Read the On the Market “Facebook’s IPO” in Section 13.4. What effect did the wide range of estimates have on the value of the Facebook IPO? What equity pricing model would you select to price Facebook? Justify your answer.
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The wide range of estimates for Facebook's IPO likely created uncertainty among investors regarding the company's true value. This uncertainty can lead to volatility in the stock price as investors may have differing opinions on Facebook's growth potential and Show more…
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Facebook's Initial Public Offering: Calculate the discount rate, estimate future cash flows, and then use the discounted cash flow method to value Facebook. Determine the best comparable companies and use multiples to value Facebook. What is the appropriate price for the Facebook IPO? Why?
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3. One of the theories regarding initial public offering (IPO) pricing is that the initial return y (the percentage change from offer to open price) on an IPO depends on the price revision x (the percentage change from pre-offer to offer price). Another factor that may influence the initial return is a high-tech dummy variable that equals 1 for high-tech firms and 0 otherwise. The following table shows a portion of the data on 264 IPO firms from January 2001 through September 2004. Initial Return Price Revision High-Tech 39.68 -8.73 0 10.42 −32.88 0 ⋮ ⋮ ⋮ 9.60 −22.11 1 a-1. Estimate y = βo + β1x + β2d + ε where the dummy variable d equals 1 for firms that are high-tech. (Round your answers to 2 decimal places.) a-2. Use the estimated model to predict the initial return of a high-tech firm with a 10% price revision. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) a-3. Find the corresponding predicted return of a firm that is not high-tech. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) b-1. Estimate y = βo + β1x + β2d + ε where the dummy variable d equals 1 for firms that are not high-tech. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal place.) b-2. Use the estimated model to predict the initial return of a high-tech firm with a 10% price revision. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) b-3. Find the corresponding predicted return of a firm that is not high-tech. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.)
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Gary Hansen is a securities analyst for a mutual fund specializing in small-capitalization growth stocks. The fund regularly invests in initial public offerings (IPOs). If the fund subscribes to an offer, it is allocated shares at the offer price. Hansen notes that IPOs frequently are underpriced, and the price rises when open market trading begins. The initial return for an IPO is calculated as the change in price on the first day of trading divided by the offer price. Hansen is developing a regression model to predict the initial return for IPOs. Based on past research, he selects the following independent variables to predict IPO initial returns: Underwriter rank = 1–10, where 10 is highest rank Pre-offer price adjustment = (Offer price – Initial filing price)/Initial filing price Offer size ($ millions) = Shares sold × Offer price Fraction retained = Fraction of total company shares retained by insiders (Expressed as a decimal) Hansen collects a sample of 1,725 recent IPOs for his regression model. Regression results appear in Exhibit 1, and ANOVA results appear in Exhibit 2. Exhibit 1: Hansen’s Regression Results Dependent Variable: IPO Initial Return (Expressed in Decimal Form, i.e., 1% = 0.01) Variable | Coefficient (bj) | Standard Error | t-Statistic Intercept | 0.0477 | 0.0019 | 25.11 Underwriter rank | 0.0150 | 0.0049 | 3.06 Pre-offer price adjustment | 0.4350 | 0.0202 | 21.53 Offer size | −0.0009 | 0.0011 | −0.82 Fraction retained | 0.0500 | 0.0260 | 1.92 Exhibit 2: Selected ANOVA Results for Hansen’s Regression Source | Degrees of Freedom (df) | Sum of Squares (SS) Regression | 4 | 51.433 Residual | 1,720 | 91.436 Total | 1,724 | 142.869 Multiple R-squared = 0.36 Hansen wants to use the regression results to predict the initial return for an upcoming IPO. The upcoming IPO has the following characteristics: ■ underwriter rank = 6; ■ pre-offer price adjustment = 0.04;
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