IPO underpricing algorithm: Difference between revisions

Revision as of 00:03, 18 November 2017

Revision as of 06:49, 14 December 2019

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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 |title=Commercialization Strategy and IPO Underpricing|journal=Research Policy|year=2017|volume=46|issue=6|pages=1133–1141 | urldoi=https://doi.org/10.1016/j.respol.2017.04.006 |doiurl=10.1016http://jcdm-it.respolepfl.2017ch/RePEc/iip-wpaper/commercialization_strategy_and_IPO_underpricing.04.006pdf }}</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.

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===Rule-based system===

For example, Quintana<ref>{{cite journal|last=Quintana|first=David |author2=Cristóbal Luque |author3=Pedro Isasi|title=Evolutionary rule-based system for IPO underpricing prediction|journal=In&nbsp;Proceedings of the 2005 conferenceConference on Genetic and evolutionaryEvolutionary computationComputation&nbsp;(GECCO '05)|year=2005|pages=983–989|doi=10.1145/1068009.1068176 |hdl=10016/4081 |isbn=1595930108 }}</ref> first abstracts a model with 7 major variables. The rules evolved from the Evolutionary Computation system developed at Michigan and Pittsburgh:

* Underwriter prestige – Is the underwriter prestigious in role of lead manager? 1 for true, 0 otherwise.

* Price range width – The width of the non-binding reference price range offered to potential customers during the roadshow. This width can be interpreted as a sign of uncertainty regarding the real value of the company and a therefore, as a factor that could influence the initial return.

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===Two-layered evolutionary forecasting===

Luque<ref>{{cite journal|last=Luque|first=Cristóbal|author2=David Quintana |author3=J. M. Valls |author4=Pedro Isasi |title=Two-layered evolutionary forecasting for IPO underpricing|journal=In&nbsp;Proceedings of the Eleventh conferenceConference on Congress on Evolutionary Computation&nbsp;(CEC'09)|year=2009|pages=2374–2378|publisher=IEEE Press|___location=Piscatawy, NJ, USA|doi=10.1109/cec.2009.4983237|isbn=978-1-4244-2958-5}}</ref> approaches the problem with outliers by performing linear regressions over the set of data points (input, output). The algorithm deals with the data by allocating regions for noisy data. The scheme has the advantage of isolating noisy patterns which reduces the effect outliers have on the rule-generation system. The algorithm can come back later to understand if the isolated data sets influence the general data. Finally, the worst results from the algorithm outperformed all other algorithms' predictive abilities.

==Agent-based modelling==