The Long-Term Effects of Venture Capital Rounds

Transcription

1 PRELIMINARY DRAFT PLEASE DO NOT CITE The Long-Term Effects of Venture Capital Rounds G. Nathan Dong * Alejandro Serrano Columbia University University of Texas-Pan American January 15, 2014 ABSTRACT Venture capitalists (VCs) attempt to mitigate principal-agent conflicts with entrepreneurs through pre-investment screening and post-investment monitoring. It has been shown that VCs use staged financing as a perceived effective monitoring mechanism to fund investment opportunities; as a result, younger firms require more rounds prior to IPO. However, the question of whether entrepreneurs actually benefited from staged financing in a long-run remains unanswered. This paper finds somewhat mixed evidence: VC-backed firms with more capital rounds prior to IPO are more likely to be delisted for insufficient capital and number of shareholders (core violations of the exchange s continued listing rules). In addition, legal and regulatory compliance added extra cost to the already lengthy, complex and expensive process of going public: after the enactment of Sarbanes-Oxley, VCs required more financing rounds prior to IPO. Keywords: Securities regulation, Entrepreneurial Finance, Stage Financing, Delisting JEL Codes: G24, G23, G38 * Dept. of Health, Policy & Management, Columbia University, New York. gd2243@columbia.edu. Corresponding author: Dept. of Economics & Finance, University of Texas-Pan American, 1201 W University Drive, Edinburg, TX serranoa@utpa.edu.

2 Corporate America, its public boards, and now, the United States government would be well served to take a few pages on governance from America s venture capital-backed companies. The conversations in the body politic today are about government regulation and government ownership as solutions to our crisis of governance. While there may be short-term needs, true governance reform is not a government program. Reform must begin in the boardroom, not in a Congressional hearing room. - New York Time, May 11, 2009 I. INTRODUCTION The venture capitalist-entrepreneur relationship is that the entrepreneur needs financing for his venture and the venture capitalist provides funding for the venture. The principal-agent problem naturally arises when the venture capitalist (principal) hires the entrepreneur (agent) to run the business but can not observe the entrepreneur (agent) s effort without cost. This agency problem and its practical solutions have been amply studied in the economics and finance literature. For example, Kaplan and Strömberg (2001) suggest three ways that the venture capitalist can mitigate the principal-agent conflicts: sophisticated contracting, preinvestment screening, and post-investment monitoring. The focus of this paper is on how venture capitalists use capital staging to monitor entrepreneurs and its long-term effects on business. Changes in the number of financing rounds, recognized as an internal force of corporate governance, can provide venture capitalists an effective mechanism to monitor and discipline entrepreneurs. Tian (2010) contributes to the literature by including the effects of distance on capital staging. He finds that capital rounds reduce the probability of liquidation three years after the IPO only if the firm is far from its VC. However, to the knowledge of the authors, no evidence has been presented yet that capital staging has a positive or negative impact on long-run firm performance. In this paper we collect the incidents of firms being delisted from stock exchanges to study the association between the various reasons of delisting and the number of capital round prior to their IPO. However, studying the long-term real effects of the internal force of corporate governance on firm outcomes is limited due to the fact the ability of the government to influence firms behavior has been found to be significant, even when regulations have not been enacted and no specific penalties are imposed. Specifically in financial market regulation, Sarbanes Oxley (SOX) is considered one of the most important and significant laws affecting publicly held companies in recent history. The recent literature has extensively studied both the 2

