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Proprietary costs and the equity financing choice

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Abstract

We develop a firm-level private information index with three unique features. First, the index is a direct measure of the nature and magnitude of private information that is proprietary. Second, it is multi-dimensional. Third, it is not specific to any setting. We conduct several tests to validate this index against firm-specific and industry-level proprietary cost proxies. We then use it to examine the role of proprietary information in a firm’s equity financing choice between private investments in public equity (PIPEs) and seasoned equity offerings (SEOs). In this context, the proprietary cost hypothesis posits that firms wanting to avoid public dissemination of sensitive private information can better do so by choosing PIPEs. Our tests strongly support the hypothesis. The results are robust to alternate index specifications and other factors that influence this choice.

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Notes

  1. The premise is that the nature of private information that managers possess can affect their stock ownership. We thank a reviewer for suggesting this test.

  2. Firms that refer to trade secrets (redact information from their 10-Ks) comprise 40% (13%) of our overall sample.

  3. Interestingly, Ali et al. (2014) provide evidence of a positive association between industry concentration and the probability that a firm would choose a PIPE over an SEO and interpret this evidence as supporting the proprietary cost hypothesis. We revisit their findings in Section 4.4. We also discuss private equity market reforms that occurred after their sample years.

  4. We thank a reviewer for suggesting this point.

  5. For robustness, we also use alternate one-year (t + 1) and two-year (t + 1 to t + 2) future time horizons.

  6. Confidential treatment requests can be made pursuant to Rule 406 of the Securities Act of 1933 or Rule 24b-2 of the Securities Exchange Act of 1934 with respect to information required to be filed with the SEC, such as a material agreement filed as an exhibit to a periodic report.

  7. We thank a reviewer for suggesting this analysis.

  8. The results are similar for our main FPI measure.

  9. We re-estimate this specification using probit, and the results (untabulated) are materially the same.

  10. These three regulations potentially affect firms based on the states where their headquarters are located. Compustat headquarters’ data is subject to errors because Compustat backfills this data when firms change their headquarters. We correct this error using hand-collected, firm-headquarters data, as described by Heider and Ljungqvist (2015), and 10-K header data, collected by Bill McDonald from the SEC EDGAR. Data from Heider and Ljungqvist (2015) was provided upon request. The header data is available on the University of Notre Dame Software Repository for Accounting and Finance website. We thank Bill McDonald, Alexander Ljungqvist, and Florian Heider for making their data available.

  11. We re-estimate Eq. (2) using a linear probability model and obtain materially the same inferences.

  12. In Columns (2) and (4), we control for four specific measures of product market conditions faced by a firm following Boone et al. (2016).

  13. We demean the dependent variables for estimation purposes.

  14. The Census Bureau measure is only available for the manufacturing sector.

  15. Trade Secrets is positively correlated with Redacted 10-K, which is consistent with the evidence of Glaeser (2018).

  16. In 2008, Warren Buffett’s Berkshire Hathaway bought $3 billion worth of stock from General Electric and $5 billion from Goldman Sachs through PIPE transactions (Sorkin 2008).

  17. Indeed, Gibson et al. (2004) observe: “The prospectus, road shows, and conference calls that are part of the firm’s and underwriter’s SEO marketing efforts provide outside investors with extraordinary opportunities to interact with firm management and members of the underwriting team to elicit information.”

  18. Credible sales and profitability forecasts may also inform competition of industry and demand conditions and potentially encourage entry (Darrough and Stoughton 1990).

  19. Billett et al. (2015) find that 76% of their observations overlap in the two databases. They report that PrivateRaise covers larger PIPE issuers and a noticeably higher percentage of convertible securities (debt or preferred stock). They also document differences in the way the closing/announcement date is collected. Lastly, the menu of contractual terms is not identical across the two databases, with PrivateRaise containing some additional terms for PIPEs and registered directs (i.e., scheduled multiple PIPEs or limitations for public issuances for the immediate future).

  20. We do not use a probit model since the inclusion of fixed effects in nonlinear probability models has been a subject of concern (Cornelli et al. 2013). All our results are robust to using a probit model instead of the linear probability model.

  21. The latter point reflects the notion that different firms have proprietary information along different dimensions and each variable by itself only captures proprietary information for a small subset of firms.

  22. In related literature, Kankanhalli et al. (2019) document a positive association between information redaction and SEOs. Boone et al. (2016) find that nearly 40% of firms redact information from their IPO filings and that redacting-firm insiders reduce underpricing-related wealth transfers at the IPO stage by raising more equity financing in later SEOs. However, to our knowledge, this is the first time in the related empirical literature that these measures of proprietary costs have been used to explain the equity financing choice.

  23. The coefficients on Redacted 10-K and Trade Secrets remain insignificant, even when we do not include FPI in these models.

  24. We also use the U.S. Census measure of industry concentration as another proxy for proprietary costs, noting that it is only available for firms in the manufacturing sector. We obtain the measure from the Census Bureau website for the 2002, 2007, and 2012 census years that fall within our sample period. We follow prior work and use it as a measure for industry concentration for each of the five years in a five-year window centered on the year a census is published (Aggarwal and Samwick 1999; Campello 2006; Ali et al. 2009). In untabulated results, we fail to detect any significant association between Census HHI and a firm’s propensity to choose a PIPE over an SEO.

  25. The internet appendix provides a detailed discussion of the institutional aspects of private and public registered directs.

  26. Shares offered via traditional SEOs are typically sold to a wide variety of institutional investors without a clear lead investor. Therefore, although we cannot incorporate investor-level fixed effects directly in our financing-choice model for traditional SEOs, but we can do so for public registered directs.

  27. Another way to address this issue is to identify investors unlikely to influence firms’ corporate and in- vestment policies, such as hedge funds that are typically short-term investors (Brophy et al. 2009; Dai 2007). Accordingly, we replicate our main analysis (Table 7) by constraining our PIPE sample to hedge fund-led PIPEs. Our findings (untabulated) remain qualitatively unchanged from what we present in Table 7.

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Acknowledgements

We thank Stephen Penman (editor) and two anonymous reviewers for many constructive comments and suggestions. We also thank Brian Akins, Ramji Balakrishnan, Philip Berger, Edwige Cheynel, Somnath Das, John Huck, Donghyun Kim, Nisan Langberg, Patricia Naranjo, Leila Peyravan, K. Ramesh, Tharindra Ranasinghe, Brian Rountree, Ehsanolah S. Soofi, Andrew Sutherland (discussant), Richard Swartz, Joshua White, Emmanuel Yimfor, Stephen Zeff, and seminar participants at Rice University, Cyprus University of Technology, HEC Paris, University of Wisconsin-Milwaukee as well as the 2019 FARS Midyear Meeting and the 2019 Lone Star Accounting Research Conference for helpful comments.

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Authors and Affiliations

  1. UW-Milwaukee, Milwaukee, WI, USA

    Ioannis V. Floros

  2. Rice University, Houston, TX, USA

    Konduru Sivaramakrishnan

  3. University of Illinois at Chicago, Chicago, IL, USA

    Rustam Zufarov

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  1. Ioannis V. Floros
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Appendices

Appendix 1

Table 11.

Appendix 1 Constituent Variables of FPI
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Appendix 2

Table 12.

Appendix 2 Description of Main Variables
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Floros, I.V., Sivaramakrishnan, K. & Zufarov, R. Proprietary costs and the equity financing choice. Rev Account Stud 29, 1276–1319 (2024). https://doi.org/10.1007/s11142-022-09745-6

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