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The performance of angel-backed companies “Journal of Banking & Finance 2019. Stefano Bonini, Vincenzo Capizzi, Paola Zocchi
The paper analyzes the performance of angel-backed companies, identifying key factors that influence their success, including co-investor participation, business angel network membership, and active involvement of investors. It introduces a performance index as a predictive measure of company survival and growth post-investment. The study suggests that a combination of investment practices and mentoring by business angels significantly enhances company performance, providing insights for designing effective funding strategies in the entrepreneurial ecosystem.
The performance of angel-backed companies “Journal of Banking & Finance 2019. Stefano Bonini, Vincenzo Capizzi, Paola Zocchi
- 1. “The performance ofangel-backed companies “ Journal of Banking & Finance 2019 Stefano Bonini, Vincenzo Capizzi, Paola Zocchi Shiva Indukoori 1 58 Citations
- 2. Early studies onangel investments • opaqueness of the market (Harrison and Mason, 2008; Capizzi, 2015; Lerner et al., 2016). • narrow representativeness of survey-based samples (Harrison and Mason, 2008; Capizzi, 2015; Lerner et al., 2016). • rely on anecdotal or case-based evidence (Hellman et al., 2013; Kerr et al., 2014; Mason et al., 2016). • Difficulty in finding empirical confirmation for some emerging trends in business angels’ investment process (Carpentier and Suret, 2015; Landström and Mason, 2016; Lerner et al., 2016; Harrison and Mason, 2017; Bonini et al., 2018) Shiva Indukoori 2
- 3. Measurement methodologies. • Survival(after 4 years of funding) and success (IPO or acquisition) as the binary indicators. • Employment, patents, and website traffic are the 3 outcome variables for growth. • Capability of an angel-backed company to raise subsequent venture financing as a performance measure. • Successful exits (IPOs or acquisitions) as a proxy for the performance of angel investments (Cumming and Zhang (2018) ) • Rate of growth in sales, employment, and tangible capital assets as the impact of BAs on firm growth, as measured by the (Levratto et al. (2017)) Shiva Indukoori 3
- 4. This Paper • Overcomesthe difficulty of data and provides qualitative and quantitative study. • The main unit of analysis is the investee company, instead of determinants of BAs investment decision. • Selection of an accurate set of metrics to measure performance. • “Performance Index” is the proxy for the performance and the probability of subsequent survival of investee companies Shiva Indukoori 4
- 5. Hypothesis H1. The performanceof angel-backed companies is positively affected by the number of co-investors joining a given deal. There is a positive relationship between capital invested by BAs and co- investments, consistent with prior work on venture capital and private equity (Lerner, 1994; Brander et al., 2002; Cumming and Walz, 2010; Tian, 2011). Shiva Indukoori 5
- 6. Hypothesis H2. The performanceof angel-backed companies is positively affected by the membership of Business Angels (BAs) in a given Business Angels Network (BAN). BAN managers (gatekeepers) organize periodic training meetings and pitching events aimed at stimulating the interaction between angel investors and entrepreneurs looking for funding (Aernoudt et al.,2007; Ibrahim, 2008; Paul and Whittam, 2010; Brush et al., 2012;Mason et al., 2016). BAN membership, which gives the possibility to finetune and optimize BAs’ decision-making styles according to their specific investment behavior in a trust- based environment, ultimately increasing the probability of the company toraise additional growth capital (Wiltbank et al., 2009; Fili et al.,2013; Bonnet et al., 2013; Bammens and Collewaert, 2014). Shiva Indukoori 6
- 7. Hypothesis H3. The performanceof angel-backed companies is negatively affected by a temporally deferred equity infusion pattern: fractioning the committed equity provision decreases the performance of the investee companies. VCs exploit the option to differ their equity contributions over time, conditional on the venture reaching some target milestones, typically related to financial profitability (size or revenue goals) or technological or scientific achievements (Sahlman, 1990; Gompers, 1995; Bergemann and Hege, 1998; Gompers and Lerner, 2001; Tian, 2011). This may not apply to BAs as they fund the companies that are capital- constrained and don’t have other alternatives like debt in the initial stages. Shiva Indukoori 7
- 8. Hypothesis H4. The performanceof angel-backed companies is positively affected by BAs’ active involvement over the three-year observed time period. Chua and Wu (2012) show that post-investment involvement and mentoring, rather than monitoring – positively impact business angels’ return on their investments. Landstrom and Mason (2016) show BAs’ “hands-on” involvement in company operations can meaningfully add value to the target venture. Shiva Indukoori 8
- 9. Hypothesis H5. The performanceof angel-backed companies is negatively affected by BAs’ soft monitoring. Earlier literature investigated the effectiveness of contingent contracts and financing mechanisms implemented by VC firms to decrease asymmetric information and moral hazard problems (Sahlman, 1990; Triantis, 2001; Kaplan and Stromberg, 2003; Gompers and Lerner, 2004; Wong et al., 2009; Puri and Zarutskie, 2012; Cumming and Johan, 2013; Chemmanur et al., 2014). Chua and Wu (2012) and Bammens and Collewaert (2014) show that a tightening of the degree of soft monitoring over the investee companies could damage the trust- based relationship between the founder and the angel investor, Shiva Indukoori 9
