Long-Term Investment Performance of Founder-CEO, a Comparison of High and Low Growth of New Technology-Based Firms

Long-term investment performance of founder-CEO, a comparison of high and low growth of new technology-based firms

An event study of venture capital backed IPO firms in the United States.

MSc Thesis Finance Department of Finance TiU, Tilburg University Fall Semester 2019

Author: Serife Özdemir Administration number: 928717 (SNR: 2004044) Supervisor: Dr. Marco Da Rin Second reader: Dr. Julio Crego Date of Submission: November 21th, 2019

ABSTRACT

This study examines the long-term aftermarket performance of Initial Public Offering (IPO) firms in high and low technology environments by running a comparative analysis between founder CEO and non-founder CEO led firms in their survivability and performance. The research includes IPOs based on the US market in the period from January 1, 2007 until December 31, 2013. Empirical techniques selected in this event time study are the buy-and-hold abnormal returns and the calendar-time-portfolio method. The results measuring the long-run abnormal returns highly depend on the benchmarks and measurement methods. Hence, I use a multivariate regression analysis as an additional robustness check on the dependent variables of both methods employed for long-run aftermarket performance. The main finding is in support of the hypothesis that founder CEO presence is beneficial for high technology IPO firms. Moreover, the overall results show evidence that founder CEO firms consistently deliver significant higher long-run returns, compared to a successor CEO.

Keywords: initial public offering, long-term performance, founder CEO, abnormal returns

TABLE OF CONTENTS

ABSTRACT 2 TABLE OF CONTENTS 3 1. INTRODUCTION 4 1.1 BACKGROUND AND MOTIVATION 4 1.2 RESEARCH PROBLEM AND OBJECTIVES 6 1.3 STRUCTURE OF THE RESEARCH PAPER 6 2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT 7 2.1 WHY DO PRIVATE FIRMS GO PUBLIC? 7 2.2 OWNERSHIP STRUCTURE IN THE LONG RUN PERFORMANCE 7 2.2.1 VENTURE CAPITAL THEORY 8 2.2.2 HUMAN CAPITAL THEORY 8 2.2.3 FOUNDING TEAM COMPOSITION 9 2.3 AFTERMARKET PERFORMANCE MEASURES OF IPO FIRMS 10 2.4 RESEARCH HYPOTHESIS 11 3. DATA ANALYSIS 12 3.1 SAMPLE SELECTION AND DATA SOURCES 12 3.2 DESCRIPTIVE STATISTICS 14 4. ECONOMETRIC METHODOLOGY 19 4.1 MEASURES OF LONG-RUN PERFORMANCE 19 5. EMPIRICAL RESULTS AND LIMITATIONS 25 5.2 REGRESSION RESULTS 25 6. CONCLUSION 32 6.1 LIMITATIONS 33 6.2 FURTHER RESEARCH 33 APPENDIX 33 REFERENCES 34

1. INTRODUCTION

1.1 BACKGROUND AND MOTIVATION

This chapter focuses on the design, and the importance of analysing the relationship between post- IPO firm investment performance and inside ownership in technology-based firms. An IPO stands for Initial Public Offering, which is the first moment that shares of a private company are publicly sold to investors, and thereafter traded on the stock market where its shares become listed on the stock exchange market. Since an IPOs is the first time a company is obliged to disclose financial and business information, it provides valuable information to investors regarding the company's financial performance and condition. Therefore, a vast amount of financial literature written over the past decades has been about the pricing anomalies or return behaviour of IPOs, testing for the Market Efficiency hypothesis for the firm's stock measured by price changes around event time. This thesis aims to contribute to the theory by evaluating the effectiveness of alternative governance structures in IPO firms when going public by providing arguments on the positive or negative effect of Founder-CEO led firms in the long-run post-IPO aftermarket investment performance.

There are numerous reasons why companies decide to go public through an IPO. The most common reason is to acquire capital or to increase publicity, whereas some drawbacks associated with going public are; the dilution of ownership, control and the high costs. Various studies on corporate governance in the context of ownership structure have proven to have an effect on firm performance (Demsetz & Villalonga, 2001) (Bhagat & Bolton, 2008) (Gompers, Ishii, & Metrick, 2003). Particularly in cases of diffused ownership, where the majority of shareholders are insiders or family representative, have been found to influence firm performance (McConnell & Servaes, 1990) . According to (Fama & Jensen, Agency problems and residual claims. , 1983), the agency problem between managers and owners exist because of the separation in ownership and management. In a post-IPO period in which controlling owners are active by controlling rights, a separation in ownership and management will likely occur which might influence on operations and management arising the agency problem and will lead to controversy between owners,

management and minority shareholders according to (Edwards & Weichenrieder, 2009) (Dyck & Zingales, 2004) (Shleifer & Vishny, 1997) (Fama & Jensen, 1983) Especially in complex high growth technology-driven companies, information asymmetry arises between the owners´ goals and risk attitude in contrast to the management, resulting in agency problems (Wintoki, Linck, & Netter, 2012) (Jungwirth & Moog, 2004). A study on IPOs and acquisitions from past venture capital investments by (Wang & Wang, 2011) revealed that previous served founder CEOs with general experience are likely to pull off a successful exit strategy and lead to a shortage of the investment horizon. Yet entrepreneurs of high growth ventures who are in need of resources often relinquish a large proportion of their equity ownership for multiple rounds of funding (e.g. through angel investors or venture capital firms) or decide to stock related incentives such as stock options, grants and ownerships that are leading the founder’s equity stake in the firm being diluted. Since the founder will have less decision power as its equity in the firm is diluting, the founder may be in a position where the stakeholders could bring in a professional management team or could be forced to exit in order to bring in a senior level executive. However, in studies it shows that founder-CEO are valuable for high technology-based IPO firms since founder-CEO leadership leads to lower agency risks, greater power, influence within the firm and a lower risk position. Therefore, it is interesting to investigate the difference in the long run investment performance of founder CEO or successor CEO for high growth technology IPO firms through aftermarket performance of companies conducting an IPO (Baker & Gompers, 2003).

In academic research the market performance is usually measured over the short and long horizon. For this research study, data from recent venture capital backed undertakings in the U.S. has been used with longer datasets showing the differences between the two groups of founder-CEOs (non- founder) to make comparison analysis. In addition, an investigation of the relationship between the founder-CEO leadership or successor CEO in high or low growth technology industry are compared for the long-run performance.

1.2 RESEARCH PROBLEM AND OBJECTIVES

In a transitional event such as an IPO, the ownership structure and organizational form are one of the major areas that are subject to change. The long-run survival and growth of a new technology firm depends on the offer characteristics, which are the underwriter reputation, ownership characteristics and venture capital backing (Jain & Kini, 2000).To what extent does a Founder- CEO ́s ability such as managerial incentives and risk behaviour outperform a successor-CEO in high technology growth industries?

1.3 STRUCTURE OF THE RESEARCH PAPER

The following chapters will include a review of existing literature in the long run investment performance of IPO firms, from where the hypothesis for this research will be derived. The third chapter will describe the data and descriptive analysis from the data, followed by the empirical methodology. The fifth chapter describes the empirical results of the analysis from the long-run IPO firm performance measures, as well as the regression design and results of the analysis. The conclusion in chapter six, includes a short summary of the chapters and implications for further research. The final section of this paper consists of the appendices and a list of references.

