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IPO Valuation in the New and Old economies

IPO Valuation in the New and Old economies. Sanjai Bhagat Srinivasan Rangan University of Colorado at Boulder. Outline. Motivation Research Questions Prior Literature Model Specification Sample and Data Results Conclusions. Motivation.

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IPO Valuation in the New and Old economies

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  1. IPO Valuation in the New and Old economies Sanjai Bhagat Srinivasan Rangan University of Colorado at Boulder

  2. Outline • Motivation • Research Questions • Prior Literature • Model Specification • Sample and Data • Results • Conclusions

  3. Motivation • The second half of the 1990s, which witnessed several significant innovations in the technology sector and the rise of the internet sector, has been labeled as the new economy period. • In the new economy period (or boom period), equity values, especially those of initial public offering (IPO) firms, reached unprecedented heights and outpaced fundamentals • This has prompted several commentators to raise questions about whether traditional valuation methods remain valid for IPOs.

  4. Motivation • “Early profitability is not the key to value in a company like this (Inktomi).” • Jerry Kennelly, Chief Financial Officer of Inktomi Inc (1999) • “But valuations are just as often based on gut feel. As one entrepreneur told me, “Its as if everybody just settles on a number that they are comfortable with.” • Gove (2000)

  5. Motivation • Were traditional value-relevant variables such as income and book value of equity valued differently in the boom period relative to an earlier time period for IPO firms? • Also beginning from March 2000, the stock market in general took a dive (crash period). So the other question is “How did these variables fare in the crash period?” • In this paper, we seek to provide descriptive evidence on shifts in the IPO valuation function in the boom period and crash period relative to a more stable period – the late 1980s.

  6. Motivation • Another question that we consider is whether investment bankers and first-day investors agree on their valuations of different variables. • We conduct this analysis by regresssing first-day market values on offer values and other variables. • If the two sets of individuals agree with each other, the coefficient on offer value should equal one and the coefficient on other variables should equal zero.

  7. Motivation • Why is this important/interesting? • Separate fact from fiction/anecdotes • Fills gaps in the IPO valuation literature • Stimulate further research into what factors drove the shifts in the valuation function

  8. Discounted Cashflow Valuation where, n = Life of the asset CFt = Cashflow in period t r = Discount rate reflecting the riskiness of the estimated cashflows

  9. Research Questions • Were the following variables valued differently by investment bankers and first-day investors in the boom and crash periods relative the second half of the 1980s? • Income • Book value of equity • Sales • R&D • Industry price-to-sales ratios • Insider retention • Were the valuation of these variables different for tech firms, internet firms, and loss firms?

  10. Priors / Expectations • Based on anecdotes, we expected that income would be valued less in the boom period relative to the 1980s • Based on anecdotes, we expected that sales would be valued more in the boom period relative to the 1980s • We had no priors on how things would change in the crash period and so we let the data speak. • We also expected insider retention to be valued more in the boom period relative to the 1980s (substitution) • For technology and internet firms, we expected income and sales to be less valuable and insider retention to be more valuable (substitution) • For loss firms, we expected income to be valued less (Hayn (1995) and Basu (1997)) and insider retention to be valued more (substitution).

  11. Prior Literature • Valuation of ownership • Leland and Pyle (1977) • Downes and Heinkel (1982) • Ritter (1984) • Feltham, Hughes, and Simunic (1991) • Valuation of financial information • Klein (1996) • Kim and Ritter (1999) • Beatty, Riffe, and Thompson (2000)

  12. Prior Literature • Valuation of internet IPOs • Hand (2000) • Bartov, Mohanram, and Seethamraju (2002) • Inter-temporal changes in the valuation function • Core, Guay, and Buskirk (2003) • Demers and Lev (2001) • Keating, Lys, and Magee (2003)

  13. Prior Literature How do we extend the literature? • First, sample periods of most prior studies of IPO valuation do not cover the late nineties, 2000, and 2001. • Second, none of the prior studies share our research focus, which is to examine inter-temporal shifts in the IPO valuation function. • Third, with the exception of BRT, sample sizes in prior studies are small or based on one industry, the internet, and hence limit the generalizability of their conclusions.

  14. Prior Literature • Fourth, while prior studies have examined the determinants of the first-day closing value, they have not modeled this value conditional on the offer value. • Fifth, prior research has examined ownership retention by pre-IPO shareholders only as an aggregate signal; we extend this research by studying the value implications of the ownership of four classes of shareholders: CEOs, other officers and directors, venture capitalists, and other five percent blockholders.

  15. Model Specification • Dependent variable choices: • Price-to-earnings ratios or Price-to-sales ratios • Price per share • Offer value in millions of dollars • Logarithm of Offer value • Independent variables (expected signs) • Year -1 Income before extraordinary items and R&D (+) • Year -1 Book value of equity (+) • Year -1 Sales (+) • Year -1 R&D (+) • Pre-IPO Industry median price-to-sales ratio (+) • Post-IPO insider retention (+)

  16. Model Specification • Basic Model: OV = Offer value INCBRD = Income before extraordinary items and R&D in year –1 BV = Book value equity at the end of year –1 SALES = Sales for year –1 R&D = Research and development costs in year –1 INDPS = Median industry price-to-sales comparable of recent IPOs INSRET = Percentage of the post-IPO firm owned by pre-offering shareholders.