- 10. Data Sources Italian BusinessAngels Network Association (IBAN) : Sequential Survey info Orbis : Financial data, IPOs and Acquisitions Lexis / Nexis : Financial data, IPOs and Acquisitions Shiva Indukoori 10
- 11. Sample size andstudy period DEALS: 695 After excluding 393 companies as the name was not specified or incorrectly specified: 302 Companies with 3 years of financial data available: 111 ANGEL INVESTORS: 380 STUDY PERIOD: 2008 (t0) to 2012 (t3) Shiva Indukoori 11
- 12. Table 1 –Sample Filtering Shiva Indukoori 12
- 13. Shiva Indukoori 13 Table2 – Panel A – Industry Distribution Industry
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- 15. Variables Dependent Variable: PerformanceIndex which is a Set of Indicators composed of the following • Revenue • Net Assets • Net Income Shiva Indukoori 15
- 16. Shiva Indukoori 16 PerformanceIndex Ordinal Value Performance Description 2 Net asset value, Net income and Revenues are positive 1 Net asset value and revenues are positive but net income is negative 0 Both net asset value and net income are negative but revenues are positive - 1 Net asset value is positive revenues are equal to zero and net income is negative. - 2 Both net asset value and net income are negative and revenues are equal to zero
- 17. Shiva Indukoori 17 PerformanceIndex Ordinal Value Revenue Net Assets Net Income 2 + + + 1 + + - 0 + - - -1 0 + - -2 0 - -
- 18. Table 3 :Panel A Performance Index Shiva Indukoori 18 Companies without FS
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- 20. Independent Variables 1. Coinvestors 2. Membership 3. Equity infusion pattern 4. Active Involvement (Dummy): 1 for BAs active managerial contributions. sharing financial knowledge (32.9%) sharing industrial experience (27.6%) sharing marketing knowledge (22.4%) offering strategic and management advice (75.0%). 5. Soft Monitoring: Ordinal 1 – 5 Shiva Indukoori 20
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- 22. Table 5: EquityInfusion Pattern on BA is 1 for multiple installments Shiva Indukoori 22
- 23. Methodology Set of ordinallogistics (Ologit) regressions analysis Yi = BX + FirmControls + AngelControls + τ + θ + e Yi is Performance Index (Ordinal) X is Vector of explanatory variables (Coinvestors, BAN_membership, Equity infusion pattern, Active Involvement, and Soft-Monitoring). FirmControls is a Vector of control variables(Age, Equity, Foreign, and Pre-investment Revenues) AngelControls is a Vector of controls (Age, Experience, and Share) τ is time effect θ is industry fixed effect Shiva Indukoori 23
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- 25. Table 6a: OrdinalRegression Results Shiva Indukoori 25
- 26. Robustness Check Sub-sampling byyear, age, revenues, size of investment and monitoring differentiate the whole sample in 3 different subsamples. The results confirm that the selected explanatory variables Co- investors, Equity infusion pattern, and Soft Monitoring are significantly related to firm performance and survival independently of the investment year. Shiva Indukoori 26
- 27. Conclusions • The studyprovides the first evidence of the performance of angel-backed companies, overcoming the severe data limitations affecting previous studies. • Performance Index offers substantial predictive power, being able to predict survival up to four years after the investment. • Valuable BA investments need to be characterized by a balanced blend of investment practices, networking skills, background experience, and investment style. • “Angel investment formula” is more effective in generating positive performance than a stand-alone capital contribution. • One consequent policy implication to boost the entrepreneurial environment is to design of focused financing facility schemes leveraging on the value-adding potential of BAs like the creation of public-private angel co-investment funds. Shiva Indukoori 27
- 28. Limitations and Futureresearch More detailed data and longer time series may allow more structured survival analyses such as the Cox proportional hazards model, as in Manigart et al. (2002) and Pommet (2012). Shiva Indukoori 28
- 29. Limitations and Futureresearch Differential impact on the performance of angel groups and networks has only been marginally explored in this study. More evidence is needed to highlight whether and how different association rules, membership and services structures and BAN management practices can affect the survival and performance of new ventures, and the type of securities contracts underwritten when funding a company. Shiva Indukoori 29
- 30. Limitations and Futureresearch Additional insights may come from the collection of additional variables capturing more granularly angel investment practices such as BAs previous investment experience, the different personal backgrounds of BAs, and the type of securities contracts underwritten when funding a company. Shiva Indukoori 30
Editor's Notes
- #2 Stefano Bonini - School of Business at Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA Vincenzo Capizzic SDA Bocconi School of Management, Via F. Bocconi, 8, Milano 20136, Italy. Paola Zocchi Department of Economics and Business Studies, Università del Piemonte Orientale, Via E. Perrone, 18, Novara 28100, Italy It says how to measure the performance of angel-backed companies. This paper cited 5 papers of Dr Cumming (One of them coauthored with Dr.Johan)
- #3 VC Performance measure: Exits through IPOs and Acquisitions Financial: Turnover, market share, capital assets Non Financial: Operating performance and innovation, employment growth, productivity BAs Start at pre-revenue and no financial information. Very few are till the end of the investment cycle with IPO / acquisition
- #4 Earlier studies had these methods and methodologies. The first three are the sets of measures with heterogeneous metrics and methodologies. One of our other papers today by KERR had employment, patents and website traffic.
