IPO underpricing algorithm: Difference between revisions

Revision as of 06:59, 30 September 2022

Revision as of 11:32, 30 April 2023

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[[Underwriters]] and investors and corporations going for an [[initial public offering]] (IPO), issuers, are interested in their market value. There is always tension that results since the underwriters want to keep the price low while the companies want a high IPO price.

Underpricing may also be caused by investor over-reaction causing spikes on the initial days of trading. The IPO pricing process is similar to pricing new and unique products where there is sparse data on market demand, product acceptance, or competitive response. Besides, underpricing is also affected by the firm idiosyncratic factors such as its business model.<ref>{{cite journal|last=Morricone|first=Serena|author2=Federico Munari|author3=Raffaele Oriani|author4=Gaétan de Rassenfosse|author-link4=Gaétan de Rassenfosse|year=2017|title=Commercialization Strategy and IPO Underpricing|url=http://cdm-it.epfl.ch/RePEc/iip-wpaper/commercialization_strategy_and_IPO_underpricing.pdf|journal=Research Policy|volume=46|issue=6|pages=1133–1141|doi=10.1016/j.respol.2017.04.006|hdl=11585/627229 }}</ref> Thus it is difficult to determine a clear price which is compounded by the different goals issuers and investors have.

The problem with developing algorithms to determine underpricing is dealing with [[Statistical noise|noisy]], complex, and unordered data sets. Additionally, people, environment, and various environmental conditions introduce irregularities in the data. To resolve these issues, researchers have found various techniques from [[artificial intelligence]] that [[normalization (statistics)|normalizes]] the data.