The effects of VC involvement on the follow-on financing rounds and exit outcomes of angel-backed ventures

Transcription

1 The effects of VC involvement on the follow-on financing rounds and exit outcomes of angel-backed ventures Master Thesis Tilburg University Department of Finance, Tilburg School of Economics and Management Author: Christos Raptis ANR: Publication date: 22/10/2014 Supervisor: Dr. Marco Da Rin

2 Table of Contents Executive Summary... 2 A. Introduction... 3 B. Literature Review... 4 B. 1. Angel Investors... 4 B. 2. Stage Financing... 5 B. 3. The Angel Investor Venture Capital Mix... 5 C. Research Approach... 7 C. 1. Data... 7 C. 2. Research Questions and Hypotheses... 7 D. Methodology and Empirical Analysis D. 1. Data Analysis D Dependent Variables D Independent Variables and Control Variables D Descriptive Statistics D. 2. Univariate Analysis D. 3. Regression Analysis D VC Involvement, Angel Specific and Deal Specific Characteristics effects on the Financing Rounds Post Investment D VC Involvement, Angel Specific and Deal Specific Characteristics effects on Investment Outcomes D Timing of VC Involvement and Angel Investment Outcomes D Checking for robustness E. Conclusions E. 1. Summary E. 2. Limitations References Appendix

3 Executive Summary In this study, using a hand collected dataset of angel investor profile, investment process and portfolio companies we examine the effects of VC Involvement in angel-backed ventures. The thesis focuses on two fundamental aspects of angel investments; the amount of financing rounds a portfolio company receives relative to VC Involvement and the effect of VC Involvement on the probability for a successful investment outcome. We find that VC involvement is associated with a higher amount of financing rounds, a higher propensity for angel investors to syndicate their investments and a substantially more passive and cautious approach from angel investors towards their funded ventures, both in terms of interaction as well as total exposure to idiosyncratic risk. Although VC Involvement shows a positive relation with better investment outcomes, the effect is not statistically significant. Controlling for the timing of VC Involvement, we find that mixing angel capital in a venture that has already acquired VC funds is associated with a higher probability of an unsuccessful outcome. On the contrary, VCs entering later in the venture stage are associated with a higher probability of a successful outcome. Furthermore, higher interaction of angel investors Although investor activism is positively related with successful outcomes, our findings support the results of Goldfarb (2013) and Hellmann & Thiele (2013) that mixing angel investors and VCs impedes successful exits, a finding driven by differences in investment strategies, information asymmetry, control mechanisms and agency costs. From our analysis there is no evidence of complementarity between these two sources of finance, further supporting the theoretical model developed by Hellmann, Shure & Vo (2013). No definitive conclusion can be drawn on the substitutes proposition also; while there is evidence of the sign of the effects being positive or negative, the overall fit of the models cannot support it. 2

4 A. Introduction Innovation is one of the key drivers of economic growth, a measure that can be captured by the amount of business start-ups. New (or better versions of existing) ideas emerge craving for much sought initial venture capital. The main source of venture capital in the seed or startup stage of a firm comes through business angels (Smith et al. (2011)). Due to disclosure matters, until the late end of the 90s little was known for the business angels market size (Mason & Harrison (2000)), although significant research has taken place the last years to define its measure (Wong et al. (2009)). The Guardian reports that; [ ]the UKBAA estimates that each year in the UK some angels invest about 850m. (The Guardian, 13/11/2013). Without business angels involvement in early stage financing, very few companies would have the possibility to grow as business angels, business angel networks (syndicates) and the lately growing crowd-funding platforms fill in the financing gap related with early stage investments (OECD (2011)). Being such an important part of the economy, business angels get less than the credit they deserve for providing this service. As Anthony Clarke, CEO of Angel Capital Group elaborately summarizes; Companies that are suitable for angel investment are the top 3% because these are the high risk businesses, they do need the money to develop their products and services before they can go out and get proper sales. That is high risk, not suitable for debt finance, so you ve got to have a scalable idea, a business to attract angel money. We are, as angel investors, looking for a ten times (10x) return on the money on a 5% radius [ ]. Low entrybarrier industries are not interesting for angel investors [ ] (Bloomberg TV, 10/02/2014). Moreover, attractive and scalable ideas attract Venture Capital (VC) funds. Typically VCs invest in later stages of ventures, opting for short-term financial returns relative to angel investors. Given their lagged involvement in a firm, they often impose terms incorporating strong control rights, while also forcing pre-existing investors to either exit or drop some of their control rights over the investment. VCs and angel investors make an explosive mix that when combined timely and correctly can propel a firm to success, whereas a bad mix attributes to longer resolution times, problematic cooperation between business angels and VCs and ultimately unsuccessful venture outcomes. In this study, using standard OLS techniques, we try to analyze some of the key factors related with the financing rounds a firm receives, followed by an analysis of the exit outcomes of business angels investments using ordered probit regressions. Furthermore, we will attempt to test the substitutes/complements hypothesis of business angel and venture capital financing in the context of the study conducted by Hellmann, Shure & Vo (2013). 3

