The determinants of business angels investment choices: the role of experience, networking monitoring, and co-investments

The determinants of business angels’ investment choices: the role of experience, networking monitoring, and co-investments Vincenzo Capizzi1 Departme...
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The determinants of business angels’ investment choices: the role of experience, networking monitoring, and co-investments

Vincenzo Capizzi1 Department of Economics and Business Studies Università del Piemonte Orientale Via E.Perrone, 18, 28100, Novara, Italy

Mario Valletta Department of Economics and Business Studies Università del Piemonte Orientale Via E.Perrone, 18, 28100, Novara, Italy

Paola Zocchi Department of Economics and Business Studies Università del Piemonte Orientale Via E.Perrone, 18, 28100, Novara, Italy

This draft: April 24th 2016 JEL Codes: G24, G32, M13 Keywords: business angels, business angel networks, investment process, experience, monitoring, networking, co-investment



1

Corresponding Author. Tel.: +39 0321 375.438. Email: [email protected]

Abstract This paper provides evidence on the determinants of investment choices of business angels, either when investing alone or when co-investing together with other informal investors. Furthermore, the possibility to join a business angel network (BAN) seems to constitute a valuable source of deal flow, information, knowledge and monitoring mechanism ultimately affecting the amount of capital invested by business angels themselves. An econometric analysis using a unique data-set covering a representative sample of the main actors in the Italian informal venture capital market examines the returns on business angels’ investments and their major determinants, making reference to an original set of independent variables. One of the major contributions of the paper to finance literature is the possibility to shed some light on the determinants of business angels’ investments thanks to an empirical analysis performed over a unique dataset provided by IBAN that collects qualitative and quantitative information on over 800 investments by 625 business angels from 2008 to 2014. The second contribution added by the present paper is to provide statistical evidence about the relationship existing between business angels’ investment decisions and some both angel-specific characteristics (age, wealth, experience, education and previous background) and investment specific characteristics (stage and location of investee company, intensity of monitoring, presence of co-investors, BAN membership). As a final contribution provided to finance literature, the paper for the first time find evidence of different causal relationships for angels joining BANs when compared to angels not members of any single group or network of business angels. More in details, it comes out that being part of an angel group does generate valuable information, monitoring, networking and risk reduction effects which, ultimately, affects the amount of financial resources available to invest as well as the equity stake to assume in the investee company



1.

Introduction

In the last few years one emerging topic investigated by scholars as well as financial institutions, regulators and policy makers has been dealing with the business angels and their operations, either as individual investors or as groups of investors. As yet shared by the reference literature, business angels are high net worth individuals who invest their own money in small unlisted companies, with no family connection, assuming typically a minority equity stake as well as an active involvement in the financed companies themselves (Mason, 2006). They respect a code of ethics including, among others, rules for confidentiality and fairness of treatment (vis-à-vis entrepreneurs and other BAs), and compliance with anti-moneylaundering (Capizzi, 2015). Over time, a growing number of angel investors started organizing themselves into groups (also referred to as syndicates or networks or clubs, depending on their level of internal structure), usually on a territorial or industrial basis, sharing presentation pitches from potential entrepreneurs, due diligence over the potential investment opportunities, transaction costs and investment deals to be implemented by group members (Mason, 2006; Sohl, 2007; Paul and Whittam, 2010; Gregson et al., 2013). These associations, called Business Angels Networks (BANs), have grown to regional, national (for instance, ACA in the US, BBAA in the UK, IBAN in Italy) and even continental proportions (among them, EBAN and BAE in Europe) increasing also their internal structure and coordination among the members as well as the quality and variety of the service provided (deal flow, education, legal and advisory services). Thanks to BANs and angel groups the informal venture capital market is nowadays much more visible and, hence, easy to get access to by both demand and supply side (Mason, Botelho and Harrison, 2013). Market data at both the US and European level (US ACA, 2014; EVCA, 2014; EBAN, 2015; Kraemer-Eis et al., 2015; OECD, 2016) provide evidence of the growing and significant relevance of business angels as one major segment of the capital market industry, capable of allocating financial resources to one of the riskiest asset class – startup companies – though crucial for the development of economic and social systems. As such, Regulatory Authorities all around the world have been paying attention to these investors – also called “informal investors”, in order to differentiate them from venture capitalists and other financial intermediaries investing on a professional and institutionalized basis capital raised from third parties (Wetzel, 1986; Freear et al., 1993; Landstrom, 1993; Mason, 1996) – who nowadays are officially recognized as one component of the shadow banking industry, which in turn represents as a whole more than one third of the almost 60 thousand billions of outstanding financial assets in the Euro Area (ECB, 2015). Furthermore, governments and policymaker, when designing industrial policies aimed at stimulating entrepreneurship and economic growth inside a given country, explicitly begun considering angel financing as one major and powerful tool to activate and empower, most of all through focused fiscal policies, support to business angels networks as well as government backed equity co-investment funds (Mason, 2009; Baldock and Mason, 2015). In fact, it is now widely accepted that business angels are amongst the most suitable actors of the ecosystem for entrepreneurial businesses, considering their capability to fill the socalled “funding gap” existing between demand and supply of early stage equity capital (Mason and Harrison, 2000; Sohl, 2012; Capizzi, 2015). First of all, business angels satisfy a size of investment need (usually falling in the range 100k – 300k euros) which is not typically considered interesting as well as profitable for venture capitalists, due to both its relatively low cash flows’ generation potential and the relatively high costs for due diligence, contracting and monitoring given the relevant adverse selection and moral hazard issues affecting small-scale young businesses (Carpenter and Peterson, 2002; Mason, Jones and Wells, 2010). Second of all, alongside the capital injection, business angels provide non-monetary resources deemed highly valuable for