2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

In this section an overview of the existing literature on IPO performance and inside ownership is presented. Firstly, a discussion on the theoretical background on inside ownership and the way it influences firm performance and entrepreneurial endeavours will be discussed. The following section deliberates the aftermarket performance measures in the long run. In the final section, the hypothesis development will be derived from the theoretical framework. The purpose of this section is to obtain general understanding in the dynamics of inside ownership and its impact in post-IPO aftermarket performance.

2.1 WHY DO PRIVATE FIRMS GO PUBLIC?

One to the major events during a private company’s lifecycle is considered to be the moment of an initial public offering. After the event takes place, the company its listed on the public market for the first time. The ownership structure and organizational form change after its transition to a public firm. In general, an IPO process is a costly, time consuming process were various parties are involved from the underwriters to legal offices. Through an IPO, usually the company intends to raise capital, increase publicity and create liquidity for the founders and shareholders. Some downsides associated with going public is the dilution of ownership and loss of control for the original owners (Ritter & Welch, 2002), (Pagano & Röell, 1998) and (Ibbotson & Ritter, 1995).

2.2 OWNERSHIP STRUCTURE IN THE LONG RUN PERFORMANCE

Previous literature on corporate governance in the context of ownership structure have proven to have an effect on firm performance (Demsetz & Villalonga, 2001), (Bhagat & Bolton, 2008), (Gompers, Ishii, & Metrick, 2003). Particularly in cases of diffused ownership, where the majority of shareholders are insiders or family representative, have been found to influence firm performance (McConnell & Servaes, 1990). According to (Fama & Jensen, 1983), the agency problem between managers and owners exist because of the separation in ownership and management. In a post-IPO period in which controlling owners are active by controlling rights, a separation in ownership and management will likely occur which might influence on operations and management arising the agency problem and will lead to controversy between owners,

management and minority shareholders according to (Edwards & Weichenrieder, 2009) (Dyck & Zingales, 2004) (Shleifer & Vishny, 1997) (Fama & Jensen, 1983). Especially in complex high growth technology-driven companies, information asymmetry arises between the owners´ goals and risk attitude in contrast to the management, resulting in agency problems (Wintoki, Linck, & Netter, 2012) (Wintoki et al., 2012) (Jungwirth & Moog, 2004). (Howton, Howton, & Olson, 2001) examined the relationship of ownership structure to long run performance based on measurement of the initial day, the first year and the three-year aftermarket Market adjusted returns. The outcomes indicated that ownership roles of the board in combination with the inside members relate significantly to all the return measures. The potential reasoning was given that board members that are also insiders can better align shareholders’ and management team’s interest, which lead to superior long-run IPO performances.

2.2.1 VENTURE CAPITAL THEORY

Venture capital firms play an important intermediate role in the financial markets for providing capital to early-stage technology firms that might otherwise gone unfunded. The scarce amount of capital for some firms at their early-stage require multiple rounds of financing and for growth- oriented firms the additional capital can ensure its survival. Venture capitalists (VCs) acquire equity stakes of an early stage company by taking a risk for a potential trade-off for above-average returns in the future. Venture capitalists (VCs) are monitoring, screening and offering support to the start-up companies to professionalize early in order to get their product to market. Moreover, VCs influences the composition of the board of directors, according to (Baker M. &., 2003) boards backed by venture capitals have fewer inside directors or founders as directors compared to those not backed by venture capital. The degree to which VC are involved after the investments has been made in terms of governance, depends strongly on how founder-friendly a VC firm is (Da Rin & Hellmann, 2019).

2.2.2 HUMAN CAPITAL THEORY

Technology firms are noticeable for high innovativeness in a knowledge intensive industry with rapid technological changes that require skilled human capital. A study on VC investments by (Kaplan & Stromberg, 2005) suggested that the experience of the management team at start-up is of importance as in guiding investment decisions of the firm. Experience is one of the highly sought

requirements for venture capitalist when considering a start-up investment. VCs consider experienced entrepreneur or (co-) founder as one of the successful venture backed firm that went public as experienced managers on the other side the uncertainties and risks stated by VCs are stated as a lack of operating experience, executive experience of the management team and youth of the founder (Hsu, 2007). The major determinants of start-up success in venture performance according to entrepreneurship literature stated that factors of high-technology venture success lies in forming larger and more complete teams, management team with prior joint experience, technical knowledge, skills, and prior experience in high-growth firms that competed in an equivalent industry as the start-up firm (Deeds, DeCarolis, & Coombs, 2000).

The key drivers of growth performance of technology-based firms are according to entrepreneurship literature known to be the human capital of founders and access to venture capital (VC) (Grilli & Colombo, 2010). The role of founders’ human capital can be divided into general and specific human capital. General human capital is the reputation built through experience and the skills gained across industries. Specific human capital is associated with industry specific know-how that contributes to growth and survival e.g. specifically in the technology sector (Gimeno, Folta, Cooper, & Woo, 1997).

2.2.3 FOUNDING TEAM COMPOSITION

Empirical evidence revealed that high-technology firms are more likely to be set-up by teams or as a partnership in which each individual conveys his/her own particular expertise in either management, finance or technology (Feeser & Willard, 1990). Prior research has stressed the connection between a positive venture outcome with human capital and therefore venture capitalist value human capital when gaining an early-stage approval to invest (Dalton, Lester, Certo, Dalton, & Cannella, 2006). Founders’ human capital play an important role for the competitive advantage of a technology-based firm. Empirical evidence generally supported the positive relationship between founders’ competences and firm performance. The founders’ competences measured by professional experience and educational level have proven to have a positive statistically significant impact on the firm’s growth. Especially founders’ that have former industry specific skills gained by self-employment or a prior managerial position were leadership competences were gained are positively influenced by performance. The entrepreneurs that share the same

background in the industry of operations as the start-up firm are more likely to be a high-growth tech firm, as a deep understanding of the market and customer needs are essential in defining the firm’s performance. Specifically, founders’ previous work experience and entrepreneurial experience matching the start-up’s sector influences the new technology firm’s success and survival (Colombo & Grilli, 2006) (Colombo & Grilli, 2007) (Wasserman, 2003). The founding team structure of high-technology firms are formed based on skills, abilities and experiences that supplement the founders’ own assets.

2.3 AFTERMARKET PERFORMANCE MEASURES OF IPO FIRMS

In recent studies it has been documented that long-term IPO performance on average underperform in the long-run, relative to the returns for matched firms portfolio or benchmark returns (Ritter 1991). For investing in IPO shares for a long period while holding the shares, would result in low returns. Prof J. Ritter used three year buy and hold returns against comparable firms and his results indicated the IPOs significantly underperformed and claimed that the results were sensitive to the benchmarks used and to the methodology and time-periods chosen. Therefore, he concluded that using the Fama French multifactor regressions could produce biased results. Findings from (Schultz, 2003) argues that event time IPO performance measures result in biased returns from pseudo market timing, which can be mitigated using calendar-time returns instead of event-time returns. Further findings on long-run performance measurement techniques in measuring abnormal returns have all its advantages and disadvantages. Therefore, implementing various measurement techniques can affect the results substantially.

Academic literature suggests that the decision of the entrepreneur to exit the market does depend on the entrepreneur human capital and thus not solely by financial performance. The key drivers of this decision are the entrepreneurs’ human capital and exit strategies such as an acquisition, M&A and initial public offering (IPO). Whereas, bankruptcy and liquidation represent as a failure for the founder. Success of the venture exit depends on the entrepreneur human capital in characteristics such as prior education level, prior experience, age, areas of study and industry experience (Stam, Thurik, & Van der Zwan, 2010) (Lee & Lee, 2015).