5 B. Literature Review The analysis presented in the upcoming parts of this study is based on the theoretical background regarding Angel Investors, Stage Financing and lastly the effects of VC Involvement in angel-backed firms. Due to the difficulty of gathering representative data regarding business angels investments their perspective regarding investment outcomes is vaguely and shortly relative to the VC perspective analyzed. Firstly, a theoretical analysis regarding angel investors is presented. Next we examine the theory backing stage financing and the amount of financing rounds a start-up firm receives until exit. Finally, theory in the context of VC Involvement and the effects on financing rounds and exit outcomes is discussed. B. 1. Angel Investors Multiple research papers are dedicated in defining business angels profile, the business angel market, the investment process and exit, however, the vast majority is limited to descriptive analysis due to unrepresentative samples (Da Rin & Penas (2011)). Angel investors are high net-worth individuals and are widely recognized as the first source of outside financing for firms at seed or start-up stage (Wong (2009); Osnabrugge & Robinson (2000)), investing typically small amounts of money ranging from $ $ (Wiltbank et al. (2009); OECD (2011)) in private firms for an investment horizon of five to ten years (Smith et al. (2011)). However, little is known about the types of individuals that provide angel financing (Prowse (1998)). A study from OECD (2011) outlines angel investors as experienced entrepreneurs who invest in innovation in a broader perspective than VCs, both in terms of industries and locations. Moreover, angel investments are less sensitive to market cycles given their longer investment horizon. Typical firms that receive angel financing are high growth, high risk start-ups in the prototype or pre-sales stage (Ibrahim (2008)), that provide high probability of significant return on investment (Mason & Harrison (2002)). In the last years even more business angels are syndicating, providing empirical evidence of improved firm survivability for at least four years and superior follow-on investments (Kerr et al., 2014). Chemmanur (2006), however, finds evidence that syndication is associated with poor exit results. Additionally his analysis confirms that entrepreneurs have superior information over their business, although its effect diminishes as the VC or the business angel interacts with the firm (and the follow-on investments). In terms of contracting, business angels make smaller investments while exerting weaker cash-flow and control rights (Chemmanur, 2006; Goldfarb, 2013). Goldfarb (2013) also suggests that weaker rights are a manifestation of angels alignment with the entrepreneur s preferences. 4

6 B. 2. Stage Financing Angel investors are the first source of financing an entrepreneur turns into after the entrepreneur s own wealth, as well as the financing capabilities of his close environment (namely family and friends) are depleted, and before VCs come into play (Mason & Harrison (2000), OECD (2011)). Schwienbacher (2007) additionally notes that entrepreneurs face a choice of either waiting until they have gathered the required capital, or move fast into achieving certain milestones before contracting larger investors such as VCs, resulting in different shapes of firms. In a later study he also confirms that angel investors finance only the first rounds of investment due to limited resources (Schwienbacher (2009)). Thus angel investors fill the financing gap between seed stage investments (Founder, friends and family funds), and VC funding. In the recent years angel investing is becoming far more visible and professional as angel investors start forming syndicates or networks (OECD (2011)), while according to Hellmann, Shure & Vo (2013) angel financing is a springboard towards subsequent VC financing rounds. VCs offer larger amounts of capital, typically investing in the stage after angels provide funds (OECD (2011) and is the first indication of a project entering the upscale phase. (Smith et al. (2011)). The most important factor for VC investments is economic potential, whereas angel investors are more interested in identifying interesting investments and providing their human capital (Hsu (2014). Finally Sohl (2011) states that in 2010 a total of firms received angel funding, an increase from 2009 indicating the market s rebound after the start of the recent financial crisis. B. 3. The Angel Investor Venture Capital Mix As VCs invest in later stages, common wisdom implies it is almost certain that an angel investors will have already provided capital. The mixing of these discrete sources of finance, nonetheless, is of great interest as research is expanding upon the effects of their interaction. Entrepreneurs selecting angel financing as their initial funding benefit from the increased incentives for the angel investors, generating additional value-adding services, while also angel investors are more aligned to the entrepreneurs preferences (Schwienbacher (2009), Goldfarb (2013)). On the other hand, choosing VCs secures further funding, however, the investors incentives to put effort diminishes greatly (Schwienbacher (2009). Mason & Harrison (2002) suggest that angel investors and VCs complement each other in terms of financing and co-investing, however, recent studies propose that the mix of angels and VC is most of the times impeding successful exits (Goldfarb (2013), Hellmann & Thiele (2013)). Furthermore, screening and monitoring are two important services VCs provide that contribute in the higher overall efficiency of VC-backed firms compared to non-vc-backed firms, which in turn positively affect the probability of a successful exit (Chemmanur (2011), Hellmann & Thiele (2013)). Interaction between investors and the entrepreneur is a highly significant factor 5

7 towards a venture s success or failure. Angel investors offer tutoring especially in the first stages of a venture, whereas active VCs provide a different set of services, mainly through better recruiting and networking. Experience in the industry is the critical trait for both investors, nonetheless, the resulting effect is different (Gompers (2008)). Bottazzi, Da Rin & Hellmann (2008) also find evidence that business experience is linked to an active investment style, as well as positively related to successful investment outcomes, indicating the economic significance of human capital. Finally, Gompers (2008) confirms that experienced VCs within a certain industry are more responsive to market signals of investment opportunities, whereas overall experience has no effect on identifying opportunities. Mergers and acquisitions accounted for 66 % of angel exits, of which roughly half came with a profit and annual returns between 24% and 36%. Yield rates on investment opportunities have increased from 2009 by almost 4% to reach 18,4% (Sohl (2011)). 6

