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Incremental impact of venture capital financing

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Abstract

This paper investigates the differences in the return generating process of venture capital (VC)-backed firms and their peers that operate without VC financing. Using a unique hand-picked database of 990 VC-backed Belgian firms and a complete population of Belgian small and medium-sized enterprises (SMEs), we focus on the extent to which the presence of a VC investor affects the sensitivity of a firm’s returns to the changes in the capital structure, in the operating cycle, and in the industry dynamics. The differences may stem from the (self-) selection of better companies into VC portfolios, from the venture capitalists’ (VCs) value-adding activities, and/or from both. We examine these factors in the context of a complex simulation procedure which allows separating selection from value-adding when traditional approaches are difficult to implement. Our results indicate that VC-backed firms are able to extract more rent from the changing industry conditions and from the optimizations in their capital structure. The presence of VCs in the firm’s equity seems to have only a marginal effect on the operating cycle efficiency. Overall, the results are suggestive of the value-adding being the main driver of the VC-backed firm’s performance.

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Notes

  1. Scholars have also documented that prospective entrepreneurial firms may self-select themselves into better VCs (Hsu 2004).

  2. The term “first-round financing” is equivalent to the terms “initial capital injection”, “initial investment”, etc.

  3. The classical solutions to this problem, i.e., the instrumental variable (IV) approach and Heckman’s sample selection models (Heckman 1976, 1979) are discussed in the respective sections.

  4. In this sense, VC financing serves as a viable alternative to the bank capital.

  5. The literature suggests that traditional instruments are convertible securities, such as convertible preferred equity (Cornelli and Yosha 2003; Kaplan and Strömberg 2003; Hellmann 2006). Some scholars argue, however, that this conjecture is particular to the U.S. VC industry (Cumming 2005).

  6. Even if the efficiency is not an objective per se, we may expect that VCs’ involvement still benefits the operating process via the cost-reductions and value enhancements.

  7. It is worth noting that this list is all we have as an initial input. Unfortunately, we do not possess any information on the investor or the deal itself, e.g., the valuation, the number of subsequent financing rounds, the investor type, the syndication, etc.

  8. The coverage of Belgian VC deals in these databases is far from complete.

  9. All companies in Belgium, regardless their listing status or size, are obliged to file complete financial statements with the National Bank of Belgium. Bureau Van Dijk next compiles these into the commercially available BELFIRST electronic database.

  10. For start-ups, we took the corresponding values in the injection year.

  11. If a portfolio firm belongs to the first or the last decile of its industry, only the following or preceding decile, respectively, is taken.

  12. All random integers here are generated from the uniform distributions.

  13. \(R_{3_{i}}\) depends on the size of the PG corresponding to \(R_{2_{i}}.\)

  14. The sectors are aggregated using the first three digits of the NACE-BEL code. This ensures consistency with MS-PG matching procedure, which is also based on the three-digit correspondence.

  15. Some companies in the sample show zero values of AGE and EMPL. AGE is 0 if a company is VC-backed from inception; we force NA initial value for the AGE in such cases. EMPL may take the value of 0 when the company does not employ staff in the legal sense, e.g., contract workers with the status “independent”. We add 1 to the EMPL variable to force the existence of logs.

  16. We checked for the partial correlations with lags (Ljung–Box Q stat) as well as for the presence of the unit roots (Im, Pesaran and Shin W stat, ADF Fisher χ2, PP Fisher χ2) in all series. Data and tests are available upon request.

  17. The first loading (β i ) represents the cross-section fixed effect constant, followed by the common factor betas, which are assumed to be constant over time and cross-sections. The term \(u_{i,t}\) should be considered as an independent variable in Eq. (3), since its value is determined earlier in Eqs. (1) and (2).

  18. For convenience, we use terms “simulated mean”, “simulated average”, “mean of the simulated distribution”, and “average of the simulated distribution” interchangeably.

  19. For example, the median regression is a particular case of the quantile regression when the dependent variable is the conditional median.

  20. For instance, if we are interested in the effect of the VA_TA, E_TA, and \(\Updelta({\rm LOGIND})\) on the performance of the most performing firms, we specify and estimate the parameters for the 0.9th quantile over the complete sample, and not over the firms in this quantile only.

  21. The estimates of the simulated distributions for control variables are available upon request.

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Acknowledgments

This research is supported by the Belgian Fund for Fundamental Scientific Research (Fonds de la Recherche Fondamentale Collective, FRFC). We are grateful to Sophie Manigart, Mike Wright, Bernard Surlemont, Pierre-Armand Michel, Michael Ghilissen, and anonymous referees for their useful comments and remarks on earlier versions of this paper. Georges Hübner thanks Deloitte Luxembourg for financial support.

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Authors and Affiliations

  1. EMLYON Business School, 23, Avenue Guy de Collongue, 69134, Ecully Cedex, France

    Yan Alperovych

  2. HEC Management School, University of Liège, 14, rue Louvrex, 4000, Liège, Belgium

    Georges Hübner

  3. School of Business and Economics, Maastricht University, Maastricht, The Netherlands

    Georges Hübner

  4. Gambit Financial Solutions, Liège, Belgium

    Georges Hübner

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  1. Yan Alperovych
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  2. Georges Hübner
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Correspondence to Yan Alperovych or Georges Hübner.

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Alperovych, Y., Hübner, G. Incremental impact of venture capital financing. Small Bus Econ 41, 651–666 (2013). https://doi.org/10.1007/s11187-012-9448-6

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