2.4 RESEARCH HYPOTHESIS

Several studies found evidence on performance differences of founder CEO in terms of higher firm valuation better stock market performance and better investment behavior compared to successor CEOs (Fahlenbrach, 2009). Academic papers advocate that the superior founder CEO performance relies on lower agency risks, greater power, influence within the firm and a lower risk position (Nelson, 2003). The findings by (Shleifer & Vishny, 1997) state that a founder CEO has a negative influence on post-IPO performance as a consequence of lack of diversification, distorting investment decisions and the lack of experience in running a public firm. In short, relative little is known about the relationship of founder CEO presence in the performance of IPO firms. Since an effective governance structures is essential around a corporate event such as an IPO when the firm goes through an organizational transition, this research aims to evaluate significant pot-IPO aftermarket performance in founder CEO led firms.

The research question derived for evaluating whether the relationship between post-IPO firm investment performance and founder CEO presence is positively related to the growth of high technology IPO firms. The hypothesis resulting from the literature is as follows:

H0: an IPO firm led by a founder CEO has significant post-IPO performance in a high technology environment.

3. DATA ANALYSIS

This chapter provides an extensive description of the empirical foundation for this research that will help to identify whether founder-CEO leadership is related to a higher post-IPO investment performance. The first section includes the data collection process, where the data and data sources will be discussed. The following section explains the variables that will define the significant post- IPO aftermarket performance of founder CEOs for this research study. In conclusion, section 3.2 presents the descriptive statistics of the variables used for the analysis.

3.1 SAMPLE SELECTION AND DATA SOURCES

This research study covers initial public offerings in the United States of America that received venture capital funding and underwent an IPO between 2007 and 2014, with a decrease in 2008 in the number of IPOs due to the global financial crisis. This timespan includes all recent initial public offerings to obtain a sufficient number of founder-CEOs still running the firm within a period of up to three and five years after going public for the long-run investment performance measures. In order to test the hypotheses and evaluate the long-run performance of IPO firms, all technology IPOs listed on the three major U.S. exchanges; NASDAQ, AMEX and NYSE, will be collected on their abnormal returns using the database Center for Research in Securities Prices (CRSP) and COMPUSTAT company financials database. The outcomes are further analysed in Stata and Excel and compared between the panel groups within the investigation period after going public.

The initial sample is a random sample selected from the Security Data Corporation (SDC) Platinum database of Thomson Reuters. This dataset consists of all venture-capital backed IPO issues within the sample period that are solely listed on primary stock exchanges in the United States and filtered upon Standard Industrial Classification (SIC) code for technology firms. The SIC classification is further used to determine the influence of founder CEOs in high technology firms and to make comparisons with the low technology firms. Therefore, the sample size has been divided into three categories based on the SIC code of the IPO firms: High- Technology, Low-Technology and the full sample. In order to divide the sample into high and low technology firms, the codes for internet and technology firms specified by (Ljungqvist & Wilhelm Jr, 2003) has been used for high-technology companies that are active in SIC codes: “3571, 3572,

3575, 3577, 3578, 3661, 3663, 3669, 3674, 3812, 3823, 3825, 3826, 3827, 3829, 4899, 7370, 7371, 7372, 7373, 7374, 7375, 7378 and 7379”. These classifications are based on Loughran and Ritter (2004) and all remaining technology or internet related firms are classified as low technology firm.

The seven-year sample period after the dot com bubble is chosen deliberately to avoid any significant effect on the number of IPOs in order to reach a sufficient number of firms to conduct this research. Several IPOs were eliminated to obtain a representative sample selection were corporate disclosed information is available from the listings. All stocks from the initial dataset not listed in the AMEX, NASDAQ or NYSE were eliminated from the sample. Financial institutions (SIC codes of 6000-6999), insurance and real estate investment trusts (REIT ́s), foreign issues (FI), limited partnerships (LP ́s), reverse leveraged buyouts (LBOs), Seasoned Equity Offerings (SEO's) and unit offerings were all excluded from the sample. The reason for exclusion is because the characteristics of these issues differ compared to other issuing firms, therefore the focus is on common stocks. The data collected from the SDC database on IPO offering characteristics holds: the offer price, first trading date, and ticker symbol, SIC codes and technology firm classification. Furthermore, all offerings with an offer price below $5 per share were eliminated from the sample because penny stocks cause the firm valuation to be problematic (Chambers & Dimson, 2009) (Liu & Ritter, 2011). In order to calculate the firm maturity, company founding date has been collected from the SDC database and any missing information has been supplemented via Google Finance. To obtain the age of the firm at the time of going public, the IPO date on SDC was taken as the base date and subtracted from the founding date.

Once the dataset of IPO firms is derived, the next step is to collect firm specific data of the sample selected IPO firms. I manually collect the data related to the founding team or executive management team and accounting data from the IPO prospectuses of each firm from the SEC EDGAR database. Subsequently, both datasets are then merged with publicly available information and any accounting data in the range of at least five years after the IPO listing of each firm. Since this study focuses on determining whether founder or non-founder CEO led firms differ in investment performance in the long run after going public. The treatment group would be the founder CEO led sample firms over which the post-IPO investment performance is being evaluated. The post IPO investment performance is assessed over a five-year post IPO window

until the CEO departure, IPO firm delisting date, or five-year founder CEO anniversary. The post- IPO period takes five years where the founder CEO remains as a CEO to ensure that the returns are tracked for the founder CEO portfolio and aimed at capturing the founder CEO effect on the long run performance. IPO prospectus of each listed firm from the dataset has been consulted in order to acquire data on: (1) the status of the founder CEO at IPO date (such as; CEO, Top Management Executive or director, or no role in the firm), (2) CEO ownership, (3) CEO duality, (4) board composition. Additional information required on the founders presence of three and five years post-IPO is backed by using the company website or LinkedIn, the world’s largest professional social network platform or Bloomberg for each IPO founder individually. All IPO firms without an IPO prospectus available on SEC EDGAR database or SDC database were excluded from the sample.

The following step is to download historical stock information. The share price, company financials and information on several benchmark index returns has been collected from the CRSP and Compustat database. The share prices and returns are collected by a run on the event study regression on CRSP Wharton where the CRSP company identifier code is collected from the list of founding dates for firms that went public in the U.S. during (1975-2017)1. Data retrieved from CRSP and Compustat contains the initial start date (IPO date or first trading day) the first trading day closing price and the standard deviation of the stock price (by retrieving the closing prices of the first 30 trading days). The firm has been excluded from the dataset when financial information is not available or is missing from an IPO firm. The Fama-French global factors are obtained from Kenneth R. French data library online. As a result after the adjustments listed above the final sample totalled 211 IPO firms, which is close to the statistics collected on technology IPOs by Professor Jay R. Ritter’s website2.

