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Patents, R&D investments and post-IPO strategies

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Abstract

This paper empirically investigates the effect of going public on the evolution of high-tech entrepreneurial firms, focussing in particular on the interaction between innovation variables and financing and investment strategies. Specifically, I confront the effects of the IPO on firms with higher R&D investments versus firms with more patents. Firms with higher R&D investments typically view the IPO as a mechanism to raise external equity, used to pursue investments and to acquire participation in other companies, whereas those with more patents raise more debt capital and invest less after the IPO, as compared to high-tech entrepreneurial firms. I suggest that a large number of patents is an index of technological maturity for high-tech ventures, even more than age and size, that helps investors to individuate firms with a lower level of risk.

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Notes

  1. Cash flows generated by companies may be not sufficient to sustain the investments necessary to maintain a competitive advantage in innovative industries. Debt lenders may also be reluctant to fund entrepreneurial firms with new and innovative products because of the difficulty associated with evaluating the risk of such products, or because of the sizeable time lag between investments and cash generation. Even when additional commercial credit is available to the entrepreneur, indeed, the covenants attached to the loan may be too restrictive for him or her to pursue opportunities with high-growth prospects, but also with high risk.

  2. See Audretsch et al. (2009) for the theoretical implications of the patent ownership of the CEO versus by the firm.

  3. High-tech entrepreneurial firms are defined as small, young firms active in high-tech industries. Small firms are identified, according to the definition of EU definition of SMEs, as firms with (pre-IPO) sales inferior to 50 €m. As for age, I refer to the definition of New Technology-Based firms (less than 25 years old at IPO), as in Little (1977) and Colombo et al. (2010). In line with other studies (e.g., Cloodt et al. 2006), I consider as high-tech sectors electronics and industrial machinery, information and communication technology, and pharmaceutical and biotech. The industry classification is the official one adopted by the European stock exchanges, namely the ICB–Industry Classification Benchmark.

  4. The list of IPO firms is from the EURIPO database that provides the IPO prospectus as well as very detailed information on the companies and their management. See Cogliati et al. (2011) and Vismara et al. (2012) for a detailed description of the database.

  5. The purpose of the prospectus is to sell stock. Therefore, it is assumed that all relevant information will be included.

  6. The number of registered patents are obtained from IPO prospectuses. When missing, the patent variable was completed by measuring the number of patents as reported by the US and by the European Patent Office issued to the firm up to the date of the IPO.

  7. Firm size is measured by the natural logarithm of market capitalization at the IPO; firm age is measured by the natural logarithm of company’s years since incorporation at the IPO; firm profitability is measured by return on assets at the IPO; firm leverage is measured by the natural logarithm of the ratio between debt and total assets at the IPO.

  8. In unreported results, I also controlled for the role played by TMT human capital. Specifically, I considered the variable Education constructed using the factor analysis considering the proportion of directors with MBA and Ph.D. degrees. I also controlled for the variable Experience, constructed with factor analysis including measures ofTMT experience and relational capital. For TMT structure, I considered the number of directors and the proportion of independent directors. The results with reference to patents and R&D investments, which constitute the objective of this paper, did not change significantly.

  9. No variables had an average variance inflation factor (VIF) higher than 10, which is usually considered the threshold of attention for collinearity problems.

  10. I included only companies with (pre-IPO) sales lower to 50 €m are selected. As a consequence, the average market size in the sample is higher for industries with high relevance of intangible assets, such as IT and biotech, and lower for machinery and electronics. This is due to the higher price-to-sales ratio in more intangible industries. Similar considerations can be drawn for the market-to-book ratio.

  11. In the OLS regression on the dilution ratio, the dilution ratio itself and the participation ratio are not considered among the independent variables.

  12. The authors demonstrated dramatic improvement in performance of the system estimator compared to the usual first-difference GMM estimator. Furthermore, in system GMM it is possible to include time-invariant regressors, which would disappear in difference GMM. Asymptotically, this does not affect the coefficients estimates for other regressors. This characteristic is of particular interest since most of the independent variables in this study refer to characteristics at the IPO, and thus are time invariant.

  13. The total volume of acquisitions for each firm is calculated as the natural logarithm of the total monetary value of acquisitions. Since I have not all the data on the monetary volume of deals, I use as dependent variable the ratio between the total volume of acquisitions and the availability of deals values (expressed in percentage of the total number of deals for each firm). The correlation between the total number of deals and the available total value of deals is 65.71 %.

  14. See Paleari et al. (2008) for an analysis of the borrowing behavior of recently listed firms in Europe.

  15. In unreported results, I also show that firms with high R&D investments tend to pursue intra-industry deals, probably to complete their set of technological competencies.

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Authors and Affiliations

  1. Department of Economics and Technology Management, University of Bergamo, Bergamo, Italy

    Silvio Vismara

  2. CCSE, University of Bergamo, Bergamo, Italy

    Silvio Vismara

  3. University of Augsburg, Augsburg, Germany

    Silvio Vismara

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  1. Silvio Vismara
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Correspondence to Silvio Vismara.

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Vismara, S. Patents, R&D investments and post-IPO strategies. Rev Manag Sci 8, 419–435 (2014). https://doi.org/10.1007/s11846-013-0113-5

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