entrepreneurs like industrial knowledge, management experience, advice and mentoring, standing and personal relationship networks (Harrison and Mason, 1992; Landstrom, 1993, Politis, 2008). As far as the literature is concerned, which will be briefly reviewed in the following section, a clear signal for the growing deal of attention disclosed by the scientific finance community is the increasing number of articles dealing with business angels published over the last 10 years in the top rated journals, such as Venture Capital, Journal of Business Venturing, Review of Financial Studies, Entrepreneurship Theory and Practice, Journal of Corporate Finance, International Small Business Journal.2 Indeed, one major factor negatively affecting the quality of the research is the possibility to rely upon rigorous databases, given the high opaqueness of the market and the sample bias intrinsic in the surveys normally used to collect data from angel investors, making it difficult to build and analyze representative samples of the population of business angels both at the national and the international level (Harrison and Mason, 2008; Capizzi, 2015). As such, one of the major contributions of the paper to finance literature is the possibility to shed some light on the determinants of business angels’ investments thanks to an empirical analysis performed over a unique dataset provided by IBAN that collects qualitative and quantitative information on over 800 investments by 625 business angels from 2008 to 2014. The second contribution added by the present paper is to provide statistical evidence about the relationship existing between business angels’ investment decisions and some both angel-specific characteristics (age, wealth, experience, education and previous background) and investment specific characteristics (stage and location of investee company, intensity of monitoring, presence of co-investors, BAN membership). As a final contribution provided to finance literature, the paper for the first time find evidence of different causal relationships for angels joining BANs when compared to angels not members of any single group or network of business angels. More in details, it comes out that being part of an angel group does generate valuable information, monitoring, networking and risk reduction effects which, ultimately, affects the amount of financial resources available to invest as well as the equity stake to assume in the investee company. The remainder of the paper is structured as follows: the second paragraph will derive the research hypothesis to be tested from the literature dealing with business angels and the informal venture capital. In the third paragraph will be performed the empirical analysis, while in the final paragraph will be presented authors’ conclusive remarks and suggestions for future research.



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All the above cited journals are ranked “A” or “A+” according to the Australian Business Dean’s Council quality list for 2014, except for Venture Capital which is ranked “B”.

2.

Related literature and hypotheses development

Business angels have attracted several studies during the last four decades, with the first contributions dating back to the 80s. There are different ways of classifying major streams of contributions which have risen over time and which deal with business angels, most of all making reference to a chronologic and evolutionary approach (Harrison and Mason, 1999; Freear et al., 2002; Mason, 2008; Johnson and Sohl, 2012). In this paper – consistent with the approach followed by Macht (2006) and Capizzi (2015) –a classification approach based upon the research areas under investigation will be used, which the author identifies in the eight groups described as follows. The first group of contributions is made up by the very first studies on business angels, aimed aimed at identifying and profiling business angels on the one hand, and studies about the role played by business angel networks on the other hand. In fact, it is widely accepted now that the unit of analysis and measurement when studying informal venture capitalists can be either single angels or angels groups Among the most important studies, it is possible to make reference to Wetzel (1986), Gaston (1989), Landstrom (1993), Mason and Harrison (1994, 1997,2007), Coveney and Moore (1998), Aernoudt (1999), Visser and Williams (2001), Paul et al. (2003), Sohl and Hill (2007), Morrisette (2007), Sohl (2007), Mason (2009), Paul and Whittam (2010), Christensen (2011), KfW (2011), Festel and De Cleyn (2013), and Li et al. (2014). The second stream of contributions measures size, composition and growth trends of the informal venture capital markets, either at the national or international level, also breaking down the investments by industry, holding period, features of investee firms and other angel specific characteristics. Among the most relevant publications are those by Wetzel (1987), Landström (1993), Reitan and Sørhein (2000), Mustilli and Sorrentino (2003), Bygrave et al. (2003), Fleming (2004), Harrison and Mason (2008), Capizzi and Giovannini (2010), OECD (2011), Kraemer-Eis and Schillo (2011), Scheela and Jittrapanum (2012), Romani et al. (2013), and Carpentier and Suret (2015). The third group investigates the relationship between business angels and hi-tech firms, trying to answer research questions such as: “do business angels investments boost technology development?”, “do hi-tech firms grant a higher IRR when compared with non-hi-tech start-ups?”, “do business angels share peculiar technology-specific knowledge with target companies?”. The most relevant contributions include Freear and Wetzel (1990), Manigart and Struyf (1997), Fenn et al. (1998), Freear et al. (2002), Harrison and Mason (2003), Erikson and Sørheim (2005), Madill et al. (2005), Shane (2008), Metrick and Yasuda (2011), and Festel and De Cleyn (2013). The fourth group of contributions includes all those studies aimed at comparing operations and performances of business angels with those of venture capitalists. Furthermore, it is possible to consider in this group contributions investigating the effect of co-investments which are realised simultaneously by BAs and VCs and contributions which measure the impact of formal and informal investors on venture-backed and angel-backed companies. The most important publications are: Harrison and Mason (2000), Sohl (2003), Chahine et al. (2007), Sudek et al. (2008), Johnson and Sohl (2012), Bonnet and Wirtz (2012), Goldfarb et al. (2012), Hellman et al. (2013), Kerr et al. (2014), Hsu et al. (2014). The fifth group of contributions deals with the determinants of performance of business angels’ investments, answering to these research questions: “what are the major factors affecting the performance of BAs’ investments?”, “what are the functional forms of the determinants of IRR of BAs’ investments?”. Some