3 short-term and long-term impacts of SOX on firms behavior and performance. It is argued that the increase in regulation imposes complying costs that are increasingly taxing for smaller firms to comply with. However, Leuz, Triantis, and Wang (2008) and Leuz (2007) defy the perception that SOX is to blame for the increased voluntarily delisting after the inception of SOX. There are two main explanations for the increase of voluntarily delisted firms: first, firms that withdraw from a major exchange were very weak in terms of financial performance and corporate governance; hence it could be delisted at anytime, irrelevant to the enactment of SOX. Fama and French (2004) suggest that the increasing number of firms being delisted is due to the increasing number of small firms being listed on stock exchanges. Bhattacharya, Borisov, and Yu (2010) study the lifespan of new firms and find that new IPOs are most unstable during their first three years. Second, the availability of debt financing due to the ever increasingly active privateequity markets fostered a worldwide boom of going-private activities. This paper contributes to the literature in two ways. It studies the association between the number of capital rounds a firm received from VCs prior-ipo and the outcome of being delisted post-ipo. If capital staging by VCs can strengthen the firms in terms of financial performance and corporate governance, those received more funding rounds that are eventually delisted should be due to less serious violations of listing standards than those received fewer capital rounds. Following the same reasoning, firms with more financing rounds should be more likely to continue trading in a secondary exchange that continues reporting to the SEC rather than on the Pink Sheets if they continue trading at all. Finally, if SOX increases the costs of getting listed, we should expect an increase of capital rounds for VC-backed IPOs. Given that the prior research only studies the short-term performance after IPO, this paper is focused on long-term impact of capital staging. Such an update in the literature is critical to understanding how contributing factors of VC governance on long-term firm performance have changed over the past two decades. In addition, we provide new evidence on quantitatively understanding how governance regulation (SOX) negatively impacted the entrepreneur finance. The remainder of the paper is organized as follows. Section II surveys the literature on venture capital and delisting. Section III describes the delisting structure in NASDAQ. Section IV develops the hypotheses. Section V describes the data and explains the methodology. Section VI presents the results on capital rounds and delisting. Section VII concludes. 3

4 II. RELATED LITERATURE Despite the abundance of papers on VC governance and performance (see Baker and Gompers 2003, Sahlman 1990, Kaplan and Stromberg , and Cumming and Johan 2007), prior literature on the governance implication of capital staging are scarce and little research has been done into aspects of what the determinants and consequences of financing rounds are, especially the long-term consequence: delisting. Gompers (1995) analyzes the dynamics behind capital stages or rounds by VCs. He states that VCs invest in firms where asymmetric information is very high and they overcome this asymmetry by monitoring through frequent financing rounds. If the VC believes a firm has very little probability of going public, the VC will discontinue funding. According to Sahlman (1990), VCs structure their financing to diminish the agency costs that arise from the asymmetric information such as convertible securities, syndicating of loans, and capital stages. Gompers focuses on the last instrument and asserts that the duration of the round, the size of the investment per round, and the number of financing rounds are relevant elements of capital staging. Gompers collects a sample of 794 firms backed by venture capital between January 1961 and July Amongst his results, he finds that firms that go public receive more VC funding than firms that remain private. IPO firms also receive more financing rounds than those that remain private. Gompers believes that these results are the consequence of the constant interaction between the VC and the firm, the VC continues to fund the project if it believes it has potential to go public. VCs do several financing rounds to acquire information; if the firm is likely to succeed, the VC will continue providing funding. Other stream of literature focuses on the security design of startups. Bergemann and Hege (1998), study the effects of moral hazard and learning on financial contracting. Moral hazard emerges when the entrepreneur diverts capital for his private ends or simply by reducing his level of effort. To start the project, the enterprise requires seed financing and as the venture capitalist acquires information about the project, the characteristics of financing evolve. The evolution of the project decreases the information asymmetry and the VC capitalist can withhold or expedite the distribution of capital. This asymmetry arises from the unobservability of the allocation of funds. The rate of investment can determine the fate of a project. If the probability of success becomes negligible, funding stops. The authors argue that short-term refinancing is not optimal and it should be replaced by a long-term contract with inter-temporal risk-sharing. Repullo and Suarez (2003) study the design of securities where double-sided moral 4