8 C. Research Approach This part of the thesis is dedicated to the discussion of the sources and the form of the data used, the formation of the main research question as well as the hypotheses used in the analysis. C. 1. Data The thesis is based on hand-picked data originating from two main sources; a) an online survey conducted by Dr. Da Rin and Drs. Penas of Tilburg University focusing on UK-based angel investors and business angel networks (syndicates) and b) data retrieved from ORBIS database regarding firm performance and the confirmed financing rounds, resulting in a dataset of 77 angel investors and 108 portfolio companies structured as panel data. The survey is anonymous and is being circulated among UK business angel networks, providing insightful information about the angel investors profile, syndication traits and their investment process. It comprises of three parts; a) angel investor demographic, educational, professional background and investor profile information, b) investment process details, entailing information such as contractual terms, deal flow sources and typical amount of capital provided, and c) portfolio company details, where thorough information are provided covering every aspect and characteristic of the financed firm. Although not required, most of the investors provided the names of their portfolio companies, thus making it possible to connect the information retrieved through the questionnaire, with scarcely available financial information on the financed firms from Orbis. Orbis is an online database containing comprehensive information on companies worldwide, with an emphasis on private companies information (orbis.bvdinfo.com), encompassing all data that used to be in AMADEUS database, as well as data from ZEPHYR database, which in turn contains M&A deals and rumors where info on VC-backed private placements are available. From within ZEPHYR database I collected all available information regarding the financing rounds prior, at and post the angel investment for the respective companies included in the dataset. Additionally the angel investors views on individual portfolio companies performance as stated in the questionnaire are compared with the information obtained through Orbis, and wherever possible verified and adjusted to current company performance. C. 2. Research Questions and Hypotheses Attributable to scarcity and disclosure of information regarding angel-backed financing, research on the perspective of successful investment outcomes of business angels is rather limited. However, recent studies (Goldfarb (2013), Hellman, Shure & Vo (2013), Hellmann & Thiele (2013)) have emerged shedding light to this yet-to-be explored field. As results keep pouring in, different datasets are used and analyzed giving ambiguous results with respect to 7

9 how business angels and VCs mix and the effects of fusing these distinct means of financing on subsequent financing rounds and successful investment outcomes. Extending the work of Goldfarb (2013) and Hellmann & Thiele (2013) the main research question of this thesis is formed as to test their results on a different dataset; Does mixing angel and VC financing have a significant positive correlation with the amount of subsequent financing rounds and a significant negative correlation with successful investment outcomes for Angel Investors? Building upon the enhanced dataset provided by the online survey and the information retrieved through Orbis, several factors affecting the number of follow-on financing rounds and the probability of a successful investment outcome are analyzed and tested, forming subquestions and hypotheses that will facilitate answering the main research question, while also validating the results of the papers upon which this thesis is trying to extend. Data regarding Angel Investor profile, investment process and investment outcomes as reported in the online questionnaire are fused with information from Orbis. Relevant variables impacting both aspects of the research question are categorized in two distinctive groups; Angel Investor characteristics and Deal Specific characteristics, both of which are further controlled for robustness of their results with Angel Investor syndication effects, as well as firm fixed effects, as denoted by the industry the portfolio companies are operating. VC Involvement, especially by VCs that have prior business experience, is associated with securing follow-on venture financing rounds (Schwienbacher (2007) & (2009), Goldfarb (2013), Botazzi et al. (2008)). Hence, VC involvement should have a positive relation with the amount of financing rounds a firm receives; Hypothesis I: VC involvement increases the amount of subsequent financing rounds of business-angel-backed firms Additionally, firms that are financed by VCs alone are more likely to be successfully exited. However, when mixed with angel funds, the misalignment of interest between investors impeded successful outcomes. VCs are more interested in short-term economic returns, whereas angel investors team up with the entrepreneur s vision, opting for longer investment horizon (Goldfarb (2013), Kerr (2014)). The introduction of the VC involvement variable in the models regarding investment outcomes should yield a negative relation between VC involvement and the investment outcomes; Hypothesis II: VC involvement impedes successful investment outcomes for angel investors Similar to research on VCs, angel investors provide value-adding services to their portfolio companies through interaction. Their previous business experience is the greatest asset they 8