examples of empirical analysis focused on this research topic can be found in Lumme et al. (1996), Harrison and Mason (2002), Mason (2005), Heukamp et al. (2007), Wiltbank and Boekor (2007), Wiltbank et al. (2009), Capizzi (2011, 2015). The sixth group of studies explores the negotiation and contractual issues characterizing the relationship between BAs and entrepreneurs, considering information asymmetries and opportunistic behaviour. Examples of research questions investigated are: “how do angels monitor investee companies?”, “what kind of contingent contracts or clauses do BAs require?”, “how do BAs protect their investments?”. The most relevant contributions belong to Van Osnabrugge (2000b), Chemmanur and Chen (2006), Cumming (2008), Ibrahim (2008), Wong et al. (2009), Erzurumlu et al. (2013), Cumming and Johan (2013), and Caselli et al. (2013). The seventh stream of contributions addresses an issue that is also widely investigated in the private equity and venture capital literature, namely the impact of the hands on contributions of business angels on the value creation process and profitability of their investee companies. The typical research questions are: ”what is the performance of angel-backed firms?”, “does BAs’ experience matter?”, “do angel-backed firms deliver better post-IPO performance?”, “what are the appropriate financial indicators to use in order to measure the impact of BAs post-investment?”. Among the most important publications in this recent research field are: Ardichvili et al. (2002 ), Davila, Foster, and Gupta (2003 ), Delmar and Shane (2006 ), Chahine, Filatotchev, and Wright (2007 ), Johnson and Sohl (2012 ), Macht and Robinson (2009 ), Goldfarb et al. (2012 ) and Vanacker et al.(2013 ), Collewaert and Manigart (2015). In the seventh research theme, we find studies focusing on both the investment process and evaluation procedures undertaken by either business angels or business angel networks, trying to identify the major determinants of angel investments. The main research questions are: “how do business angels select target companies?”, “how do business angels’ experience and background affect their investment criteria?”, “what are the drivers of the capital invested?”. Among the major contributions, it need to remember Landström (1995), Feeney et al. (1999), Van Osnabrugge (2000a), Harrison and Mason (2002a, 2003, 2007), Mason and Stark (2004), Sohl (2006), Paul et al. (2007), Wiltbank et al. (2006), Riding et al. (2007), Sudek et al. (2008), Harrison et al. (2010), Capizzi and Tirino (2011), Macht (2011a), Collewaert (2012), and Bammens and Collewaert (2013), Croce et al (2016), Mason and Botelho (2016). In the eight group, finally, we find studies focusing on both the investment process and evaluation procedures undertaken by either business angels or business angel networks, trying to identify the major determinants of angel investments. The main research questions are: “how do business angels select target companies?”, “how do business angels’ experience and background affect their investment criteria?”, “what are the drivers of the capital invested?”. Among the major contributions, it need to remember Landström (1995), Manigart et al. (1997), Feeney et al. (1999), Van Osnabrugge (2000a), Harrison and Mason (2002a, 2003, 2007), Mason and Stark (2004), Maula et al. (2005), Sohl (2006), Wiltbank et al. (2006), Paul et al. (2007), Riding et al. (2007), Sudek et al. (2008), Harrison et al. (2010), Capizzi and Tirino (2011), Macht (2011a), Collewaert (2012), and Bammens and Collewaert (2013), Croce et al (2016), Mason and Botelho (2016). This study falls inside the last mentioned group of contributions, in that it aims at testing the existence of a significant causal relationship between the investments of business angels and a number of factors firm related to the investee firms, the business angels’ features and the investment decision making process. More in detail, as evidenced by the above mentioned related studies, we expect that the equity stake in the target company acquired by a business angel is negatively affected by the size of the company itself, as well as by it stage in the life cycle and its location (Maula et al., 2005; Wiltbank et al., 2006). There are,

furthermore, a number of angel specific factors we do expect have an impact on their investment choices, such as experience (Mason and Harrison, 1996), age and education (Shane, 2000), as well as the previous background, which could be a managerial one, an entrepreneurial one or a financial one (Collewaert and Manigart, 2016). Among this second series of factors, the original one added by our contribution is the membership to a business angel community (network or syndicate or group), which can positively affect the share of the equity investment, due to the risk sharing and risk reduction effect produced inside the angel community, coupled with the increased information generated by sharing knowledge, experience and the quality and cost of the due diligence. A third series of factors deals with the angels’ investment decision making process, such as the quality and intensity of monitoring (Hsu, 2004), the active or passive approach to the investment and the presence of other co-investor, where last two factors are an original contribution of the present empirical analysis. This leads to our first first research hypothesis H1: The amount of capital invested by business angels as a share of the equity capital of the investee company is negatively affected by (1) company size, (2) company stage of life cycle, (3) company home country, (4) age, (5) presence of co-investors, (6) investment motivation, and positively affected by (7) previous investment experience, (8) BAN membership, (9) education, (10) monitoring and (11) personal wealth. Our second investigation area deals with an alternative dependent variable – the invested capital as a share of business angels’ personal wealth – with the independent variable remaining the same. Accordingly, we formulate the following research hypothesis H2: The amount of capital invested by business angels as a share of their personal wealth is negatively affected by (1) company stage of life cycle, (2) company home country, (3) age, (4) presence of coinvestors, (5) investment motivation, and positively affected by (6) company size, (7) previous investment experience, (8) BAN membership, (9) education, (10) monitoring and (11) personal wealth.



3.

