IPO underpricing algorithm: Difference between revisions

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{{Short description|Increase in stock value}}

{{Multiple issues|

{{original research|date=April 2011}}

{{essay-like|date=April 2011}}

{{cleanup|date=April 2011}}

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'''[[Initial public offering#Pricing|IPO underpricing]]''' is the increase in stock value from the [[initial public offering#Pricing|initial offering price]] to the first-day closing price. Many believe that underpriced IPOs[[initial public offering|IPO]]s leave money on the table for corporations, but some believe that underpricing is inevitable. Investors state that underpricing signals high interest to the market which increases the demand. On the other hand, overpriced stocks will drop long-term as the price stabilizes so underpricing may keep the issuers safe from investor litigation.

==IPO underpricing algorithms==

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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.

== Evolutionary models ==

==Artificial neural network==

[[Evolutionary programming]] is often paired with other algorithms e.g. [[artificial neural network|ANNnetworks]] to improve the robustness, reliability, and adaptability. Evolutionary models reduce error rates by allowing the numerical values to change within the fixed structure of the program. Designers provide their algorithms the variables, they then provide training data to help the program generate rules defined in the input space that make a prediction in the output variable space.

[[Artificial neural networks]] (ANNs) resolves these issues by scanning the data to develop internal representations of the relationship between the data. By determining the relationship over time, ANNs are more responsive and adaptive to structural changes in the data. There are two models for ANNs: supervised learning and unsupervised learning.

In [[supervised learning]] models, there are tests that are needed to pass to reduce mistakes. Usually, when mistakes are encountered i.e. test output does not match test input, the algorithms use [[back propagation]] to fix mistakes. Whereas in [[unsupervised learning]] models, the input is classified based on which problems need to be resolved.

For example, Chou<ref>{{cite journal|last=Chou|first=Shi-Hao|coauthors=Yen-Sen Ni and William T. Lin|title=Forecasting IPO price using GA and ANN simulation|journal=In&nbsp;Proceedings of the 10th WSEAS international conference on Signal processing, computational geometry and artificial vision&nbsp;(ISCGAV'10)|year=2010|pages=145–150|publisher=World Scientific and Engineering Academy and Society (WSEAS)}}</ref> discusses their algorithm for determining the IPO price of [[Baidu]]. They have a three layer algorithm which contains—input level, hidden level, and output level:

* Input level, the data is received unprocessed.

* Hidden level, the data is processed for analyses

* Output level, the data goes through a sigmoid transition function

They reduce the amount of errors by trying to find the best route and weight through the neural network which is an evolutionary algorithm.

==Evolutionary models==

[[Evolutionary programming]] is often paired with other algorithms e.g. [[artificial neural network|ANN]] to improve the robustness, reliability, and adaptability. Evolutionary models reduce error rates by allowing the numerical values to change within the fixed structure of the program. Designers provide their algorithms the variables, they then provide training data to help the program generate rules defined in the input space that make a prediction in the output variable space.

In this approach, the solution is made an individual and the population is made of alternatives. However, the outliers cause the individuals to act unexpectedly as they try to create rules to explain the whole set.

===Rule-based system===

For example, Quintana<ref>{{cite journalbook|last=Quintana|first=David |coauthorsauthor2=Cristóbal Luque and |author3=Pedro Isasi|title=Evolutionary rule-based system for IPO underpricing prediction|journal=In&nbsp;Proceedings of the 20057th annual conference on Genetic and evolutionary computation&nbsp;(GECCO '05)|chapter=Evolutionary rule-based system for IPO underpricing prediction |year=2005|pages=983–989|doi=10.1145/1068009.1068176 |hdl=10016/4081 |isbn=1595930108 |s2cid=3035047 |hdl-access=free}}</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 journalbook|last=Luque|first=Cristóbal|author2=David Quintana |author3=J. M. Valls |author4=Pedro Isasi |title=2009 IEEE Congress on Evolutionary Computation |chapter=Two-layered evolutionary forecasting for IPO underpricing|journal=In&nbsp;Proceedings of the Eleventh conference on Congress on Evolutionary Computation&nbsp;(CEC'09)|year=2009|pages=2384–23782374–2378|publisher=IEEE Press|___location=Piscatawy, NJ, USA|doi=10.1109/cec.2009.4983237|isbn=978-1-4244-2958-5|s2cid=1733801}}</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==

Currently, many of the algorithms assume homogeneous and rational behavior among investors. However, there's an approach alternative to financial modeling, and it's called [[agent-based model]]ling (ABM). ABM uses different autonomous agents whose behavior evolves endogenously which lead to complicated system dynamics that are sometimes impossible to predict from the properties of individual agents.<ref>{{cite journal |last=Brabazon |first=Anthony |author2=Jiang Dang |author3=Ian Dempsy |author4=Michael O'Neill |author5=David M. Edelman |title=Natural Computing in financeFinance: aA reviewReview |journal=Handbook of Natural Computing |year=2010 |url=httphttps://irserverresearchrepository.ucd.ie/dspace/bitstream/10197/2737/1/NCinFinance_v8.pdf |deadurl=yes}} {{dead link |date=September 2013}}</ref> ABM is starting to be applied to computational finance. Though, for ABM to be more accurate, better models for rule-generation need to be developed.

== References ==

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{{Corporate finance and investment banking}}

[[Category:CorporateInitial financepublic offering]]

[[Category:Stock market]]

[[Category:Artificial neural networks]]

[[Category:EvolutionaryApplications of evolutionary algorithms]]