10 provide as it can make the difference between failure and success. Moreover, the more experienced an investor is within the sector they invest, the higher the value-adding services. Motivated by the variables used in Gompers (2008) and Bottazzi et al. (2008) we suggest that the older an investor starts providing angel funding, the more valuable the services she will provide. Also, industry-specific experience will have a high impact on the amount of financing rounds through the increased network as well as on the eventual investment outcome. Still, mixing VCs and angels amplifies information asymmetries, agency costs and the importance of the control mechanisms an angel investor is forced to drop once VCs start investing; Hypothesis III: Total investor business experience, interaction and industry specific experience positively influence the amount of subsequent financing rounds of business-angel-backed firms. Hypothesis IV: Total investor business experience and relevant sector are positively related with the probability of a successful investment outcome for their investments, whereas interaction has a negative relation. An important factor determining the amount of subsequent financing rounds, as well as the investment outcome is deal specific characteristics. Angel investors impose their weaker compared to VCs control rights over their investments, which leaves a great amount of freedom to entrepreneurs choosing their next steps. On the other hand choosing for VCs early on, limits greatly this freedom, however, it is offset by the security of financing and the upscale potential of the projects. Angel investors typically invest small amounts, and an indication of how highly a project is regarded is given by the amount of capital they provide relative to the total equity of the portfolio firm. Since angel investments entail high risk, exposing an investment greatly is probably counterbalanced by the potential in the venture. Furthermore, angel investors acknowledge the importance of VC networks and value-adding services, as well as the stringent contract terms in their deals. Providing capital to a firm that has already acquired VC funds is tempting for angel investors, to take a passive role, withdrawing from their perceived value-adding services and merely provide capital without exerting effort a free-rider approach; Hypothesis V: Exposure to investment is positively related with the amount of subsequent financing rounds of business-angel-backed firms. On the contrary, free-riding has a negative effect on the follow-on financing. Hypothesis VI: Exposure to investment has a positive relation with the probability of a successful investment outcome for angel investors, whereas free-riding has a negative effect. 9

11 D. Methodology and Empirical Analysis Within this chapter, the hypotheses are tested empirically. The analysis is arranged in three parts. In the first part the variables used in the models are defined and interpreted, while in the following part the models constructed are discussed and deciphered. In the last part the results are considered and the hypotheses are contested. D. 1. Data Analysis In order to test the validity of the hypotheses set in the previous section of this study, two models are formulated using relevant variables carved out of the online survey. The models will examine the importance of certain factors that influence primarily the amount of financing rounds a company receives and consequently the outcome of the investment for angel investors. The interrelation between these two models in strong as exhibited in relevant literature (Kerr (2014), Hellmann, Shure & Vo (2013), Chemmanur (2006), Hellmann & Thiele (2013)), hence the dependent variables should examine both aspects of angel investments towards exit. First the dependent variables in the two models are specified and explained, followed by a discussion on the independent and the control variables used. Finally descriptive statistics for all variables are reported. D Dependent Variables The dependent variable used in the first model is straightforward and is the amount of financing round a portfolio company received after the business angel investment (Table 1). Given the nature of angel investments, the amount of financing rounds as obtained through the survey had to be refined. Also disclosure over private investments had to be taken into account before classifying a cash inflow as financing round. Hence, sources of financing that are classified as eligible for consideration into the model and relevant for the purpose of the analysis are; VC firms, corporate investors, bank loans, public grants and other (than the one answering the survey) business angels, thus excluding entrepreneur s own wealth and family and friends as sources of finance. Where information was not available, particularly in the cases were a portfolio company received exclusively bank loans, public grants and/or capital provided by other business angels, the amount of financing rounds has been merged into one entry to prevent double counting and align to market practices as especially in startups these sources are used in combination. The dependent variable of the second model is ordinal and is the performance of the portfolio company, split into three levels; Negative (1) if the portfolio company has performed disappointingly or was liquidated, Neutral (2) for the companies that are still active but they did not achieve any substantial increase in their realized revenues and/or total assets relative to the investment, and Positive (3) for portfolio companies that have been exited through an IPO or an acquisition, bought back by the entrepreneur, and 10

12 lastly if they have achieved a substantial increase in revenues and/or total assets relative to the investment from the angel investor, or more than 3 million in revenues or total assets in absolute numbers. As it is difficult to measure financial returns due to disclosure and unreliable information, focusing on nominal outcomes of the investments is prudent. D Independent Variables and Control Variables The next step in the construction of the empirical models is selecting and evaluating the factors that affect bilaterally the amount of financing rounds a portfolio company receives after the angel investment and the outcome of the angel investment as a whole. Gompers (2008) finds that experienced VC firms increase their investment activities in response to positive economic signals without this increase corroding the success of these investments. Additionally, industry-specific experience is a decisive factor for VC firms increasing investment activity. However, investment decisions between angel investors and VCs differ greatly, given their decision encompass vastly different perspectives on agency costs, market risks, information asymmetry and control mechanisms (Hsu (2014)). Motivated by the aforementioned studies the factors deemed relevant are introduced into the models on the basis of their additive power to the model, their significance levels, their interaction with the dependent and other independent variables as well as their contribution in supporting the theoretical background set. The main independent variable upon which the thesis analysis is going to focus is VC Involvement, a dummy variable capturing the effect of VC involvement regardless of the timing of the VC providing capital to a portfolio firm to test the value added effect of the VC involvement in an angel-backed investment. The other variables revolving around this central concept that utilized are grouped into two categories, angel investor characteristics and deal specific characteristics. The variables meeting these criteria regarding the angel investor characteristics are; The age of the angel investor at her first investment, as a proxy for experience accumulated in the work field that is brought into their portfolio companies through interaction as well as their mean risk-aversion level regarding bet-investments, similar to the TOTALEXPERIENCE variable used in Gompers et al. (2008) study Interaction, a nominal variable indicating the frequency of interaction an angel investor has with a portfolio company, as a proxy for value adding services the angel investor offers to portfolio companies. Relevant sector, a dummy variable projecting the angel investor s knowledge and exposure to the challenges their portfolio companies sectors, assimilating to the INDEXP variable used by Gompers et al. (2008) 11