Data and Methodology

The dataset used in this paper was created by processing data and qualitative information provided by IBAN (Italian Business Angels Network), the national association whose members include, on a voluntary basis, regional business angel networks, business incubators, angel groups and single angel investors. IBAN carries out a yearly survey, through an on-line questionnaire – investigating the nature, size and structure of the informal venture capital market in Italy. As part of the survey, questionnaires are also proposed through a variety of distribution channels to a large number of individuals believed – or reported to by IBAN members – to be business angels operating in Italy. (Capizzi, 2015). Data referring to the 2008–2014 time horizon were collected in the early months of 2009, 2010, 2011, 2012, 2013 and 2014 and were properly processed, thus allowing to extrapolate key features and expected trends in business angels’ behaviour. The author received authorisation to further process IBAN data in order to build a dataset consistent with the kind of empirical analysis to be performed in this paper under the explicit – and highly reasonable – restriction that the confidentiality of single invested companies and business angels remain secure. The final dataset used for this paper includes 808 investments carried out by 625 BAs during the period 2008-2014. The target ventures are very heterogeneous, as they could be seed projects, start-up or developed firms. Moreover, they belong to various industries, among which the most represented ones are: Manufacturing of Food and Beverage products (20%), Biotech (17.1%) and Cleantech (13.1%). The geographical distribution of ventures is quite concentrated, since approximately the 88% of them is located in Italy. The business angels joining an angel community (herein after “BAN members”) are almost the 54% of the sample, giving the possibility to empirically investigate the role of the business angel networks in the investment decision making process. However, it is worth underlining that, despite the high response rate, sample representativeness can still be an issue: in fact, all of the information which was processed include evidence of activity which took place mostly within the IBAN network, and as such by no means represent the full extent of the overall theoretical business angel activity which exists in Italy: in other words, everyone has to be aware of the existence of an unobservable invisible market of informal investors, at least as far as there are not public policy measures aimed at stimulating the creation of an officially legitimated visible market of business angels (Harrison and Mason, 2008). Table 1 provide descriptive statistics for the two dependent variables built for the empirical analysis: PARTICIPATION%, which is computed as the amount invested in a venture as a share of its net-asset-value, and WEALTH%, which is the share of a BA’s financial wealth invested in a venture. The descriptive statistics related to the dependent variables show that the relative incidence of BAs’ investments widely varies in the sample, both in terms of participation in the venture and in terms of personal wealth of the BAs. Nevertheless, the majority of the investments are relative small. In fact, the participation in a venture is lower than 10% in half of the cases and lower than 20% in three quarters of them and the share of wealth invested in a venture is lower than 15% in the 50% of the investments and lower than 17% in three quarters of them. Being member of an angel community significantly indeed affects the amount of wealth invested in a venture. This evidence emerges by performing a two-group-mean comparison test on the variables PARTICIPATION% and WEALTH%, between those BAs which are members of a Business Angel Network (BAN) and those which

are not members of any BAN. As presented in Table 1, the test shows that being a member of an angel community positively affect the share of wealth invested in the investee company, while it does not influence the size of the participation in the investee company. Table 1 – Dependent variables: descriptive statistics. Breakdown BAN members vs non-BAN members

Total sample

BAN members

Research Question No.1 – Dependent variable = PARTICIPATION% Mean 14.74 14.87 Maximum 100 100 Minimum 1 1 Standard deviation 19.54 18.30 No. observation 808 436 Two-group mean-comparison test It-statI Research Question No.2 – Dependent variable = WEALTH% Mean 15.48 17.09 Maximum 60 60 Minimum 5 5 Standard deviation 11.80 13.13 No. observation 669 354 Two-group mean-comparison test It-statI

Non-BAN members

14.59 100 1 20.93 372 0.197 13.67 60 5 9.80 315 3.480***

As anticipated in the previous section, the aim of the empirical analysis is to investing the existence of a causal relationship between the two above mentioned dependent variables and a series of independent variables related to the investee firms, the business angels’ features and the investment decision making process. We also add to the dataset four control variables measuring, respectively, the market interest rate, the industry market capitalization, the industry price-to-book value and the industry average capital intensity. Table 2 describes in details the explanatory variables used in the present analysis and Table 3 presents some descriptive statistics. Since many angels did not answer to a number of questions proposed in the survey, Table 3 also indicates the number of available observations for each explanatory variable. Table 2 – Explanatory variables Variables

Firm specific variables NET_ASSET_VALUE SEED

Description

Enterprises’ net asset value in the year of the BA’s investment Dummy = 1 if the BA has invested in a seed enterprise

Expected sign Research Research Hypothesis 1 Hypothesis 2 (Dependent (Dependent variable = variable = PARTICIPATION%) WEALTH%) -

+

-

-

FOREIGN

Dummy = 1 if the BA has invested in a foreign enterprise Variables related to business angels’ characteristics EXPERIENCE Number of BA’ investments in lifetime BAN_MEMBERSHIP Dummy =1 if the BA is a BAN member WEALTH BAs’ financial wealth in the year of the investment AGE Age of the BA EDUCATION Dummy = 1 if the BA holds a high school diploma or a lower educational qualification ENTREPRENEUR Dummy =1 in case of prevalent working occupation as entrepreneur MANAGER Dummy =1 in case of prevalent working occupation as manager Variables related to the investment decision making process CO-INVESTORS Number of co-investors MONITORING Ordinal variable ranging from 1 to 5, where 1 means monitoring very low or absent and 5 means monitoring very high, with a constant presence in the firm PASSIVE_INVESTOR Dummy =1 if the investment is exclusively driven by capital gain motivations Control variables (Market and industry indexes) MSCI_YEAR_INV Industry market capitalization as measured by the MSCI, in the investment year 5Y_EURIRS Average 5y Eurirs, in the investment year INDUSTRY PBV Industry price-to-book value, in the investment year CAPITAL INTENSITY Industry net capital assets to sales, in the investment year

-

-

+ + +/-

+ + +/-

- +/-

- +/-

+/-

+/-

+/-

+/-

- +

- +

-

-

+/-

+/-

-

+/-

+



As far as the size of the investee venture is concerned, we expect the share of participation to decrease as the size of a firm increases. In contrast, since it is likely that bigger firms are perceived as less risky investments, it is possible that the relationship between the dependent variable WEALTH% and the NET_ASSET_VALUE variable is positive. As one can see from Table 3, the net-asset-value of the ventures varies from 1.000 euro to more than 160 millions of euro. However, 75% of the ventures show a net-asset-value lower than 1,400,000 euro. It is possible to argument the such a high variability depends on the great heterogeneity of ventures belonging to the sample in terms of industry and stage of the life cycle. Approximately the 36% of the investments mapped in dataset are addressed to seed projects, while in the other cases the target firms are start-up, early growth enterprises, turn-around and buy-out projects. Since, investing in a seed enterprise is likely to be riskier than investing in a well-established entrepreneurial project, the expected relationship between the dummy SEED and the dependent variables PARTICIPATION% and WEALTH% is negative. Foreign ventures represent only the 12% of the projects financed. We believe that considerations related to geographical, cultural and juridical distance between the residence of the BA and the location of the venture are likely to shrink the amount invested in a venture. Thus, the expected sign for the dummy FOREIGN is negative.

