The IPO window of opportunity for digital product and service firms

Abstract

Recognizing the window of opportunity to go public for digital product and service (DPS) firms is especially critical because they have high fixed-to-variable cost (FCVC) ratios and winner-take-all industry competition. Too early and their very risky nature means a steep discount to their stock. Too late and they may not be able to sell stock or it may be too late to take advantage of a new product or service. We find that the size of the run-up in stock price at the IPO is higher (lower) for DPS firms the earlier (later) they go public and significantly more than for traditional firms. We also find that the DPS firms that go public earlier or later outside the window of opportunity are more likely to fail. This result is also stronger for DPS firms than for traditional firms.

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Notes

  1. 1.

    IPO timing usually refers to synchronizing the offer with a receptive market (Schultz 2003).

  2. 2.

    There is evidence that early decisions of high-technology firms contribute to their survival (Wilbon 2002).

  3. 3.

    See Ritter and Welch (2002) for a discussion of IPOs and offer price discounting.

  4. 4.

    The notion that IPO offer prices impound risk is consistent with arguments found in Beatty and Ritter (1986), Rock (1986), and Carter and Manaster (1990).

  5. 5.

    It is difficult to pin-point exactly when to separate DPS periods. One likely development was the development of the World Wide Web in the early 1990s and the Mosaic Web browser in 1993. In addition to these developments we chose the 1997 separation because of an increase in DPS firm IPOs from 6.44 per month before 1997 to 11.7 per month thereafter.

  6. 6.

    The relationship between risk and these variables are provided in Johnson and Miller (1988), Carter and Manaster (1990), and Ritter (1984), among others.

  7. 7.

    The values for the industry variable were obtained from K. French’s website.

  8. 8.

    See www.nyse.com for a series of requirements.

  9. 9.

    See, for example, Carter and Manaster (1990), Hanley et al. (1993), Johnson and Miller (1988), and Ritter (1984).

  10. 10.

    The dot com bubble period is identified in Ljungqvist and Wilhelm (2003) as January 1998 to June of 2000.

  11. 11.

    The general model is \( {{\mathrm{Y}}_t}={\beta_1}+{\beta_2}*{D_t}+{\beta_3}*{X_t}+{\beta_4}*{D_t}*{X_t}+{\varepsilon_t} \) where D is a dummy variable and X and Y continuous variables. The coefficient β2 interprets how Y changes with respect to D. The coefficient β3 interprets how Y changes with respect to X where D=0. The coefficient β4 provides an interpretation of whether there is a significant change in the X/Y relationship for levels of D and the sum of β3 and β4 interprets how Y changes with respect to X where D=1. In all tables we have summed the β3 and β4.

  12. 12.

    As an alternative test we estimated the model using both EARLY and LATE in the same model and assuming two breaks. The results were qualitatively similar.

  13. 13.

    See for example Carter and Manaster (1990), Hanley et al. (1993), Johnson and Miller (1988), and Ritter (1984).

  14. 14.

    Because it is possible that unobserved variables may potentially impact unobserved characteristics, we used month and underwriter identification variables as fixed/random effects in regression models. While both variables provided explanatory power, they did not qualitatively alter the results reported.

  15. 15.

    As in footnote 13 above, fixed-effects regression models were run using the same fixed/random effects variables with no qualitatively differentiating results.

  16. 16.

    As an alternative test, we estimated the model using both EARLY and LATE in the same model and assuming two breaks. The results were qualitatively similar.

  17. 17.

    We did not use a heteroscedasticity correction in these empirical models because statistical analysis of the data did not identify heteroscedasticity.

  18. 18.

    \( {\lambda_i}={{{-\varphi \left( {\gamma {Z_i}} \right)}} \left/ {{\left[ {1-\varPhi \left( {\gamma {Z_i}} \right)} \right]}} \right.} \), where [ ϕ ]is the standard normal density function and [Ф] is the standard normal cumulative distribution function.

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Correspondence to Troy J. Strader.

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Responsible Editor: Roger W.H. Bons

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Carter, R.B., Strader, T.J. & Dark, F.H. The IPO window of opportunity for digital product and service firms. Electron Markets 22, 255–266 (2012). https://doi.org/10.1007/s12525-012-0109-z

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Keywords

  • Digital product
  • Digital service
  • Digital technology
  • Financing strategy
  • Initial public offering
  • IPO timing

JEL classification

  • G24 – Investment Banking