13 As for deal specific characteristics that meet the criteria to be included into the model, the following are selected; Investment to Equity, which is the ratio of the total capital amount invested in a portfolio company to the total equity of the portfolio company, both measures reported in the online survey as nominal values, and operates as a proxy for angel investor exposure to the idiosyncratic riskiness of an investment, a variable important in determining investment appeal. A free-rider variable, controlling for the free-rider effect of investors delegating due diligence, while also syndicating their investment, thus exerting minimal effort and using limited value-adding services through networking. Three dummy variables; VC prior, VC at and VC post the angel investment, capturing the effect of the timing the VC-business angel mix in the financing rounds has in the models, an extension of the main independent variable VC Involvement. Timing of the investment is of great importance for both angel investors and VCs since it is a determinant of a break-even analysis for both investors (Van Osnabrugge (2000)) Finally, four control variables are introduced to the models to check for robustness of results. Syndication, a dummy variable controlling for the effect of business angel syndication as specified in firm-level investments (thus whether an angel investor syndicated for the specific investment, regardless of her propensity to syndicate), a variable having a positive effect in investment outcomes (Wong et al (2009), Kerr (2014) Whether a firm has generated sales at the time of the angel investment, as a proxy for a portfolio company s ability to produce tangible evidence of its vision to potential investors. Entrepreneurship dummy variable as a proxy for investor experience in startup capital needs and financing procedures and pitfalls, a similar variable to TOTALEXPERIENCE (Gompers at al. (2008)) and documented in Bottazzi et al. (2008) as a predictor for investor activism, related with positive investment outcomes. A sector cluster nominal variable, grouping similar companies into broad industries as indicated in the survey and cross-checked with their respective NACE code in Orbis. Several other factors could be obtained through the online survey, however, their additive power to the models, as well as their relevance to the research question is minimal. The selection of the criteria, as well as the variables that are left out of the models could be of importance to other studies but are left out intentionally in this thesis due to insignificance relative to the models. 12

14 Variable Dependent Variables Description Table 1: Variables Description Financing Rounds Post Outcome Independent variable reporting the amount of financing rounds a portfolio company received post the angel investment Independent variable accounting for the outcome of the angel investment. Allocated in three categories; Negative (1), Neutral (2) and Positive (3) Independent Variables: Angel Investor Characteristics Age At First Investment Dependent variable reporting the angel investor's age at the moment of their first angel investment Interaction Nominal variable indicating the frequency the angel investor interacts with the respective portfolio company; (1) substantially passive, (2) less than a quarter, (3) quarterly, (4) monthly, (5) weekly Relevant Sector Dummy variable reporting whether the angel investor has working experience within the industry the portfolio company is operating (1/0) Independent Variables: Deal Specific Characteristics VC Involvement Dummy variable indicating VC investing at any time at a portfolio firm (1/0) Investment To Equity The ratio of total capital provided by the angel investor as indicated in nominal values; (1) , (2) , (3) , (4) , (5) over , proportional to the total equity of the portfolio company reported in nominal values; (1) , (2) , (3) , (4) , (5) , (6) , (7) over Free Rider VC Prior VC At VC Post Control Variables Dummy variable indicating angel investors exploiting "free-riding", as they delegate due diligence and have syndicated for the specific investment (1/0) Dummy variable reporting whether a VC has provided capital to the portfolio company prior to the angel investment (1/0) Dummy variable reporting whether a VC has provided capital to the portfolio company at the time of the angel investment (1/0) Dummy variable reporting whether a VC has provided capital to the portfolio company post the angel investment (1/0) Syndication Dummy variable whether the angel investor has syndicated for the specific investment (1/0) Sales Dummy variable whether the portfolio company had realized sales at the time of the angel investment (1/0) Entrepreneur Dummy variable whether the angel investor has ever started their own firm (1/0) Sector Cluster Industry classifications based on NACE Rev 2 (1) Consulting (management, technical) - NACE codes; 7022 (2) Consumer Products and Services (incl. Retail) - NACE codes; 321, 2751, 3299, 4631, 4799, 7490, 8299 (3) Electronics - NACE codes; 4643, 6201 (4) Financial Services - NACE codes; 6430, 7010 (5) Health/Pharma/Biotech - NACE codes; 2059, 2110, 4690, 7010, 7120, 7211, 7219, 7490, 8623 (6) Industrial Products & Services - NACE codes; 910, 2630, 2651, 2896, 2920, 3250, 5010 (7) Media & Entertainment - NACE codes; 5911, 5913, 6010, 6201, 6209, 6311 (8) Professional Services (accounting, law) - NACE codes; 2673 (9) Software (incl. Internet) - NACE codes; 6202, 6499 (10) Telecommunications - NACE codes; 2620, 6190 D Descriptive Statistics The dataset comprises of 77 angel investors providing information over 108 portfolio companies spread among 10 broad industry sectors. Descriptive statistics for the dataset are 13

