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The role of venture quality and investor reputation in the switching phenomenon to different types of venture capitalists

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Abstract

We examine how the perceived quality of a venture and the reputation of the incumbent Venture Capitalist (VC) are affecting the propensity to switch from an independent VC (IVC) to another IVC, to a governmental (GVC), a bank-affiliated (BVC) and a corporate (CVC). We find that high perceived quality ventures are more likely to switch from a IVC to another IVC, for whatever level of reputation of the incumbent. The quality of the venture matters in the switching to a BVC too, when the reputation of the incumbent IVC is not particularly high. Conversely, the lower is the perceived quality of the venture and the lower is the reputation of the incumbent IVC, the higher is the likelihood to rematch with a GVC. Finally, results also suggest that the probability of successful exit increases when a switching occurs from an IVC to a new lead IVC or to a CVC.

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Notes

  1. However, this might not always happen in thin VC markets where the matching process between ventures and VC investors is often driven by the selection of companies in need, rather than best performing companies (see the “frog kissing” versus “cherry picking” arguments developed in the theoretical model by Bertoni et al. 2016).

  2. In our analysis, we cannot distinguish whether the board of invested ventures evaluates ex-ante (before the decision to switch) or ex-post (after the decision to switch) the opportunities that are available on the market (in terms of the full range of investors). We can just model the fundamental option of switching or not lead investor and, in case the switching takes place, the type of switching.

  3. The extraction refers to April 2012. We decided to compare the switching dynamics affecting the only VC industry, by considering the heterogeneity within the industry and thus the different types of VCs, without introducing other forms of financing (e.g. business angels, buyouts and non-equity based funding). The VC industry presents specific characteristics and patterns that are not directly comparable with other forms of financing. Adding other forms of equity or non-equity financing would make such comparison less interesting. Moreover, the Venture Expert’s data coverage on business angels and non-equity deals is today still insufficient.

  4. Following Cumming and Dai (2013), as a robustness check, we also considered a lead VC as the one that invested the largest cumulative amount of capital. However, we did not find any significant difference in the results discussed in the next section.

  5. Note that a venture may switch more than once in the observed period.

  6. We followed the industry classification provided by the Thomson One database.

  7. In Appendix A (Table 1A) we report the distribution of the number and percentage of switching rounds in the different US regions.

  8. This approach has been followed also in Abrardi et al. (2018).

  9. This number is estimated by year, by considering those IPOs for which the focal VC was the lead investor at the time of the last funding round.

  10. Lee et al. (2011)’s VC reputation index is available for public use at www.timothypollock.com. VC investors in our sample are not all included in Lee et al. (2011)’s index. The presence of these missing values explains why we replicate Lee et al. (2011)’s procedure instead of directly using their index.

  11. Results of this regression are not reported in the text for the sake of brevity but are available from the authors upon request.

  12. Please refer to the previous Section for details on the estimation of incumbent IVC reputation and perceived venture quality.

  13. The concept of switching is unavoidably linked to follow-on rounds because we only observe switching behaviour for the restricted, nonrandom group of ventures which successfully obtained more than one round of financing. Accordingly, estimates of the multinomial logit only refer to ventures obtaining a follow-on round of financing. In order to control for this selection effect, as a robustness check, we resorted to an Inverse Mill’s ratio sample selection control. We ran a first stage probit model predicting the probability to obtain more than one round of financing and we derived an inverse Mills’ ratio (IMR), which is the estimated value of the generalized residual. Then, to correct for the selection bias, the inverse Mills’ ratio control factor was included in the second probit models predicting the probability to switch to the different types of VC investors. Estimates results, that are very similar to those obtained with the multinomial logit, are reported in Appendix B.

  14. The marginal effect of venture quality is not significant for a short interval of incumbent VC reputation, ranging from 4.6 to 5.4. Only the 0.219% of observations in our sample assume values in this range of non-significance.

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Authors and Affiliations

  1. Politecnico di Milano, DIG, Milan, Italy

    Annalisa Croce

  2. Politecnico di Torino, Turin, Italy

    Elisa Ughetto

  3. Bureau of Research on Innovation, Complexity and Knowledge (BRICK), Collegio Carlo Alberto, Turin, Italy

    Elisa Ughetto

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  1. Annalisa Croce
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  2. Elisa Ughetto
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Correspondence to Elisa Ughetto.

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Appendices

Appendix A

See Table 10 here.

Table 10 Number and % of switching rounds in the different US regions
Full size table

Appendix B

Tables 11 and 12 illustrate the results of a sample selection model based on the Heckman two-stage framework. The model consists of two simultaneous equations to distinguish between the determinants of successfully obtaining more than one round of financing in the sample period and those influencing the occurrence of the switching event. In the first step, the binary dependent variable is a dummy which is equal to one if the venture has successfully obtained more than one round of financing during the sample period. The independent variables include: a dummy indicating the stage of the venture at the time of the financing round (i.e. Acquisition/Buyout, Early stage, Later stage, Other stage), the geographical location of the venture (i.e. in particular, two dummies indicate whether the venture is located in California or Massachusetts), a dummy variable which is equal to 1 if the deal is syndicated, the amount of financing received by the venture in the round (in logarithms), industry and year controls.

Table 11 Probability to receive more than one round of financing
Full size table
Table 12 Probability to switch
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In the second step, the probability of switching was regressed on a set of variables concerning the reputation of the incumbent lead IVC, the perceived quality of the entrepreneurial venture, the interaction between the two variables and the IMR previously estimated.

See Tables 11, 12 here.

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Croce, A., Ughetto, E. The role of venture quality and investor reputation in the switching phenomenon to different types of venture capitalists. J. Ind. Bus. Econ. 46, 191–227 (2019). https://doi.org/10.1007/s40812-019-00112-2

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  • DOI: https://doi.org/10.1007/s40812-019-00112-2

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