Syndication and Venture Capital Finance: Evidence from the U.S. Market


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Master project, Finance 2011

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Syndication and Venture Capital Finance: Evidence from the U.S. Market

  1. 1. SYNDICATION AND VENTURECAPITAL FINANCE: EVIDENCEFROM THE US MARKETBy G. Klinger, P. Otero and G. PratobeveraMSc in Finance, 29/06/2010
  2. 2. PRESENTATION OUTLINE1. Introduction2. Related Literature3. Description of dataset4. Statistical Analysis5. Conclusion
  3. 3. Introduction Syndication is a widespread phenomenon among the Venture Capital Industry Different hypothesises have been developed by research The aim of our research is to evaluate which hypothesis is the most important one to explain our results
  4. 4. PRESENTATION OUTLINE1. Introduction2. Related Literature3. Description of dataset4. Statistical Analysis5. Conclusion
  5. 5. Related Literature Venture Capital  Investment in private companies  “Money of invention”  Supply value-added resources  Investor takes active role in development  Maximize Capital gain by exiting through a sale or IPO
  6. 6. Related Literature Syndication  Joint investment by at least two VC firms  Most commonly soon after first investment  Repeated inside the same VC network  Structure can solve free-riding problem Existing literature identifies three main reasons, but differs on their ranking:  Project Selection and Screening  Value added (management skills)  Risk Sharing
  7. 7. Reasons for Syndication Project Selection and Screening  Joint expertise facilitates the spotting of profitable projects  Second opinion improves evaluation  Several separate screenings are more efficient  Evaluation costly: loss of monopoly (competitor) Value-added  VC brings additional value  VC have different resources, knowledge, skills  Syndication reduces risk via superior management of investments Risk Sharing  intuitive, although not necessarily most important reason  syndication to reduce total capital in investment  diversification by syndication is need to share risk  liquidity constrains
  8. 8. Determinants of syndication Difficult to evaluate the prevalent reason for syndication  in high uncertain projects, selection and screening is more difficult  second opinion of other VC more valuable  high agency costs: monitoring difficult and asymmetric information problems  benefits of risk sharing increase more frequent and intense in uncertain environment Different relationship under the hypothesises (Brander 2002)  Selection hypothesis higher syndication in unsuccessful projects (project quality relative low)  Value-added hypothesis high syndication in successful projects (value and expertise added by each member)
  9. 9. PRESENTATION OUTLINE1. Introduction2. Related Literature3. Description of dataset4. Statistical Analysis5. Conclusion
  10. 10. Description of the Dataset 2692 US firms that received venture capital financing (2001-2005) This dataset includes firms that are no longer VC backed The data was downloaded from the Thomson One Banker database
  11. 11. Description of the Dataset Name of the company Founding date of the venture firm First and the last investment date (by VC) Total amount of funding to date Industry in which the venture firm operates Exit type of the investors (acquisition, IPO,…) The number of investors in each venture firm
  12. 12. Description of the Variables n_synd: is the number of investors d_synd: indicating syndication (1) or not (0) types of exit: o M&A o IPO o Defunct o Private
  13. 13. Description of the Variables age: difference between the first investment date and the founding date fund: total amount of funding to date in million pounds d_round: indicating one round (0) or more than one round (1) industry dummies: 9 main industries year dummies
  14. 14. Summary Statistics Investments decreased from 925 to 382 Syndicated investments remained relatively constant Liquidity constraints  Control for year
  15. 15. Summary Statistics Missing values for age and fund No logarithmic transformation of the variable age High skewness in funding (apply logarithm to funding) The average number of investors among the syndicated ones is 3.99
  16. 16. Summary Statistics Syndicated investments are more present when the exit is an M&A or an IPO Standalone investments are more present in case of defunct and private firms IPOs yield the highest return (59.5%) for venture investors Acquisitions offer average returns of only 15.4% Liquidations lose 80 percent of their value (Gompers, 1995)
  17. 17. Summary Statistics VC specialize in industries in which monitoring and information evaluation are important Syndication is a way to evaluate projects better, especially in uncertain environments Control for industry fixed effects
  18. 18. PRESENTATION OUTLINE1. Introduction2. Related Literature3. Description of dataset4. Statistical Analysis5. Conclusion
  19. 19. Empirical Strategy Does syndication varies across firms accordingly with the three motivations discussed before? Determinants of syndication Which one of the three motivations is the most suitable for explaining US data? Relation between syndication and the success of the firm: o if positive => Value-added hypothesis; o if negative => Selection and/or risk-sharing hypothesis.
  20. 20. Determinants of Syndication Poisson regression for the number of syndicate members; probit regression for the syndication dummy (with marginal effects) Robust standard errors
  21. 21. Determinants of Syndication The two models give the same qualitative results. The measures for syndication:  decrease with the age of the firm at the first investment (0.12 points): more information available and lower uncertainty for older firms  increase with the total amount of funding (0.71 points): opinion and expertise more valuable  increase with the number of rounds (2.35 points): syndication after the first investment This evidence does not reject any of the motivations for syndication (value-added, selection, risk sharing)
  22. 22. Determinants of Success Probit regressions reporting marginal effects Robust standard errors
  23. 23. Determinants of Success Statistical significance partially different in the two models:  model2: probability of success higher when there is more then one investment round => acquisition of new info  both models: probability of success higher when the total funding increases => favourable prospects  both models: no relation between success and age  model1: probability of success higher when the number of syndicate members increase => value-added hypothesis The last result does not hold anymore in model2. Hence, it is the addition of a member that is related to the success of the firm more than the decision to syndicate itself
  24. 24. Robustness Checks These results are robust to (and in some cases enhanced by) several robustness checks:  missing values  classification of “active” firms  definition of success  other regression models We also discuss some concerns regarding endogeneity problems  omitted variables: - experience of the VC and - control rights of the VC  reverse causality?
  25. 25. PRESENTATION OUTLINE1. Introduction2. Related Literature3. Description of dataset4. Statistical Analysis5. Conclusion
  26. 26. Conclusions Syndication is more common in indutries that face a greater uncertainty, in investments about which little information is available and the required amount of funding is relatively high These results are consistent with the three main hypothesis explaining syndication However, we find that the probability of success is higher when the number of syndicate members increase This result prefer the value-added hypothesis over the other two: venture capitalists syndicate in order to exploit complementary skills and knowledge, thus enhancing the projects probability of success
  27. 27. Thank you! Vielen Dank!Muchas gracias! Grazie mille!
  28. 28. APPENDIX: RobustnessChecks
  29. 29. Missing Values
  30. 30. Classification of Private Firms
  31. 31. Definition of Success
  32. 32. Other Regression Models (1)
  33. 33. Other Regression Models (2)