IPO Underpricing and Information Disclosure. Laura Bottazzi (Bologna and IGIER) Marco Da Rin (Tilburg, ECGI, and IGIER)

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1 IPO Underpricing and Information Disclosure Laura Bottazzi (Bologna and IGIER) Marco Da Rin (Tilburg, ECGI, and IGIER)

3 !! Work in Progress!!

4 Motivation IPO underpricing (UP) is a pervasive feature of primary equity markets. It has implications for investors, companies, and regulators. It is the object of a large literature, both theoretical and empirical.

5 Motivation Asymmetric information is at the core of all theories of IPO underpricing. Asymmetric information may exist among all market participants: Founders (sellers) may know more about the firm than investors (buyers), leading to a lemons problem. UP is then a credible signal of quality.

6 Motivation Some investors may know more about the firm than the others, leading to a winner s curse. UP then induces participation by the uninformed. In the same situation, the repeated interaction of underwriting banks with institutional investors through the bookbuilding process may generate UP as a compensation to informed investors revelation of information.

7 Motivation If it is the underwriting bank to be better informed, and if its marketing efforts are non contractible, then UP provides a viable second best solution by compensating the underwriter. If investors care about liquidity in the secondary market, then UP is a compensation for the risk of future illiquidity. Such risk depends on post-ipo asymmetric information.

8 Motivation Despite its central role in explaining UP, asymmetric information is very difficult to measure. Typically, studies resort to indirect proxies: Company characteristics: age, size, sector, venture backing or auditor reputation. Issue characteristics: bookbuilding, underwriter reputation, insiders secondary sales After-market characteristics: return volatility

9 Our contribution We provide a direct measure of asymmetric information, which is gathered from the IPO prospectus. We build an index of the quantity of information provided in the prospectus. This is information which is provided voluntarily, not mandated by law. We measure voluntary information disclosure through a direct measure gathered from the IPO prospectus.

10 Our contribution We find three main (preliminary) results: Disclosure negatively affects UP. The effect of disclosure is asymmetric: low disclosure increases UP, high disclosure has virtually no effect. The effect of disclosure appears to be robust to controls for endogeneity of the regressors.

11 The Data Hand-collected database with information from IPO prospectuses of all 515 companies which went public on Neuer Markt, Nouveau Marché, Nuovo Mercato until the end of Disclosure Index Balance sheet information Ownership structure pre- and post-ipo Pricing mechanism, greenshoe option, lead underwriter, primary and secondary shares sold Sector of activity

12 The Data From stock exchanges we obtain: Date of listing Issue price First closing price Closing prices in first 12 weeks of trading Seasoned equity offerings

13 The Disclosure Index The DI is modeled on similar metrics for corporate governance quality: S&P Transparency Disclosure Index (US, EU) AIMR reports (US listed companies) IRRC Governance Index (US listed companies) CIFAR disclosure index intended use of funds, number of risk factors These metrics have been used in previous studies

14 The Disclosure Index 1. the company's corporate strategy is discussed. 2. the company's core/additional business segments are identified. 3. the nature of competition in the core market is discussed. 4. the main competitors in the core market are identified. 5. the barriers to entry to the core market are discussed. 6. the source(s) of competitive advantage are identified. 7. the intended use of IPO proceedings is clearly stated.

15 The Disclosure Index 8. an estimate of the company's core market value is provided. 9. the company's market share in its core market is quantified. 10. the expected future value of the core market is estimated. 11. sales for the company's core market are given. 12. the value/share of the company's foreign sales (if any) is given. 13. business evolution since the last annual report is discussed. 14. a quarterly breakdown of the financial accounts for the year before the IPO is provided.

16 The Disclosure Index Disclosure: Frequency plot ** 1 5 *** 2 6 **** 3 7 ***** 4 10 ****** 5 26 ***************** 6 36 *********************** 7 46 ****************************** 8 60 *************************************** 9 77 ************************************************** ***************************************************** ********************************************** ****************************** ********************* 14 7 ***** Total 515

17 The Disclosure Index Panel A: Item composition Item: All C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 Mean St.Dev

18 The Disclosure Index Panel B: Sectoral composition Sector: Biomed Soft./Internet Technology Media Telecom Traditional n.: Mean St.Dev

19 The Disclosure Index Panel C: Market composition Market: Germany France Italy n.: Mean St.Dev

20 Dependent variable We measure underpricing as the logarithm of the ratio of the first day s closing price to the issue price. This ratio is closer to a normal distribution than the traditional measure of underpricing.