  17. Model Specification • Measuring Median Industry price-to-sales ratio • Based on five most recent IPOs within the last two years from the same four digit SIC code • Multiplied by Sales of Firm • R&D add back. • Using R&D stock instead annual R&D in year -1 does not change results.

  18. Model Specification • We substitute aggregate insider retention with ownership levels of four categories of owners • CEO • Officers & Directors • Venture Capitalists • Other 5% Blockholders • We also examine the impact of changes in percentage ownership around the IPO. • We include sales growth as an additional proxy for growth prospects and our results are unchanged.

  19. Model Specification • Our main research goal is to test for inter-temporal shifts in the valuation function. • Therefore, we expand the basic model by adding binary dummy variables for the boom period and the crash period. • We also construct dummies for loss firms, technology firms, internet firms and for interactions of these dummies with the six basic variables.

  20. Model Specification • Expanded Model:

  21. Model Specification • Boom = 1 if the offer date is during 1/1997-3/2000, and 0 otherwise. • Crash = 1 if the offer date is during 4/2000-12/2001, and 0 otherwise. • Loss = 1 if income before extraordinary items is negative, and 0 otherwise. • Tech = 1 if a firm belongs a technology industry, and 0 otherwise. • Internet = 1 if a firm belongs to an internet industry, and 0 otherwise.

  22. Model Specification • Logarithmic specification • Hand (2000) and Ye and Finn (2000), Beatty, Riffe, and Thompson (2001). • Reduces heteroscedasticity and influence of outliers • L(W) = loge(1+W) when W >= 0 in $millions; L(W) = -loge(1-W) when W < 0 in $millions. • The transformation is monotone and one-to-one and ensures that L(W) is defined when W is zero or close to zero. • Is the best model based on Box-Cox analysis.

  23. Model Estimation • Robust regression • OLS is justified by the fact that it is best linear unbiased estimate of linear model coefficients, and the overall best estimate when regression residuals are normally distributed. • Additionally, if residuals are normally distributed we have convenient access to a distribution theory for inference. • However, when residuals are non-normal, OLS is no longer the most efficient estimator. • In contrast to OLS which estimates the conditional mean, quantile regression estimates the conditional median. • It is less sensitive to outliers in the dependent variable. • It may be more efficient than OLS.

  24. Sample and Data Eighties Nineties • Initial Sample: non-financial companies, firm-commitment offerings, US companies, not units, not spinoffs, not LPs, proceeds >= $5 million 718 1,381 Delete misclassifications 51 64 Delete not listed on Compustat 26 0 Delete dual-class IPOs 0 87 No prospectuses 2 10 Final sample 633 1,222 Final sample with data for all variables: (1855-230) = 1,625 IPOs

  25. Sample and Data • Data sources • Financial Data (years -1,-2,-3): prospectuses • Ownership data: prospectuses • Offer date, offer price, shares issued: SDC • Shares Outstanding: Ljungqvist and Wilhelm (2003), prospectuses • Stock price and return data: CRSP • Industry comparables: COMPUSTAT • Industry classification: Loughran and Ritter (2003) • In process of collecting data on cash flows, age, underwriter reputation, long-term debt.

  26. Results – Offer Values • For profitable non-tech firms in the 1980s • income, sales, R&D, industry price-to-sales ratios, and insider retention are positively related to offer values; • book value of equity is unrelated to offer value of equity. • Income is valued more in the boom period, for tech firms, and internet firms (contrary to expectation). Valuation of income has remained stable across the boom and crash periods. • Income of loss firms is valued negatively.

  27. Results – Offer Values • In the boom period, sales were valued less in the boom period relative to the 1980s (contrary to expectation). Coefficients on sales are lower for tech and internet firms; Sales became more valuable in the crash period. • Consistent with expectation, insider retention is valued higher in the boom period, for tech firms, for internet firms, and for loss firms.

  28. Results – Market Values • In regressions of first-day market values conditional on offer values we find that • Except for sales and R&D in the crash period, investment bankers and first-day investors tend to value financial and growth variables similarly • In general, first-day investors and investment bankers tend to disagree about the value relevance of insider retention. For example, for profitable non-tech firms first-day investors value insider retention less by $1.65 million • Value differential became smaller in the boom period relative to the 1980s • First-day investors assign a lower valuation than do investment bankers for tech firms, for internet firms, and for loss firms.

  29. Conclusions • Shifts in firm characteristics contributed to shifts in offer values in recent times. • We also find evidence of parameter variation for several financial variables across time-periods and industries. • Detailed information on ownership structure is incrementally useful in explaining IPO values (compared to one aggregate number). • We believe the analysis of first-day market values conditional on offer values is a fruitful way to understanding differences between investment banker and market assessments of IPO prospects.

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