15 provided in Table 2, while a pairwise correlation matrix for the independent variables and the control variables is provided at the Appendix (Table 3). Table 2: Descriptive Statistics Table 2 reports descriptive statistics for dependent, independent and control variables. Median is omitted for all dummy variables. For the categorical variable Sector Cluster only the number of observations per group is provided. Variables Number of Observasions Means Median Standard Deviation Minimum a) Dependent Variables Financing Rounds Post 102 0, , Outcome 105 1, , Maximum b) Independent Variables: Angel Investors Characteristics Age At First Investment , , Interaction 107 3, , Relevant Sector 106 0,2358-0, c) Independent Variables: Deal Specific Characteristics VCInvolved 105 0,5429-0, Investment To Equity 105 0,4374 0,4000 0,2521 0, Free Rider 104 0,2788-0, VC Prior 105 0,1619-0, VC At 108 0,3056-0, VC Post 102 0,3824-0, d) Control Variables Syndication 104 0,6250-0, Sales 108 0,5000-0, Entrepreneur 108 0,8704-0, Sector Cluster Number of Companies a) Consulting 1 b) Consumer Products & Services 22 c) Electronics 6 d) Financial Services 4 e) Health/Pharma/Biothech 20 f) Industrial Products & Services 19 g) Media & Entertainment 10 h) Professional Services 1 i) Software 16 j) Telecommunications 7 As shown in Table 2, portfolio companies received on average one financing round after the angel investment. Also the mean outcome for the portfolio companies is slightly Negative, as it is just below 2. For informational reasons the dummy variables Negative, Neutral and Positive are reported, showing that from the 105 portfolio companies whose investment outcome could be obtained, 38 companies turned out into Negative outcomes, while 30 companies turned out to have a Positive outcome. Lastly, 37 portfolio firms performance is 14

16 classified as Neutral. The outcome variable is classified as Negative if the portfolio company has performed disappointingly or was liquidated, Neutral for the companies that are still active but they did not achieve any substantial increase in their realized revenues and/or total assets relative to the investment, and Positive for portfolio companies that have been exited through an IPO or an acquisition, bought back by the entrepreneur, or if they have achieved a substantial increase in revenues and/or total assets relative to the investment from the angel investor, or more than 3 million in revenues or total assets in absolute numbers. Looking at the independent variables in Table 2 we can see that the angel investors in the dataset have accumulated on average at least 20 years of working experience before they start doing angel investment, while angel investors in the dataset interact on average on a quarterly basis with their portfolio companies. However, what is striking is that only 24% of the portfolio companies operated in a sector where the angel investor has some kind of exposure. From the perspective of deal characteristics, for more than 50% of the portfolio companies a VC firm provided capital, with figures standing at 16% of the portfolio companies receiving VC funds prior to the angel investment, more than 30% obtaining VC funds alongside business angel funds and around 39% of the firms obtaining VC money after the angel investment. Furthermore, angel investors contributed a substantial proportion of the equity of their portfolio firms reaching close to committing close to 44% of the company s total equity, whereas almost 28% of the portfolio companies received business angel funds where the angel investor providing the capital engaged in a free-rider style of investing, delegating due diligence and syndicating the investment. Finally descriptive statistics regarding the control variables suggest that more than 62% of the portfolio companies received angel financing through syndicated investment, 87% of the angel investors in the dataset has started their own company, and half of the portfolio companies had generated sales by the time of the angel investment, a factor indicating the riskiness of the investment, as realized sales may be regarded as a risk reducing factor given that the firm has already produced a product or service that customers have an interest in buying. D. 2. Univariate Analysis Table 4 reports univariate parametric tests for difference in means of two groups. The variable of interest is VC Involvement and the difference in means of the other variables for firms where a firm has received funds from a VC source in excess of business angel funds and firms which only received business angel funds. The mean financing rounds is 1,2963 for business-angelbacked portfolio companies where a VC firm is involved, whereas a portfolio company not receiving VC funds only has a mean of 0,625 financing rounds. This difference in means is statistically significant, indicating that VC involvement almost doubles the mean amount of financing rounds a portfolio company receives. However, apart from a slight increase in the 15

