10.82% 0.9974 115 40 0.39% 0.5411 11.21% 1.0284 116 40 0.06% 0.0591 11.27% 1.0296 117 40 0.01% 0.0144 11.28% 1.0265 118 40 -0.45% -0.4964 10.83% 0.9811 119 40 0.10% 0.1101 10.93% 0.9857 120 40 -0.37% -0.3075 10.56% 0.9481 121 40 0.58% 0.5393 11.13% 0.9957 122 40 -0.34% -0.4182 10.79% 0.9612 123 40 0.05% 0.0533 10.84% 0.9615 124 40 -2.31% -2.2746 8.53% 0.7538 125 40 0.44% 0.3774 8.97% 0.7897

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27 126 40 0.91% 1.159 9.88% 0.8662 127 40 -0.06% -0.078 9.82% 0.8575 128 40 1.06% 1.4156 10.88% 0.9463 129 40 0.30% 0.3662 11.18% 0.9684 130 40 -0.14% -0.1552 11.04% 0.9529 131 40 0.26% 0.2602 11.30% 0.9717 132 40 1.38% 1.7266 12.68% 1.0858 133 40 -0.34% -0.3267 12.34% 1.0530 134 40 -1.90% -2.3727 10.44% 0.8874 135 40 0.53% 0.5725 10.97% 0.9287 136 40 -0.53% -0.7553 10.43% 0.8802 137 40 -1.97% -1.8725 8.46% 0.7115 138 40 0.54% 0.7282 9.00% 0.7542 139 40 1.05% 0.7286 10.05% 0.8392 140 40 -0.22% -0.3365 9.83% 0.8178 141 40 0.25% 0.2386 10.08% 0.8353 142 40 -0.44% -0.5315 9.64% 0.7959 143 40 -0.87% -1.0792 8.77% 0.7217 144 40 1.72% 1.1882 10.49% 0.8601 145 40 1.10% 0.9262 11.59% 0.9472 146 40 -2.59% -1.6213 9.01% 0.7334 147 40 -0.81% -0.8915 8.20% 0.6654 148 40 1.39% 1.4685 9.59% 0.7756 149 40 -1.22% -1.4316 8.37% 0.6747 150 40 0.61% 0.755 8.98% 0.7213 151 40 1.09% 1.0836 10.07% 0.8063 152 40 1.54% 2.1426 11.61% 0.9265 153 40 0.61% 0.5447 12.22% 0.9721 154 40 -0.40% -0.4373 11.82% 0.9374 155 40 -0.06% -0.0883 11.76% 0.9297 156 40 -0.03% -0.0251 11.74% 0.9247 157 40 -0.55% -0.6981 11.19% 0.8787

Appendix 3 Benchmark adjusted return of all FinTechs against NYSE (long run performance) Event

week

Number of FinTech IPOs

AR(%) t-statistic(AR) CAR(%) t-statistic CAR

1 40 0.49% 0.4873 0.49% 0.4809 2 40 0.60% 0.5767 1.09% 0.7607 3 40 0.11% 0.1021 1.20% 0.6830 4 40 1.85% 1.8080 3.05% 1.5048 5 40 1.52% 1.4032 4.58% 2.0168 6 40 2.42% 2.2006 6.99% 2.8137 7 40 2.11% 1.3123 9.10% 3.3895 8 40 -1.03% -0.8509 8.07% 2.8134