21 Independent variables We look at four sets of explanatory variables: Disclosure: DISCLOSURE (index 1 to 14) DISCLOSURE-POS (positive deviation from median) DISCLOSURE-NEG (negative deviation from median)

22 Independent variables Issue characteristics: IPO-SIZE (log of gross proceedings) DILUTION (primary sale, percent) INSIDERS SALES (secondary sales, percent) RETURN VOLATILITY over 12 week (return SD) UNDERWRITER (above median mkt share) UNDERWRITER 2 (international bank)

23 Independent variables Issue characteristics: PREVIOUS IPOs over 4 and 12 weeks (by mkt) STABILIZATION ( greenshoe potential, perc.) SECONDARY OFFERINGS dummy PRICE-REVISION dummies (UP/DOWN) (no bookbuilding)

24 Independent variables Company characteristics: AGE at IPO (log of months) SIZE at IPO (log of assets, in euros) VC dummy (at least 12 months presence) R&D dummy SECTOR (6 sectors, from FT classification)

25 Independent variables Market characteristics: MARKET dummies (France residual) YEAR dummies ( residual)

26 Table 1: Descriptive statistics VARIABLE MEAN MEDIAN MIN MAX OBS Underpricing Disclosure Disclosure pos Disclosure neg IPO Size Dilution Insiders sales Return volatility Underwriter share Underwriter intern Previous IPOs Stabilization Seasoned Offerings P revision up P revision down

27 Table 1: Descriptive statistics VARIABLE MEAN MEDIAN MIN MAX OBS Age , Venture backed R&D dummy Assets Leverage ROA Germany Italy France Year Year Year Year Year Year Biomed Soft./Internet Technology Media Telecom Traditional

28 Table 2: Correlations Table Disclosure Dilution Insiders IPO Age Assets Venture Underwriter Return Previous Sales Size Backed Share Volatility IPOs Disclosure Dilution Insiders Sales IPO Size Age Assets Venture Backed Underwriter Share Return Volatility Previous IPOs

29 Empirical Strategy We start performing (robust) OLS: UP c = α + β 1 DIS c + ISSUE c β 2 + COMPANY c β 3 + MKT m β 4 + ε c We then add: Additional regressors Control for additional financial characteristics We then control for endogeneity: Simultaneity of disclosure and IPO features Effect of VC and underwriter on disclosure Selection on observables

30 Table 3: Base Model (i) Coeff. (St.Err.) Disclosure (0.006) Dilution (0.049) Insiders Sales (0.203) IPO Size (0.026) Age (0.021) Assets (0.019) Venture Backed (0.037) Underwriter share (0.036) Return Volatility (0.502) Previous IPOs (0.004) Germany (0.045) Italy (0.069) Intercept (0.340) Year / Industry effects Yes Number of obs. 506 F(21, 482 ) 6.47 R-square 0.178

31 Table3:BaseModel (i) (ii) Coeff. (St.Err.) Coeff. (St.Err.) Disclosure (0.006) Disclosure pos (0.012) Disclosure neg (0.010) Dilution (0.049) (0.049) Insiders Sales (0.203) (0.202) IPO Size (0.026) (0.026) Age (0.021) (0.022) Assets (0.019) (0.019) Venture Backed (0.037) (0.037) Underwriter share (0.036) (0.036) Return Volatility (0.502) (0.507) Previous IPOs (0.004) (0.004) Germany (0.045) (0.045) Italy (0.069) (0.069) Intercept (0.340) (0.325) Year / Industry effects Yes Yes Number of obs F(21, 482) R-square

32 Table 4: Main Model Additional Regressors Coeff. (St.Err.) Coeff. (St.Err.) Disclosure (0.007) Disclosure pos (0.013) Disclosure neg (0.011) Dilution (0.056) (0.232) Insiders Sales (0.233) (0.056) IPO Size (0.029) (0.029) Age (0.022) (0.022) Assets (0.021) (0.021) Venture Backed (0.040) (0.040) Underwriter share (0.039) (0.038) Return Volatility (0.515) (0.522) Previous IPOs (0.004) (0.004) Germany (0.047) (0.048) Italy (0.073) (0.073) Stabilization (0.642) (0.639) P Revision up (0.048) (0.048) P Revision down (0.055) (0.055) Seasoned Offerings (0.051) (0.052) Intercept (0.363) (0.349) Year / Industry effects Yes Yes Number of obs F(25, 453 ) F (26, 452 ) R-square

33 Table 5: Main Model Additional Financial Characteristics Coeff. (St.Err.) Coeff. (St.Err.) Disclosure (0.007) Disclosure pos (0.013) Disclosure neg (0.011) Dilution (0.056) (0.056) Insiders Sales (0.240) (0.238) IPO Size (0.030) (0.030) Age (0.022) (0.023) Assets (0.021) (0.021) ROA (0.016) (0.016) Leverage (0.010) (0.010) Venture Backed (0.041) (0.040) Underwriter share (0.040) (0.040) Return Volatility (0.603) (0.613) Previous IPOs (0.004) (0.004) Germany (0.049) (0.049) Italy (0.082) (0.081) Stabilization (0.683) (0.676) P Revision up (0.052) (0.052) P Revision down (0.056) (0.055) Seasoned Offerings (0.054) (0.054) Intercept (0.370) (0.355) Year / Industry effects Yes Yes Number of obs F(27, 419 ) F (28, 418 ) R-square