3.2 DESCRIPTIVE STATISTICS In this section, the results of the descriptive statistics from the sample analysis are presented. The IPO sample is sorted by year, and founder or non-founder CEO groups. In order to investigate what role a founder or successor CEO has during an IPO, the sample has been divided by three categories

1 Dataset CRSP permanent IDs: https://site.warrington.ufl.edu/ritter/files/2018/04/FoundingDates.pdf 2 Professor Jay R. Ritter’s website: https://site.warrington.ufl.edu/ritter/files/2017/03/IPOs2016Statistics_Mar29_2017.pdf 14

of all IPO firms, high technology and low technology. The high technology companies are selected based in the CIS code specification on high technology firms, based on the classification of (Loughran & Ritter, 2004). An overview of all IPO firms in this sample with corresponding data are shown in the table below. Table 1 IPO firms that went public between 2007 and 2014. The sample consists of 211 firms and the table below shows the yearly distribution of Founder versus Non-Founder CEO led IPO firms with a division in the full sample as well as the subsamples of High-technology and Low-technology IPO firms. Year All IPO firms Founder CEO IPOs Non-Founder CEO IPOs Panel A: Overall sample 2007 43 18 (41.86%) 25 (58.14%) 2008 5 2 (40.00%) 3 (60.00%) 2009 8 5 (62.50%) 3 (37.50%) 2010 28 11 (39.29%) 17 (60.71%) 2011 30 14 (46.67%) 16 (53.33%) 2012 39 19 (48.72%) 20 (51.28%) 2013 58 27 (46.55%) 31 (53.44%) Total 211 96 (45.50%) 115 (54.50%)

Panel B: High-Technology IPO firms 2007 18 9 (50.00%) 9 (50.00%) 2008 2 0 (0.00%) 2 (100.0%) 2009 4 3 (75.00%) 1 (25.00%) 2010 9 3 (33.33%) 6 (66.67%) 2011 18 11 (61.11%) 7 (38.89%) 2012 23 10 (43.48%) 13 (56.52%) 2013 19 10 (52.63%) 9 (47037%) Total 93 46 (49.46%) 47 (50.54%)

Panel C: Low-Technology IPO firms 2007 25 9 (36.00%) 16 (64.00%) 2008 3 2 (66.67%) 1 (33.33%) 2009 4 2 (50.00%) 2 (50.00%) 2010 19 8 (42.11%) 11 (57.89%) 2011 12 3 (25.00%) 9 (75.00%) 2012 16 9 (56.25%) 7 (43.75%) 2013 39 17 (43.59%) 22 (56.41%) Total 118 50 (42.37%) 68 (57.63%)

Information obtained from the biological section of the prospectus or LinkedIn helped to identify the founder CEO presence of each firm that went public. Around 49.5% of the sample represent a founder CEO led IPO firm and a similar share for founder and non-founder groups are noticed in the high technology environment. However, panel B and C show that the proportion of the sample includes a slightly higher amount of non-founder CEO in low technology firms.

In table 2 the comparison between a founder or non-founder subsample is provided including the offering characteristics. The results indicate the difference between the founder CEO and non- founder CEO is highly significant at 1% level, where on average founder CEOs with a mean(median) of 48.21(48.00) are younger compared to non-founder CEOs with a mean (median) of 51.30 (51.00). On average the equity share of a founder CEO is higher than non-founder CEOs, which is reasonable since founder CEOs usually have more stakes in the firm at moment of IPO. Additionally, the gross proceeds raised at IPO are significantly higher for founder CEOs than for non-founder CEOs and the difference is statistically significant at 1% level. Furthermore, the firm risk for founder CEO firms appears on average to be lower than for non-founder CEO firms at a low significance level. The remaining results show weak evidence for any difference between the founder and non-founder CEO groups. Table 2 Descriptive Statistics This table demonstrates the results of the Comparison of variables between Founder CEO and Non-founder CEO IPO firms. Founder CEO IPOs Non-Founder CEO IPOs Variables Test of difference t-test Mean (Median) Mean (Median) N (Z-score) 99 112 CEO Age 48.21 (48.00) 51.30 (51.00) 2.982 (2.46)*** CEO Ownership (%) 0.11 (0.07) 0.06 (0.03) -2.582 (-2.39) CEO duality 0.99 (1.00) 0.94 (1.00) -1.918 (-0.35) Gross IPO Proceeds ($M) 47.81 (15.14) 26.01 (17.02) -0.999 (-1.76)* Risk of firm 0.19 (0.18) 0.21 (0.20) 1.616 (1.87)* Age of IPO firm 3.20 (3.24) 3.17 (3.18) -0.100 (-0.50) EBITDA / Assets -0.07 (-0.01) -0.17 (-0.04) -1.351 (-1.24) Cash / Assets 0.65 (0.70) 0.66 (0.70) 0.0307 (-0.12) Capital Expenditures / 0.03 (0.02) 0.04 (0.03) 0.980 (1.18) Total Assets N of Observations 211 *, **, ***, levels of significance indicated at the 10, 5, and 1 percent level respectively.

A similar comparison analysis on the differences is conducted in the characteristics of founder and non-founder CEOs in high and low technology IPO firms. The results are presented in table 3, where the mean (median) and the significances from the mean comparison tests are reported.

Table 3 Comparison analysis Analysis between high and low technology industries and between founders versus non founder CEO firms Variables High technology IPOs Mean (Median) Low technology IPOs Mean (Median) All firms Founder Founde High Non- Non- Founde versus Founde r versus versus low All Founde All Founde r CEO non- r CEO non- technolog firms r CEO firms r CEO firms founder firms founder y firms firms firms t(Z) t(Z) t(Z) 48.45 45.65 50.85 3.25 51.45 51.22 51.62 0.29 2.80 CEO Age (48.0) (44 ) (50.00) (3.2)*** (51) (50) (52) (0.27) (21.04)***

CEO 0.08 0.12 0.06 -3.29 0.08 0.11 0.06 -1.39 -0.09 Ownership (%) (0.06) (.077) (0.04) (-0.3)*** (0.03) (0.06) (0.02) (-0.25) (-0.01)

0.98 1 0.95 -1.31 0.95 0.98 0.93 -1.21 -1.06 CEO duality (1) (1 ) (1 ) (-0.19) (1) (1 ) (1 ) (-0.27) (-0.21) Log of 41.03 60.6 24.38 -1.02 30.98 36.68 27.14 -0.62 -0.57 Gross IPO Proceeds (17.9) (97) (19.2) (-.0)***) (15.5) (14.50) (16.15) (-50) (-70.6)*** ($M) 0.20 0.2 0.21 1.55 0.20 0.19 0.20 1.18 -0.60 Risk of firm (0.20) (.19) (0.20) (0.10) (0.20) (0.18) (0.20) (0.08) (-0.04)

3.26 3.30 3.22 -0.76 3.13 3.12 3.13 0.14 -1.95 Age of IPO firm (3.30) (10.76) (3.19) (-0.35) (3.16) (3.14) (3.17) (0.06) (-0.91)**

0.10 0.25 -0.02 -0.90 -0.08 -0.03 -0.14 -0.86 -1.31 EBITDA / Assets (0.29) (.029) (0.24) (-1.26) (-0.12) (-0.09) (-0.14) (-0.43) (-1.31)

0.70 0.70 0.69 -0.05 0.59 0.61 0.60 -0.44 2.77 Cash / Assets (0.77) (0.85) (0.75) (-0.01) (0.62) (0.68) (0.65) (-0.08) (0.67)*** Capital 0.04 0.04 0.05 0.63 0.03 0.03 0.04 1.18 -1.81 Expenditure s / Total (0.03) (.04) (0.03) (0.03) (0.02) (0.00) (0.02) (0.05) (-0.09)* Assets *, **, ***, levels of significance indicated at the 10, 5, and 1 percent level respectively.