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28 9 40 -1.30% -0.9383 6.78% 2.2264 10 40 2.13% 1.8209 8.91% 2.7773 11 40 -0.08% -0.0820 8.83% 2.6254 12 40 -0.79% -0.9827 8.04% 2.2881 13 40 1.61% 1.2064 9.65% 2.6396 14 40 1.28% 1.0908 10.93% 2.8809 15 40 -1.92% -1.9685 9.02% 2.2948 16 40 1.04% 0.9281 10.06% 2.4793 17 40 -0.87% -1.0096 9.19% 2.1982 18 40 0.09% 0.0945 9.29% 2.1578 19 40 1.45% 1.5958 10.73% 2.4275 20 40 -0.30% -0.2711 10.43% 2.3004 21 40 2.32% 1.1747 12.76% 2.7449 22 40 0.98% 0.7460 13.74% 2.8873 23 40 0.59% 0.6337 14.33% 2.9449 24 40 -2.68% -2.4133 11.64% 2.3434 25 40 -1.70% -1.3522 9.95% 1.9612 26 40 -1.04% -1.0242 8.91% 1.7219 27 40 0.33% 0.4355 9.23% 1.7516 28 40 1.60% 1.4384 10.83% 2.0181 29 40 0.08% 0.0899 10.91% 1.9971 30 40 -0.22% -0.2209 10.69% 1.9236 31 40 0.44% 0.5141 11.12% 1.9694 32 40 0.06% 0.0531 11.18% 1.9493 33 40 0.20% 0.2040 11.38% 1.9532 34 40 1.14% 1.3194 12.52% 2.1176 35 40 -0.78% -0.6232 11.74% 1.9569 36 40 1.98% 1.9677 13.72% 2.2549 37 40 -0.37% -0.3505 13.36% 2.1650 38 40 2.89% 1.4118 16.24% 2.5978 39 40 -0.01% -0.0075 16.23% 2.5630 40 40 0.33% 0.3022 16.56% 2.5823 41 40 -0.78% -0.5850 15.79% 2.4310 42 40 -0.13% -0.1126 15.66% 2.3821 43 40 1.39% 1.7838 17.04% 2.5625 44 40 -1.00% -0.7026 16.04% 2.3846 45 40 0.77% 0.5276 16.81% 2.4713 46 40 -0.48% -0.4531 16.33% 2.3745 47 40 -0.60% -0.6204 15.73% 2.2624 48 40 2.09% 2.4735 17.82% 2.5364 49 40 0.10% 0.1142 17.93% 2.5250 50 40 -1.07% -1.3017 16.86% 2.3510 51 40 -0.70% -0.6223 16.16% 2.2317 52 40 -0.11% -0.1068 16.05% 2.1947

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29 53 40 -1.66% -1.2316 14.39% 1.9486 54 40 0.60% 0.5945 14.99% 2.0113 55 40 1.61% 0.9433 16.60% 2.2066 56 40 -0.01% -0.0097 16.59% 2.1856 57 40 1.56% 1.3275 18.15% 2.3706 58 40 -0.31% -0.3144 17.84% 2.3096 59 40 -0.43% -0.4592 17.41% 2.2343 60 40 -0.13% -0.1370 17.27% 2.1985 61 40 1.07% 1.4676 18.34% 2.3150 62 40 0.34% 0.4252 18.68% 2.3393 63 40 -1.12% -1.3321 17.56% 2.1814 64 40 0.67% 1.0185 18.23% 2.2465 65 40 1.03% 1.0008 19.26% 2.3556 66 40 0.21% 0.1615 19.47% 2.3635 67 40 1.78% 1.4417 21.26% 2.5603 68 40 0.66% 0.7022 21.92% 2.6206 69 40 -0.80% -0.9955 21.11% 2.5060 70 40 1.19% 1.0181 22.30% 2.6284 71 40 -0.85% -0.7352 21.45% 2.5098 72 40 0.26% 0.3030 21.71% 2.5231 73 40 -0.05% -0.0708 21.66% 2.4997 74 40 -0.02% -0.0224 21.65% 2.4809 75 40 0.22% 0.2128 21.86% 2.4890 76 40 0.22% 0.2716 22.08% 2.4976 77 40 -0.45% -0.5232 21.63% 2.4307 78 40 0.60% 0.6786 22.24% 2.4823 79 40 -0.30% -0.3368 21.93% 2.4327 80 40 -0.47% -0.5443 21.46% 2.3655 81 40 1.34% 1.4508 22.80% 2.4979 82 40 -1.42% -1.5582 21.38% 2.3280 83 40 -1.62% -1.9295 19.76% 2.1383 84 40 1.29% 1.5975 21.04% 2.2638 85 40 -1.93% -1.4122 19.12% 2.0445 86 40 -0.14% -0.1771 18.98% 2.0175 87 40 -0.48% -0.4951 18.50% 1.9552 88 40 1.44% 1.7126 19.93% 2.0950 89 40 -0.43% -0.5459 19.50% 2.0384 90 40 0.01% 0.0084 19.51% 2.0276 91 40 -0.55% -0.7743 18.96% 1.9598 92 40 0.07% 0.0804 19.04% 1.9567 93 40 -0.52% -0.5138 18.51% 1.8927 94 40 -0.67% -0.8673 17.85% 1.8149 95 40 1.05% 1.4072 18.90% 1.9119 96 40 1.55% 2.0442 20.45% 2.0576