5 hazard prevails between entrepreneurs and venture capitalists due to the unobservability of their efforts during the expansion stage. Another element in the interaction between both parties is the verifiability of the information on the probability of success. If it is verifiable, then the entrepreneur and the financier can sign a contract contingent on the profitability of the project. If it is not verifiable, then stage financing arises and it is dependent on the expansion requirements. Given that incentives for both parties increase as the probability of success increases, it is optimal to concentrate the rewards of the venture capitalists in the highest profitability states. Tian (2010) analyses the three main streams of literature that are relevant to capital staging. Venture capital is normally staged in that a venture will receive an initial investment from one or more venture capitalists, with additional investments from those (and possibly other) venture capitalists in subsequent stages. First, is a group of agency models that Tian refers to as the monitoring hypothesis. Capital rounds are expensive for many reasons amongst them is the constant negotiating and contracting costs. However, monitoring the entrepreneur is costly and the VC must balance between both forces. The second hypothesis is the hold-up problem from the entrepreneur. The VC invests in the firm and the entrepreneur can negotiate for a better deal or it can threaten to leave. Staging diminishes this problem through incremental investment rather than a onetime deal that would erode its bargaining power. Finally, the learning hypothesis states that staging is convenient for the VC because it can learn more about the firm it is financing through each capital round. By increasing its knowledge on the firm, the VC can leave or engage further with the firm with better valuation knowledge. Tian compiles data from 1980 to 2006 and includes a determinant instrument variable in his paper, distance. The further a firm is from its VC investor, the more rounds the firm will use for its financing. Furthermore, the duration between rounds will be shorter and also the capital attracted on each round. Tian later draws a relation between capital staging and IPOs. He finds that many capital rounds decreases a firm s probability of going public if it is located close to the VC but the opposite results are obtained when the firm is far from the VC. Finally, Tian wants to make a connection between capital staging and Post-IPO survival. He relies on Bhattacharya, Borisov, and Yu (2010) to build a test that measure IPO survival within three years. According to Bhattacharya, Borisov, and Yu (2010), new firms go through their must unstable period in the first three years. His results show that capital rounds reduce the 5

6 probability of liquidation 3 years after the IPO only if the firm is far from its VC. Therefore, the monitoring hypothesis holds, capital rounds on the firm s performance is dependent on the monitoring costs to the VC as measured by the distance between the VC and the firm. Given the private nature of VCs, investments are the product of lengthy negotiations between VC investors and entrepreneurs. There are no liquid financial markets that can determine the investment. However, that might change with the emergence of the company Secondary Markets in NY that trades Private equity investments. There are different models for the negotiation process between a VC investor and an entrepreneur that incorporate the bargaining position of both parties. Prior literature assumes that either the entrepreneur or the VC investor influences the outcome of the contract [(Admati & Pfleiderer, 1994; Amit et al., 1990)]. However, the latest literature suggests a more nuanced view on the VC investment process. The investment decision is the outcome of a double selection process (Eckhardt et al., 2006). First, entrepreneurial companies apply for VC funding, while VC investors choose those companies in which they will invest in a second phase. Sorensen (2007) states that in the first phase, entrepreneurs compete for the best possible VC investor and VC investors compete for the strongest investment opportunities. According to Casamatta (2003) and Kaplan & Strömberg, (2004), there is a double-sided moral hazard problem in the venture capital model. Different from a pure principal-agent model in which VC firms are the principals and entrepreneurs are the agents, there is a moral hazard problem with each other. Nehrer (1999) studies the structure of venture capital by focusing on capital rounds. The entrepreneur embeds the project with his human capital but may walk out at any time. If the assets invested in the projected are valuable only because the entrepreneur utilizes them, then the investor loses bargaining power over future returns assuming the entrepreneur keeps working. Therefore, it is said that the entrepreneur can hold up the investor after the initial investment. Staging can reduce the hold up problem by limiting the amount of capital at the beginning of the project when the entrepreneur possesses inalienable human capital that later becomes embodied in the physical assets. This is why the entrepreneur gets replaced by a manager once the company has become stable. Nehrer refers to the value of the physical assets without the entrepreneur as collateral. This increased collateral, enables outside investment in the final rounds. However, staging comes at a cost because it delays the venture s return. Wang and Zhou (2004) state that stage financing is a useful mechanism that reduces agency costs and controls risk. Furthermore, partitioning cash flows by allocating less capital at 6