17 mean, no significant difference is noted in the outcome between companies that have a VCbusiness angel mix and those with no VC funds source. Looking at the difference in means for Age At First Investment we can see that far more experienced business angels tend to mix their investments with VCs, with the difference between them being significant. Furthermore, significant differences in means are reported for Syndication, Interaction, Investment To Equity and Free Rider variables. These statistically significant differences proclaim that when a VC firm gets into the financing mix, the mean angel investor will take a far more passive approach to the investment, as denoted by the means in the respective variables; the propensity to syndicate the investment (as well as VCs investing in firms that already have received business angel funds through syndicated investment) is substantially higher, while the capital provided by the mean business angel relative to the total equity of the firm is much lower, she will interact less with the portfolio company and will in general engage in a free-rider style of investing. Additionally two groups are formed to compare the means of other variables using the Relevant Sector dummy variable so as to capture any value-adding services provided by a business angel with expertise within the industry. Measuring the means for all variables against each other we can observe that there is when a business angel is investing in a firm which operates in a sector she is familiar with, she is going to assume a more active approach dropping completely the free-rider style of investing while also being less likely to syndicate the investment, interact with more frequency with the portfolio firm and rarely joining forces with a VC at the time of her first investment. However, aligning with literature, she is unable to make a connection between her expertise within the sector and the twofold focus of the thesis; either leading to more financing rounds, or contributing to a better performance/outcome for the portfolio company as the difference in means for both variables are not statistically and economically significant. Finally, using the VC Prior dummy variable we form two groups to identify the power of interaction between a business angel investing in a firm which has already received VC funds and a firm without receiving VC funds prior to the business angel investment. Denoted by the respective figures, a firm that receives money from an angel investor after it has already received funds from a VC firm will raise additional capital easier, however, the mean outcome of the angel investment will shift towards negative, a notion also documented in the research of Goldfarb (2013). Moreover, more than 58% of the firms that received business angel funds while also having previously received VC funds, saw angel investors mixing their investments with a VC firm at the time of their investment, compared to a figure of below 23% for firms without a VC prior to the angel investment. 16

18 Table 4: Univariate Tests Table 4 provides results from Univariate Parametric Tests (t-test) for the difference in means by VC Involvement, Relevant Sector and VC Prior dummy variables. Testing with VC Involvement dummy variable, VC relevant indepentent variables are ommited. Significance of differences are reported at the 10%, 5% and 1% level identified by *, **, and *** respectively. VC Involvement Dummy Relevant Sector VC Prior No Yes No Yes No Yes Financing Rounds Post 0,6250 1,2963*** 0,9737 1,0420 0,8916 1,3750* Outcome 1,8261 2,0000 1,9747 1,7600 1,9765 1,5294** Syndication 0,4783 0,7636*** 0,6667 0,4583* 0,6072 0,6471 Sales 0,5208 0,4912 0,5309 0,4000 0,5227 0,4118 Entrepreneur 0,8958 0,8421 0,8642 0,8800 0,8864 0,7647 Age At First Investment 44, ,3158*** 47, , , ,0000 Interaction 3,5208 2,9107** 3 3,8400*** 3,2414 3,4706 Relevant Sector 0,2609 0, ,2442 0,2353 Investment To Equity 0,5333 0,3404*** 0,4227 0,4877 0,4596 0,3758 Free Rider 0,1304 0,4182*** 0,3590 0,000*** 0,2619 0,2353 VC Prior - - 0,1667 0, VC At - - 0,3704 0,1200** 0,2273 0,5882*** VC Post - - 0,3816 0,4167 0,3494 0,4375 VC Involvement - - 0,5641 0, The above analysis depicts that there are significant differences in means between both dependent and independent variables for two groups of the dummy variables used; VC Involvement, Relevant Sector and VC Prior. Although results cannot be drawn from these settings as other variables that might also be important or be of interest are missing in the above settings, the analysis provides intuitive signs of alignment with prior research on the scope of business angel-vc financing mix. D. 3. Regression Analysis Following the preceding univariate analysis, the VC Involvement, Relevant Sector and VC Prior independent variables are integrated in models to examine the magnitude of their effect against the amount of financing rounds a business-angel-backed firm receives and the eventual outcome of the angel investment. Robust standard errors are implemented into the OLS models to correct for heteroskedasticity, and clustered robust standard errors are used in the ordered probit regressions to account for within industry standard error correlations. 17

19 D VC Involvement, Angel Specific and Deal Specific Characteristics effects on the Financing Rounds Post Investment To discover the relationship of VC Involvement with the amount of financing rounds post angel investment we run OLS regressions, adding consequently angel investor and deal specific characteristics variables into the model to test the first three hypotheses. In all specifications an additional control is added in the form of industry fixed effects. Results are shown in Tables 5 and 6. Table 5: VC Involvement, Angel Investor Characteristics and Amount of Financing Rounds Table 5 reports results from OLS regression of VC involvement and Angel Investor characteristics on the Amount of Financing Rounds. The dependent variable is Financing Rounds Post. VC Involvement, Age At First investment, Interaction and Relevant Sector are the independent variables. Syndication, Sales and Entrepreneur are control variables for all regressions. Use of Industry Fixed Effects is indicated at the bottom of each column. For each independent variable the estimated coefficient and the t-stastistic (in parentheses), computed using robust standard errors, are reported. Coefficients significant at 10%, 5% and 1% level are identified by *, ** and *** respectively. Variable Amount of Financing Rounds (a) (b) (c ) (d) (e) (f) VC Involvement 0,749*** 0,781*** 0,742** 0,802*** (2,878) (4,397) (2,507) (3,705) Age At First Investment -0,014-0,025** -0,020-0,031** (-0,974) (-2,108) (-1,329) (-3,144) Interaction -0,254*** -0,271*** -0,227*** -0,243** (-2,882) (-3,236) (-3,043) (-2,983) Relevant Sector 0,215 0,296 0,177 0,262 (0,958) (1,553) (0,821) (1,288) Syndication -0,208-0,188-0,035-0,01-0,218-0,166 (-0,692) (-0,442) (-0,146) (-0,026) (-0,792) (-0,407) Sales -0,199-0,197-0,353-0,323-0,311-0,315 (-0,852) (-0,786) (-1,516) (-1,214) (-1,400) (-1,368) Entrepreneur 0,324 0,416 0,609 0,721 0,694* 0,810-0,856 (1,025) (1,553) (1,745) (1,840) (2,103) Constant 0,524 0,415 2,074** 2,493** 1,894** 2,292*** (1,141) (1,018) (2,355) (3,843) (2,467) (4,265) Observations Adjusted R-quared 0, , , , , , F-statistic 5,136 5,162 2,916 3,128 4,880 4,723 Industry Fixed Effects No Yes No Yes No Yes Results from Table 5 indicate that portfolio companies that received funds from a VC firm experience a 76,4% increase in the amount of financing rounds they receive post the angel investment. Given that the mean amount of financing rounds post angel investment is around 1, VC involvement almost doubles the amount of financing rounds. The effect of VC involvement is both statistically and economically significant. When controlling for industry fixed effects, the respective coefficient is magnified without losing any statistical significance. 18