34 Extension/Robustness We consider several alternative models: We control for R&D Activity (dummy) Include US-GAAP and IAS dummies Employ pre-ipo average of financial data Employ financial data at IPO Secondary sales by founders and managers Underpricing as percentage, not logarithm

35 Endogeneity First, we recognize that disclosure is choice which is simultaneous to other choices of the IPO process. For this we use a 2SLS where the first stage estimates how much is disclosed

36 Table 6(a): Simultaneous regressions Coeff. (St.Err.) Coeff. (St.Err.) Coeff. (St.Err.) Disclosure (0.040) Disclosure pos (0.073) Disclosure neg (0.088) Dilution (0.076) (0.074) (0.078) Insiders Sales (0.227) (0.210) (0.257) IPO Size (0.033) (0.030) (0.039) Age (0.023) (0.024) (0.025) Assets (0.019) (0.019) (0.020) Venture Backed (0.055) (0.055) (0.060) Underwriter share (0.047) (0.050) (0.047) Return Volatility (0.730) (0.700) (0.826) Previous IPOs (0.004) (0.004) (0.004) Germany (0.076) (0.079) (0.075) Italy (0.089) (0.105) (0.086) Intercept (0.703) (0.461) (0.455) Year / Industry effects Yes Yes Yes Number of obs Wald χ

37 Table 6(b): First stage regressions Coeff. (St.Err.) Coeff. (St.Err.) Coeff. (St.Err.) Age (0.147) (0.077) (0.098) ROA (0.126) (0.066) (0.080) Leverage (0.071) (0.037) (0.046) R&D (0.256) (0.130) (0.173) Assets (0.106) (0.055) (0.070) Venture Backed (0.339) (0.178) (0.225) Underwriter share (0.286) (0.150) (0.190) Return Volatility (4.261) (2.235) (2.835) Previous IPOs (0.025) (0.013) (0.017) Germany (0.328) (0.172) (0.218) Italy (0.521) (0.273) (0.347) Year (0.544) (0.285) (0.362) Year (0.545) (0.286) (0.362) Year (0.550) (0.288) (0.366) Year (0.810) (0.425) (0.539) Intercept (1.919) (1.007) (0.455) Industry effects Yes Yes Yes Number of obs Wald χ R-square

38 Endogeneity Second, we recognize that disclosure is a choice which is affected by some other regressors, most importantly: The presence of a venture capitalist The choice of underwriter For this we use a 2 step treatement regression model

39 Table 7(a): Treatment Effects Model Disclosure Coeff. (St.Err.) Coeff. (St.Err.) Disclosure (0.006) (0.006) Dilution (0.068) (0.060) Insiders Sales (0.203) (0.230) IPO Size (0.030) (0.027) Age (0.023) (0.024) Assets (0.025) (0.019) Venture Backed (0.176) (0.043) Underwriter share (0.044) (0.268) Return Volatility (0.719) (0.617) Previous IPOs (0.004) (0.004) Germany (0.052) (0.070) Italy (0.092) (0.108) Intercept (0.409) (0.373) Year / Industry effects Yes Yes Lambda (0.142) (0.151) Number of obs Wald χ

40 Table 7(b): Treatment Effects Model Disclosure deviations Coeff. (St.Err.) Coeff. (St.Err.) Disclosure pos (0.015) (0.012) Disclosure neg (0.012) (0.013) Dilution (0.069) (0.038) Insiders Sales (0.199) (0.238) IPO Size (0.027) (0.027) Age (0.023) (0.025) Assets (0.019) (0.021) Venture Backed (0.221) (0.043) Underwriter share (0.047) (0.270) Return Volatility (0.602) (0.630) Previous IPOs (0.005) (0.004) Germany (0.059) (0.076) Italy (0.085) (0.095) Intercept (0.355) (0.390) Year / Industry effects Yes Yes Lambda (0.129) (0.156) Number of obs Wald χ

41 Endogeneity With propensity score we check that our results are not due to selection in the amount to disclose due to observable firm characteristics.

42 Conclusions Voluntary information disclosure seems to reduce UP, even accounting for observable and unobservable selection effects. Disclosure also improves on previous explanations in terms of goodness of fit. We are currently working to extend the analysis of endogenous effects

Stock price synchronicity and the role of analyst: Do analysts generate firm-specific vs. market-wide information?

Stock price synchronicity and the role of analyst: Do analysts generate firm-specific vs. market-wide information? Yongsik Kim * Abstract This paper provides empirical evidence that analysts generate firm-specific