7 the beginning of the project allows the financing of companies that otherwise wouldn t have become profitable. However, if the initial capital is insufficient, the enterprise may fail. Other properties that they find are that moral hazard creates a greater possibility for the project to be abandoned, and the VC uses his investment patterns to control moral hazard. The delisting literature focuses on the deleterious effects of delisting for investors (Macey, O Hara, and Pompilio, 2008, Harris, Panchapagesan, and Werner, 2008). Macey, et al., (2008) study the delisting of 58 stocks that moved from the NYSE to the Pink Sheets in They find that the NYSE does not apply its continued listing standards consistently. Some firms are removed almost immediately after violating listing standards whereas other firms linger for months in the NYSE. After the stocks are traded on the Pink Sheets, average percentage spreads are 40 percent and the median is 23 percent. On the last day of trading the mean closing price in the NYSE is $0.94 and falls to $0.48 on the first day of trading on the Pink Sheets. Closing price volatility doubles on the first day. The authors suggest a stronger supervision by the government to assure a more objective application of the continued listing standards. Harris, et al., (2008) do a similar exercise as Macey, et al., (2008) but they focus on the delistings from NASDAQ. The goal of their paper is to determine if a liquidity shock, a transition from NASDAQ to the OTCBB or Pink Sheets, leads to a decline in shareholder s wealth. Their sample includes 1,098 stocks that were delisted between 1999 and Their window includes three months before and after delisting. They observe that quoted spreads increase from to 33.6 percent, effective spreads rise from 3.3 to 9.9 percent, and intraday volatily surge from 4.4 to 14.3 percent. Furthermore, they divide the cause of delisting according to degree of violation. They find that stocks that violate core listing standards are affected more by the delisting than stocks delisted for nonstick violations. Also, stocks that continue trading on the OTCBB have a smaller liquidity impact than stocks that have to trade on the Pink Sheets. Another aspect of the increase of delistings from major U.S. exchnages is explored by Fama and French (2004). They study new listings on major U.S. exchanges in the 1980s and 1990s. They find that the average number of new listings per year from 1973 to 1979 was 156 but in the 1980s and 1990s, this number increased to 549. Fama and French show that there has been a decrease in the profitability of new firms, which has impacted the amount of delistings. They argue that this phenomenon is due to smaller firms benefitting from lower cost of capital. 7

8 III. DELISTING STRUCTURE Most of the firms that are delisted from the sample were trading on Nasdaq. Following the methodology of Harris, Panchapagesan, and Werner (2008), we classify the delisting causes in 4 main categories that are shown in the descending order of the severity of the violation of Nasdaq s listing standards. Table 1 provides a comprehensive list of delisting categories and their corresponding CRSP delisting code and description. The first category belongs to those firms that were delisted for Bankruptcy or Liquidation reasons. CRSP assigns the Delisting Code 574, which is described as Delisted by current exchange - bankruptcy, declared insolvent. The second category is for firms that were delisted for Corporate Governance reasons such as filing, Public Interest, Shareholder Meeting, Disclosure, etc. CRSP assigns the Delisting Code 585, which is described as Delisted by current exchange - protection of investors and the public interest. We also include the Code 587: Delisted by current exchange - corporate governance violation. The third category pertains to firms delisted for Core-listing reasons. Amongst these reasons Harris, Panchapagesan, and Werner (2008), mention low market capitalization, net tangible assets below minimum, net income below minimum, etc. CRSP Code 560: Delisted by current exchange - insufficient capital, surplus, and/or equity. Code 582: Delisted by current exchange - failure to meet exception or equity requirements. Code 551: Delisted by current exchange - insufficient number of shareholders. The final category is for firms that had non-core listing reasons for getting delisted such as bid price below $1, fees, low float, etc. The CRSP codes are the following: Code 552 Delisted by current exchange - price fell below acceptable level, Code561 Delisted by current exchange - insufficient (or non-compliance with rules of) float or assets, Code 580 Delisted by current exchange - delinquent in filing, non-payment of fees, and Code 584 Delisted by current exchange - does not meet exchange's financial guidelines for continued listing. IV. HYPOTHESES DEVELOPMENT Analyzing Given the results from previous literature that show that more capital rounds strengthen the IPO, it can be expected that VC backed IPOs with more capital rounds that get delisted will be for noncore violations. Given that noncore violations are the least serious of 8