20 Looking at the Angel Investor characteristics that influence the amount of financing rounds post angel investment regardless of VC Involvement, we see that there is a significant negative relation between the interaction an angel investor has with their portfolio company and the post investment financing rounds. The magnitude and statistical significance of the coefficient suggest that the more angel investors interact with their portfolio companies, the less the portfolio companies will raise additional capital through financing rounds. This can be explained twofold. In one hand, as angel investors interact with their portfolio companies, they also offer mentoring to management among other services, thus enhancing management performance which results in a smoother transition from the seed stage to the actual generation of sales, minimizing the need for additional capital since companies start sustaining themselves through sales. On the other hand, greater interaction can also lead to higher probability for an unsuccessful venture for angel investors as they many times fail to connect their experience with the entrepreneur s ability to make things happen. The negative effect even magnifies when controlling for industry fixed effects. Combining the effect of VC involvement with angel investor characteristics, the economic effect of VC involvement with the amount of financing rounds is even greater without losing statistical significance. The same applies to the effect Interaction has upon financing rounds. Regarding the Age At First Investment variable, we see that in a fixed effects setting, it has a statistically significant effect on the amount of financing rounds, however, the economic effect is marginally different than zero, thus omitted from further analysis. Table 6 below reports results on a setting of Deal Specific independent variable. Again the strong relationship between VC Involvement and the amount of financing rounds post investment is projected in the results as VC Involvement leads to a 76,4% increase in the amount of financing rounds post angel investment. However, no conclusion can be drawn from the integration of the Deal Specific independent variables into the model. This is most probably attributable to the complexity of the angel investment deals, giving little explanatory power to just a few individual variables and the deal must be considered in its whole. For example, the amount of capital provided by an angel investor relative to the total equity of the portfolio company is intuitively important, however, its power diminishes as many other factors have to be taken into account such as contracting terms, the total riskiness and even more importantly the timing of the investment. 19

21 Table 6: VC Involvement, Deal Specific Characteristics and Financing Rounds Table 6 reports results from OLS regression of VC involvement and Angel Investor characteristics on the Amount of Financing Rounds. The dependent variable is Financing Rounds Post. VC Involvement, Investment To Equity and Free Rider are the independent variables. Syndication, Sales and Entrepreneur are control variables for all regressions. Use of Industry Fixed Effects is indicated at the bottom of each column. For each independent variable the estimated coefficient and the t-stastistic (in parentheses), computed using robust standard errors, are reported. Coefficients significant at 10%, 5% and 1% level are identified by *, ** and *** respectively. Variable Amount of Financing Rounds (a) (b) (c ) (d) (e) (f) VC Involvement 0,749** 0,781*** 0,715** 0,749*** (2,878) (4,397) (2,449) (4,127) Investment To Equity -0,364-0,265 0,101 0,230 (-0,868) (-0,536) (0,239) (0,634) Free Rider 0,293 0,339 0,222 0,289 (1,079) (0,938) (0,787) (0,729) Syndication -0,208-0,188-0,194-0,204-0,312-0,307 (-0,692) (-0,442) (-0,703) (-0,487) (-1,073) (-0,693) Sales -0,199-0,197-0,301-0,254-0,221-0,208 (-0,852) (-0,786) (-1,151) (-0,790) (-0,858) (-0,669) Entrepreneur 0,324 0,416 0,225 0,344 0,288 0,387-0,856 (1,025) -0,579 (0,948) (0,774) (1,081) Constant 0,524 0,415 1,133 0,970 0,553 0,368 (1,141) (1,018) (1,752) (1,597) (0,926) (0,732) Observations Adjusted R-quared 0, , , , , , F-statistic 5,136 5,162 2,251 13,285 3,824 44,434 Industry Fixed Effects No Yes No Yes No Yes From the above analysis we can support Hypothesis I, while even though Interaction has a significant effect on the amount of post investment financing rounds and can partially confirm Hypothesis III on this aspect, we cannot definitively draw any conclusions for the rest of the independent variables. As far Hypothesis IV, we can reject the hypothesis for the given setting of variables. 20