The analysis also investigates the relationship between amount invested in a venture and experience in angel investments. We measure the experience considering the number of investments made in the past, consistently, consistently with Hsu (2004) and Capizzi (2011). We believe that more experienced BAs find easier to identify worthy investment opportunities. Moreover, a greater experience could also increase selfconfidence. For both of the reasons, more experienced BAs are likely to invest greater amounts than less experienced BAs. Thus, we expect a positive relationship between the dependent variables PARTICIPATION% and WEALTH% and the variable EXPERIENCE. The role of BA networks is strictly connected with the above considerations. Angel communities propose investment opportunities and also support their members in different stages of the decision making process. Therefore, it is reasonable that their members feel more confident in deciding where invest their money. In this case, the sign of the BAN_MEMBERSHIP dummy should be positive. As shown in Table 3, the financial wealth of BAs varies form 250,000 to 7,500,000 euro, however, for the three quarters of them it is lower than 1,450,000 euro. The wealthier BAs could both choose to invest higher amounts in single ventures, in order to detain higher participations, and to select a higher number of investment opportunities, in order to achieve a better portfolio diversification. Thus, the sign of the relationship between the dependent variables PARTICIPATION% and WEALTH% and the WEALTH variable is not plain. The dataset shows that co-investments are very frequent. More specifically, the 70% of the investments are characterized by the presence of at least two co-investors. Since co-investing favors risk-sharing, the expected relationship between the dependent variables and the explanatory variable CO-INVESTORS is negative for both the research questions. The survey also offers interesting evidences regarding the role played by BAs in the monitoring of the participated firms. The ordinal variable MONITORING graduates the frequency of the visits that a BA made in a participated venture, from 1 to 5, where 1 means a very limited involvement (no or very few visits) and 5 means a very high involvement (a constant presence in the firm). Though the survey collects this information ex-post, asking the effective involvement in the participated firms by BAs, we believe that they already know the future degree of involvement in a venture also at the time when the investment decision is taken. Moreover, it is likely that it influences the choice concerning the amount to invest. In particular, a higher degree of monitoring is expected to decrease the investment risk perceived by a BA. Therefore, we assume the variable MONITORING as a proxy of the agreed degree of monitoring at the time when the investment decision was taken. The expected sign for the variable MONITORING is positive, in both the analysis. Finally, we expect to find a negative relationship between the dependent variables PARTICIPATION% and WEALTH% and the dummy PASSIVE INVESTOR, which indicates that the investment decision is exclusively driven by capital gain motivations. Table 3 – Descriptive statistics Numeric and ordinal variables Firm specific information NET_ASSET_VALUE (in euro) Angel’s characteristics AGE

Observations

Mean

Min

Max

806

2,385,167

1,430

167,000,000

668

48.32

28

71

WEALTH (in euro) 669 EXPERIENCE 668 Factors related to the investment decision MONITORING 668 CO-INVESTORS 809 Market and industry indexes MSCI_YEAR_INV 810 5Y_EURIRS 810 INDUSTRY PBV 810 NET CAPEX/SALES 810 Dummy variables Observations Firm specific information SEED 810 FOREIGN 711 Angel’s characteristics BAN_MEMBERSHIP 810 ENTREPRENEUR 668 MANAGER 668 Factors related to the investment decision PASSIVE INVESTOR 668

1.480.682 6.36

250.000 0

7.500.000 26

2.75 4.45

1 0

5 37

58.93 2.251 3.05 0.80

45.78 0.73 0.71 -4.47 Dummy = 1 (percentage)

81.81 4.31 8.62 22.96

35.7 12.1 54.1 37.7 16.8 22.0



4.

Results

Research hypothesis No.1 The first part of the empirical analysis explores the factors affecting the amount invested in a venture by a BAs. For this purpose, we estimate the relationship between the dependent variable PARTICIPATION% and a wide set of explanatory variables, by running a standard OLS regression. We firstly assume the existence of a linear relationship between the dependent and the explanatory variables. However, the presence of heteroskedasticity, induces us to search for non-linear relationships. To this end, we transform the numerical variables, by adopting different functional forms. This way, we obtain a better model specification by considering the natural logarithm of the following variables: PARTICIPATION%, NET_ASSET_VALUE , WEALTH and EXPERIENCE. Moreover, by searching for interaction effects among the independent variables, we find a significant difference between those BAs that act as a part of a pool of co-investors and those that invest alone. By adding the variable COINV*NET_ASSET_VALUE to the model, the level of heteroskedasticity significantly decreases3. Finally, we estimate robust standard errors for every model specification. In particular, we run the following three equations. Equation (1) comprises a few number of explanatory variables with the widest number of available observations. Equation (2) comprises the whole set of explanatory variables described in Table 2, while equation (3) adds to the above base model the interaction

3

The Breusch-Pagan test for heteroskedasticity shows a Chi2 = 6.68 and a Prob>Chi2 = 0.0098, for the base model described in equation (2).