9 violations for getting delisted, then this will be the type of violations for firms with plenty of capital staging. H1: Firms with more financing rounds that get delisted will be for non-core violations. Following the same logic from the first hypothesis, firms with more rounds are the strongest. If these firms get delisted but continue trading, it will be in stronger secondary exchanges such as OTCBB, firms with fewer rounds that get delisted might end up in smaller secondary exchanges like the Pink Sheets. It might also be reasonable to expect a smaller price decline and a smaller increase on spreads after the firm begins trading on the secondary exchange if they had more financing rounds. H2: Firms with more financing rounds that get delisted from a major exchange will continue trading on the OTCBB rather than the Pink Sheets. Prices should decline less and spreads should also increase less when firms with more rounds begin trading on the secondary exchange. Finally, most of the literature pertaining to SOX, states that firms that voluntarily delist from SOX is due to higher compliance costs. Therefore, these are the smaller firms that may think twice before going public. If an IPO is harder due to the increased difficulties presented by SOX, then firms will have to go through more capital rounds prior to getting listed. H3: After SOX, VC backed IPOS will require more capital stages. V. DATA AND METHODOLOGY We compile a list of firms from SDC Global New IPOs that were backed by Venture Capital but were eventually involuntarily delisted between 1990 and We remove securities that are not common shares, finance (SIC between 6000 and 6999) and utilities (SIC between 4900 and 4999), and firms with offer price of less than $5. Finally, we corrected SDC mistakes following Jay Ritter s guide Corrections to Security Data Company s IPO database. The final sample consists of 448 firms that were delisted. From this sample, 43 firms were delisted for bankruptcy or liquidation; 18 for corporate governance issues; 103, for violating core listing standards; and 284 for violating noncore requirements. We include controls for sizes (the natural log of market 9

10 value, the natural log of shares outstanding, and stock price), controls for liquidity (the natural log of trading volume), controls for age (the number of years from IPO date to delisting date), and use delisting-year fixed-effects to control for the time varying nature of stock delisting. Market value, shares outstanding, stock price, trading volume are from the first trading day of the IPO as observed on CRSP. Given that we just want to focus on the difference between core and noncore violations, we remove governance and bankruptcy related delistings. We perform logistic regression because the dependent variable noncore delisted firms is binary. As a robustness check, we also conduct probit and OLS regression analysis. The model is the following: Non-core delisted = β 0 + β 1 log(mv i ) + β 2 log(shares Outstanding i ) + β 3 Price i + β 4 log(volume i ) + β 5 Age i + β 6 Rounds i + FE(Delisting Year) + ε i The second test measures the impact on financing rounds for VC backed firms after SOX. We collect IPOs between 1990 and 2011 backed by Venture Capital from SDC Global New Issues database. We apply the same filter as in the previous test and collect market value and offer price from the first trading day of the IPO as observed on CRSP. The resulting sample size is 320 IPOs after SOX and 2013 before SOX. Following Tian (2010) and Gompers (1995), we include a control variable for size and incorporate the log of the market value. We finally include a Dummy variable that equals 1 if the firm was listed after July 29th, 2002 the day that SOX was enacted. The model is the following: Rounds = β 0 + β 1 log (MV i ) + β 2 Price i + β 3 DummySox i + ε i We perform Poisson regressions with the dependent variable being an ordinal discrete variable. VI. RESULTS Descriptive Statistics The descriptive statistics show there is a wide disparity between VC backed firms that get listed. The standard deviation is almost three rounds and there is a 300% increase on rounds between the first and third quartile. Finally, table 4 shows that the correlation between the variables is very low. In terms of delisted firms, it can be observed from the figure 1 that most 10