Table 4 specifies several differences between the founder and non-founder CEOs in both high-and- low technology environments. The outcomes show that the age of founder CEOs is significantly lower in high technology firms. The founder CEOs in high technology firms hold significantly more ownership stakes than successor CEO in high technology firms. Furthermore, table 3 shows significant differences between high technology and low technology firms in terms the IPO firms’ age, the cash balance and the capital expenditures. The high technology IPO firm seems significantly younger and have higher cash balances and higher capital expenditures compared to low technology IPO firms, which is consistent with previous literature stating that high technology firms hold more cash balances to react to the fast changing technological environment with new investments (Chen, 2009). Moreover, high technology firms raise significantly higher gross proceeds from the IPO than low technology firms. The results indicate that founder CEO leadership is significantly higher for CEO ownership stakes in high technology firms. Further, the gross proceeds raised at IPO for founder CEO firms in high technology environment were significantly higher compared to successor led IPO firms. To conclude, the sample IPO firms indicate significant differences between the two founder groups in either the CEO and firm characteristics between high and low technology firms that possibly may signify a superior founder CEO leadership in the performance of IPO firms in the two types of situations.

4. ECONOMETRIC METHODOLOGY

4.1 MEASURES OF LONG-RUN PERFORMANCE

The main objective of this study is to compare the founder-CEO versus successor-CEO of high- and low technology firms in venture capital backed IPOs, over a period of three years and five years respectively. Long-term investment performance of corporate events, such as the IPO offering, will be measured using two econometric methods to identify the long-run performance: 1.) The buy-and-hold abnormal returns (BHAR) approach and 2.) An event study on the calendar time-based factor regression model, as visualizes in figure 1 (appendix). To evaluate the long-run performance of IPOs following previous literature, starting with Ritter (1991), the long-term performances were measured up to three years post-IPO using an event study methodology by calculating the buy-and-hold returns (BHAR) and wealth relatives (WR) against various market indices. The benchmarks used for the BHAR results are the CRSP value-weighted index, the equally weighted index and the size and book-to-market matched control as a benchmark were the conventional t-statistics have been calculated for all the measures.

1) Holding period returns The buy-and-hold abnormal return (BHAR) approach is based on calculating the average difference between “raw” buy-and-hold returns of IPO firms and respective buy-and-hold returns of matched control firms. The BHAR method reflects the experience of a long-run investor by capturing the long-run buy-and-hold returns through compounding the short term returns (Kooli and Suret, 2004). The BHAR model will be run in a five-year window for the founder CEO and non-founder CEO group based on the average difference between buy-and-hold geometric returns of the IPO firms against the value weighted index benchmark and the size and book-to-market control group benchmarks which are collected from CRSP database. In case the firm delist or the founder-CEO turnover occurs within the five year post-IPO investment horizon, the delisting date and CEO turnover date has been taken as ending date for the buy-and-hold return period. Share prices of firms in dataset and the index prices are collected from Thomson Financial’s Datastream. The returns of the buy-and-hold abnormal returns (without rebalancing of the portfolio weighting

of the return on event firms) is constructed until the end of the event period, which is usually set as 36 and 60 months.

Firstly, for every sample firm the first listing date on CRSP is defined as the event date indicated as t=0. The event window initiation ( t1 ) from the following trading day after going public until fifth anniversary date, or until delisting date, or founder CEO departure date post-IPO is given as

( t2 ). The IPO firm stock after going public is purchased at the closing market price at (t1) and held until anniversary T, as defined in the following formula:

푇 (1) 푅𝑖푇 = ∏ (1 + 푟𝑖푡) − 1 𝑖=1 The cross –sectional differences in the buy-and-hold abnormal returns between founder and non- founder-CEO for the event time regressions are acquired by subtracting the benchmark index. Likewise, the difference between the buy and hold abnormal returns of the sample firm and the benchmark firms are computed within this five year event window, however I also computed the post-IPO investment performance over a three year event window. According to Barber and Lyon (1997) the BHAR for the event firm i is calculated as follows:

푡2 푡2 (2) 퐵퐻퐴푅 (푡1, 푡2) = ∏ [(1 + 푅𝑖푡)] − ∏ [(1 + 푅푏푒푛푐ℎ푚푎푟푘,푡)]

푡=푡1 푡=푡1 The section of the equation: ∏푡2 [(1 + 푅 )] is given as the compounded returns of IPO firm (i) 푡=푡1 𝑖푡 from (t=1) up to t=T, where (t) is monthly. The return of the IPO firm (i) on date (t) is given as Rit and the return on the benchmark is given as Rbenchmark,t. The benchmark uses the matching index returns obtained from CRSP based on the annual market value index returns. The average BHARs for founder and non-founder CEO firms are reported and the differences in means and the results of the t-tests will be presented. The significance of the BHAR is calculated using the following test statistic to test the null hypothesis that the mean buy-and-hold return equals zero:

퐵퐻퐴푅𝑖푡 푡 − 푠푡푎푡 = (3) 휎(퐵퐻퐴푅𝑖푡)/ √푁

The standard average of the abnormal returns is indicated as 퐵퐻퐴푅𝑖푡 and the standard deviation of the abnormal returns is given as: 휎, where N is the number of IPO firms in the sample.

Previous literature on IPO literature starting from (Loughran & Ritter, 2004) adopted wealth relatives (WR) as an additional measure of performance for BHAR estimations. The formula in the study of Ritter (1991) is given as:

1 + 푎푣푒푟푎푔푒 푓𝑖푣푒 푦푒푎푟 푡표푡푎푙 푏푢푦 푎푛푑 ℎ표푙푑 푟푒푡푢푟푛 표푛 (푛표푛)푓표푢푛푑푒푟 퐶퐸푂 퐼푃푂푠 푊푅 = (4) 1 + 푎푣푒푟푎푔푒 푓𝑖푣푒 푦푒푎푟 푏푢푦 푎푛푑 ℎ표푙푑 푟푒푡푢푟푛 표푛 푏푒푛푐ℎ푚푎푟푘 This formula calculates the holding period return over the whole period relative to the benchmark total holding period return over the same period. The outcome of the wealth relative greater than 1 indicates that the founder or non-founder CEO in IPO firm outperforms the benchmark and an outcome of less than one implies underperformance of the benchmark (Jakobsen & Voetmann, 2005).

2) Calendar-time portfolio returns The second approach for the long run performance measure of aftermarket performance of IPO firms is mainly based on the (Loughran & Ritter, 2004) method that takes the difference in the portfolio excess returns (dependant variable) relative to the risk free rate 푅𝑖푡 − 푅푓푡to obtain the abnormal performance of the sample IPO firms for comparing the performance between the portfolios. I use a (Fama & French, 1996) three factor model, which is often used as benchmark in time serial regressions to generate the normal returns with Newey-West standard errors, however I employ equal-weighted observations and the market model in robustness tests. The calendar-time portfolio approach is used to estimate the factor risks by the (Fama & French) factor models to control for the heteroscedasticity problem, which is called the calendar-time portfolio approach for measuring the long-run anomalies in which the time serial monthly portfolio returns are developed. The calendar-time portfolio (CTP) return will be initiated by creating returns in month i (of 21- trading days) as the average equal weighted return of each IPO firm in the portfolio, 퐶푇푃푡 = 푛 ∑𝑖=0 푊𝑖 푅𝑖,푡. In addition to the regression of the abnormal returns of the IPO firms against the risk-free rate, a second regression is run to capture the differences between the excess returns of the portfolio of the founder and non-founder CEO firms comparative to the risk free rate.

