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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 ...

Clustering issuers by

Clustering issuers by SIC code is important as issuers from different industry sectors may experience systematically different return outcomes. The fixed effect coefficient estimates may change substantially due to covariance with issuer industry sector, such as industry specialization in the case of private equity and venture capital investors. For example, perhaps the Growth logic exerts its effect primarily because venture capital firms disproportionately invest in technology companies, and technology companies as a group experience higher pricing above the range? Furthermore, the effects of the institutional logics may themselves vary by industry sector, with no fixed effect remaining after controlling for these random effects. For example, perhaps the Income logic exerts its effect primarily because LBO firms primarily target sectors they can successfully perform leveraged recapitalizations (e.g., utilities), but when they invest outside these preferred sectors (e.g., technology), they are unable to avoid pricing above the range. While we have clustered issuers by four-digit SIC codes (in models 6 and 7 of Tables 3 and 4), we have not allowed the logic coefficients to vary by cluster. Also, more aggregated sector clusters would simplify the analysis for potential bias due to industry specialization by investment firms. I use the ten main industry sectors for the Dow Jones sector indices to cluster issuers, conducting four additional pricing above the range hierarchical mixed effect logit models (see Table 6). Model 1 clusters issuers by Dow Jones industry sector, model 2 further allows the Income logic coefficient to vary by sector, model 3 allows Growth to vary, and model 4 allows both institutional logic coefficients to vary. All models also control for offering year fixed effects. The mixed effect logit models with random slopes for the institutional logic variables strongly confirm hypotheses H1 and H2, with the fixed effect coefficient estimates of Income (-0.82 to - 0.96; p

Hence, the impact of institutional logics on pricing above the range is not due to sector-level variation in issuers. [Insert Table 6] We can also utilize SEC filing rules to mitigate another source of potential bias: unforeseen circumstances arising during the roadshow. Underwriters must re-file with the SEC if the offer price is priced more than 20 percent outside the price range; issuers and underwriters seek to avoid re-filing as it significantly delays the IPO process. As such, issuers pricing more than 20 percent outside the price range are doing so due to unforeseen circumstances. While not outliers in the statistical sense, these extreme situations could nevertheless unduly influence our coefficient estimates for the logic variables. I conduct two-limit Tobit regressions restricting the offer-price return to +/-30 percent to test the robustness of our inferences to these situations and the exogenous shocks they may represent (see Table 7). For the majority of price ranges in the data, +/-30 percent offer-price returns approximates pricing more than 20 percent outside the price range. Again, the analyses strongly confirm hypotheses H1 and H2. Of particular note the proxy for prospect theory (secondary portion) becomes significant once we limit the response variable. [Insert Table 7] In sum, the findings are decidedly mixed for the alternative hypotheses from behavioral finance for all five sets of regressions, but in particular for our primary response variable of 31

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