11 firms are delisted for violating noncore standards but the group of firms delisted for violating core standards is more than one third of the noncore delisted firms. Multivariate Analysis rounds. Table 5 test the first hypothesis, the delisting reason as having an effect on the number of [Insert Table 5 Here] Contrary to the hypothesis, there is some evidence that an increase on capital rounds is actually leading to a decrease on noncore violations and given that the sample only includes core and noncore delistings, capital rounds have a positive and significant effect on core violations. A possible explanation to these results might be that venture capital rounds affect the size of the company as shown through market value. Therefore an increase on size would lead to more noncore delistings as shown by the significant and positive sign of the log of market value. It would also be important to study the number of years after an IPO that the firm got delisted. If the firm is delisted after several years from its IPO then the effects of VC rounds might get diluted. Table 6 shows the results to test the third hypothesis. As previously shown by Tian (2010) and Gompers (1995), stronger firms receive more capital rounds. [Insert Table 6 Here] The table shows that market value has a positive significant effect on the capital rounds. After SOX, smaller firms complain about the difficulty on getting listed due to the higher compliance costs that result from enacting SOX. The variable shows that IPOS after SOX have more capital rounds than before the law was passed. VII. CONCLUSION Despite a large body of papers on VC governance and performance, prior literature on the governance implication of capital staging are scarce and little research has been done into aspects of what the determinants and consequences of financing rounds are. This paper is to 11

12 take a step further to study the long-term consequence of VC staging: delisting. This paper provides evidence that there is a relation between the delisting causes and venture capital staging. Amongst the capital staging or venture capital literature, there is scant mention of delisting. We extend Tian (2010) which briefly investigates the relation between capital staging and delisting to explore the governance implication of capital staging, specifically how the VC funding rounds affect the long-term (post-ipo) consequence of delisting. Given that the prior research only studies the short-term performance after IPO, this paper contributes to the literature by focusing on long-term impact of capital staging. Such an update in the literature is critical to understanding how contributing factors of VC governance on long-term firm performance have changed over the past two decades. In addition, we provide new evidence on quantitatively understanding how governance regulation (SOX) negatively impacted the entrepreneur finance. Most entrepreneurs complain that listing has become terribly costly due to the requirements imposed by SOX. Most of the literature explores the effects from SOX on firms that are already listed. This paper attempts to explore the effects of SOX on firms that haven t been listed yet, especially firms that receive VC funding. The results show that after SOX, VC backed firms require more capital rounds prior to their IPO. 12

13 REFERENCE Admati, Aanat, and Paul Pfleiderer, 1994, Robust financial contracting and the role of venture capitalists, The Journal of Finance 49, Amit, Raphael, Lawrence Glosten and EitanMuller, 1990, Entrepreneurial ability, venture investments, and risk sharing, Management Science 36, Angel, James J., Harris, Jeffrey H. Harris, Venkatesh Panchapagesan, and Ingrid Werner, 2004, From Pink Slips to Pink Sheets: Liquidity and Shareholder Wealth Consequences of Nasdaq Delistings, Working paper. Ohio State University. Baker, M., and P. A. Gompers, 2003, The Determinants of Board Structure of the Initial Public Offering, Journal of Law and Economics 46, Bhattacharya, U., Borisov, A., Yu, X., 2010, Do financial intermediaries during IPOs affect longterm firm mortality rates? Working paper. Indiana University. Casamatta, Catherine, 2003, Financing and Advising: Optimal Financial Contracts with Venture Capitalists, The Journal of Finance 58, Cumming, D. J., and S. A. Johan. 2007, Advice and Monitoring in Venture Finance, Financial Markets and Portfolio Management 21, Eckhardt, Jonathan, Scott Shane and Frederic Delmar, 2006, Multistage selection and the financing of new ventures, Management Science 52, Engel, Ellen E., Rachel M. Hayes, and Xue Wang, 2007, The Sarbanes-Oxley Act and firms' going-private decisions, Journal of Accounting and Economics 44, Fama, Eugene F., and Kenneth R. French, 2004, New lists: Fundamentals and survival rates, Journal of Financial Economics 73, Gompers, Paul, Optimal investments, monitoring, and the staging of venture capital. Journal of Finance 50, Kaplan, Steven, and Per Strömberg, 2001, Venture Capitalists as Principals: Contracting, Screening, and Monitoring, American Economic Review 91, Kaplan, Steven, and Per Strömberg, 2003, Financial Contracting Theory Meets the Real World: An Empirical Analysis of Venture Capital Contracts, Review of Economic Studies 70, Kaplan, Steven, and Per Strömberg, 2004, Characteristics, Contracts and Actions: Evidence from Venture Capitalist Analyses, The Journal of Finance 59, Leuz, Christian, 2007, Was the Sarbanes-Oxley Act of 2002 really this costly? A discussion of evidence from event returns and going-private decisions, Journal of Accounting and Economics 44,