The models described above are estimated as follows:

(5) 푅𝑖푡 − 푅푓푡 = 훼 + 훽(푅푚푡 − 푅푓푡) + 푠푆푀퐵푡 + ℎ퐻푀퐿푡 + 푒푡

(6) 푅푓퐶퐸푂 − 푅푛푓퐶퐸푂 = 훼 + 훽(푅푚푡 − 푅푓푡) + 푠푆푀퐵푡 + ℎ퐻푀퐿푡 + 푒푡 The intercept ( 훼 ) is the result of monthly (in event time) cross-sectional regression as seen in the equation provides an estimate of the abnormal returns from the founder and non-founder CEO firm portfolio and at the second equation the alpha in explaining the excess return of a portfolio of founder CEO related to the non-founder CEO firms. The sign of the alpha will explain the significance in terms of the hypothesis developed to prove if founder CEO firms outperform non- founder CEO firms. To point this out, the alpha is used to test the null hypotheses of the monthly excess return on calendar time portfolio being equal to zero. The monthly returns of the calendar- time portfolio which is the sample of IPO firms in month t which is given as Rit and the return of the value weighted market index found on CRSP database Rmt in month t. The return of the risk- free rate Rft is based on the three-month Treasury bill in month t. The normal return for each firm i for given month t is constructed as follows:

(7) 푁푅𝑖푡 = 푅푓푡 + 훽̂𝑖 (푅푚푡 − 푅푓푡) + 푠̂𝑖 푆푀퐵푡 + ℎ̂𝑖 퐻푀퐿푡 The abnormal returns obtained from the three-factor model are more accurate than the market model and show less cross-sectional correlation. As omitting size and value factors for IPO firms that are usually growth firms, from the normal return benchmark model will lead to correlated abnormal returns across firms that go public in the same month. Furthermore, the factor regression models are estimated using either equally or value weighted returns. The factors such as the SMBt and HMLt, which can be further explained as the “small minus big” stocks and the “high minus low”, which is the difference in return between a portfolio of firms with a high book-to-market minus low book-to-market stocks level for firm i in month t. Can also be explained as the difference between “value” stocks and “growth” stocks as described by Bodie, Kane and Marcus (2005, section 13.3).

In order to test the significance of the long-run IPO calendar-time portfolio returns (Agrawal, Jaffe, & Mandelker, 1992) and (Fama E. F., 1998) approach provides a monthly time-series of event portfolio returns defines as: 푅푝푡, which is regressed on the Fama-French factors. This method

overcomes the serial-correlation problem and the cross-sectional correlation issue that occurs in event-time BHARs and CAR methods3.

(8) 푅𝑖푡 − 푅푓푡 = 훼𝑖 + 훽𝑖(푅푚푡 − 푅푓푡) + 푠𝑖 푆푀퐵푡 + ℎ𝑖 퐻푀퐿푡 + 푒𝑖푡

(9) 푅푓퐶퐸푂 − 푅푛푓퐶퐸푂 = 훼𝑖 + 훽𝑖(푅푚푡 − 푅푓푡) + 푠𝑖푆푀퐵푡 + ℎ𝑖퐻푀퐿푡 + 푒𝑖푡

The intercept (훼푝) measures the abnormal performance with respect to the three factor benchmark, where its significance is tested by the t-test. This is a slightly similar case as the BHAR method described above however, in the calendar-time portfolio returns the time series of portfolio returns are constructed for each month where all firms had an IPO in the last H months (usually H=36 or H=60 months) and in cases where there was no IPO in a particular month, the portfolio return equalled the risk free return. The returns are regressed against the Fama-French three factor model U.S. returns data collected from the prof. Kenneth R. French website4 in order to obtain the alpha.

To test the hypothesis, I will use CRSP, Stata and Excel. However, as a robustness check of the hypothesis, I ran another multivariate regression analysis with Stata to test the link between the post- IPO investment performance and founder-CEO presence and the interaction relation of a founder-CEO to a high technology environment. Since, literature on the determinants that influence long-term aftermarket investment performance usually adopt a Wealth relative or BHAR method, (Loughran & Ritter, 2004), I use the following empirical model with the five-year- BHAR or Wealth relative with the value-weighted index as benchmark as a dependent variable:

퐿표푛푔 푟푢푛 𝑖푛푣푒푠푡푚푒푛푡 푝푒푟푓표푟푚푎푛푐푒

= 훼 + 훽1퐹표푢푛푑푒푟퐶퐸푂 + 훽2퐻𝑖푔ℎ푡푒푐ℎ푛표푙표푔푦 푓𝑖푟푚

+ 훽3(퐹표푢푛푑푒푟퐶퐸푂 ∗ 퐻𝑖푔ℎ 푡푒푐ℎ푛표푙표푔푦) + 훽4퐶퐸푂 푑푢푎푙𝑖푡푦 (10) + 훽5 퐿표푔(푃푟표푐푒푒푑푠) + 훽6 퐹𝑖푟푚 퐴푔푒 + 훽7 푅𝑖푠푘 표푓 푓𝑖푟푚

+ 훽8퐶푎푠ℎ / 푇표푡푎푙 퐴푠푠푒푡푠 + 훽9 퐸퐵퐼푇퐷퐴 / 푇표푡푎푙 퐴푠푠푒푡푠

+ 훽10 퐶푎푝𝑖푡푎푙 퐸푥푝푒푛푑𝑖푡푢푟푒푠 / 푇표푡푎푙 퐴푠푠푒푡푠 The long-run investment performance is a regression model where I test the post-IPO performance and take the five-year BHAR with the value weighted benchmark. In this model the variable

3 Lecture notes course Empirical Methods in Finance in MSc Finance program at Tilburg University, “Event Studies Methodology” by F. De Jong and P. De Goeij (2011) 4 K. R. French, “ Current Research Returns” U.S. Research Returns Data. http://mba.tuck.dartmouth.edu/, 2019, http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. 23

FounderCEO is a dummy variable that takes the value of one when the firm is led by a founder CEO after going public during the whole investigated duration period, and zero otherwise. The Hightechnology firm dummy variable is one when the IPO firm is a high technology firm and zero otherwise. The interaction of two dummy variables are estimated to test the additional effect of Founder CEOs in high technology environment given as (FounderCEO*Hightechnolgoy firm). CEO duality variable is included, because if the CEO is holding the chairman of the board next to its position as a CEO, the CEO does get more power relative to the other board members, therefore this variable is included as a dummy variable. Additionally, some controlling variables are included in the regression model that might have an influence in the long-term investment performance, such as the size of the firm or age of the IPO firm. As a proxy for the pre-IPO operating performance the controlling variables from the fiscal year of IPO are included, such as EBITDA/Total Assets, Cash/Total Assets and Capital Expenditures/ Total Assets.

5. EMPIRICAL RESULTS AND LIMITATIONS

In this chapter I will present the findings of the analysis from the methodology as described in chapter 4. The results from the regression to answer the hypothesis is presented by tables and a short description. The empirical results outlined in this chapter are further discussed in detail in chapter six.

5.2 REGRESSION RESULTS

The findings from the long-run buy-and-hold abnormal returns and the wealth relatives are reported in table 4. The returns for each founder and non-founder CEO sample are reported separately in order to make comparisons. The first column presents the equally weighted five year raw return from the IPO firms sample dataset. The second column report the buy-and-hold abnormal returns including a t-test for equality of means between the two groups. Further, the third column displays the wealth relatives between the founder-and non-founder CEO IPO firms. The IPO returns, BHARs and wealth relatives are subsequently reported against the three benchmark divisions of the overall IPO firms BHARs, the five year BHARs for high technology and the five year BHARs for low technology.

