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5 years ago

* I would like to thank Frank Dobbin, Christopher Marquis, Peter ...

* I would like to thank Frank Dobbin, Christopher Marquis, Peter ...

macroeconomic variables.

macroeconomic variables. I have done so and the inferences remain unchanged from those presented in the “Findings” section (available upon request). For non-SEU issuers, I code from the prospectuses secondary portion (secondary shares divided by total offering shares) and management ownership (percentage of total shares outstanding held by senior management and board members unaffiliated with institutional shareholders prior to the offering). For information asymmetry, I measure underwriter status with the average Carter-Manaster rank for the lead underwriters. I use Carter-Manaster ranks (the standard economics reputation score for the investment banking industry) rather than the Eigenvector centrality measure due to the availability of periodically updated rank lists of Carter- Manaster scores throughout the period of study. Carter-Manaster ranks are calculated based on the same tombstone advertising data as Eigenvector centrality, and attempts to measure the same hierarchical placement of underwriters relative to each other. 20 Carter-Manaster ranks range from 0 to 9, with investment banks scoring 8 or higher generally considered to be high-status lead underwriters and those scoring 6 or less low-status co-managers. Status signal theory also predicts that underwriter status should lower the cost of underwriting (Podolny 1993, 2005). High-status investment banks can syndicate offerings at lower cost, and these efficiency gains can be traced to the effectiveness of expanded syndicates in marketing and distributing stock. 21 I control for broader market price movements with same-day and one-month returns to the Standard and Poor (S&P) 500 index, the log S&P 500 index, and the three-month return on the Dow Jones industry sector index relevant for the issuer based on SIC code. Dow Jones industry sector indices include: basic materials, consumer goods, consumer services, financials, healthcare, industrials, energy, technology, telecommunications, and utilities. To measure market volatility, I utilize the Chicago Board Options Exchange Volatility Index (VIX), which captures 26

the implied volatility of index futures reflecting forward-looking investor risk sentiment. 22 Some analysts believe VIX and sector-specific index returns could also proxy for sentiment. I also code fundamental issuer characteristics from prospectuses, including offering size (log millions), revenues (standardized), operating cashflow 23 (standardized), positive earnings (dichotomous), age since founding (log years), debt-to-capitalization ratio (total debt divided by capitalization, inverse coded for negative), and SIC code. Finally, I include the four macroeconomic variables orthogonal to the Baker-Wurgler index: employment growth, industrial production growth, total consumption growth, and a dummy variable for National Bureau of Economic Research (NBER) recession month. Employment growth is the percentage increase in monthly total nonfarm payroll employment from the Bureau of Labor Statistics. Industrial production growth is the percentage increase in the monthly industrial production index from the Federal Reserve Statistical Release. Finally, total consumption growth is the percentage increase in monthly consumer durables, non-durables, and services data deflated by CPI from the BEA National Income Accounts. FINDINGS Regression analyses of IPO price outcomes over the past ten years strongly support the institutional logic hypotheses. The following table presents Pearson correlations and descriptive statistics for the 34 variables of interest. [Insert Table 2] 27

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