14 Leuz, Christian, Alexander Triantis, and Tracy Yue Wang, 2008, Why do firms go dark? Causes and economic consequences of voluntary SEC deregistrations, Journal of Accounting and Economics 45, Marosi, Andras, and Nadia Massoud, 2004, Why do firms go dark? Working paper, University of Alberta and York University. Sahlman, William, 1990, The structure and governance of venture-capital organizations, Journal of Financial Economics 27, Tian, Xuan, 2011, The Causes and Consequences of Venture Capital Stage Financing, Journal of Financial Economics 101,

15 Figure 1. This figure shows the delisting reason of ventured backed firms involuntarily delisted between 1990 and

16 Table 1 Delisting categories and descriptions This table describes the categories for getting delisted according to NASDAQ. The categories are filled by the delisting cause as reported by CRSP that best matches the categories as stated by NASDAQ. Category Non Core Violation CRSP code Description 552 Delisted by current exchange price fell below acceptable level. Delisted by current exchange insufficient (or non compliance with rules of) float or 561 assets. 580 Delisted by current exchange delinquent in filing, non payment of fees. Delisted by current exchange does not meet exchange's financial guidelines for 584 continued listing. Core Violation CRSP code Description 551 Delisted by current exchange insufficient number of shareholders. 560 Delisted by current exchange insufficient capital, surplus, and/or equity. 582 Delisted by current exchange failure to meet exception or equity requirements. Corporate Governance CRSP code Description 585 Delisted by current exchange protection of investors and the public interest. 586 Delisted by current exchange composition of unit is not acceptable. 587 Delisted by current exchange corporate governance violation. Bankruptcy or Liquidation CRSP code Description 574 Delisted by current exchange bankruptcy, declared insolvent. 16

17 Table 2. Venture backed IPOs that were involuntarily delisted between 1990 and This table reports the summary statistics for the log of market value on the firm's first trading day, stock price on the first trading day, and venture capital rounds. Data about VC firms is obtained from SDC Global New Issues database. Variable 25% Median Mean 75% S.D. Log of Market Value Price Number of financing rounds

18 Table 3 Summary statistics for IPOs backed by venture capital between 1990 and This table reports the summary statistics for the log of market value on the firm's first trading day, stock price on the first trading day, and venture capital rounds. IPOs are divided before and after Sox was passed on July Data about VC firms is obtained from SDC Global New Issues database. Before Variable 25% Median Mean 75% S.D. Log of Market Value Price Number of financing rounds N 2013 After Variable 25% Median Mean 75% S.D. Log of Market Value Price Number of financing rounds N

19 Table 4 Correlation of variables Log of market value on the firm's first trading day, stock price on the first trading day, and venture capital rounds. Data about VC firms is obtained from SDC Global New Issues database. Rounds Market Value Price Rounds 1 Market Value Price

20 Table 5. Relationship between delisting cause and financing rounds Section 1. Logistic regressions This sample includes firms that violated listing requirement of both core and non-core as defined in Table 1. The dependent variable is a dummy variable with its value being 1 if it is delisted from the stock exchange for violating a non-core requirement. The independent variables include the number of funding rounds from VCs, log of market value on the firm s first trading day (IPO date), stock price on the first trading day, and the age defined as the years from the founding date to the IPO date. All specifications use logistic regression and specification (6) also uses delisting-year fixed-effects to control for the time varying nature of stock delisting. Coefficient t-statistics are shown in the parentheses with ***, ** and * indicating its statistical significant level of 1%, 5% and 10% respectively. Dependent Variable: Non-core Delisting (1) (2) (3) (4) (5) (6) Funding Rounds * (-1.741) * (-1.900) * (-1.893) ** (-1.983) ** (-1.969) ** (-1.994) Log (Total Assets) *** (-2.737) *** (-3.304) *** (-3.162) *** (-3.351) *** (-3.143) *** (-2.908) Log (Total Shares Outstanding) 0.267** (2.162) 0.266** (2.156) (1.298) (1.329) Stock Price (-0.716) (-0.662) (-0.659) (-0.702) Log (Trading Volume) (0.132) (0.123) (0.123) Firm Age (-0.325) (-0.325) Delisting-Year Fixed-effects Yes Yes Yes Yes Yes Yes Sample Size LR Chi-square 98.82*** *** *** 85.34*** 85.45*** 83.66*** Pseudo R-square