The findings indicate that for all three panels the long run the IPO firms underperform the value- weighted index benchmark, which is consistent with the literature the (Ibbotson & Ritter, 1995). Panel A, B and C presents negative BHARs results of the five-year post IPO performance against the value weighted benchmark. The wealth relative values for all panels are below one indicating underperformance in the long-run for the five-year period. The BHARs and the wealth relatives for founder-CEO IPO firms are in all benchmarks higher than that for non-founder led firms, which indicates an outperformance of non-founder CEO led firms. In panel C the BHARs show a statistically significant difference for the low technology benchmark, where the founder CEO led firms have a positive BHARs in founder CEO presence for the long-run performance. This finding indicates a five-year post-IPO investment performance difference between founder CEOs and non- founder CEOs, against the value-weighted benchmark. Evidently the outcomes are prone to the benchmark used. To conclude, founder CEO leadership indicate a positive significant effect over

non founder CEO leadership in low technology IPO firms. This finding is in line with the research of (Fahlenbrach, 2009) that found evidence on the performance differences of founder CEO and successor CEO. Founder CEO showed difference in higher stock market performance, better investment behavior in terms of acquisitions and R&D and higher firm valuation compared to successor CEO firms.

Table 4 Post-IPO long-term buy-and-hold abnormal returns and wealth relative for (non) founder CEO venture capital backed IPO firms The table reports the long-run post-IPO buy and hold returns and wealth relatives for IPO firms with a division in founder CEO and non-founder CEO led IPO firms. The sample consists of 211 firms that went public in the United States between 2007 and 2014. The benchmarks used are the value-weighted index withdrawn from the daily CRSP files from Wharton database. The benchmarks are subsequently divided into high and low technology value-weighted buy and hold abnormal returns for the IPO firms and presented in the second and third column for which this benchmark is used. The columns represent the equally weighted holding period IPO returns, along with the five-year- buy and –hold abnormal returns and wealth relative results. For the BHARs the mean comparison analysis with the t-statistics is included for comparison between the founder CEO and non-founder CEO IPO firms.

IPO returns BHAR Wealth Relatives

Benchmark Founder Non- Founder Non- Founder Non- BHAR CEO Founder CEO Founder CEO Founder t-test firms CEO firms firms CEO firms firms CEO firms Panel A: five-year-buy-and-hold returns for IPO firms Value- -7.77 -14.35 -3.02 -3.29 0.39 0.94 0.93 Weighted index

Panel B: five-year-buy-and-hold returns for high technology IPO firms Value- -10.78 -17.37 -0.23 -0.54 -.072 0.98 0.97 Weighted index

Panel C: five-year-buy-and-hold returns for low technology IPO firms Value- 1.81 -17.74 0.68 -0.12 -1.80** 0.90 0.89 Weighted index *, **, ***, significant at the 10, 5, and 1 percent level, respectively.

Table 5 presents the results of the three-factor regression model where excess return of both founder and non-founder CEO is the dependent variable and the (Fama & French, 1993) factors are the independent variables. The excess returns from the equally weighted portfolio relative to the risk-free rate with the three-factor model are reported for both founder CEO led firms in panel A, and non-founder CEO led firms in panel B. Findings showed that the intercept for both subsamples are significant. The benchmark chosen affects highly the significance levels for a positive effect on post-IPO performance, therefore I ran additional one model with the equally weighted, however the result were similar to the value weighted.

The results for the founder CEO panel show that the founder CEO presence is statistically significant throughout the subsamples which indicates a positive founder CEO effect. In the high technology subsample, the founder CEO effect signifies superior post-IPO investment performance and is significant on all factors. The results are statistically significant with a positive intercept from the regression of portfolio returns that is used to test for abnormal performance. Similar to the outcomes of the study of (Gao & Jain, 2011) where a similar methodology was implemented for the time period 1997 to 2000 also indicated superior founder CEO leadership and performance in high technology IPO firms. Therefore, I find evidence of a positive founder CEO effect in a high technology environment. Based on the factor regression model of the (Fama & French, 1993) on excess returns, Panel B presents the results for the non-founder in the high-and-low technology environments and show less evidence compared to the founder subsample for superior investment performance. However, the intercepts are positive for each subsamples, meaning that there is an effect of post-IPO investment performance for the non-founder CEO IPO firms however relative to panel A of the founder CEO sample the results shows less magnitude and no difference between the high and low technology IPO firms.

Table 5 Factor regressions for portfolio excess returns The Fama-French three factor model regression for excess returns for founder-CEO and non-founder CEO post IPO firm’s portfolio. The total sample consist of 211 IPO firms that went public on primary stock exchanges between 2007 and 2014 in the United States. The returns on a portfolio are based on monthly returns from event date based on calendar-month of 60 months. The return on the calendar-time portfolio returns of founder (non-

founder) CEO IPO firms are calculated for each panel in month i, 푅푚푡 and subtracted from the Fama-French risk-

free rate of three-month Treasury Bills in month t. The monthly market returns 푅푚푡 are composed from the CRSP value-weighted market index return on the states risk-free rate. The difference of returns on small stocks and big stocks is given as SMB t in month t and the difference in high book-to-market stocks in month t are reported as HML t. In all corresponding coefficient the t-statistics is provided in parentheses.

푅 𝑖푡 − 푅푟푓 = ⍺ + 훽(푅푚푡 − 푅푓푡) + 푠푆푀퐵푡 + ℎ퐻푀퐿푡 + 휀푡 Dependent All IPO firms High-Technology Low-Technology variable IPO firms IPO firms

(푅𝑖푡 − 푅푓푡) FAMA-French three FAMA-French three factor FAMA-French three factor Model parameters factor model (1993) model (1993) model (1993) Panel A: Return difference between founder CEO IPO firms and Risk-free return Intercept 0.0006 0.0007 0.0006 (3.78) *** (3.70) *** (2.55) *** RMRF 0.0002 -0.0003 0.0004 (1.25) (-1.74)* (1.37) SMB 0.0001 0.0009 0.000 (0.34) (2.11)** (0.04) HML -0.0008 0.011 -0.0012 (-2.16)** (2.56)*** (-2.01)** Adjusted R2 0.21 0.19 0.19 Panel A: Return difference between non-founder CEO IPO firms and Risk-free return Intercept 0.0007 0.0006 0.0007 (-2.18) *** (2.88) *** (3.21) *** RMRF -0.0004 0.00008 -0.0008 (-2.18) ** (0.36) (0.34) SMB -0.0003 -0.0004 -0.0001 (-0.95) (-0.77) (-0.26) HML 0.0007 0.0005 -0.000 (1.86) (1.03) (-0.00) Adjusted R2 0.01 0.03 0.01 *, **, ***, significant at the 10, 5, and 1 percent level, respectively. The difference between the founder and non-founder CEO IPO firms can be compared in more detail with a factor regression model of the differences in the post-IPO performance of founder

CEO and non-founder CEO led firms. The results are reported for the overall sample, as well as the high and low technology IPO firms. The additional factor regression model will account for the difference between the founder CEO and non-founder CEO firms within a high technology or low technology environment. The return difference between the founder CEO portfolios relative to the non-founder CEO portfolio returns were set as dependent variable. The findings provide weak evidence that indicate significant post-IPO performance, as the intercept term is insignificant in all subsamples, as shown in table 6 below. Table 6 Factor regressions for return difference between founder and non-founder CEO IPO firms The difference between the returns of each sample group portfolio consisting of founders and non-founder CEO IPO firms are calculated in calendar time period for 211 firms in the sample that have conducted an IPO in the United States between 2007 and 2014. A calendar month of 60 months has been considered for this analysis, where the dependent variable is the return difference of founder and non-founder CEO IPO firms

(푅푓표푢푛푑푒푟 푐푒표 − 푅푛표푛−푓표푢푛푑푒푟 푐푒표) .