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30 97 40 -0.96% -1.4140 19.49% 1.9512 98 40 0.13% 0.1217 19.62% 1.9540 99 40 0.53% 0.7874 20.15% 1.9962 100 40 -0.16% -0.1930 19.99% 1.9709 101 40 0.77% 1.4354 20.76% 2.0362 102 40 0.32% 0.3195 21.07% 2.0571 103 40 -1.79% -1.8978 19.28% 1.8733 104 40 -0.12% -0.1576 19.16% 1.8523 105 40 1.18% 1.2612 20.34% 1.9570 106 40 1.26% 0.6888 21.60% 2.0686 107 40 -0.32% -0.4130 21.28% 2.0286 108 40 -0.62% -0.7746 20.67% 1.9607 109 40 1.34% 1.7657 22.01% 2.0784 110 40 0.10% 0.1127 22.10% 2.0779 111 40 1.16% 1.3069 23.26% 2.1770 112 40 0.41% 0.5478 23.67% 2.2056 113 40 0.15% 0.1912 23.82% 2.2093 114 40 1.08% 1.4921 24.90% 2.2994 115 40 0.52% 0.7117 25.42% 2.3368 116 40 0.23% 0.2110 25.65% 2.3480 117 40 0.09% 0.0864 25.74% 2.3458 118 40 -0.36% -0.3844 25.38% 2.3034 119 40 0.22% 0.2539 25.60% 2.3138 120 40 -0.31% -0.2616 25.29% 2.2760 121 40 0.80% 0.7620 26.09% 2.3387 122 40 -0.25% -0.3261 25.84% 2.3065 123 40 0.28% 0.3089 26.11% 2.3215 124 40 -2.20% -2.2031 23.92% 2.1177 125 40 0.58% 0.4808 24.50% 2.1603 126 40 1.11% 1.4429 25.60% 2.2489 127 40 -0.04% -0.0496 25.57% 2.2368 128 40 1.10% 1.5592 26.66% 2.3236 129 40 0.71% 0.8800 27.37% 2.3761 130 40 -0.15% -0.1724 27.22% 2.3542 131 40 0.36% 0.3609 27.58% 2.3761 132 40 1.61% 2.1073 29.19% 2.5053 133 40 -0.16% -0.1585 29.03% 2.4822 134 40 -1.73% -2.1553 27.30% 2.3253 135 40 0.73% 0.8159 28.03% 2.3784 136 40 -0.35% -0.4924 27.68% 2.3400 137 40 -1.61% -1.5228 26.07% 2.1963 138 40 0.63% 0.8398 26.71% 2.2413 139 40 1.17% 0.8175 27.88% 2.3312 140 40 -0.10% -0.1540 27.77% 2.3144

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31 141 40 0.40% 0.3801 28.17% 2.3393 142 40 -0.34% -0.4178 27.83% 2.3026 143 40 -0.67% -0.8042 27.16% 2.2394 144 40 1.64% 1.1310 28.80% 2.3661 145 40 1.43% 1.1796 30.22% 2.4746 146 40 -2.44% -1.5991 27.78% 2.2669 147 40 -0.84% -0.9264 26.95% 2.1913 148 40 1.31% 1.3345 28.25% 2.2896 149 40 -0.98% -1.1707 27.27% 2.2030 150 40 0.73% 0.9398 28.01% 2.2545 151 40 1.30% 1.3093 29.30% 2.3510 152 40 1.78% 2.4194 31.08% 2.4856 153 40 0.78% 0.7057 31.86% 2.5395 154 40 -0.13% -0.1516 31.73% 2.5207 155 40 -0.05% -0.0693 31.68% 2.5089 156 40 -0.07% -0.0682 31.61% 2.4953 157 40 -0.69% -0.8625 30.92% 2.4331 References

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Post-IPO stock performance for FinTech firms listed on US market between 2008 and 2016

Post-IPO stock performance for FinTech firms listed

on US market between 2008 and 2016

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