21 Section 2. Probit regressions This sample includes firms that violated listing requirement of both core and non-core as defined in Table 1. The dependent variable is a dummy variable with its value being 1 if it is delisted from the stock exchange for violating a non-core requirement. The independent variables include the number of funding rounds from VCs, log of market value on the firm s first trading day (IPO date), stock price on the first trading day, and the age defined as the years from the founding date to the IPO date. All specifications use probit regression and specification (6) also uses delisting-year fixed-effects to control for the time varying nature of stock delisting. Coefficient t-statistics are shown in the parentheses with ***, ** and * indicating its statistical significant level of 1%, 5% and 10% respectively. Dependent Variable: Non-core Delisting (1) (2) (3) (4) (5) (6) Funding Rounds * (-1.689) * (-1.882) * (-1.862) * (-1.950) * (-1.933) * (-1.939) Log (Total Assets) *** (-2.773) *** (-3.390) *** (-3.246) *** (-3.433) *** (-3.209) *** (-2.955) Log (Total Shares Outstanding) 0.159** (2.197) 0.158** (2.193) (1.310) (1.343) Stock Price (-0.675) (-0.610) (-0.606) (-0.660) Log (Trading Volume) (0.121) (0.112) (1.067) Firm Age (-0.328) (-0.145) Delisting-Year Fixed-effects Yes Yes Yes Yes Yes Yes Sample Size LR Chi-square 98.79*** *** *** 85.25*** 85.36*** 83.54*** Pseudo R-square

22 Section 3. OLS regressions This sample includes firms that violated listing requirement of both core and non-core as defined in Table 1. The dependent variable is a dummy variable with its value being 1 if it is delisted from the stock exchange for violating a non-core requirement. The independent variables include the number of funding rounds from VCs, log of market value on the firm s first trading day (IPO date), stock price on the first trading day, and the age defined as the years from the founding date to the IPO date. All specifications use OLS regression and specification (6) also uses delistingyear fixed-effects to control for the time varying nature of stock delisting. Coefficient t-statistics are shown in the parentheses with ***, ** and * indicating its statistical significant level of 1%, 5% and 10% respectively. Dependent Variable: Non-core Delisting (1) (2) (3) (4) (5) (6) Funding Rounds * (-1.721) * (-1.928) * (-1.911) ** (-1.988) ** (-1.972) * (-1.966) Log (Total Assets) *** (-2.710) *** (-3.341) *** (-3.182) *** (-3.367) *** (-3.152) *** (-2.891) Log (Total Shares Outstanding) ** (2.180) ** (2.171) (1.285) (1.317) Stock Price (-0.664) (-0.632) (-0.635) (-0.682) Log (Trading Volume) (0.174) (0.160) (1.073) Firm Age (-0.328) (-0.137) Delisting-Year Fixed-effects Yes Yes Yes Yes Yes Yes Sample Size F-test 3.70*** 3.76*** 3.66*** 2.87*** 2.78*** 2.81*** Adjusted R-square

23 Table 6 Relationship between financing rounds and IPO timing This table reports the Poisson regression results of IPOs before and after SOX was approved on July The firms on this table include IPOs between 1990 and 2011 that were backed by venture capital. The dependent variables is the number of financing rounds. The dependent variables include the log of market value on the firm's first trading day, stock price on the first trading day, and the dummy variable PostSOX that equals 1 if the IPO began after SOX. Data about VC firms is obtained from SDC Global New Issues database. ***,**, Dependent variable: Financing Rounds Intercept (2.62)*** Price ( 3.73)*** Log of Market Value (6.02)*** PostSOX (4.32)*** Pseudo R N