- #5 Early studies had heterogeneous metrics and measurement methodologies. The 3 different sets of measures are 1. They were built two binary indicators for survival and success (survival after 4 years from the funding event; successful exit through IPO or acquisition) 2. they employed 3 outcome variables for growth (employment, patents, website traffic) 3. they treated the capability of an angel-backed company to raise subsequent venture financing as a performance measure. Performance Index is an ordinal variable with a set of 5 indicators
- #6 The study had 5 hypotheses related co investors, BAN, Equity Infusion pattern, Active participation, soft monitoring
- #7 The importance of BAN is considered as they provide networking and training platforms for the BAs
- #8 Unlike VC, this will not apply to BAs as the companies are capital-constrained due to their significantly high intrinsic riskiness and cannot finance their investment needs through debt capital or other sources of financing facilities. Thus, the only other financing alternative beyond the initial monetary infusion made by the founders (plus possibly the family and friends tranche) is constituted by the intervention of the angel investors.
- #9 BAs exposure and experience is useful for the companies through advisory and mentoring.
- #10 SOFT MONITORING : Geographical proximity, BAs’ knowledge of the industry, Experience gained from previous investments, Interactions with entrepreneurs Hard monitoring : VCs Venture capitalists exploit the option to differ their equity contributions over time, conditional on the venture reaching some target milestones, typically related to financial profitability (size or revenue goals) or technological or scientific achievements (Sahlman, 1990; Gompers, 1995; Bergemann and Hege, 1998; Gompers and Lerner, 2001; Tian, 2011)
- #12 Number of angel investors is more as it includes co investors.
- #13 LOW NUMBERS IN THE FIRST YEAR (2008) 2008 was the inception year for IBAN surveys The second half of 2008 lowered the number of new firms due to the eruption of the financial crisis This concern is addressed by introducing year-fixed effects in all regressions that should absorb a significant portion of such heterogeneity. Robustness check on three subsamples obtained by restricting the year of the BA’s investment. The results are qualitatively unchanged.
- #14 63.9% of Tech Companies (Traditional Ventures or startups) : ICE, Biotech and Cleantech 50.4% ICE and biotech 41% ICE 33.3%ICT, Electronics 18% of Non-Tech industries not mentioned.
- #15 Revenues 23% of the firms showed no revenues in t1 which is one year after BA investment. Even after 3 years of BAs participation 8 % are still with no revenues: BAs are patient enough to wait for revenues. Net Earnings: More than 70% - Ve indicate substantial level of risky investments It shows How tough it is for angel investors.
- #17 Results may yield diverging conclusions if revenues, net assets, and net income are taken individually
- #18 Measured at the launch of initial equity crowdfunding offering.
- #19 In all the years more observations with –ve net income followed by all positives. Though data was limited to 111 companies that had financial info, only t2 had that fulfilled and rest fell short of information.
- #20 Panel B shows diverging conclusions if Revenues, Net assets, and Net Income are taken Individually with mean values. Stars represent the significance tests. Panel B. Within each class, the top/bottom quartile group includes those companies with 2 out of 3 indicators in the top 25% of the within-class distribution. Worst companies of the P.I. class 1 exhibit higher revenues but lower asset value than the best companies of the lower
- #21 Survey is done for active involvement and soft monitoring.
- #24 Yi Is dependent variable performance index
- #25 Low correlation among the variables is exhibited. Soft monitoring has a positive correlation
- #26 (1) H1. higher number of co-investors positively affects the performance of angel-backed companies, thus confirming our first research hypothesis. By getting access to equity capital raised by a syndicate of BAs, a company can also leverage on a wide set of non-monetary contributions, leading to an increase in its performance and probability of survival. (2) H2. Different from H2, affiliation to a BAN does not seem to affect the probability of success of angel-backed firms. (3) H3. Equity_infusion_pattern is negative and strongly significant in all model specifications. (4) H4. Active Involvement (5) Soft Monitoring has negative impact.