Management Guidance and the Underpricing of Seasoned Equity Offerings*
Bibliographic record
Abstract
investors is high, those who trade against an informed party will demand compensation by lowering (increasing) the price at which they are willing to buy (sell) assets, consequently affecting liquidity and asset prices (Amihud and Mendelson 1986).Similarly, when firms issue new shares in the presence of information asymmetry between managers and outside investors, rational investors will pay a lower price for the shares to compensate for their information disadvantage, therefore increasing the cost of raising external capital.Prior theoretical studies have argued that IPO or SEO underpricing is a result of information asymmetry.Most of the theories concerning IPOs also apply to SEOs (Loderer, Sheehan, and Kadlec 1991).Rock (1986) argues that uninformed investors face the winner's curse problem.To ensure that uninformed investors participate in IPOs and thus shares can be sold, the offer price has to be set below the intrinsic value of the shares so that uninformed investors at least break even.This causes stock prices to increase on the first day of trading and results in underpricing.Benveniste and Spindt (1989) argue that investment banks induce asymmetrically informed investors to reveal truthfully the information they hold about the value of IPOs.To reward these investors for truthful reporting, IPO offer prices are set below expected first-day closing prices.Parsons and Raviv (1985) use information asymmetry among investors to explain the underpricing of SEOs.In their model, an underwriter chooses a sufficiently low offer price to attract investors who attach a high value to the firm's new project.These investors realize that they can buy shares at the offer price only when the issue is undersubscribed.To avoid oversubscription, their demand drives the market price over the offer price, causing underpricing.While the above models are based on different assumptions and offer different explanations for underpricing, they all agree that information asymmetry is a source of underpricing.Many empirical studies on IPOs or SEOs have shown that various measures of information asymmetry are positively associated with underpricing.In the SEO setting, firm size and pre-SEO stock return volatility are often used to proxy for information asymmetry or uncertainty (Corwin 2003).Other proxies of information asymmetry include analyst forecast dispersion (Marquardt and Wiedman 1998), the bid-ask spread (Corwin 2003), and accruals quality (Lee and Masulis 2009).These studies find a positive association between information asymmetry proxies and SEO underpricing.Certain mechanisms can help reduce information asymmetry concerning firm value and thus the magnitude of underpricing.Titman and Trueman (1986) present a model in which the quality of auditors and ⁄ or investment bankers provides information useful to investors in valuing new issues.Beatty (1989) finds support for this model by documenting that the extent of IPO underpricing is lower for firms that hire more reputable auditors.Many IPO and SEO studies employ underwriter rank to measure the quality of investment banks and find that it is negatively associated with underpricing (Kim and Shin 2004;Mola and Loughran 2004).Information intermediaries such as financial analysts act as another uncertainty reduction mechanism.Analyst coverage can improve a firm's information environment and potentially reduce information asymmetry among investors, thereby reducing SEO underpricing.Mola and Loughran (2004) find that underwriters with toptier analysts are associated with a lower level of SEO underpricing.Bowen et al. (2008) show that analyst coverage, as well as certain attributes of analysts such as those working for the lead underwriters and those having a reputation for superior ability, lowers the magnitude of SEO underpricing. Management guidance and the underpricing of seasoned equity offeringsAs discussed above, auditor quality, underwriter reputation, and analyst coverage all represent information asymmetry reduction mechanisms provided by third parties: auditors, investment banks, and financial analysts, respectively.Firms can also reduce information asymmetry by voluntarily disclosing more information, including their own predictions of 712
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".