I have used the following regression: 푅푓푐푒표 − 푅푛푓푐푒표 = ⍺ + 훽(푅푚푡 − 푅푓푡) + 푠푆푀퐵푡 + ℎ퐻푀퐿푡 + 휀푡

The monthly market returns 푅푚푡 are composed from the CRSP value-weighted market index return composed

monthly i, where the risk-free rate of three-month Treasury Bills in month t is given as 푅푓푡. The difference of returns on small stocks and big stocks is given as SMB t in month t and the difference in high book-to-market stocks in month t are reported as HML t. In all corresponding coefficient the t-statistics is provided in parentheses. Dependent All IPO firms High-Technology Low-Technology variable IPO firms IPO firms

(푅푓푐푒표 − 푅푛푓푐푒표) FAMA-French three factor FAMA-French three factor FAMA-French three factor Model parameters model (1993) model (1993) model (1993)

Intercept -0.0003 -0.0002 0.0000 (-0.78) (-0.32) (-0.00) RMRF 0.0004 0.0004 0.0004 (4.33) (2.61)*** (3.33)*** SMB 0.0088 0.0062 0.0055 (2.30)** (0.95) (0.98) HML -0.0069 -0.0041 -0.0080 (-2.68)*** (-0.92) (-2.10)*** Adjusted R2 0.30 0.0900 0.15 *, **, ***, significant at the 10, 5, and 1 percent level, respectively.

The cross-sectional results from the multivariate analysis are presented in table 7 below. This regression provides a more robust analysis to answers the hypothesis on the significant relation between post-IPO performance and founder CEO presence. The dependent variable employed in the first model is the BHAR and in the second model the wealth relatives. Post -IPO investment performance is related to the founder CEO involvement and high technology environment. The coefficients are mainly insignificant, however the interaction term of high technology with founder CEO is significant indicating that the founder CEO presence is beneficial to high technology firms. As well as in the wealth relative model, which has a positive intercept, the same interaction term of founder CEO presence and high technology firm indicate a statistically significant post-IPO investment performance. On the whole, the analysis of post-IPO performance with the employed methodologies of both the BHAR and calendar-time regression models provide weak evidence to point out superior investment performance in founder CEO IPO firms, since the benchmark weights used, methodology and factor regression model influence the significance level of the results. Given these points, it can be observed that in the two methods there is an indication of significant superior founder CEO leadership in the long-run performance in high technology firms, regardless of the methods used.

Table 7 Cross-sectional regression of long-term aftermarket investment performance. This table demonstrates the results of the regression with the dependent variable of two models from the sample consisting of 211 IPO firms that went public between 2007 and 2014 in the United States. The dependent variable in the first model is the buy-and-hold abnormal return with the CRSP value weighted index as benchmark for a period of five years and the second column demonstrates the results of the five-year wealth relative model which also takes the CRSP value weighted index as benchmark. The independent variables including dummy variables such as, Founder and High technology. The variable Founder takes the value of one if the IPO firm is led by a Founder CEO after going public and zero otherwise. The variable High technology takes the value of one if the IPO firm is a high technology firm and zero for low technology based on the SIC code classification. The dummy variable CEO duality takes the value of one if the CEO and Chairman position title is held by the same person and zero otherwise. The variable Log (Gross Proceeds) signifies the log of the gross proceeds raised at the year of IPO. Risk of IPO firm is the standard deviation of the first thirty days aftermarket returns. In this research study, firm age is operationalized as the log of one plus the difference between the IPO firms’ founding date and its IP date. The following variables are accounting data measured in the fiscal year of the IPO for each firm. The variable EBITDA/Total Assets is the ratio of IPO firm’s earnings divided by total assets and the same denominator division goes for Cash and short-term investments and Capital Expenditures. In all corresponding coefficient the t-statistics is provided in parentheses. BHAR Wealth relatives Independent variables Model (1) Model (2) Intercept -0.05 0.59 (-0.30) (5.34)*** Founder 0.02 0.01 (0.31) (0.31) High technology -0.11 -0.07 (-2.14)** (-2.14)** Founder*High technology 0.03 0.02 (0.41) (0.41) CEO duality -0.07 -0.04 (-0.66) (-0.66) Log (Gross Proceeds) 0.00 0.00 (-1.28) (-1.28) Firm Age 0.04 0.02 (0.94) (0.94) Risk 0.15 0.09 (0.49) (0.49) Cash/Total Assets -0.03 -0.02 (-0.35) (-0.35) EBITDA/Total Assets -0.05 -0.03 (-1.40) (-1.40) Capex/Total Assets 0.68 0.42 (1.54) (1.54)* Adjusted R2 0.06 0.06 N of Observations 211 *, **, ***, levels of significance indicated at the 10, 5, and 1 percent level respectively.

6. CONCLUSION

The objective of this research paper was to investigate the long-run aftermarket performance of IPO firms by comparing founder and non-founder CEO led firms in their survivability and performance in high and low technology environments, by addressing the research question whether an IPO firm led by a founder CEO has significant post-IPO performance in a high technology environment.

The aim of this paper was to find out whether post-IPO investment performance and the growth of a high technology firm is positively related to founder CEO presence in high and low technology firms. To test the hypothesis, a sample of 211 IPO firms that went public from 2007-2014 is selected to calculate and analyse the post-IPO abnormalities with an event window of five years. The methods used to estimate the long-run post-IPO investment performance relative to the benchmark are the buy-and-hold abnormal returns and the calendar-time portfolio returns methodology that both comply with previous literature on post-IPO performance measures.

The buy-and-hold abnormal returns and the factor regression model showed evidence that the founder CEO presence surpasses the long-run performance of successor CEO led IPO firm, based on the benchmark used to estimate the abnormal returns. As has been noted in the descriptive statistics, a significant difference in the CEO and firm performance of the two successor CEO and founder groups between high and low technology environment indicated a superior performance of founder CEO leadership. Therefore, founder CEO provide consistently significant higher long- run returns compared to successor CEO. As the results from the methods used rely highly on the benchmark chosen, the consistency of the measures for long-run IPO performance was questionable. Therefore, to provide a more robust test of the hypothesis in the final analysis the results on their validity were confirmed by a cross-sectional regression. Henceforth, it can be concluded that this paper adds empirical evidence on the impact of founder CEO presence which is beneficial for high technology IPO firms that have transitioned from private to public ownership.

6.1 LIMITATIONS

In the light of the findings claimed by (Ritter & Welch, 2002) the long run IPO performance is highly sensitive on the sample period used as the market conditions vary over time that might influence the IPO stock performance. Moreover, the study also claimed that the long run IPO performance outcomes is sensitive to the choice of methodology to estimate the abnormal returns. Therefore, the analysis was conducted with the by comparing the equally weighted and value weighted returns on the IPO performance. However, a more sophisticated approach to estimate the significance of the results from the BHAR method could have been by adding additional benchmarks, such as the control group based on size and book-to-market.

6.2 FURTHER RESEARCH

This research study adds to the existing literature on long-run investment performance of IPO firms by evaluating the probable economic impact of founder CEO leadership in the management team of firms that transition from private to public ownership. The analysis could be improved by including the influence of venture capitalists in the performance of the IPO firm. Further areas of improvement and further research should include measurements that calibrate a more precise performance measurement by use of a control group benchmark based on matching size and book- to-market portfolio. Future research could involve an in depth analysis on the factors that influence significant founder CEO aftermarket performance in high technology firm.

APPENDIX

Figure 1. Two econometric methods of event study techniques used in this dissertation. The figure on the left displays the event- time approach and the figure on the right displays the calendar-time portfolio approach

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