term between CO-INVESTORS and NET_ASSET_VALUE. In every model specification, we add as controls the following variables: MSCI_YEAR_INV, INDUSTRY PBV and NET CAPEX/SALES. PARTICIPATION% = f (NET_ASSET_VALUE, SEED, BAN_MEMBERSHIP, CO-INVESTORS, MSCI_YEAR_INV, INDUSTRY PBV, NET CAPEX/SALES) (1) PARTICIPATION% = f (NET_ASSET_VALUE, SEED, FOREIGN, BAN_MEMBERSHIP, AGE, EDUCATION, WEALTH, ENTREPRENEUR, MANAGER, EXPERIENCE, PASSIVE_INVESTOR, CO-INVESTORS, MONITORING, MSCI_YEAR_INV, INDUSTRY PBV, NET CAPEX/SALES) (2) PARTICIPATION% = f (NET_ASSET_VALUE, SEED, FOREIGN, BAN_MEMBERSHIP, AGE, EDUCATION, WEALTH, ENTREPRENEUR, MANAGER, EXPERIENCE, PASSIVE_INVESTOR, CO-INVESTORS, MONITORING, MSCI_YEAR_INV, INDUSTRY PBV, NET CAPEX/SALES, COINV*NET_ASSET_VALUE) (3) Table 4 presents the results of the model. The analysis shows a high explanatory power: the adjusted R2 is greater than 50% in the model specification (2) and (3) and the majority of the independent variables are significant. In general, the outcomes suggest that BAs are very well conscious about the risks inherent in investing in ventures and that they maintain their risk exposure under specific limits. In particular, it results that the share of participation in a venture decreases as the net-asset value of the firm increases and the degree of monitoring decreases. Moreover, the participation diminishes by 15% if the target venture is a seed projects and by 21% if it is a foreign enterprise. Co-investing appears an effective way for pursuing risk limitation goals: in fact, as the number of co-investors increases the individual participation shows a 7% decrease. However, the positive sign of the interaction variable COINV*NET_ASSET_VALUE means that, in presence of a pool of investors, the share of the individual participation grows as the net-asset-value of the venture increases, probably in consideration of the aim of reaching a relevant participation in the firms’ net-assetvalue. The degree of experience in BAs’ investments positively affects the amount invested. The same evidence emerges if the BA is a manager or an entrepreneur. The motivation at the basis of an investment decision is also relevant in explaining the amount of money that a BA invests in a venture. In fact, if the investment is only driven by capital gain motivations (when the dummy PASSIVE_INVESTOR is equal to 1) the dependent variable shows a 19% reduction. As far as the personal characteristics of the BA are concerned, the model results display a progressive reduction of the amount invested in a venture as the age of the investor increases. It also emerges that, less educated BAs show a greater risk exposure. In contrast, no significant evidences emerge for the level of financial wealth. Finally, regarding the market conditions and the sector specific indexes, the outcomes highlight that BAs invest growing amounts in presence of positive market trends and prefer sectors characterized by a lower price-to-book value and a higher capital intensity.

Table 4 – Research Question No.1: Results The regressions are all conducted with the ordinary least square method (OLS), using White heteroskedasticity-consistent standard errors and covariances. The dependent variable, PARTICIPATION%, is the amount invested in a venture by an Angel as a share of its net-asset-value. Equation (1) comprises a limited number of explanatory variables with the widest number of available observations. Equation (2) comprises the whole set of explanatory variables described in Table 2, while equation (3) adds to the above base model the interaction term between CO-INVESTORS and NET_ASSET_VALUE. The t-stat are reported in brackets under each coefficient.

Indipendent Variables NET_ASSET_VALUE

(1) -0.120*** (-5.72) 0.069 (1.24)

Coefficients (t-stat) (2) -0.243*** (-10.43) -0.150** (-2.08) -0.217** (-2.03) -0.071*** (-10.56) 0.168*** (5.18) -0.119 (-1.44) -0.011*** (-2.87) 0.354*** (2.82) 0.030 (0.47) 0.326*** (4.24) 0.246** (2.29) 0.200*** (3.14) -0.220*** (-2.37) 0.017*** (3.88) -0.084*** (-3.16) 0.030*** (2.90)

(3) -0.325*** (-11.58) SEED -0.158*** (-2.34) FOREIGN -0.205** (-1.99) CO-INVESTORS -0.084*** -0.193*** (-12.28) (-7.06) MONITORING 0.143*** (4.24) BAN_MEMBERSHIP -0.119 -0.124 (-1.38) (-1.44) AGE -0.009** (-2.35) EDUCATION 0.280** (2.21) WEALTH 0.036 (0.67) ENTREPRENEUR 0.333*** (4.42) MANAGER 0.253** (2.38) EXPERIENCE 0.222*** (3.72) PASSIVE_INVESTOR -0.244*** (-2.62) MSCI_YEAR_INV 0.014*** 0.017*** (3.87) (4.00) INDUSTRY PBV -0.167 -0.078*** (-0.88) (-3.11) NET CAPEX/SALES 0.034*** 0.031 (2.97) (1.63) COINV*NET_ASSET_VALUE 0.019*** (4.73) CONS 2.380*** 2.856*** 3.256*** (8.09) (5.90) (6.85) Observations 798 542 542 Prob F 0.0000 0.0000 0.0000 Adjusted R2 0.2587 0.5210 0.5383 * = significant at 10% level; ** = significant at 5% level; ***= significant at 1% level



Research hypothesis No.2 The second analysis investigates the determinants of the share of personal wealth invested in a venture by a BA. To this end, we run a OLS regression between the dependent variable WEALTH% and a set of explanatory variables related to the venture, the investor and the investment decision. Similarly to what we did in the previous analysis, we manage heteroskedasticity by computing the natural logarithm of the dependent variable and of the explanatory variables NET_ASSET_VALUE, WEALTH and EXPERIENCE. Moreover, since the twogroup-mean comparison tests on the dependent variable WEALTH% presented in Table 1 shows that being member of an angel community positively affect the share of wealth invested in a venture, we also split the dataset on the basis of the BAN_MEMBERSHIP dummy. Since after the above transformations, heteroskedasticity still persists, we estimate the model with robust standard errors. In every model specification, we also add as controls the following variables: MSCI_YEAR_INV, and 5Y_EURIRS. In fact, it is likely that BAs decide the amount of money to invest in a venture considering the level of returns resulting from alternative investments. Equation (4) comprises a limited number of independent variables, which have a wider number of observations. Equation (5) represents the base model and comprises all the explanatory variables described in Table 2. Finally, for the two sub-samples originated by grouping BAs on the basis of the BAN_MEMBERSHIP dummy we run equation (6). WEALTH% = f (NET_ASSET_VALUE, SEED, BAN_MEMBERSHIP, CO-INVESTORS, INDUSTRY PBV, NET CAPEX/SALES, MSCI_YEAR_INV) (4) WEALTH% = f (NET_ASSET_VALUE, SEED, FOREIGN, BAN_MEMBERSHIP, AGE, EDUCATION, LNWEALTH, ENTREPRENEUR, MANAGER, EXPERIENCE, PASSIVE_INVESTOR, CO-INVESTORS, MONITORING, INDUSTRY PBV, NET CAPEX/SALES, MSCI_YEAR_INV) (5) WEALTH% = f (NET_ASSET_VALUE, SEED, FOREIGN, AGE, EDUCATION, LNWEALTH, ENTREPRENEUR, MANAGER, EXPERIENCE, PASSIVE_INVESTOR, CO-INVESTORS, MONITORING, INDUSTRY PBV, NET CAPEX/SALES, MSCI_YEAR_INV) (6) Table 5 presents the results of the analysis. The model is significant in all the specifications and shows a R2 of roughly 18% in specification (2) and above the 20% in specification (3). In general, it emerges that the amount of wealth invested in a venture depends on the personal characteristics of BAs, while it is not influenced by the firms’ characteristics. In particular, the dependent variable increases as the experience of the BA grows and diminishes as her/his age and her/his financial wealth decrease. In addition, managers and entrepreneurs invest 10% more than other categories of workers. Other conditions being equal, one unit increase in the number of co-investors reduces by 2% the amount of money invested in a venture. In addition, being member of an angel community increases the share on wealth invested by approximately 12%. By comparing BAN members with non-BAN members, we observe some interesting differences. Firstly, it emerges that those BAs that invest alone, and thus do not benefit from the prescreening analyses that BANs make for their members, consider a wider set of information. In fact, the variables related to the net-asset value of the firm and the level of the medium-term interest rates are significant for this group of investors, while they are not for the other sub-sample. In particular, the amount invested by non-BAN members decrease as the net-asset-value of the venture increases. This evidence, however, does not subtend that they

invest in ventures characterized by higher dimensions. In fact, the regression (equations 7 and 8) presented in Table 6, that we run as robustness check, shows that the firms proposed by BANs have, on average, a greater dimension that those identified by single investors. PROPOSAL = f (NET_ASSET_VALUE, SEED, INDUSTRY PBV, NET CAPEX/SALES, MSCI_YEAR_INV)



PROPOSAL = f (NET_ASSET_VALUE, SEED, FOREIGN, INDUSTRY PBV, NET CAPEX/SALES, MSCI_YEAR_INV)

(7) (8)

Secondly, the decision about the amount to invest is affected by the presence of co-investors only for the sub-sample of the BAN members, implying that there could be a positive effect played by trust inside a given angel community, whereas non BAN member could be prevented for co-investing fearing relevant the issue of free riding and opportunistic bahaviour. Thirdly, the MONITORING variable shows a positive sign for the group of BAs not affiliated to an angel community and a negative sign for the BAN members. This evidence is apparently controversial, but it is probably an indirect result of the screening support made by the BA network to their members. In fact, it is likely that BAN members impose higher level of monitoring only to ventures that are more opaque. If this is true, the negative sign is related to the perceived investment risk (which require more monitoring). In contrast, since non-BAN members do not benefit from the soft-information given by angel communities, they probably compensate this greater information asymmetry by imposing more extensively high level of monitoring. In this case, higher monitoring is not necessarily associated to higher risk. In order to be sure that the above results do not depend on differences related to personal characteristics between the two groups of investors, we also run, as a robustness check, a logistic equation with the dummy BAN_MEMBERSHIP as dependent variable and a set of personal characteristics as independent variables. As displayed in Table 7, only the fact of being a manager shows a significant relationship with the affiliation to an angel community. BAN_MEMBERSHIP = f (AGE, EDUCATION, GENDER, WEALTH, EXPERIENCE, ENTREPRENEUR, MANAGER)



(9)

BAN_MEMBERSHIP = f (AGE, EDUCATION, GENDER, WEALTH, EXPERIENCE, ENTREPRENEUR, MANAGER, CENTER OF ITALY, SOUTH OF ITALY (10) Table 5 – Research Question No.2: Results The regressions are all conducted with the ordinary least square method (OLS), using White heteroskedasticity-consistent standard errors and covariances. The dependent variable, WEALTH%, is the share of Angels’ Wealth invested in each BA enterprise. Equation (4) comprises a limited number of independent variables, which have a wider number of observations. Equation (5) represents the base model and comprises all the explanatory variables described in Table 2. For the two sub-samples originated by grouping BAs on the basis of the BAN_MEMBERSHIP dummy we run equation (6). The t-stat are reported in brackets under each coefficient.

Indipendent Variables NET_ASSET_VALUE SEED

Whole sample (4) -0.004 (-0.44) 0.029

(5) -0.004 (-0.42) -0.023

Coefficients (t-stat) BAN members

Non-BAN members (6)

-0.033 (-1.42) 0.017

-0.043** (2.12) -0.107

(-0.31) (0.36) 0.063 0.033 (0.68) (-0.21) CO-INVESTORS -0.020*** -0.017*** -0.037*** (-4.62) (-4.07) (-4.58) MONITORING -0.016 -0.094** (-0.21) (-2.56) BAN_MEMBERSHIP 0.119** 0.165*** (2.27) (3.99) AGE -0.015*** -0.011*** (-6.14) (-3.04) EDUCATION 0.071 0.191** (1.35) (2.07) WEALTH -0.062** -0.029 (-2.49) (-0.91) ENTREPRENEUR 0.087* 0.087 (1.88) (0.98) MANAGER 0.110** 0.374*** (2.12) (4.08) EXPERIENCE 0.293*** 0.339*** (6.88) (5.82) PASSIVE_INVESTOR -0.074 0.039 (-1.42) (0.64) MSCI_YEAR_INV -0.003 -0.010*** -0.017*** (-0.99) (-3.94) (-4.16) 5Y_EURIRS -0.003 -0.001 -0.041 (-0.15) (-0.42) (-1.23) CONS 2.748*** 3.656*** 4.353*** (14.65) (12.30) (10.35) Observations 667 542 285 Prob F 0.0000 0.0000 0.0000 Adjusted R2 0.0435 0.1834 0.2706 * = significant at 10% level; ** = significant at 5% level; ***= significant at 1% level FOREIGN

(1.05)

(-0.60) 0.071 (0.05) 0.002 (0.80) 0.122*** (3.93) -0.018*** (-6.36) -0.100 (0.39) -0.108 (-0.71) 0.103* (1.71) -0.082 (-0.43) 0.231*** (2.90) -0.192** (-2.34) -0.009*** (-2.79) 0.043* (1.94) 3.512*** (8.96) 226 0.0000 0.2142

Table 6 – BA’s investments: probability of being proposed by a BA network Logistic regression with the dummy PROPOSAL as dependent variable, which corresponds to 1 if the project financed by an Angel has been proposed by a BA network or an Investors’ club. Columns 2 and 3 report results of the equations 7 and 8, which differ for the number of available observations. The z-scores are reported in brackets under each odds-ratio.

Indipendent Variables NET_ASSET_VALUE SEED FOREIGN INDUSTRY PBV

Odds-ratio (z-score) (7) 1.14** (2.40) -1.95 (-0.30) - 1.07 (1.09)

(8) 1.16** (2.43) 1.07 (0.36) 0.004 (0.02) 1.01 (0.23)

NET CAPEX/SALES

-0.97 -0.96 (-1.14) (-1.34) Observations 668 569 Prob chi2 0.0639 0.7529 Pseudo R2 0.0106 0.0037 * = significant at 10% level; ** = significant at 5% level; ***= significant at 1% level Table 7 – Probability of being a BAN member Logistic regression with the dummy BAN_MEMBERSHIP as dependent variable, which assumes the value 1 in case of BAN membership. Columns 2 and 3 report results of the equations 9 and 10, which differ for the number of available observations. The z-scores are reported in brackets under each odds-ratio.

Indipendent Variables AGE

Odds-ratio (z-score) (9) 1.005 (0.60) -0.959 (-0.13) -0.867 (-0.45) -0.962 (-0.45) -0.979 (-1.03) 1.112 (0.50) -0.581** (-2.05) -

(10) -0.998 (-0.22) EDUCATION -0.846 (-0.52) GENDER -0.929 (-0.23) WEALTH -0.977 (-0.27) EXPERIENCE -0.982 (-0.89) ENTREPENEUR 1.202 (0.85) MANAGER -0.593* (-1.94) CENTER OF ITALY 1.044 (0.24) SOUTHOF ITALY - -0.987 (-0.04) Observations 668 655 Prob chi2 0.2664 0.3342 Pseudo R2 0.0108 0.0125 * = significant at 10% level; ** = significant at 5% level; ***= significant at 1% level

5.

Conclusive remarks

In this paper we presented evidence of the existence of a number of factors producing a significant impact on the business angels’ investment. More in detail, by making reference to a unique dataset which provides data on the Italian informal venture capital market over a seven-year time period, we contributed to finance literature by showing the role in the business angels’ investment decision making process played by

experience, networking (as argued from the membership to an angel community), monitoring and coinvesting. In fact, the result of the econometric analyses performed in order to test the two research hypothesis, show that the amount of capital invested by business angels as a share of the equity capital of the investee company is negatively affected by the company size, its stage in the life cycle, its home country, the presence of co-investors in the deal, the angels’ age and a passive approach to the investment itself; on the other hand it is positive the impact on the invested capital played by the angels’ experience, their education, their past background and, most of all, by the quality and intensity of monitoring. When considering the second research hypothesis, our analysis shows that the share of the personal wealth invested by business angels does not depend on firm-specific features, rather it depends on angels’ characteristics as well as on factors related to the investment decision making process. In detail, business angels increase their availability to invest their financial resources when it increases their previous experience and they belong to an angel community; the presence of co-investors, the angels’ age and their overall financial wealth negatively affects the asset class represented by informal venture capital. Furthermore, the co-investment appears to be a significant determinant of investment choices only for BAN members, implying that there is a role for angel community in spreading information, reducing incentives to opportunistic behaviors and enhancing trust among investors. Such an argument is consistent also with another evidence, that is the lower role played by monitoring inside a business angel network when compared to that played outside the network: angel communities, thus, can decrease the need for individual monitoring effort, increasing at the same time the members’ confidence in the investments made. In contrast, since non-BAN members do not benefit from the soft-information given by angel communities, they probably compensate this greater information asymmetry by imposing more extensively high level of monitoring. Our findings have relevant implications for academicians, policy makers and entrepreneurs seeking finance, given the insight on business angels’ investment decisions. Stimulating and strengthening the networks could be a major goal in the future for the growth of entrepreneurship.



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